Bruno Marques
email: b.marques@orange.fr

TRANSPORT-TOURISM: CAPACITY COORDINATION

Keywords: coordination, capacity, profit, tourism type, installation costs

Two major findings emerge from the theoretical analysis of Transport-Tourism link via a game theory type model of capacity coordination. Firstly the model explains the optimal capacities ratio of Transport and Tourism by the quotient of the ratio of tourism type (ratio of the length of stay in the destination and of transport duration) divided by the installation costs ratio (of transport and tourism).The corollary of this first finding gives the second outcome:

The optimal transport and Tourism profit ratio is the product of the ratio of the type of tourism by the index of overcapacity conditions (a non linear combination of installation costs, and durations). From these results, it follows an interpretive grid that allows, according to tourism types, firstly to identify the optimal overcapacities by the difference between installation costs; and secondly the equilibrium ratio of profits according to the overcapacity direction and the tourism types.

Introduction

The link between Tourism and Transport is a major issue of the tourism economics of a destination. According to the definition of tourism adopted by the United Nations Statistics Commission, WTO, OECD, Eurostat (2001), tourism is intrinsically linked to traveling and transport as well. Asserting that there is no tourism without transport is not unfounded; for the intensive and/or extensive development of tourism requires the existence of transport capacities preceding or accompanying it. The dynamics of Transport and Tourism and the issue of potential overcapacity are at the heart of the analysis of Transport-Tourism link, which can be expressed as follows: Should Transport capacities precede or follow those of tourist reception, to cause tourism development?

The advent and development of Internet profoundly changed the nature of the Transport-Tourism link and suggests to re-examine the study of its dynamics.

Disintermediation and new pricing practices initiated by the Internet impacted doubly the link between Transport and Tourism.

Internet primarily affected the coordination of the three private tourism agents:

Transport, Tourism Characteristic Industries [producing "tourism characteristic products and services" as defined by the WTO(1999)] and Intermediaries Branches [Tour Operators (TO) and travel agents (AV)]. As documented by [START_REF] Duncan | The Internet Effects on Tourism Industry[END_REF] and [START_REF] Buhalis | The Strategic Impact of the Internet on the Tourism Industry, Information and Communication Technologies in Tourism[END_REF], internet reduced the role of intermediaries in the organization of tourism. Before the advent of internet, the intermediaries aggregated or in the words of [START_REF] Klein | Concatenate coordination and mutual coordination[END_REF] coordinated transport services with tourism characteristic products (accommodation and other leisure activities, attractions ...). More than an interface between supply and demand, intermediaries were the coordinating tool of private productions, regulating capacities and quantities and price. Through the direct access to demand (clients), it provides to transport and tourism characteristic industries, internet restricts or annihilates the coordinating (aggregation) role of intermediaries. Therefore direct prices interactions began to govern coordination between market agents. By adopting the categorization Orsborn and [START_REF] Klein | Concatenate coordination and mutual coordination[END_REF], the link between transport and tourism previously a coordination / aggregation organized by intermediaries became, with the advent of Internet, a mutual coordination ruled by the interactions between tourism players.

The increased uses of Yield and/or Revenue Management [Cf. Deksnyte and Lydeka (2012), Bitran and Caldentey (2003), [START_REF] Chiang | An overview of research on revenue management: current issues and future research[END_REF]] by the transport and tourism characteristic branches is the second Internet byproduct. Such pricing behavior based on price discrimination, customer segmentation, real-time knowledge of available capabilities are only possible through the Information and Communication Technology, of which internet is quintessential. [START_REF] Sahut | The Impact of Internet on Pricing Strategies in the Tourism Industry[END_REF] details the impact of Internet on the expansion of dynamic pricing in tourism. This pricing method is based on the disintermediation effects of Internet, which promoted the development of transport lowcost.

Disintermediation and its byproduct new pricing methods re-question the link between transport and tourism because it eases entries/exits of firms on the tourism market and consequently influences capacities and tourism development. Recent works [START_REF] Wachsman | Strategic interactions among firms in tourist destinations[END_REF], Candela et al. (2008), Alvarez-Albelo and[START_REF] Alvarez-Albelo | The commons and anti-commons problems inthe tourism economy[END_REF]

Transport-Tourism: a capacity coordination problem

According to [START_REF] Klein | Concatenate coordination and mutual coordination[END_REF], as a methodological approach, implemented by the game theory, mutual coordination analyzes economic phenomena as the result of interactions between systemically dependent actors: firms, individuals, sectors ... Consequentely the coordination is apprehended as the mechanical adjustment of interacting systemically linked agents, producing the state of the system at a given moment. Coordination does not necessarily mean cooperation which require a will, whereas the former is a process explanatory.

The literature of the last ten years dedicated to the study of Transport-Tourism link is mainly based upon the methodological approach of mutual coordination. Macintosh et al. (1995) define tourism (including transport) as a system to coordinate. [START_REF] Andergassen | An economic model for tourism destinations: product sophistication and price coordination[END_REF] round off this research field and acknowledge the need to analyze the tourism through the prism of coordination. According to these authors, the concept of Tourist Destination and the definition of the tourism product as a complementary bundle of goods necessarily entail the study of tourism as a coordination problem; because the complex lattice of products and services to tourists generates inter-branch interactions bounding the tourism system and its performances [START_REF] Candela | The Economics of Local Tourism Systems[END_REF][START_REF] Candela | Destination unknown. Is there any economics beyond tourismareas?[END_REF]]. The specific study of the link between Transport and Tourism also uses the coordination approach. [START_REF] Prideaux | The role of the transport system in destination development[END_REF] and [START_REF] Lumsdon | Tourism and transport: issues and agenda for the new millennium[END_REF] conclude that transport and tourism are structurally dependent, in an "asymmetric" relationship; demand and revenue of the latter being fixed by the former, via infrastructure and the decisions of carriers. Beyond the search for a unidirectional causality, [START_REF] Gay | Transport et mise en tourisme du monde[END_REF] indicates that the links between tourism and transport are "cumulative" and that there is a need to avoid the "mediological trap... that would make tourism and tourists determined by the medium, namely the transportation modes ". The concepts of Tourist Transport and of Supply Chain [START_REF] Page | Transport and tourism: Global perspectives[END_REF]] recognize inherently the systemic dimension of Transport-Tourism relation and thus justifies the coordination methodological approach. For [START_REF] Lohmann | Critical Aspects of the Tourism-Transport Relationship[END_REF], the Transport Tourism relationship is "symbiotic" and is a "codependency".

Keeping with the literature cited above, the paper adopts the mutual intersectoral coordination as the methodological framework for the theoretical analysis of the Transport-Tourism relationship. Consequently the Tourist Destination1 is a system of two players: the Transport branch and the Tourism industry2 , conceived as the aggregation of tourism characteristic branches (accommodation and other leisure activities, attractions ...). The branches interact through their price behavior and the capacity implementation of firms. With fixed capacities, price interactions decide the short run arrivals in the destination. The dynamic adjustment of transport and tourism capacities sets the long-term tourism development (arrival trend, production and capacity). Insufficient transportation capacity hinders the development of tourism by limiting arrivals. Conversely scarce tourism capacity do not encourage adding transport capacity. The adjustment of capacities depends on interactive sector strategies or on the coordination of transport and tourism operators. The paper considers capacity coordination, the suitable methodological framework to study of Tourism-Transport link, and consequently adopts the long run perspective. Thus it aims to identify the determinants for optimal capacity that arise from interactions between the two sectoral operators of the destination and as such its long-term tourist arrivals.

Like transportation, whose capacity is measured by offered seats, tourism characteristic activities instantly have a limited receiving capacity; equivalent to the maximum number of visitors during a given period. For tourism characteristic activities, the notion of receiving capacity is equally suitable for a firm or a sector; and can be extended to physical or sustainable capacity of the destination. It allows to consider hosting capabilities of hotels or the maximum receiving capacity of an area measured by the number of physically afforded excursionists, as well as the concept of carrying capacity [Cf. [START_REF] Sayre | The Genesis, History, and Limits of Carrying Capacity[END_REF]]. Thus, the notion of capacity used by the paper provides an interpretive spectrum large enough to be applied for the study of links between transport and accommodation or the relationship between transport and a recreation park.

The analysis of direct sectoral interactions with the methodological framework of non cooperative mutual coordination allows to consider the shrinking role of intermediaries, generated by internet. In addition, the prism of transport and tourism capacities of the paper proposes a new approach compared with recent works emphasizing the interactions of price and production and justifying the role of intermediaries [Cf. [START_REF] Wachsman | Strategic interactions among firms in tourist destinations[END_REF], [START_REF] Candela | The Economics of Local Tourism Systems[END_REF], [START_REF] Alvarez-Albelo | The commons and anti-commons problems inthe tourism economy[END_REF] and [START_REF] Andergassen | An economic model for tourism destinations: product sophistication and price coordination[END_REF]].

The following section provides a model of mutual intersectoral coordination Transport-Tourism via the interactions of transport and tourism capacities.

A coordination model of transport and tourism capacities

The model of mutual intersectoral coordination of Transport and tourism capacities is conducted via a two step non-cooperative game, with simultaneous choice of the players. The search of the Nash equilibrium in perfect sub-games provides the optimal ratio for the two strategic variables of the destination system: price and capacities. The following paragraphs describe the description the game: players, their behavior and its resolution mode. § The players are the two sectors Transport and Tourism (aggregation of tourismcharacteristic branches). At the sectoral level the two branches can be considered as two monopolistic firms, supplying transport capacities and tourism capacities during a given period. These capacities result from single or repeated use of the production factor combination in a given period. In fact, the sectors-firms offer a capacity utilization of transport capacity (T 1 ) and of Tourism capacity (T 2 , which is the aggregation of all the capacities of firms producing characteristics tourism goods). In the modeling, the index 1 indicates the transport industry and 2, the tourism industry. § The behavior of sector-firms follows a dual perspective. Firstly, each sector-firm decides the capacity allocated to the destination and secondly they set their prices in order to ensure the highest load factor. The first step materializes the long-run behavior of the players and the second its short-term dimension. The dual perspective behavior requires a two steps model.

The first step determines the optimum capacity through the potential-profit maximizing behavior of each sector-firm, since each sector-firm do not know ex ante the level of demand, dependent on price alone. The form of the sector profit function is:

𝜋 𝑖 = 𝑝 𝑖 𝑇 𝑖 -𝑐 𝑖 𝑇 𝑖 (1) 𝑖 = 1,2
𝑝 𝑖 are the sale prices of each unit of capacity. 𝑇 𝑖 are transport and tourism capacities.

