Nabila Djennane
email: djennanenabila@gmail.com

Rachida Aoudjit
email: aoudjitrachida@yahoo.com

Samia Bouzefrane
email: samia.bouzefrane@lecnam.net

Energy-Efficient Algorithm for Load Balancing and VMs Reassignment in Data Centers

Keywords: Cloud Data Centers, Consolidation, Energy, Load Balancing, Migration, Optimization

Nowadays, cloud computing is an effective solution for providing computing services to consumers. However, data centers that host computing resources are still faced with a misuse of resources and a workload imbalance of servers, where some servers become overloaded while others are underloaded or even idle. This results in performance degradation and resource wastage. The load balancing is a key aspect and has an important role in the management of cloud data centers. It allows an optimal use of the resources and improves the desired Quality of Service (QoS) using optimal methods for allocating resources and distributing workload.

In this paper, we propose a load-balancing algorithm that is based on a new parameter called the balance factor of the data center, introduced here, to determine if a data center is imbalanced or not, in order to redistribute the workload equally over all the hosts. To minimize the energy consumption of the data center, our strategy relies on the live migration of virtual machines (VMs) while using a mathematical model. The simulation results, using the CloudSim toolkit, have shown that the energy efficiency can be managed by reassigning VMs to the data-center hosts.

I. INTRODUCTION

The increasing proliferation of large data centers within which a great number of physical servers are deployed, has allowed cloud computing to gain more attention these last decades. Cloud computing consists of delivering available services over the Internet using hardware and software resources of the data centers according to a service model (SaaS, PaaS, and IaaS).

Behind cloud computing, virtualization techniques [START_REF] Bouali | Virtualization Techniques: Challenges and Opportunities[END_REF] are used allowing to host different tenants virtual resources within the same physical resource, hence facilitating the orchestration of these virtual resources and the transformation of the delivery of these resources into a service billable for consumption.

As the demand for resources by customers increases exponentially, the possible reduction of IT infrastructure management costs is one of the top business priorities of cloud providers.

In this context, load balancing is considered as one of the main and biggest challenge in cloud computing because the cloud providers must ensure an equilibrium in the available resources to satisfy the client requests and to meet the clients service level agreements. This equilibrium is done through the distribution of workloads across multiple computing resources, such as computers, computer clusters, disk drives [START_REF] Bokhari | Performance tradeoffs in static and dynamic load-balancing strategies[END_REF], etc. Hence, cloud load balancing means to maintain data centers balanced among the servers in such a way that no servers will be overloaded or underloaded to reduce the risk of saturation, to minimize resource consumption, to facilitate the implementation of fault tolerance, and to guarantee scalability.

Several researches have been undertaken to find optimal and efficient algorithms with the objective of satisfying the workload-equilibrium constraint, and of optimizing the infrastructure management cost of the cloud data centers to gain credibility of customers.

In this paper, we propose to combine two algorithms to solve the problem of load balancing in the data centers, considered as an NP complete problem. The first algorithm is inspired from [START_REF] Aoudjit | Load Balancing: An Approach Based on Clustering in Ad Hoc Networks[END_REF] used to manage the load balancing in MANETs. It is based on the clustering process in order to organize the nodes into clusterheads and to coordinate other nodes in the clusters. This solution has some advantages such as reducing energy consumption and improving bandwidth utilization by reducing communications overhead. Our algorithm purpose is to rebalance the workload of the data-center servers through the migration of Virtual Machines (VMs) from overloaded servers to underloaded ones. To minimize migration costs and energy consumption, the bMatching method [START_REF] Hadji | A New Scalable and Energy Efficient Algorithm for VMs Reassignment in Cloud Data Centers[END_REF] is used to complement our loadbalancing algorithm.

The remainder of this paper is organized as follows: Section II presents the significant existing research works related to the load-balancing issue, and our motivations, followed by the description of our proposed method as detailed in section III. Section IV presents the power model of our method using the Cloudsim simulator. The conducted experiments are described in section V before concluding with some perspectives in section VI.