Variable marginal costs associated with demand (number of tourist) are assumed negligible.

The capacity installation costs (𝑐 𝑖 𝑇 𝑖 ) consists of the remuneration of production factors (capital and labor) and other fixed charges linked to installation, in case of repeated use of physical capital. They are postulated linearly dependent on the offered capacity (𝑇 𝑖 and so to physical capital and the intensity of its use) due to the complementary of production factors.

This assumption is also underpinned by the normative ratios of the number of employees to the total capacity (seats or rooms) as a quantitative or qualitative index by transport companies and hotel. Constant returns in transport activities for unique routes and with stable technology [Cf. Oum and Zhang (1997), Hensher and Button (2008, pp. 391-392) and De Palma et al. (2013, pp. 286-287)] led to consider fixed the installation marginal cost in the Transport branch(𝑐 1 ). Notwithstanding the diversity of the tourism branch, returns to scale are also postulated constant, hence the fixity of its marginal installation cost (𝑐 2 ).

The second step of the game produces the optimum price of the capacity unit by postulating that the sectors-firms minimize their revenue loss (P) at fixed capacity. P is the difference between the maximum revenue (𝑝 𝑖 𝑇 𝑖 ) and actual sales: the product price (𝑝 𝑖 ) by tourist arrivals∶ 𝐹 = 𝑎 -𝑏𝑝 1 -𝑝 2 . The formalization of this behavior is: Step 2: The loss minimization by each sector-firm provides the reaction system functions below:

[𝑀𝑖𝑛 𝑃 = 𝑝 𝑖 𝑇 𝑖 -𝑝 𝑖 (𝑎 - 𝑏𝑝 1 -𝑝 2 )]
{ 𝑝 1 = 1 2𝑏 (𝑎 -𝑑𝑝 2 -𝑇 1 ) 𝑝 2 = 1 2𝑑 (𝑎 -𝑏𝑝 1 -𝑇 2 )
that generates the equilibrium price 𝑝 1 * and 𝑝 2 * . The price of each sector-firm is negatively related to the capacity of the same branch, and positively to those of the other branch.

𝑝 1 * = 1 3𝑏 (𝑎 + 𝑇 2 -2𝑇 1 ) (2 -1) 𝑝 2 * = 1 3𝑑 (𝑎 + 𝑇 1 -2𝑇 2 ) (2 -2)
The overall equilibrium price of the tourist trip is:

𝑝̅ = 𝑝 1 * + 𝑝 2 * = (𝑑-2𝑏)𝑇 2 +(𝑏-2𝑑)𝑇 1 +𝑎²𝑑𝑏 3𝑏𝑑 .
The non-cooperative coordination framework (where Step 1: using the optimal prices of step 2 above for maximizing the potential profit of each sector-firm gives the system of reaction functions below:

{ 𝑇 1 = 1 4 (𝑎 -3𝑏𝑐 1 + 𝑇 2 ) 𝑇 2 = 1 4 (𝑎 -3𝑑𝑐 2 + 𝑇 1 )
The positive partial derivatives (

𝑇 1 * = 1 15 (5𝑎 -3𝑑𝑐 2 -12𝑏𝑐 1 ) (3 -1) 𝑇 2 * = 1 15 (5𝑎 -3𝑏𝑐 1 -12𝑑𝑐 2 ) (3 -2)
These equilibrium capacities determine the occurrence of structural overcapacities. The optimal ratio of Transport and tourism capacities (𝑇 1 * -𝑇 2 * ) depends on the ratio . The asymmetry between the type of tourism (the relative time in transport and in the destination) and installation costs regulates the optimum lag of transport and tourism capacities.

The model allows to express the following rule, summarizes in Table 1: the optimal

Transport and Tourism capacities ratio depends on the quotient of the tourism type ratio (ratio of the stay time in the destination and of transport duration) divided by the installation costs ratio (of transport and tourism). This rule induces logical necessities from which comes a reading grid of relative overcapacity (see Table 2) according to the type of tourism.

Thus when the tourism and transport are considered goods (b = d = 1), the comparative ratio of installation costs is a good predictor for the relative overcapacity. The relative superiority of tourism installation cost leads to overcapacity of transport and vice versa. This also applies when transport and tourism are assessed as durations, and are similar (b = d).

However, according to the tourism type, installation costs ratio alone does not allow for a symmetrical prediction of overcapacity; while generating corollaries in terms of price.

In Tourist-tourism situations ( 𝑑 𝑏 > 1), the superiority of Tourism installation costs (𝑐 2 > 𝑐 1 )

leads necessarily to the overcapacity of transport (𝑇 𝑡𝑟𝑎𝑛𝑠𝑝 * > 𝑇 𝑇𝑜𝑢𝑟 *

). The reverse is not proved:

𝑐 1 > 𝑐 2 leaves an uncertainty, which does not allow to conclude about the direction of overcapacity

( 𝑑 𝑏 > 𝑐 1 𝑐 2 > 1 ou 𝑐 1 𝑐 2 > 𝑑 𝑏 > 1 ).
Similarly in the case of excursionist tourism ( The transitional dynamic of step 1, the general rule and the reading grid of table 2 provide an interpretive scheme for imbalances between air transport and tourism capacities revealed by UNWTO (2012). More broadly the model allows the two following inferences: ) > 𝑎 ( 𝑏 𝑑 -1) 6.23 -1 ] . Transport profits are relatively higher if the tourism overcapacity is below the previously mentioned threshold.


According to Table 3, overcapacity is broadly a good predictor of sector profit ratio and consequently the distribution of tourism income distribution.

Concluding Remarks

Two major findings emerged from the theoretical analysis of transport-tourism link via a game theory type model of capacity coordination. These findings provide the expression and the determinants of Transport and Tourism optimal relative capacities and profits. Firstly the model explains the optimal capacities ratio of Transport and Tourism by the quotient of the ratio of tourism type (ratio of the length of stay in the destination and of transport duration)

divided by the installation costs ratio (of transport and tourism). The second outcome of the paper shows the equilibrium profit ratio as the product of the tourism type ratio by the index of overcapacity conditions (a non linear combination of installation costs, and durations).

From these overall rules, it follows an interpretive grid that identifies:

 The optimum capacities ratio by the gap between the installation costs ratio and the tourism type ratio,  The equilibrium profits ratio according to the direction of relative overcapacity and the tourism type ratio.

Finally, through the model, the paper provides different ratios for empirical tests to assess the link between capacity and profits of Transport and Tourism. The visitor buys a right to use a unit of Transport and Tourism capacity which can be considered a good (calibrated in its duration and in its composition) or a variable duration.

Given the sequential nature of the acquisition of transport and tourism services, the model assumes they are perfect complement during the journey. In order to rationally allocate goods or duration of transport and tourism, the representative visitor maximizes a utility function of the form 𝑀𝑖𝑛 (𝑏𝑋 1 , 𝑑𝑋 2 ), under the budget constraint 𝑚 = 𝑝 1 𝑋 1 + 𝑝 2 𝑋 2 , with X 1 , the good as Tourist-Tourism.

In equilibrium X i demand function is:

= 𝑚 𝑏𝑝 1 +𝑑𝑝 2 , from which it comes 𝑏𝑝 1 + 𝑑𝑝 2 = 𝑚 𝑋 𝑖
. m is the journey budget.

Following the papers of [START_REF] Sinclair | The economics of Tourism[END_REF] and [START_REF] Lim | A survey of tourism demand modelling practice : issues and implications[END_REF] , which can be considered as the primary or basic value of the profit ratio.

Equilibrium profit ratio

Le classement des deux composantes du ratio

  [START_REF] Andergassen | An economic model for tourism destinations: product sophistication and price coordination[END_REF]] justified the favorable role of intermediaries relative to price, welfare and profit. These works do not include capacity constraints intrinsically linked to tourist visits and transportation. Yet the dynamics of transport and tourism capacities shapes the tourism development of destinations. Analyzing structural trends in air transport via the concept of connectivity, UNWTO (2012) highlights the importance of capacity dynamics, evoking latent imbalances between transport and tourism offerings. Lowering entry barriers allowed by Internet and new pricing methods necessarily influence the adjustment dynamics between transport and tourism capacities and thus the tourism development of destinations. The paper proposes a theoretical analysis of the Transport-Tourism link based on the question: What determines the equilibrium ratio between transport capacity and tourist receiving capacity? Using the methodological framework of the non-cooperative mutual coordination, it proposes a "scheme of intelligibility" of the Transport-Tourism relationship through their relative capacity. It highlights the parameters of the dynamics of tourism development initiated by the interactions between transport capacities and tourist receiving capacities. The analysis provides an explanation framework for overcapacity situations and for the distribution of profits (tourism rent) between the carriers and tourism characteristic industries. The first section of the paper argues the prism of the capacity coordination as the suitable methodological framework for the study and modeling of the Transport-Tourism link. The second part presents a game theory type model of Transport-Tourism capacity coordination to answer the question. Concluding remarks complete the paper.

  . The arrivals equation (F, the number of arrivals) combines empirical tourism demand modeling with tourist's rational behavior, as presented by appendix 1. b and d are the respective weight of Transport and Tourism, considered complementary goods or durations in the representative tourist's utility function. The revenue loss minimization behavior leads in the equilibrium to a price equation that respects dynamic pricing principles (Revenue and Yield Management) mentioned in the introduction. Appendix 1 explains the two latter points. § The resolution of the game: Dynamic adjustment, relative optimal capacities and profits. The resolution of the game is reversely performed. Firstly the Nash equilibrium in price of step 2 is solved; out of which comes the Nash equilibrium in capacity of step 1.

  allows to identify when freedom of entry in the transport market or the tourism market induces a decrease of the equilibrium price of travel (𝑝̅ ), through the partial derivatives of 𝑝̅ . All thing being equal, in the case of Tourist-Tourism (Cf. Appendix 1) an increase of transport capacity always decreases price of the trip (and only if transport duration exceeds 2 times the duration of the excursion for Excursionnist-Tourism:𝑏 𝑑 ∈ ]1,2[).Symmetrically, in the case of Excursionist-Tourism (Cf. Appendix 1) an increase of tourism capacity always decreases price of the trip (and only in case of short stay, when transport lasts less than 2 times the stay for Tourist-Tourism).

Figure 1

 1 Figure 1 summarizes the effect of capacity changes on the equilibrium price of the journey, which can be stated as follows: the simultaneous increase of transport and tourism capacities generates a decrease of price of the trip if:

Figure 1 :

 1 Figure 1: Effects of capacity changes on the equilibrium price of the journey

)

  confirms the complementarity of Transport and Tourism. The reaction functions system describes the dynamics of tourism development through the capacities dynamics, illustrated in Figure 2 with given parameters [Cf. Varian (1991, p. 287) for a dynamic reading of the reaction functions system for duopolistic markets]. Three main features stem out of the transitional dynamics of Transport-Tourism capacities: It is triggered by pre-existing capacities of tourism or transport, as both capacities do not start on the y-axis (T 2 =0 in equation T 1 and T 1 =0 in equation T 2 ). As noted in the left figure, an initial transport capacity linked with resident's journey is necessary to start off the installation of tourist capacities and to launch the dynamic Transport -Tourism. Conversely (right figure) an initial capacity tourism indicating pre-existing internal tourism (as defined by WTO) triggers the installation of transport capacity thus increasing tourism capacity;  It is based solely on the observation sector-firms capacities as the partial derivatives are not weighted by the parameters (including installation costs). Thus only imperfect information can hamper Transport-Tourism dynamics. Consequently the model highlights Information as a privileged field for public policy;  It starts with a development phase (positive increase of T 1 and T 2 ) generating a stable equilibrium capacity [because the slopes are different. Cf. Varian (1991, p. 287)],which if exceeded induce adjustments to lower capacity. Thus the reaction functions system pinpoints transitional overcapacity situations.

Figure 2 :

 2 Figure 2 : Reaction fonctions in Transport / Tourisme

  relative superiority of transport price ( 𝑝 1 * > 𝑝 2 * ) . On the contrary in excursionist tourism situations ( 𝑑 𝑏 < 1), the relative overcapacity of transport is concomitant with the relative superiority of tourism prices.

5 ,-

 5 Public policies to rebalance excess capacity depends not only on installation costs but also on demand policies: d and b reflecting the nature of tourist clienteles; b for remoteness and d for the length of stay. The effectiveness of the policy is determined by the equation governing the direction of overcapacity: 𝑇 𝑡𝑟𝑎𝑛𝑠 * -𝑇 𝑇𝑜𝑢𝑟 * = (𝑑𝑐 2 -𝑏𝑐 1 ) 3 Through the lowering of installation costs it can induce and/or the shift of clientele it can foster by dynamic pricing, Internet necessarily changes the optimum capacities of transport and tourism and hence the link between the two branches Equilibrium prices and capacities provides the relative ratio of actual profits of each branch: As explained by appendix 3, β is a non linear combination of installation costs, and durations, and as such can be read as an index of overcapacity because:The interpretive grid for equilibrium ratio of the two sector-firms' profits ( 𝜋 1 * 𝜋 2 * ), summarized by table 3, is structured by tourism types and relative overcapacity.  When transport and tourism are considered goods or when their durations are the same [d=b (=1)], the overcapacity of a sector induces its relative inferiority of profit. Profits are equal if the capacities are identical,  In the situation of Tourist-Tourism (d > b), Transport-sector profit is relatively higher in case of equal capacity (𝑇 𝑡𝑟𝑎𝑛𝑠 * = 𝑇 𝑇𝑜𝑢𝑟 * ), in case of relative Tourism overcapacity (𝑇 𝑡𝑟𝑎𝑛𝑠 * < 𝑇 𝑇𝑜𝑢𝑟 * ), and if the transport overcapacity is below the maximum arrivals weighted by the inverse of the tourism type ratio [(𝑇 𝑡𝑟𝑎𝑛𝑠 * 23 -1 ] . Tourism profits are relatively higher only if Transport overcapacity exceeds the previously mentioned threshold,  In the situation of Excursionist-Tourism (d <b), tourism profit is relatively higher in case of equal capacity (𝑇 𝑡𝑟𝑎𝑛𝑠 * = 𝑇 𝑇𝑜𝑢𝑟 * ), in case of transport overcapacity, and if Tourism overcapacity is above the maximum arrivals weighted by the inverse of the tourism type ratio [( 𝑇 𝑇𝑜𝑢𝑟 * -𝑇 𝑡𝑟𝑎𝑛𝑠 *

Table 3 :

 3 Relative Profit ratio acording to tourism type and overcapacity index an "intelligibility scheme" to actual situations.Tourist-Tourism with tourism overcapacity [(1) in Table3] corresponds to the situation described byUNWTO (2012), where air transportation seats are below accommodation capacity. In this situation, the model interprets policies aiming to increase transport capacity as a profit rebalancing directed to the tourism sector since 𝜋 𝑡𝑟𝑎𝑛𝑠𝑝 * > 𝜋 𝑇𝑜𝑢𝑟 * and not necessarily as a tourism development strategy. The model explains cruiselines' fee on shore excursions during the stopovers [Cf. Petit-Charles and Marques (2012)]. This percentage of the shore excursion price is a share of the profit kept by the carrier, in order to rebalance the profit ratio in favor of tourism sector, in a situation of excursionist-tourism (d / b <1) and relative transport overcapacity ( [(2) in Table 2]. Indeed in the Caribbean islands, the capacity of cruise megaships often exceeds the capacity of ground attractions. Without the rebalancing, cruiselines stopovers are less numerous in the destination [Cf. Little Charles and Marques (2012)]. Thus the cruiselines' fee appears as a cooperation device, fully understandable in the frame of the model.

Appendix 1 :

 1 Arrivals equation and Revenue loss minimization b and d parameters in the arrivals equation. The meaning of b and d in the arrivals function is based on the complementarity of transport and tourism in the utility function of the representative visitor and hence on his/her optimization behavior.

  or transport time (the right to use one unit of capacity), X 2 , the right to use the tourism unit capacity (good or duration), with p 1 , p 2 , their respective prices. as regards to the utility function, the proportions of X 1 and X 2 are fixed and determined by b and d according to the equation: 𝑏𝑋 1 = 𝑑𝑋 2 . Hence b and d are the respective weights of Transport and Tourism. If utility is measured by the number of services then b = d = 1, since the travel requires one transport service and one tourism service (considered the aggregation of all rights of use of tourism characteristic goods, consumed during the stay). If time is the utility unit (transport hours and nights of the stay) b and d are the weighting parameters of transportation durationand stay in the destination. Thus, considering transport technology (able to connect all places within 24 hours) when 𝑑 ≤ 𝑏, (length of stay in the destination lower than or equal to the transport duration) the model considers the journey as an excursion trip, and the visitor like an "excursionist" according toUNWTO(2002) categories. Consequently in the frame of the model d > b refers to longer stays (and to some extent to more distant destinations) and to visitors categorized as "tourists" byUNWTO (2002). Within these broad limits defining the two major tourism categories (excursion and long stay), the various values for d / b signalize the variety of tourist types. In order to ease interpretations and to adopt broad categories, when 𝑑 ≤ 𝑏, the model categorizes tourism activities as Excursionist-Tourism and if d > b,



  selon leur valeur seuil permet une lecture simplifiée des rapports de profit effectif d'équilibre, que synthétise le tableau 3 :Ranking the two components of the profit ratio ( , according to their threshold values allows a simplified reading of the sectoral distribution of profit, as summarized by Table3

Table 1 :

 1 Determinants of the optimal transport-tourism ratio capacities

			𝒃 = 𝒅	𝒃 ≠ 𝒅
	𝒄 𝟐 ⋛ 𝒄 𝟏	𝑇 𝑡𝑟𝑎𝑛𝑠𝑝𝑜𝑟𝑡 *	⋛ 𝑇 𝑇𝑜𝑢𝑟𝑖𝑠𝑚 *
	( 𝒅 𝒃 ) ⋛ (	𝒄 𝟏 𝒄 𝟐	)		𝑇 𝑡𝑟𝑎𝑛𝑠𝑝𝑜𝑟𝑡 *	⋛ 𝑇 𝑇𝑜𝑢𝑟𝑖𝑠𝑚 *

Table 2 :

 2 Interpretive Grid of overcapacity depending on tourism situations Proofs supporting the table are in Appendix 2.

	Tourism type / Parameter	𝒄 𝟐 > 𝑐 𝟏	𝒄 𝟐 < 𝑐 𝟏	𝑻 𝒕𝒓𝒂𝒏𝒔𝒑 *	< 𝑻 𝑻𝒐𝒖𝒓 *	𝑻 𝒕𝒓𝒂𝒏𝒔𝒑 *	> 𝑻 𝑻𝒐𝒖𝒓 *
	Transport-Tourisme: Goods ( b=d=1)	𝑇 𝑡𝑟𝑎𝑛𝑠 *	> 𝑇 𝑇𝑜𝑢𝑟 *	𝑇 𝑡𝑟𝑎𝑛𝑠𝑝 *	< 𝑇 𝑇𝑜𝑢𝑟 *
	Touris-Tourism (	𝒅 𝒃	> 1)		𝑇 𝑡𝑟𝑎𝑛𝑠𝑝 *	> 𝑇 𝑇𝑜𝑢𝑟 *	𝑝 1 * > 𝑝 2 *
	Excursionist-Tourism (	𝒅 𝒃	< 1)			𝑇 𝑡𝑟𝑎𝑛𝑝 *	< 𝑇 𝑇𝑜𝑢𝑟𝑖𝑠 *	𝑝 1 * < 𝑝 2 *

Table 2 :

 2 on empirical tourism demand modeling, it is possible to model linearly arrivals as:𝐹 = 𝑎 -𝐹 = 𝑎 -𝑏𝑝 1 -𝑝 2 . In this perspective b and d come from the utility function and are the weights of the two goods or durations. They also materialize the sensitivity of arrivals to prices and to passenger types and to varieties of trips and visitors. 𝑝 𝑖 𝑇 𝑖 -𝑝 𝑖 (𝑎 -𝑏𝑝 1 -𝑝 2 )] is𝑇 𝑖 -(𝑎 -𝑏𝑝 1 -𝑝 2 ) = -𝑏𝑝 𝑖 3. Reading dynamically this result (projecting it in the moment) and allows T i to be an instantaneously available capacity (total capacity less total sales), it comes 𝑇 𝑖𝑡 -(𝑎 -𝑏𝑝 1𝑡 -𝑝 2𝑡 ) = -𝑏𝑝 𝑖𝑡 which is equivalent to 𝑇 𝑖𝑡 -𝐹 𝑡 = -𝑏𝑝 Interpretive grid of relative overcapacity according to tourism type

	𝑚 𝑋 2	; negatively linked

𝑖𝑡 . And the remaining capacity instantly () evolves inversely to price. In other words the lower the remaining capacity and the higher the instantaneous price; which illustrates a basic principle of the Revenue / Yield management: either a price which increases in time with the filling. As a result, moderate assumptions of minimizing Revenue loss lead to dynamic pricing, reproducing the behavior of firms initiated by internet.