II. RELATED WORK AND MOTIVATION

Given the key role of load-balancing algorithms in cloud computing, several researches have been done in this area and various classifications of load-balancing techniques have been proposed based on different criteria.

In [START_REF] Thakur | A taxonomic survey on load balancing in cloud[END_REF], various load-balancing algorithms for cloud computing environments are discussed comprehensively and systematically according to a novel taxonomy. In this study, load-balancing algorithms have been divided into two broad categories, namely, nature-inspired and statistics-based, depending on their source of inspiration and operational model.

Authors in [START_REF] Einollah | Load-balancing algorithms in cloud computing: A survey[END_REF], surveyed research literature in the loadbalancing area and have presented a new classification of the load-balancing techniques: (1) Hadoop MapReduce loadbalancing category, (2) natural phenomenon based loadbalancing category, (3) agent-based load-balancing category, and (4) general load-balancing category. In each category, some techniques were studied and analyzed in terms of metrics such as: throughput, makespan, scalability, fault tolerance, migration time, energy consumption, etc.

The authors of [START_REF] Shah | Load Balancing in cloud computing: Methodological Survey on different types of algorithm[END_REF] surveyed the load-balancing problem by distinguishing between static and dynamic load balancing. The static algorithms are appropriate to the environment with low-loads variations, which requires a complete knowledge on the features and the performances of the system resources. The drawback of the static algorithms is that, once the VMs are assigned to dedicated machines, they cannot move to other ones, while migration of VMs after initial assignment is possible in dynamic load balancing algorithms.

The authors of [START_REF] Volkova | Load Balancing in Cloud Computing[END_REF] analyzed and compared three principal algorithms, which are, the Round Robin algorithm, the Active Load Balancing Monitoring algorithm and the Throttled Load Balancing Algorithm. These algorithms are used for load balancing at the scheduling level.

To monitor and balance the load of a cloud data center, authors of [START_REF] Kanniga Devi | [END_REF] propose a graph-based solution that is composed of two phases. In the first phase, the cloud data center is modeled as a network graph that is augmented with a minimum dominating set, inspired from the concept of graph theory, for monitoring its load. The second phase focuses on the system and network aware live migration.

Authors, in [START_REF] Hong | Research and Implementation of load Balancing Technology for Cloud Computing[END_REF], combined load balancing and live migration techniques through a mathematical model that computes the average load of each server in terms of CPU, memory and bandwidth in order to determine the overloaded servers. In case of server-overload detection, a VM migration occurs once a destination server is found. The drawback of this solution is that the average resource load is computed within each server, which generates an additional overhead in terms of memory and energy consumption.

A multiple objective optimization framework named MOVMrB is presented in [START_REF] Li | A Novel Multi-Objective Optimization Scheme for Rebalancing Virtual Machine Placement[END_REF]. It aims to rebalance the placements of VMs in order to achieve load balancing of multiple resources in cloud computing. The proposed solution considers both the load balancing of each resource across Host Machines (inter-HM load balancing) and the load balancing of different resources within the same Host Machine (intra-HM load balancing) at the same time.

To reduce the number of SLA violations by migrating VMs before the host is overloaded, a proactive VM migration policy is presented in [START_REF] Sommer | Predictive Load Balancing in Cloud Computing Environments based on Ensemble Forecasting[END_REF]. This method for the dynamic and anticipatory consolidation of VMs in IaaS cloud data centers uses short-term forecasts of the future utilization of hosts, to proactively detect overloaded hosts. If a host is classified as overloaded, some VMs have to be migrated to other hosts to avoid SLA violations. The predictive overload detection is based on time series technique.