3 With 𝑑 𝑏

  and considering the linear approximation of 𝛽(𝑐 1 ) in the in theHence with 𝐴(𝑐 2 ) = 21𝑐 2 2 𝑑 2 -(8𝑎 + 54𝜃𝑑)𝑑𝑐 2 + 3.3𝑎 2 , it comes 𝐴(𝑐 2 ) > 0 ⟹ ∀𝑐 2 si (8𝑎 + 54𝜃𝑑)𝑑 2 -4 × 21𝑑 2 × 3.3𝑎 2 < 0 ⟹ 𝜃 <

	 (6-1) -(6-2) > 1 vicinity of 𝑑 𝑏 𝑑 𝑏 𝑐 2 , it comes	𝑑 𝑏	𝛽 =	𝑑 𝑏	[1 + (𝑐 1 -	𝑑 𝑏	𝑐 2 )	486𝑏𝑑𝑐 2 2 𝑑 2 -120𝑎𝑑𝑐 2 +50𝑎 2 ] hence : 315𝑐 2
		𝑑 𝑏	𝛽 > 1, equivalent to 𝜋 1 * > 𝜋 2 * ⟹ 1 + (𝑐 1 -	𝑑 𝑏	𝑐 2 )	486𝑏𝑑𝑐 2 2 𝑑 2 -120𝑎𝑑𝑐 2 +50𝑎 2 > 315𝑐 2	𝑏 𝑑
		⟹	1 𝑏	(𝑏𝑐 1 -𝑑𝑐 2 )	486𝑏𝑑𝑐 2 2 𝑑 2 -120𝑎𝑑𝑐 2 + 50𝑎 2 > 315𝑐 2	𝑏 -𝑑 𝑑
		⟹	15 9𝑏	[-(𝑇 1 * -𝑇 2 * ) ]	486𝑏𝑑𝑐 2 2 𝑑 2 -120𝑎𝑑𝑐 2 +50𝑎 2 > 315𝑐 2	-(𝑑-𝑏) 𝑑
		⟹ 𝜃 <	1 54𝑑 2 𝑐 2	(21𝑐 2 2 𝑑 2 -8𝑎𝑑𝑐 2 + 50𝑎 2 ), with 𝜃 =	𝑇 1 * -𝑇 2 * 𝑑 -𝑏
		⟹ 0 < 21𝑐 2 2 𝑑 2 -(8𝑎 + 54𝜃𝑑)𝑑𝑐 2 + 3.3𝑎 2
									𝑎	1
									𝑑	6.23
		⟺ 𝑇 1 * -𝑇 2 * < 𝑎 (1 -	𝑏 𝑑	)	1 6.23 .
		Thus if 𝑻 𝟏 * -𝑻 𝟐 * < 𝑎 (𝟏 -	𝒃 𝒅	)	𝟏 𝟔.𝟐𝟑	then 𝝅 𝟏 * > 𝝅 𝟐 * (6-1)
		and if 𝑻 𝟏 * -𝑻 𝟐 * ≥ 𝒂 (𝟏 -	𝒃 𝒅	)	𝟏 𝟔.𝟐𝟑	then 𝝅 𝟏 * < 𝝅 𝟐 * (6-2)
	 (7)	𝑑 𝑏		< 1, 𝛽 = 1 ⟹	𝑑 𝑏	𝛽 < 1 ⟹ 𝜋 1 * < 𝜋 2 *
	 (8-1) -(8-2) are are obtained with the same method (6-1) -(6-2)
		(9)	𝑑 𝑏	< 1, 𝛽 < 1 ⟹	𝑑 𝑏	𝛽 < 1 ⟹ 𝜋 1 * < 𝜋 2 *

Table 3 :

 3 Interpretive grid of profits ratio according to tourism types

	Type de tourisme / Paramètres	𝜷 = 𝟏 ⇒ 𝑻 𝒕𝒓𝒂𝒏𝒔 * = 𝑻 𝑻𝒐𝒖𝒓 *		𝜷 > 1 ⇒ 𝑻 𝒕𝒓𝒂𝒏𝒔 *	< 𝑻 𝑻𝒐𝒖𝒓 *	𝜷 < 1 ⇒ 𝑻 𝒕𝒓𝒂𝒏𝒔 *	> 𝑻 𝑻𝒐𝒖𝒓 *
	Tourisme-Transport : Biens (b=d=1 ou b=d)	𝜋 1 * = 𝜋 2 * (1)			𝜋 1 * > 𝜋 2 * (2)	𝜋 1 * < 𝜋 2 * (3)
	Tourisme de Séjour Long
	(	𝒅 𝒃	> 1)	𝜋 1 * > 𝜋 2 * (4)			𝜋 1 * > 𝜋 2 * (5)	(𝑇 𝑡𝑟𝑎𝑛𝑠 *	-𝑇 𝑇𝑜𝑢𝑟 *	(6-1) ) < 𝑎 (1 -𝑏 ) 6.23 -1 ⟹ 𝜋 1 * > 𝜋 2 * 𝑑 (6-2)
									(𝑇 𝑡𝑟𝑎𝑛𝑠 *	-𝑇 𝑇𝑜𝑢𝑟 *	) ≥ 𝑎 (1 -	𝑏 𝑑	) 6.23 -1 ⟹ 𝜋 1 * < 𝜋 2 *
	Tourisme d'Excursion ( 𝒅 𝒃 < 1)	𝜋 1 * < 𝜋 2 * (7)	(𝑇 𝑡𝑟𝑎𝑛𝑠 *	-𝑇 𝑇𝑜𝑢𝑟 *	(8-1) ) ≥ 𝑎 (1 -𝑏 ) 6.23 -1 ⟹ 𝜋 1 * > 𝜋 2 * 𝑑 (8-2)
					(𝑇 𝑡𝑟𝑎𝑛𝑠 *	-𝑇 𝑇𝑜𝑢𝑟 *	) < 𝑎 (1 -	𝑏 𝑑	) 6.23 -1 ⟹ 𝜋 1 * < 𝜋 2

A country, a sub-region, a city, or a specific space.

branch, sector and industry are equivalent in the paper.

𝑇 𝑇𝑜𝑢𝑟 * ) < 𝑎 (* (𝑇 𝑡𝑟𝑎𝑛𝑠 * -𝑇 𝑇𝑜𝑢𝑟 * ) ≥ 𝑎 (1 -𝑏 𝑑 * Excursionst-Tourism( 𝒅 𝒃 < 1) 𝜋 1 * < 𝜋 2 * (𝑇 𝑡𝑟𝑎𝑛𝑠 * -𝑇 𝑇𝑜𝑢𝑟 * ) ≥ 𝑎 (1 -𝑏 𝑑 ) 6.23 -1 ⟹ 𝜋 1 * > 𝜋 2 * (𝑇 𝑡𝑟𝑎𝑛𝑠 * -𝑇 𝑇𝑜𝑢𝑟 * ) < 𝑎 (1 -𝑏 𝑑 ) 6.23 -1 ⟹ 𝜋 1 * < 𝜋 2 * 𝜋 1 * < 𝜋 2 * (2) Proofs supporting the table

Appendix 2: Interpretive grid of relative overcapacity

The equilibrium capacities are:

They allow the calculus of equilibrium prices:

From 𝑇 1 * et 𝑇 2 * , it comes:

Appendix 3: Profits and equilibrium profits ratio.

Equilibrium Profits

The equilibrium prices and capacities from appendix 2 allow the calculus of actual equilibrium profit. hence the equilibrium profits ratio :


Roman Ursu

Khalid Latif

David Novo

Manuel Selva

Abdoulaye Gamatie

Gilles Sassatelli

Dmitry Khabi

Alexey Cheptsov

A Workflow for Fast Evaluation of Mapping Heuristics Targeting Cloud Infrastructures

Resource allocation is today an integral part of cloud infrastructures management to efficiently exploit resources. Cloud infrastructures centers generally use custom built heuristics to define the resource allocations. It is an immediate requirement for the management tools of these centers to have a fast yet reasonably accurate simulation and evaluation platform to define the resource allocation for cloud applications. This work proposes a framework allowing users to easily specify mappings for cloud applications described in the AMALTHEA format used in the context of the DreamCloud European project and to assess the quality for these mappings. The two quality metrics provided by the framework are execution time and energy consumption.

I. INTRODUCTION

Usage of cloud infrastructures is by no means uncommon in today's scientific research. A typical cloud system is a sophisticated tool that relies on the possibility to combine computing, networking and storage capabilities of multiple compute resources to deliver a significant performance advantage over traditional desktop systems. Due to the size, cost and complexity of operation, cloud infrastructures are almost never used exclusively by a single user. Instead, a number of users try to compete for access, and thus mechanisms for effective resource sharing among multiple consumers need to be in place. A process of allocating resources efficiently and scheduling computational tasks onto these allocated resources in a proper manner is based on resource allocation and scheduling priorities. A typical scheduler targets multiple goals including maximization of throughput and minimization of response time (i.e., latency). Other optimization criteria such as CPU fairness or energy-awareness are becoming more important nowadays, too. Since some of these goals are conflicting with each other, a job of the scheduler is to figure out a reasonable allocation strategy. The approach of the DreamCloud project is to enable a closed-loop control mechanism that will allow for a dynamic resource allocation. In order to assess the quality of the resource allocation policy, the use of dedicated fast simulators is of an essential importance.

Contributions of this paper. To address the above requirement this work proposes a configurable framework for fast performance evaluation of different resource allocation strategies targeting cloud infrastructures. The implemented framework connects AMALTHEA [START_REF]AMALTHEA : Model Based Open Source Development Environment for Automotive Multi-Core Systems[END_REF] application models, used to describe application as a tasks graph in the context of the DreamCloud project (see Section III for the details of the model) to the SimGrid [START_REF] Casanova | Versatile, scalable, and accurate simulation of distributed applications and platforms[END_REF] simulation platform. It aims to be an open-source and flexible framework supporting a variety of state of the art mapping algorithms.