To dynamically forecast the resource utilization and energy consumption in cloud data centers, authors of [START_REF] Tseng | Dynamic Resource Prediction and Allocation for Cloud Data Center Using the Multiobjective Genetic Algorithm[END_REF] proposed a multi-objective genetic algorithm which considers the CPU and the memory utilization of VMs and physical machines, and the energy consumption in the data center.

Our contribution, in this article, is to rebalance the workload of the data-center servers through a dynamic loadbalancing approach that will detect overloaded and underloaded servers through well-defined thresholds. In order to reduce the consumed energy in our solution, an additional method is used to take into account the migration cost due to the VM migration that is done to remedy to the server-load imbalance.

III. MODEL DESCRIPTION

The cloud computing is based on several data centers distributed geographically. Each data center is composed of a set of physical servers that host VMs. The clients request resources in the form of a service (IaaS, PaaS or SaaS) and the service is offered as a set of VMs.

Workloads of a data center might span across multiple physical servers that are consolidated through live migration to maintain the load of the data center and to avoid overloaded and underloaded servers.

According to the different levels in computer systems defined for energy consumption in Beloglazov et al. [START_REF] Beloglazov | A Taxonomy and Survey of Energy-Efficient Data Centers and Cloud Computing Systems[END_REF], our work targets the energy-aware resource management in the data centers. In this context, the load of a data center is linked to its relatively high-energy capacity caused by the physical servers consolidation. The server consolidation is used to maximize the resource utilization and to minimize the energy consumption in the meanwhile. This consolidation is ensured thanks to the VM migration so that a minimum number of servers are utilized to host the VM load, which consequently reduces the energy consumption induced by the physical servers activity. Thus, this paper focuses on the load re-balancing of data centers based on the live migration leading to the reduction of the energy consumption. As shown in Figure 1, our approach is built upon several modules. Each module is described as follows: VMs, all the servers can be candidate to host the migrated VMs. The destination server that is chosen to host the VM is the one that generates a minimum energy cost among these VMs. The energy cost corresponds to the energy consumed during the execution of the VM within the source server, plus the energy consumed during the migration of the VM from source to destination. The objective of this module is to transfer the selected VMs without interrupting their execution while minimizing their migration cost. For this purpose, we propose a smart reassignment solution that can consolidate the physical substrate in few seconds as in [START_REF] Hadji | A New Scalable and Energy Efficient Algorithm for VMs Reassignment in Cloud Data Centers[END_REF]. This reassignment allows to migrate the VMs from the overloaded source to the destination server with a minimum migration cost in term of energy.

• Check if there are underloaded hosts: After migrating the VMs from the overloaded nodes, these nodes as well as others may become underloaded due either to the VM migration or to their workload variation. Hence, the system checks if there exists underloaded hosts in order to get rid of the VMs they host by migrating them to non-overloaded nodes with the aim of obtaining some idle servers that can be powered off.

IV. POWER MODEL BASED APPROACH

The power consumption of the data centers is mostly determined by the load of the physical-servers resources such as CPU, memory, disk storage, network interfaces. The CPU capacity contributes significantly in consuming energy. Moreover, the studies in [START_REF] Beloglazov | Adaptive Threshold-Based Approach for Energy-Efficient Consolidation of Virtual Machines in Cloud Data Centers[END_REF] and [START_REF] Buyya | Energy-Efficient Management of Data Center Ressources for Cloud Computing: A vision, Architectural, and Open Challenges[END_REF] have shown that, on average, an idle server consumes approximately 70% of the power consumed by the server running at the full CPU speed, while a server in a maximum use can lead to a performance degradation hence paralyzing datacenters activities. To improve the server load balancing and in order to reduce the power consumption at the same time, we propose a power-model based approach that relies on the load-balancing algorithm that we have already used in MANETs [START_REF] Aoudjit | Load Balancing: An Approach Based on Clustering in Ad Hoc Networks[END_REF], combined with the bMatching theory for live migration to ensure the dynamic consolidation of the VMs within the servers of a data center.

A. Load-Balancing Algorithm:

Unlike the existing research works that deal with load balancing in data centers, in our algorithm we introduce a new parameter called balance factor and denoted by F in order to maintain perfectly a balanced data-center load even after VM migration. F is defined as follows:

F = n n i=1 (load i -µ) 2 (1)
n: is the number of nodes in the data center. load i : is the load of a node i.

µ = n i=1 load i n (2)
µ: is the average load of the data center.

As explained before, in order to maintain the data center stable and for a good load-balancing algorithm, the balance factor F must be a value in a range [Min, Max], because if F is less than Min value then a load imbalancing will occur causing a degradation in the performances of the servers due to the presence of a great number of underloaded servers. However, if F is greater than Max value, the load-balancing process will be inefficient due to the presence of a great number of overloaded servers.

B. Dynamic Consolidation

To achieve the load balancing, we rely on the VM consolidation used to improve the utilization of data-center resources. VM consolidation is a process that supports VM live migration to reduce the number of physical servers that are powered on while maintaining an equilibrium between the servers workloads. Beloglazov and Buyya in [START_REF] Beloglazov | Optimal Online Deterministic Algorithms and Adaptive Heuristics for Energy and Performance Efficient Dynamic Consolidation of Virtual Machines in Cloud Data Centers[END_REF] proposed different heuristics for VMs consolidation. Since we focus, in our work, on minimizing energy consumption due to the live migration, we follow some heuristics of [START_REF] Beloglazov | Optimal Online Deterministic Algorithms and Adaptive Heuristics for Energy and Performance Efficient Dynamic Consolidation of Virtual Machines in Cloud Data Centers[END_REF] either to detect overloaded and underloaded hosts, or to select some VMs candidate for migration, as presented in the following paragraph.

Detection of overloaded hosts: To decide if a host is overloaded, we use the Local Robust Regression (LRR) heuristic proposed by Beloglazov and Buyya in [START_REF] Beloglazov | Optimal Online Deterministic Algorithms and Adaptive Heuristics for Energy and Performance Efficient Dynamic Consolidation of Virtual Machines in Cloud Data Centers[END_REF]. As stated in [START_REF] Beloglazov | Optimal Online Deterministic Algorithms and Adaptive Heuristics for Energy and Performance Efficient Dynamic Consolidation of Virtual Machines in Cloud Data Centers[END_REF], "this method of LRR is an iterative method of Local Regression (LR) that fits a simple model to localized subsets of data to build up a curve that approximates the original data".

VMs Selection: Once the overloaded hosts are found, the next step is to select specific VMs to migrate from these hosts. We have opted to the Minimum Migration Time (MMT) policy to select VMs based on the minimum migration time as a selection criterion. The migration time is evaluated as a ratio of the amount of RAM currently utilized by the VM and the spare network bandwidth available for the VM host.

Live migration: During this phase, we consider the optimization technique based on the bMatching theory that we have already proposed in [START_REF] Hadji | A New Scalable and Energy Efficient Algorithm for VMs Reassignment in Cloud Data Centers[END_REF]. It consists in migrating judiciously the selected VMs from a set of servers to the optimal ones leading to reduce data-center energy consumption. We assume to have in the data center: N VMs deployed on K available servers, and a set of VMs to be migrated as selected by the MMT policy described above, noted by: T init = {V M 1 ,V M 2 ,...,V M θ } with θ <N and each VM in T init is currently hosted by one of the servers set noted by S= {S 1 ,S 2 ,...,S k } with k < K. The reassignment problem consists in placing or hosting optimally θ VMs on k available servers. We define C v i, j as the migration energy cost that is considered by our optimization algorithm to recommend the migration of a VM v from a server i to a server j. The k servers of the data center, including the source server i of VM v, are investigated to host v. The destination server j that is selected to host v is the one that will generate the minimum energy cost estimated when v is supposed to migrate from server i to server j. Hence, as explained in the following:

• C v i, j is equal to 0 if i = j, which means that, there is no transfer of v since the source host is confused with the destination host. • C v i, j is equal to a positive value that corresponds to the energy cost of v obtained when simulating the migration of v from server i to server j. Based on this configuration, we build up a new bi-partite graph noted G=(V ∪ S, E) where: V represents a set of selected virtual machines to migrate with MMT policy and S is the set of all available servers (powered on). E represents a set of weighted edges w (v i, j ) . A weighted edge between V and S depends on the power l consumed by the VM during its execution on the host, and on the energy cost C related to the VM migration, as defined in the following. 1)

w (v i, j ) =l (v i, j ) + C (v i, j ) if VM v is hosted in server j i. 2) w (v i, j ) =l (v i, j ) if VM v is hosted in server j = i.
At this stage according to different costs listed previously, we introduce the minimum weight bMatching objective to be assimilated to the reassignment problem that assigns each VM to the best server with a minimum migration cost. The objective function according to the described costs (migration cost and power consumption) is given as follows:

minObjbMatching = e ∈E,e=(v i , j) = (l e + C e 1 i j )x e (3)
Where:

1 i j = 0, If i = j
1, Otherwise e=v i, j is the weighted edge if migrating v from server i to server j. Some additional constraints are added to completely describe the reassignment requirements that are summarized below:

1) Migration with a unique destination: each VM will be assigned to one and only one server.

e∈δ(v) x e = 1, ∀v ∈ V (4)
2) Servers with power limits constraints: each server s has a resource capacity that we can't exceed.

e ∈δ(s)

x e ≤ b(s), ∀s ∈ S (5)
Where b(s) is the available resource capacity of the server s in term of CPU. More details for bMatching method can be found in [START_REF] Hadji | A New Scalable and Energy Efficient Algorithm for VMs Reassignment in Cloud Data Centers[END_REF].

Detection of underloaded hosts: Upon all the overloaded hosts are found, and the selected VMs for migration are allocated on the destination hosts, the system will try to find the hosts with the minimum utilization rate in order to reassign the VMs of these underloaded hosts to other ones while keeping these latter not overloaded. This is done by launching again the live-migration phase with the aim of switching off the source hosts that will become idle, and hence reducing the number of active hosts which will minimize the global energy consumption in the meanwhile.

V. EXPERIMENTAL RESULTS ANALYSIS 1) Experimental environment and platform: Our target system is an IaaS Cloud platform. Therefore, we choose CloudSim Toolkit [START_REF] Calheiros | CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms[END_REF] as an experimental platform to model and simulate the energy efficiency of our proposed method using the power package. We conduct the experiments using 1052 VMs collected from the workload traces of PlanetLab [START_REF] Park | CoMon: a mostly scalable monitoring system for PlanetLab[END_REF]. The VMs are of small, medium, large or extra large types. Each type of VM is assigned an energy consumption value taken from 10, 15, 20, 25 Watts respectively. Since all the servers have the same network bandwidth capacity of 1Gb/s, we assign for each configuration a random migration energy cost ranged between: 0 and 1. 2) Performance metrics: In order to evaluate the efficiency of our proposed load balancing bMatching migration cost (LBBMC) method, we made a comparison with an existing method integrated within CloudSim called Local Robust Regression Minimum Migration Time (LRRMMT) that uses LRR policy to detect overloaded and underloaded hosts, as well as MMT to select particular VMs to migrate, similarly to LBBMC. However, LRRMMT uses the best-fit decreasing strategy for the placement of the selected VMs while ours uses bMaching method. For our experiments, first we evaluate the balance factor defined in our LBBMC method. Then, the number of migrations, the energy consumption of the physical servers of the data center, and the execution time metrics are used to compare the LBBMC and LRRMMT methods. 3) Results and Analysis:

Balance Factor: We recall that the balance factor allows us to measure the load-balancing degree of the data center. According to Figure 2, the graph shows that the Factor F belongs at the beginning to high range values [50, 80] then it decreases before it stabilizes. This is explained by the consolidation of VMs in order to rebalance the workload among the servers. We can also see that the factor F is stabilized in the interval [35,55]. Therefore, in order to maintain our system balanced we have to maintain the Balance Factor F in this interval. Our work shows that load balancing is essential to improve resources utilization and energy consumption by optimally distributing a workload among multiple servers in a data center. In this paper, we have combined a load balancing algorithm with a consolidation VMs through a smart method bMatching for reassignment. The simulation results show that our method improves the management of the workload and reduces the energy consumption for a great number of servers. From a perspective point of view, we envisage to extend our work to adapt our load-balancing algorithm to multiple data centers. The proposed reassignment method takes into account the migration energy cost and the VMs consumption energy in the source server. Our next objective is to investigate new parameters to better manage the energy in cloud computing.

Figure 1 :

 1 Figure 1: The general steps of our solution

Figure 2 :

 2 Figure 2: Balance Factor

Table I :

 I CloudSim simulator is an open source project highly extensible and supported by the research community to simulate, and experiment Cloud computing infrastructures and application services. We have simulated our environment with 800 heterogeneous physical nodes of two types: HP ProLiant ML110 G4 servers, and HP ProLiant ML110 G5. The CPU frequency of the servers is respectively 1860 MIPS and 2660 MIPS. Each server is modeled to have 1GB/s network bandwidth. The characteristics of their power consumption is given in TableI: Power Consumption at different levels in Watts

	Server HPProLiantML110G4 HPProLiantML110G5
	0%	86	93.7
	10%	89.4	97
	20%	92.6	101
	30%	96	105
	40%	99.5	110
	50%	102	116
	60%	106	121
	70%	108	125
	80%	112	129
	90%	114	133
	100%	117	135

Table II :

 II Migration NumberEnergy consumption: Energy consumption is the principal parameter used to evaluate our method. As we can see in TableIII, the consolidation through LBBMC consumes less energy than through LR-RMMT. However, LBBMC has less number of shutdown servers compared to LRRMMT. This is due to the fact that there are more servers in LBBMC that are moderately loaded, which is good, because these active servers consume less energy. Also, this will allow keeping the data center balanced for longer. Execution time: The execution time (see TableIV) in our LBBMC method is greater than that in LR-RMMT method. This is due to the computing time of

	Method	Energy	Number	of
		Consump-	Shutdown
		tion(KWh)	Servers
	LRRMMT	87.67	646
	LBBMC	69.95	532
				Number Of Migration
				LRRMMT	LBBMC
				278	196

Table

II

, the number of migrations in LBBMC is lower than that in LRRMMT. As the number of migrations contributes in the energy consumption, our method reassignment avoids unnecessary migrations.

Table III :

 III Energy Consumption the balance factor F that is not taken into account in LRRMMT, as well as to the parameters of our bMatching method, used for live migration, that takes into account the energy consumption of the VM in the source server and the migration energy cost.

	Execution Time(s)
	LRRMMT	LBBMC
	86100	98290
	Table IV: Execution Time(s)
	VI. CONCLUSION AND FUTURE WORK


Elio Hbeich
email: elio.hbeich@cstb.fr

Ana-Maria Roxin

Nicolas Bus
email: nicolas.bus@cstb.fr

Semantic and Multi-scale BIM/CIM/LIM Regulation compliance checking

Keywords: BIM, CIM, CityGML, IFC, GIS, Semantic interoperability, Federation BIM, CIM, CityGML, IFC, SIG, Interopérabilité sémantique, Fédération

Compliance checking for building models, cities and territories involve formalizing a set of model schema knowledge and constraint. The objective of our study is to propose: an information model to federate heterogeneous data sources describing an urban area (building and building environment) along with a method for formally specifying of urban rules. The overall goal we pursue is to be able to query and to verify data against different regulations and/or requirements. The purpose of this article is to describe our approach for interoperability among different data sources (e.g. IFC, CityGML) thus creating a consistent description of an urban area.