Outline. In the rest of the paper, Section II discusses related work. Section III introduces the AMALTHEA application modeling language. The core engine of our simulation framework allowing to simulate AMALTHEA applications on top of SimGrid is presented in Section IV. The proposed framework is introduced in Section V. Section VI presents the results of using this flow to evaluate the quality of different mappings for a scientific application case study executed on top of a typical cloud infrastructure. Finally, conclusions and perspectives are presented in Section VII.

II. RELATED WORK

A variety of cloud application simulation frameworks has been proposed. CloudSim [START_REF] Buyya | Modeling and simulation of scalable cloud computing environments and the cloudsim toolkit: Challenges and opportunities[END_REF] is a Java-based software framework, which supports modeling and simulation of large scale cloud computing components and environments including data centers, virtual machines, service brokers, and provisioning policies. The features of CloudSim include the availability of a visualization engine that aids in the creation and management of multiple, independent, and co-hosted virtualized services on a data center node. Additionally, CloudSim provides flexibility to switch between space-and time-shared allocation of processing cores to virtualized services. These compelling features of CloudSim would speed up the development of new application provisioning algorithms for cloud computing. However, it is not possible to define the task level allocation policies for CloudSim.

iCanCloud [START_REF] Nez | icancloud: A flexible and scalable cloud infrastructure simulator[END_REF] is an open source C++ based simulation platform which has been designed to model and simulate cloud computing systems. A user can customize the core hypervisor class, which in turn is the core of iCanCloud. The Amazon model has been integrated with the simulator. From the DreamCloud perspective, being a commercial tool, it does not provide complete access to flexibly deal with application requirements. There is no support for power consumption analysis. Additionally, the focus of iCanCloud is to define and integrate new brokering policies and minimize the user cost for public cloud infrastructure based on classical scheduling heuristic. While the main focus of the DreamCloud project is dynamic resource allocation. GreenCloud [START_REF] Kliazovich | Greencloud: A packet-level simulator of energy-aware cloud computing data centers[END_REF] is a sophisticated packet-level cloud simulator with focus on energy consumption for cloud communications. It is a C++/OTcl based tool, with underlying NS-2 platform. GreenCloud provides the fine-grained modeling of energy consumption for the IT equipment in a data center, such as computer servers, network switches and communication links. From the DreamCloud perspective, GreenCloud does not provide the detailed timing analysis which is an important requirement for evaluating dynamic resource allocations. To introduce the detailed timing analysis, a designer needs to define timing models in NS2 environment, which is quite outdated and very complex tool.

SimGrid [START_REF] Casanova | Versatile, scalable, and accurate simulation of distributed applications and platforms[END_REF] is an open-source and mature C-based tool to study the behavior of large-scale distributed systems such as grids, clouds, HPC or P2P systems. It can be used to evaluate heuristics, prototype applications or even assess legacy MPI applications. Task level scheduling is supported by SimGrid. The power consumption analysis tool has been integrated to the latest version of SimGrid. Additionally, the developers are very reactive. Due to these facts, and making the basic study, SimGrid was selected for the integration to our design framework for performance evaluation of applications executed on top of cloud infrastructures.

III. APPLICATION MODEL

In the context of the DreamCloud project addressing resource allocation issues in future multicore and distributed systems. From the applications perspective, the project focuses both on automotive and on cloud computing domains. The AMALTHEA [START_REF]AMALTHEA : Model Based Open Source Development Environment for Automotive Multi-Core Systems[END_REF] application model has been chosen to capture application specifications from these two domains because it provides clean abstractions allowing to capture both of them. AMALTHEA is based on three main entities: labels representing data elements, runnables denoting the smallest units of code schedulable by an OS, and tasks defined as graphs of runnables.

In AMALTHEA, an application model is represented as a directed task graph, where vertices represent tasks, while directed edges represent either inter-task activation or communication. Figure 1 represents an arbitrary AMALTHEA application model with seven tasks and three labels. To specify the inter-task communication, it can be observed that task T1 communicates with task T3 via label L3. For this purpose, T1 will perform a write operation on L3 while T3 will perform the read operation on L3. The size of L3 represents the exchanged data volume. At lower granularity level, a task is composed of runnables as illustrated in Figure 1. Task 'T4' is composed of eight runnables. The runnable graph also provides information about precedence relationship between runnables.

A runnable is a set of "abstract" instructions. Such instructions in AMALTHEA application model are categorized as computation and communication instructions. Computation instructions feature operations such as arithmetic or logical operations. For quick simulations, the actual computational instructions are abstracted away by representing them with associated platform-dependent execution time in terms of clock cycles. Communication instructions comprise read/write accesses to labels, which can be further classified into two categories: local and remote accesses. Local accesses take place, when the target label is mapped at the same node as the requesting runnable. Remote accesses take place, when the target label is mapped on a different node, compared to that of the requesting runnable. In this case, the runnable goes to the wait state to wait for the read/write response and makes the core available for other runnables to execute their instructions. Figure 1 details the instructions of runnable R45 from the runnable graph of task T4. The computation instructions are represented with their execution delay values. The communication instructions are represented as read/write accesses to their corresponding labels. Their simulation consists of the actual transfer of data size corresponding that of accessed labels in read/write requests.

IV. FROM SIMGRID TO AMALTHEA

We now review how we implemented the simulation of AMALTHEA on top of SimGrid. Before digging into the details, we describe the basic architecture of SimGrid.

A. SimGrid Architecture

SimGrid has been an active project for more than 10 years. Since then, it has been evolving and taking new systems into consideration. At start, it was dedicated to grid simulation and its focus was to study centralized scheduling simulations (e.g., off-line scheduling of a Directed Acyclic Graph (DAG) on a heterogeneous set of distributed computing nodes). Nowadays, SimGrid deals with a variety of distributed systems ranging from grids to peer-to-peer systems, and arbitrarily defined hybrid systems combining different computing systems. The latest, stable and publicly available version of SimGrid (3.2) implements the architecture shown in Figure 2.

SimGrid provides three APIs, all based on a common kernel, allowing users to write custom simulations. SURF is this internal kernel. It provides the core functionalities to simulate a virtual platform. It has been implemented at very low level and is not intended to be used by the end users. On top of the SURF layer, SimDag, MSG, and SMPI APIs are implemented. MSG is a simple programming environment, which was the first distributed programming environment provided within SimGrid. It is still the most commonly used programming environment and can be used to build rather realistic simulations. The SimDag API provides specific programming environment for DAG applications while the SMPI one provides support for simulating MPI applications. To simulate the rich set of abstractions provided by the AMALTHEA model described in the previous section, we rely on the MSG API.

B. AMALTHEA Execution on SimGrid

To execute AMALTHEA applications on top of SimGrid, we implemented a simulation core engine that reads the inmemory object representation of an AMALTHEA model and converts it to SimGrid function calls. Each runnable of the initial application is converted into a SimGrid process. Runnables activation dependencies and communications through labels are then implemented using SimGrid messages.

In order to fit AMALTHEA applications into SimGrid, we simplified the AMALTHEA semantic by gathering runnable all the read instructions together, all the computing instructions together and all the write instructions together. From that, each runnable process first waits for receiving activation messages from all the runnables it depends on. Then, the runnable process sends messages corresponding to the read label instructions it contains. The computational load of the process required by SimGrid is then computed by adding the execution time of all the compute instructions of the runnable. Finally, the runnable process performs write label operations and activates all subsequent runnables.

V. WORKFLOW

In order to be able to simulate AMALTHEA applications on top of cloud infrastructure we propose the workflow depicted on Figure 3. This workflow comprises the 4 following steps: • Step 1. The application specifications are modeled in AMALTHEA as discussed in the previous section.

• Step 2. The AMALTHEA model is fed to a parser. The job of the parser is to translate the AMALTHEA model into a format that can then be easily simulated using SimGrid. This parser has been written in C++ to be easily integrated with SimGrid.

• Step 3. The mapper then executes the mapping algorithms by allocating tasks and labels onto the simulation platform. The description of the platform is provided to the mapper. Because this work focuses on providing a modular workflow to evaluate mapping strategies, the proposed workflow does not provide elaborated mapping strategies but rather a clean and simple interface to let users implement any mapping algorithm. Nevertheless, to illustrate the complete workflow, basic mapping strategies are proposed by default.

• Step 4. At this stage, the AMALTHEA runnables are executed on the simulation platform as described in Section IV-B. Ensuring the AMALTHEA execution semantic on top of the SimGrid MSG API was the biggest implementation challenge of the workflow.

As results, the simulation platform provides information about execution time, execution steps and the energy consumption. All these results are provided in the form of text files. The energy consumption results are provided both globally and per host. Moreover, the workflow provides a timeline view of runnable processes execution on the different hosts of the platform. This timeline view, provided as a standard timeline trace file, also includes runnables dependencies and labels reading and writing information. This timeline view is provided as a trace file that could be visualized using the Vite1 open source tool. We now show how the proposed workflow can be used to evaluate an application model and a cloud infrastructure provided by the High Performance Computing Center Stuttgart (HLRS) involved in the DreamCloud project. In all the following experiments, to illustrate the workflow, we use a mapping algorithm that randomly allocate the runnables and the labels on the underlying hardware infrastructure.

A. eScience Application

The application use case is based on an eScience application MS2 genetic algorithm [START_REF] Glass | A molecular simulation tool for thermodynamic properties, new version release[END_REF] that performs molecular dynamic simulation. The goal of the simulation is to predict thermophysical properties of condensed fluids which is required for the design and optimization of chemical engineering processes. The eScience workflow as an AMALTHEA model is depicted in Figure 4. MS2 is used to optimize the algorithm that tries to fit the parameters of an existing model to data collected through experiments. The algorithm is executed until a good enough fit is found, a time limit is hit or a fixed number of iterations have been executed. This in handled by the task called Check termination. In this application model, each AMALTHEA task contains a single runnable as depicted on Figure 4. As shown for the runnable R7, each of this runnable first reads input data from an input label, then perform a specific number of computations instructions and finally write output data to an output label. In all the following experiments, 32 MS2 tasks are considered.

B. Cloud Infrastructure

The cloud infrastructure model we use in the following experiments, also provided by HLRS, is shown in Figure 5. It contains two nodes, named node 0 and node 1. Node 0 contains three hosts (node0,0 node0,1 and node0,2) while node 1 contains two hosts (node1,0 and node1,1). The configuration details for each host can be observed. The Frontend host acts as the master, and resource allocations are performed by this host. The bandwidth requirements for each interconnection link have also been provided.

1) Preliminary mapping evaluation:

We simulate the execution of the eScience application on the cloud infrastructure described previously. During each simulation instance, we trigger only one run of the application, i.e. the execution of the application completes as soon as the task named Check termination finishes. In a more general execution setting, the eScience application could possibly iterate several times until data fit the model (see Section VI-A).

On the other hand, we consider two variants of the cloud infrastructure: heterogeneous and homogeneous platform versions. The former is depicted in Figure 5, while the latter is obtained by removing from the Heterogeneous platform the host HOST 0 2, which is a GPU.

We conducted two sets of experiments. In the first experiment, we simulated the execution of 100 random static mappings of the eScience application on the homogeneous platform. The characterization of each mapping in terms of execution time, energy dissipation and simulation time is represented in Figure 6 The second experiment is similar to the first one with the difference that the heterogeneous platform is considered. The corresponding results are shown in Figure 7. Here, for the mappings with the shortest and longest execution time, the detailed temporal evolution of the execution is depicted in Figures 8(a) and 8(b) respectively. We can notice that in this heterogeneous case, the mapping with the shortest execution time may not be the most efficient one from the energy point of view. Indeed, the mapping number 28 may be a good 2) Large-scale mapping evaluation: We extend the previous evaluation to a larger number of mappings in order to get a better insight of the mapping impact on execution time. Figure 9 shows the evaluation of 6000 mappings of the eScience application on the heterogeneous platform. As for the experiments in the previous section, these mappings are randomly choosen.

The shortest execution time is approximately 0.3064 seconds and the longest execution time is 0.6938 seconds. The reason for this difference is that in the first case, 34.4% of the MS2 tasks are mapped on the host HOST 0 2 which is almost 100 times more powerful than the other hosts. In the second case, only 18.8% of the MS2 tasks are mapped on the host HOST 0 2. Thus the execution for the first mapping finishes sooner than for the second mapping. Table I shows the execution times (E time(s)) in seconds of different mappings. Each line corresponds to one mapping. For each mapping, the percentage of the MS2 tasks that were mapped on the different hosts is specified. The mapping m good is the best mapping 9. The m bad is the worst mapping among those 6000 mappings. For the mapping m best, all the tasks of the eScience application were mapped to the most powerful host HOST 0 2. In case of m worst, all the tasks were mapped on the host HOST 0 1. When mapping all the tasks on the most powerful host, the execution time is almost 10 times shorter than if all the tasks are mapped on another host. This result was expected. Indeed, among the 5 hosts of our platform, 4 hosts have identical performances and 1 host provides 100 times more flops compared to any other host. VII. CONCLUSION AND PERSPECTIVES This work proposes an integrated simulation workflow allowing to quickly assess the quality of mapping algorithms targeting cloud infrastructures and according to different criterion (i.e. energy efficiency or execution time). This simulation workflow takes as input applications specifications expressed in the AMALTHEA model chosen as the application model to be used in the context of the DreamCloud project. The specification of custom mappings is handled by implementing a clean interface whose main role is to specify where AMALTHEA entities should be located on the targeted cloud architecture.

We plan to extend the mapping design space exploration introduced in the last section with optimization techniques such as simulated annealing to allow end users to identify what are (and why) the best mapping strategies for particular applications. We will also improve the quality of the provided results by taking into account more architectural parameters.

Fig. 1 :

 1 Fig.1: An illustrative representation of an application as a task graph and task as a runnable graph, with inter-task activation and communication.

Fig. 2 :

 2 Fig. 2: SimGrid Architecture and the contribution of this work. SimGrid provides three different APIs allowing user code to write custom simulations. This work relies on the MSG API.

Fig. 3 :

 3 Fig. 3: Simulation workflow from applications specifications to simulation results

Fig. 4 :

 4 Fig. 4: The eScience case study application

Fig. 5 :

 5 Fig. 5: The HLRS cloud platform

  . The red and blue dots in the Figure, point to the worst and best mappings in terms of the execution time.

Fig. 6 :

 6 Fig. 6: Simulation on homogeneous platform

Fig. 7 :

 7 Fig. 7: Simulation on heterogeneous platform

Fig. 9 :

 9 Fig. 9: Large scale simulation on heterogeneous platform

TABLE I :

 I Execution times and MS2 tasks mappings for different mappings

http://vite.gforge.inria.fr

ACKNOWLEDGMENT

The research leading to these results has received funding from the European Community's Seventh Framework Programme (FP7/2007-2013) under the DreamCloud Project: http: //www.dreamcloud-project.org, grant agreement no. 611411. The authors would like also to thank the SimGrid team for the provided support.


Amir Aly
email: amir.aly@ensta-paristech.fr

Adriana Tapus
email: adriana.tapus@ensta-paristech.fr

Prosody-Driven Robot Arm Gestures Generation in Human Robot Interaction

Keywords: I, 2, 9 [Computing Methodologies]: Artificial Intelligence-Robotics Experimentation

 shows that para-verbal and non-verbal communications are naturally synchronized. This research focuses on the relation between non-verbal and para-verbal communication by mapping prosody cues to the corresponding arm gestures. Our approach uses the coupled hidden Markov models (CHMMs), which could be seen as a collection of HMMs, one for the prosodic characteristics' stream and six HMMs for the arm movements' stream [4] [1].

INTRODUCTION

The important role of robots in our daily life shows the importance of making robots capable of generating intuitive multimodal communication with people under different interactional contexts. This research tries to understand the natural communication strategies of arm gestures that humans use in their daily interaction in order to transfer them to the robots. The database used in this research is the Stanford database available online [START_REF] Levine | Gesture controllers[END_REF] which is composed of many avatar videos. The data is acquired from human individuals using the PhaseSpace motion capture system and processed with the MotionBuilder system, which provides an approximation of the Euler rotations of the arms' joints.

TEMPORAL SEGMENTATION

Speech

Speech is analyzed in terms of the intensity and pitch curves of the voice signal. The inflection points (i.e., zeros crossing points of the rate of change of the curve) of the pitch and intensity curves, and the separating points between the voiced and the unvoiced parts of the voice signal are detected

Copyright is held by the author/owner(s). HRI'12, March 5-8, 2012, Boston, Massachusetts, USA. ACM 978-1-4503-0561-7/11/03.

Trajectory Class

Trajectory State

1 Pitch ↑ & Intensity ↑ 2 Pitch ↑ & Intensity ↓ 3 Pitch ↓ & Intensity ↑ 4 Pitch ↓ & Intensity ↓ 5
Unvoiced segment Table 1: Voice Signal Segmentation Labels [START_REF] Aly | Speech to head gesture mapping in multimodal human-robot interaction[END_REF]. The trajectories between the detected points in the pitch and intensity curves are then labeled according to the behavior of these trajectories together (see Table 1). Speech signal is segmented into syllables constituting the states of the HMM of the voice stream. The authors in [START_REF] Arai | The temporal properties of spoken japanese are similar to those of english[END_REF] defined the average duration of a syllable as 200ms and this duration can increase or decrease according to the nature of the syllable as being short or long. Practical tests proved that within a syllable of duration varying from 180ms to 220ms, the average number of trajectory classes in its corresponding pitch and intensity curves is around 5, therefore each 5 labels of the signal constitute a state in the HMM of the voice stream.

Arm Gestures

Arm gestures are characterized in terms of Euler angles of the six articulations (Elbow, Shoulder, and Wrist) of the two arms. Due to the mechanical limitations of the test platform (Nao robot), Euler rotations of the articulations are limited to be: Pitch and Roll -Shoulder; Yaw and Roll -Elbow; and Yaw -Wrist. Roll, Pitch, and Yaw rotations' data indicated in the database are segmented similarly to the voice signal by comparing the trajectories of the relevant Euler curves of each articulation and giving a label according to the behavior of these trajectories together (see Table 2). However, for the wrist articulation, we used the following rule: if the rate of change of the specific trajectory of the yaw curve is increasing, it takes label 1; if it is decreasing, it takes labels 2; or it takes label 3 for no change. Arms articulations are modeled in terms of six independent HMMs that construct together with the HMM of the voice stream the entire CHMM used for mapping the speech to generated mechanical rotations in each articulation in the arms (fig 1) [START_REF] Rezek | Coupled hidden markov models for biosignal interaction modelling[END_REF]. Each state in the HMMs modeling arm's articulations presents a complete performed gesture, which is presented by 4 labels of the obtained trajectory classes of each articulation.

Trajectory State Trajectory Class

Shoulder Elbow 1 Pitch ↑ & Roll ↑ Yaw ↑ & Roll ↑ 2 Pitch ↑ & Roll ↓ Yaw ↓ & Roll ↑ 3 Pitch ↓ & Roll ↑ Yaw ↑ & Roll ↓ 4 Pitch ↓ & Roll ↓ Yaw ↓ & Roll ↓ 5
No Change No Change Table 2: Shoulder and Elbow Movements Segmentation Labels Figure 1: Coupled Hidden Markov Model CHMM mapping between speech to arm gestures' sequences

SYNTHESIZED GESTURES

The synthesized Euler angles of each articulation are compared to the original Euler angles in terms of the similarity between the trajectory classes (see Table 3). However, during human-human interaction, the generated arm gestures differ from one person to another in terms of the direction and the amplitude of the performed gesture. Therefore, the obtained scores of similarity between the original and synthesized trajectories could be considered as reasonable results (see Figure 2 and3) because this research focuses on automatic robot arm gestures generation based only on human user prosody.

CONCLUSIONS

This research focuses on synthesizing robotic arm gestures based on human user speech characteristics (e.g., pitch and intensity of the signal). Our mapping system is based on the Coupled Hidden Markov Model (CHMM) that tries to find a coupling joint between the audio and gesture sequences. The obtained scores of similarity between the trajectories of the

Figure 2 :Figure 3 :

 23 Figure 2: Typical view of the database test avatar and the Nao robot performing its own generated arm gestures

Table 3 :

 3 Comparison between the original to synthesized gestures' trajectory classes

	Articulation	Similarity Scores between
		Trajectory Classes
	Left Shoulder	47%
	Left Elbow	55%
	Left Wrist	57%
	Right Shoulder	52%
	Right Elbow	59%
	Right Wrist	61%

ACKNOWLEDGEMENTS

This work is supported by the French National Research Agency (ANR) through Chaire d'Excellence program 2009.


Daniel Florentin

Pauline Gabillet

Catalina Duque Gomez

Larger, diversified and courted : the new triad of local firms of infrastructure in transition ?

Keywords:

Following Jaglin and Verdeil's argument (2013), this article considers that a certain obsession for energy transition may overlook important transformations of energy systems. A focus on largely understudies local utilities in three very different contexts (Grenoble, Magdeburg, Medellin) reveals important though silent changes that indicate new emerging infrastructure regimes and complete the literature on infrastructure firms.

infrastructure and constitute the background of many crucial aspects underpinning the various energy transitions and the evolution of urban services.

Research on infrastructure firms has been predominantly focusing on multinationals and their role in globalisation as new actors of urban capitalism (notably in Lorrain's classic work, 2002). We would like to complete this approach by looking at the other side of the pipe, focusing on local infrastructure firms in cities that are not necessarily at the head of the metropolitan archipelago (Dolffus, 1996;[START_REF] Malone | Ordinary Cities: Between Modernity and Development[END_REF]. Over the last decade, one has easily noted a renewed interest on local infrastructure firms for mainly two reasons. First, they have been at the very centre of debates of political economy on their juridical status, and much attention has been given to processes of remunicipalisation of urban utilities with the flagship examples of Berlin, Grenoble or Bogota (Blanchet, 2015;[START_REF] Hall | Re-municipalisation in the early 21st century: water in France and energy in Germany[END_REF][START_REF] Hüesker | Managing Water Infrastructures in the Berlin-Brandenburg Region between Climate Change, Economic Restructuring and Commercialisation[END_REF]. Second, they have been inserted in socio-technical debates, where local utilities are considered as levy of energy transformations at local level [START_REF] Duque Gomez | Geschichte der Stadtwerke[END_REF][START_REF] Nieswandt | Der Querverbund im Kontext kommunalen Wirtschaftens[END_REF]. Our contribution wants to go beyond these two arrays of debates to illustrate sociotechnical transformations of a different kind. Our joint work started with the comparison of our respective case studies, all of them centred on local infrastructure firms but in much differentiated contexts.

To sketch it briefly, the first one is enshrined in the typical context of post-socialist transition (Golubchikov et al., 2014 ;Sykora et Bouzarovski, 2012) in the city of Magdeburg (Germany, capital city of the Saxony Anhalt region, 100 kilometres away from Berlin). Urban services are predominantly delivered by the local multi-utility, the Stadtwerk of Magdeburg (SWM), which is a typical case of the German strong municipal2 model of urban services (Krämer, 1992;Barraqué, 1995). The Stadtwerk has had to face tremendous diminutions of consumptions of water, gas and district heating, which forced the utility to initiate a massive transformation of the urban water and energy system. The second case is situated in Grenoble, a French middlesized city close to the Alps, whose energy system does not entirely reflect the traditionally stateled French model of energy governance. Due to political decisions of the early 20 th century, the city is supplied by a local public utility (Gaz Electricité de Grenoble, GEG), while the regulation of gas and electricity markets remain national. This regulation and general organisation is currently questioned by the twofold process of liberalisation and the integration of climateenergy objectives in public policies and programmes. The third and last case is incorporated within the framework of the intensely liberalised energy market of Colombia, in the city of Medellin, the second biggest city of the country. The local multi-utility of the city (Empresas Publicas de Medellin, EPM), which historically had played a key role in the development of electricity at the regional level all the 20 th century long, has managed to span unchanged the multiple neoliberal reforms of the 1990s that deconstructed the traditional model of municipal multi-utilities.

Despite the variety of institutional and geographical contexts of these cases, something seems to emerge out of their comparison. Their respective evolution tells a somewhat common story, and important similarities arise in the strategies adopted to deal with highly competitive energy market: this goes beyond the simple coincidences. Although they obviously do not represent all the local firms of infrastructures, they do embody a trajectory followed by numerous similar utilities, which translates into a new infrastructure regime [START_REF] Monstadt | Conceptualizing the political ecology of urban infrastructures: insights from technology and urban studies[END_REF] that constitutes the essential background to decrypt the modifications of urban energy systems. This transformation rests on three pillars, which constitute the three following parts of this article: a rescaling of their supply area, a diversification of their business model and a reconfiguration of their relation with the city. These three transformations are crucial to understand the context in which energy transformations are happening.

The rescaling of local infrastructure firms

The first pillar of this fairly silent but decisive transformation is relating to a modification of the territoriality of these firms. The liberalisation of energy markets and the increasing development of new forms of energy production and distribution have contributed to new spatial arrangements in energy systems: over the last fifteen years, new scales of governance have appeared, which accelerated the production of new geographies of local infrastructural firms.

This process translates into a form of rescaling, and echoes one of Brenner's hypothesis that state rescaling processes were accompanied by new forms of (spatial) redistribution occurring at regional level, which he coins as "neo-Fordist political projects" (Brenner, 2004:466). Through the various forms of rescaling they have carried out, local infrastructure firms are increasingly becoming multi-scale actors. Our three cases reveal a twofold expansion strategy, combining an expansion at a local level in continuity with existing infrastructures and an extension at a regional if not national level.

In the case of Magdeburg, this rescaling is quite striking, yet marked by the ambivalent alternation between processes of de-territorialisation and re-territorialisation. The first steps of liberalisation of energy markets were used by the multi-utility to develop its activities in other cities to expand their markets, like in cities in the Northern part of Germany such as Hamburg or Schwerin. This went so far as to provoke a form of de-territorialisation, as SWM was selling more electricity outside Magdeburg than in its traditional core of intervention. Such a trend was considered as potentially risky by some of the directors of the company, and the general strategy developed by the company now favours a regional scale and a form of re-territorialisation. The utility's zone of influence has consequently been extended at a regional level, may that be in power, gas or water sectors. The utility has, for instance, taken over the technical management of Schönebeck's region water provider (South of Magdeburg, WZV Schönebeck) or the commercial management of Stendal's Stadtwerk. Similar evolutions have occurred in the power sector, as parts of other local multi-utilities specialised in the energy provision have been bought by SWM, in Zerbst or Stendal. This general expansion can be considered as a combined territorial consolidation and upscaling of the Stadtwerk. Its goal is clearly expressed by many representative of the company: becoming a regional utility and not only a local communal one.

As in other similar cases such as Halle, the utility of the regional metropolis progressively extends its zone of influence by buying local Stadtwerke. This allows the company to compensate a somewhat shrinking or not-growing original market quite typical of post-socialist contexts, which are characterised by emerging cold spots [START_REF] Moss | Cold spots" of urban infrastructure: shrinking processes in Eastern Germany and the Modern Infrastructural Ideal[END_REF] where demand has tremendously decreased and where the level of service has worsened. Such a strategy highly nourishes the economic viability of the company, as of its benefits now come from this network of subsidiaries. To a certain extent, this also mimics a similar path adopted by one of the shareholders of SWM, Gelsenwasser, which has also largely extend its zone of influence in the water sector in other regions to compensate a declining demand in the Ruhr area. Local multi-utility such as SWM are progressively becoming regional political actors, and consequently unavoidable actors of possible energy transitions or adaptations.

The Medellin case tells a quite similar story, although the dialectic between de-territorialisation and re-territorialisation may be slightly different. The local multi-utility has largely exceeded its original territory and operated an impressive spatial metamorphosis. Its expansion has been both incremental and monumental. The utility first extended its network at a local level, in the Aburra valley (today's Medellin's metropolitain region) and in its surrounding region of Antioquia, anchoring its influence at a regional level. The primary goal of this change was to enhance the capacity of production of the utility to satisfy the growing demand of energy of the local market. Through these extensions, EPM managed to build a strongly intermeshed power network, which now comprises 24% of the national capacity of production for electricity and commercialises 23% of the consumed electricity at the national level. This helped the utility to become not only a local actor of the energy arena, but a regional and even a national one. During the energy crisis that affected the country in the early 1990s, EPM appeared as a key actor at the national level as they led the construction of an interconnected electricity network and drew a new plan enhancing the supply capacity at the national level (López Díez, 2003;Varela Barrios, 2010). Due to reforms liberalising urban services, EPM had to reshape its administrative structure and created a holding named Grupo EPM. This holding has, through its various subsidiaries, further fostered its spatial development, so that the utility is now active in seven Colombian regions and in six Latin-American countries3 . EPM is thus by no small provider anymore, but rather an impressive firm owned by the municipality of Medellin, but whose tentacles are drawing radically transformed geographies of local firms. EPM explicitly follows a strategy of de-territorialisation of its activities: one of the objectives of Grupo EPM is that 40% of the total revenue should be generated through the subsidiary companies working at the international level. Yet, this form of de-territorialisation does not altogether mean that EPM has deserted the local arena: its spatial model has been twisted but the utility remains locally owned and deeply anchored in its regional native territory.

In the Grenoble case, the possibilities of a similar rescaling of the utility are lower, as the energy distribution remains a national monopoly and consequently locks many possibilities of extension. However, the utility has also adopted new forms of territorialisation [START_REF] Jaglin | Services d'eau en Afrique subsaharienne. La fragmentation urbaine en question[END_REF] and tries to expand its initial frame of action. The utility has installed new sources of renewable energy outside of the city, and increasingly favour the regional scale to develop its various projects. The de-territorialisation is not a relevant issue in this case, but the utility really tries to strengthen and develop its spatial core of intervention, deploying its capacities and activities in concentric circles whose vibrating heart remains the city of Grenoble. The utility has also developed new products to be commercialised in the region, which leads us to the second pillar of the transformation of local infrastructure firms.

The diversification of the business model of local infrastructure firms : a new role in the urban arena

The rescaling of local infrastructure firms produces new geographies of infrastructure management. Yet, this does not exhaust the understanding of the change towards a new infrastructure regime. This geographical mutation is accompanied by a transformation of their business model, and more precisely by the diversification of their production of value. Local infrastructure firms are no longer limited to the lonely transport and supply of a fluid going through a pipe: they have become provider of larger urban services that largely exceed their traditional mission. This diversification of the activity is the corollary of the rescaling, as both tend to anchor further the utility in a larger territory. This gives to the utilities a new role in the urban arena. To phrase it in a different way: one can even wonder whether these utilities can be characterised as simply infrastructure firms.

In the three cases, this alteration of their traditional business model follows two main lines: the expansion of services related to the provision of energy and the development of other services largely unrelated with the core mission of a utility. This process is particularly prevalent in the EPM case in Medellin.

Through its subsidiaries, Grupo EPM has developed a large range of activities and services, that include investment companies (Panama, Caiman Island, Guatemala) or the commercialisation of electric appliances (El Salvador). At the local level, EPM, like many other utilities, has started to enlarge its portfolio of services to downstream users, with offers ranging from advice to reduce consumption to the design of financial programmes to obtain credits in order to buy domestic appliances4 .

Yet, the most decisive part of this strategy of diversification has been carried out outside of the traditional role of energy provider, notably through its programmes of Corporate Social Responsibility (CSR). Most of them have been implemented through the Fundacion EPM. The utility is thereby involved in the management of public spaces and equipment, constructing and running some cultural or educative buildings such as the EPM Library, Parque de los pies descalzos or the new Unidades de Vida Articulada. Through this commitment, the utility participates to the fabric of the urban and to its physical transformation as well as its everyday management. Such a strategy reinforces the visibility of the utility within the city of Medellin, which could almost be named the EPM city. This image-based approach renders the utility both visible and closer to its inhabitants/users and strengthens the attachment of the inhabitants to the utility. In acting so, EPM accentuates its rootedness in the local context, compensating the rescaling of its management. Internationally, EPM also exports the image of Medellin, as if the utility had absorbed the city. This diversification is, in other words, a process of territorialisation, which legitimises the utility as a major player of the local governance.

If Medellin is converting into an EPM city, Magdeburg is also increasingly turning into a SWM city, as a result of a similar policy of diversification led by the utility. Beyond the expanding offer of services relating to energy provision that characterises the classic commercial turn in the energy sector, SWM has also diversified its mission by providing its customers a special card, the SWM card. This card is free of charge, yet offers no service at all as far as urban technical systems are concerned. However, this item provides its users with various commercial advantages such as discounts in some shops, or free access to several cultural events. The utility is also supporting numerous cultural events in the city. SWM is consequently slightly invading the everyday-life of Magdeburg's inhabitants, not only with the countless SWM cars of technicians that are crossing the city's streets, but also through various channels outside of the classic relationship between energy users and provider. This is primarily reinforcing the connection between the utility and its supplied area. This diversification is slightly more limited in the Grenoble story, as the utility is diversifying its activities, but concentrates this diversification around energy issues. Amongst other projects, the company has invested in the production of renewable energy and on smart grids. The transformation of activities is, in this context, mainly driven by financial opportunities relating to national public policies encouraging an energy transition.

New regulations and reconfigured relations between the city and the utility

This twofold move of rescaling and diversification of local infrastructure firms reconfigure the political arrangements between the city and the utility. A growing tension emerges between the eminently political will expresses by utility heads to remain autonomous and the inclusion of the utility in the local political landscape. As public firms and representatives of public services, the local utilities are bound to the municipal authorities. Their transformation combined with a renewed interest in energy questions accounts for new forms of regulations (and conflicts) between local powers and the utilities, In our three cases, the utility has long been conceived quite uniquely as a source of revenues for the local authority. In Grenoble, the utility certainly benefits from a certain autonomy to establish its own strategy, but it substantially nourishes at the same time the city's revenues. The city's role has long only consisted of validating strategic choices of territorial expansion and economic diversification to ensure the continuity of the city's incomes, with little interest showed to the energetic dimension of the utility.

However, the implementation of new public policies on energy and climate have changed the context. Local political actors have seized this opportunity to reconsider GEG as a potential tool for these new urban energy policies. Within the city's administration, this new discourse is advocated by departments in charge of sustainable development, environment and urban affairs, and translates into the elaboration of local climate plans. this context, GEG is encouraged to participate to urban projects such as eco-districts or innovative experiments of smart grids. Even though these changes remain marginal, they attest the emerging search to integrate the utility in urban policies, which provides it with new opportunities and are a way to acknowledge the eminently urban and political role of the utility. This transformed relationship may lead to intern frictions within the local authority, but also between the city and its utility. Conflicts arise mainly because the utility try to preserve a form of industrial and strategic autonomy in this context of growing politicisation of local energy issues. Similar frictions can be observed in Magdeburg between the city and the utility, as a result of the transformation of the latter. A large part of the SWM benefits is now accumulated outside of Magdeburg, but 60% of these benefits are transferred to the city budget according to the number of shares owned by the city authorities. This epitomises possible complex relations between Magdeburg and its region, and the complex dilemma between a traditional communal mission, supporting the local development, and new economic strategies.

The reconfiguration of the role and place of the utility in the local arena also triggers new conflicts with the city in the Medellin case. This is particularly salient with regard to the supply of certain areas of the city when the technicians of the municipal authority disagree with the technicians of EPM. In spite of attempts to exert a larger control over the firm, the city remain often inferior, as the economic, technical and symbolic power of the firm practically dispossess the city's technicians.

Conclusive thoughts

The purpose of this short paper was threefold. First, by considering the transformations of energy systems as not univocal, and by integrating it into more urban and politicized contexts to take into account its diversity, it tries to go beyond the obsession of the energy transition.Second, our sociotechnical approach was centred on one specific and understudied actor, the local utility, whose role is largely underestimated in the emergence of new infrastructure regimes. Confronting a similar type of actor in three very different contexts seemed fruitful to delineate crucial though silent ongoing metamorphosis of local firms of urban services. Deciphering the elements of such a transformation is essential to analyse the evolution of urban energy systems and to reflect the political and social issues at stake in the various local contexts.

The majority of the capital (54%) is owned by the municipality, the rest being owned by two private companies of the energy market, Gelsenwasser (originally a mix of public local entities) and E.ON Avacon.

Grupo EPM is constituted of 6 subsidiary companies in the water sector and

in the energy sector that operate outside of Antioquia at the national level. At the international level, EPM is at the head of 23 subsidiary companies, mainly in the energy sector, active in Mexico, Chile, Panama, Costa Rica, Guatemala and Salvador.

This goes up to the creation of a EPM credit card.


Amir Aly
email: amir.aly@ensta-paristech.fr

Adriana Tapus
email: adriana.tapus@ensta-paristech.fr

Towards Enhancing Human-Robot Relationship: Customized Robot's Behavior to Human's Profile

A social robot should be able to understand human's profile (i.e., human's emotions and personality), so as to make the robot able to behave appropriately to the multimodal interaction context. This research addresses the online recognition of emotions based on a new fuzzybased methodology. It also focuses on investigating how could a match between the human's and the robot's personalities influence interaction. Furthermore, it studies the automatic generation of head-arm metaphoric gestures under different emotional states based on the prosodic cues of the interacting human. The conducted experiments have been validated with NAO robot from Aldebaran Robotics and ALICE robot from Hanson Robotics.

Introduction

The increasing need for a social intelligent robot nowadays requires an advanced level of artificial intelligence functions that can enhance the interactive capabilities and the decisive criteria of the robot. In this research, we focus on making the robot able to model and understand human's emotions and personality in order to adapt its generated multimodal behavior to human's profile, which helps in building up a long-term human-robot relationship.

Research Overview

An overview of this research is illustrated in Figure ( 1). In this model, the captured audio-visual data are used to define the profile of the interacting human and to setup for generating an appropriate customized multimodal action. The outputs of the behavior generation subprocesses are modeled directly on the robot, in the presence of the personality control factor that adapts the robot's behavior to human's personality.

Online Detection of Human's Emotions

Fuzzy Logic imitates human logic by using a descriptive and imprecise language in order to cope with ambiguous input data. Emotion could be considered as a part of these complex systems that do not have clearly defined borders between Copyright c 2014, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.

Figure 1: Research Overview some of its classes. The purpose of this system is to buildup a fuzzy model that can recognize emotions and decide whether an expressed emotion is conform to a previously learned emotion clusters, or it constitutes a new cluster (not learned before) that requires a new verbal and/or nonverbal action to be synthesized [START_REF] Aly | Towards an online real time fuzzy modeling for human internal states detection[END_REF].

Customized Robot's Multimodal Behavior to Human's Personality

Personality plays an important role in the human-human communication and interaction. Various researches have demonstrated that personality is also crucial in human-robot interaction. In our recent work (Aly and Tapus 2013a), a direct relationship between the extraversion-introversion personality trait and the characteristics of the generated verbal and nonverbal behaviors has been found. The similarity attraction principle (i.e., individuals are more attracted by others who have similar personality traits) within a humanrobot interaction context has been validated (Figure 2), by modeling a combined verbal and nonverbal robot behavior based on the extraversion-introversion personality dimen-

Customized Metaphoric Gestures Generation to Human's Emotions

The system is coordinated through 3 stages, as illustrated in

Future Research Perspective

As referred to in Figure ( 1), future research orientations include developing a computational cognitive architecture that can simulate the functionalities of the Wernicke's area [START_REF] Ojemann | Cortical language localization in left, dominant hemisphere: An electrical stimulation mapping investigation in 117 patients[END_REF]) and the Broca's area [START_REF] Schaffler | Comprehension deficits elicited by electrical stimulation of broca's area[END_REF] in human brain which allow understanding and generating speech appropriately to the context of interaction, in addition to the mechanism of action imitation and emulation and the functionality of the mirror neurons that allow understanding and generating actions. A preliminary computational model is illustrated in Figure ( 5), in which the captured speech undergoes a semiotic and linguistic analysis in order to extract the semantic and pragmatic information, then the dialog manager phase which formulates a corresponding text to the context of interaction. On the other hand, action grammars (Pastra and Aloimonos 2012) are used to understand the goal of the captured actions, which will be learned by imitation or by emulating their goals. The processed synchronized speech and gestures will be stored in the multimodal action memory, ready for generating a future multimodal robot behavior whenever required.

Conclusion

The previously shortly discussed current research points aim towards creating an intelligent cognitive system that can make a robot more capable of understanding human's profile, so as to create a more natural, intuitive, and customized human-robot interaction relationship.

Figure 2 :

 2 Figure 2: Personality Matching for the Introverted and Extraverted Robot's Conditions

  Figure (3). Stage 1 represents the training stage of the system, in which the characteristics of the audio/video inputs get segmented and modeled on the CHMM, through which new adaptive head-arm metaphoric gestures are synthesized (i.e., stage 2) based on the prosodic patterns of a new speechtest signal which undergoes the same phases of the training stage. However, stage 3 represents an experimental stage in which the robot should be able to increase online its initial learning database by acquiring more raw audio and video data from humans in the surrounding of the robot(Aly and Tapus 2013b).

Figure 4 :Figure 5 :

 45 Figure 4: Synthesized Facial Expressions by ALICE Robot: Sadness, Happiness, and Anger (From Left to Right)