Résumé

La vérification de conformité des modèles de bâtiments, villes et territoires passe par la formalisation d'un ensemble de connaissances, de schémas de modèles et de contraintes. L'objectif de notre étude est de proposer : un modèle d'information permettant de fédérer des sources de données hétérogènes décrivant une zone urbaine (bâtiment, quartier, etc.) et une méthode de formalisation des règles urbaines, afin de pouvoir effectuer l'interrogation et la vérification des données au regard de différentes exigences règlementaires. Le but de cet article est de proposer une approche qui permet d'implémenter une interopérabilité entre les différentes sources de données (ex. IFC, CityGML), dans le but de créer une description cohérente d'une zone urbaine.

Introduction

In the last years, a growing demand for modelling man-made, natural and built environment has been noticed. This is mainly drawn by the need for merging outdoor and indoor applications according to different use cases such as visualization or applications related to crises, planning, construction, 3D cadaster, etc. Several attempts have been observed lately to design methods and tools integrating GIS and BIM models [START_REF]Is construction industry ready for City Information Modeling[END_REF] [START_REF]Le BIM: Signification, définition et explications[END_REF].

BIM stands for "Building Information Modelling". "Building" here is rather generic, going beyond what one would consider a building, including infrastructure elements such as roads, tunnels, bridges, etc. BIM is a combination of processes and methods along with a 3D parametric digital model. BIM aims at supporting sharing reliable information throughout the lifecycle of the considered built element, from design to demolition. Such digital model is a representation of the physical and functional characteristics of the built element (building or infrastructure). Thus, BIM allows specifying who does what, how and when, in the context of a construction project. The scale associated with such BIM projects and related specification of exchanges among actors usually corresponds to the built element itself, without necessarily including it into a bigger city, region or wide scale.

When considering a broader perspective, notably the city and the regional levels, additional approaches exist. We can mention CIM that stands for "City Information Modelling" and relies on a 3D urban model containing the various elements of a city such as buildings, roads and public spaces, streetlights, etc. Moving up to the level of a geographical area, one has LIM that stands for "Landscape Information Model". In another word by expanding the GIS coverage we can create a multiscale digital model that can cover a building, district, city, region, etc.

When considering an information model spanning over several of these perspectives (building, city, region), such model should allow representing the essential nature of the built and natural world as digital information models, which can then support various types of machine-driven computational processes to simulate, analyse, design, manage, understand, assess, test, evaluate the real world. The vision presented here of this digital twin for the environment (be it built or natural) poses several challenges to the existing BIM, CIM and GIS approaches, notably in terms of accuracy when loading objects on large sites, linking different complex objects, information about the surrounding landscape, spatial querying, etc. With respect to this, in the paper at hand, we present an approach for federating BIM and GIS worlds, with the aim to establish a consistent knowledge model of natural and built environment. The approach presented here answers the interoperability gap as identified by several factors such as international standardization organisations (e.g. ISO launching a Joint Working Group on BIM/GIS interoperability), industrial actors (e.g. Autodesk and ESRI collaboration around Project Information Modelling1 ), etc. Such interoperability problem is usually depicted between oriented object data formats (e.g. IFC) and geometrical data formats (e.g. CityGML). Following this separation, our article first introduces the data exchange approaches as done in BIM and in GIS. Section 3 further discusses concepts of interoperability and presents the standard approaches that can be used to implement it. Section 4 presents our proposal for implementing semantic interoperability among the BIM and GIS domains. We conclude this paper with a discussion about the advantages related to our approach, along with the future work needed.

Modelling Language Concept

When considering GIS, information modelling is done according to the ISO 19100 series of standards, defining the so-called General Feature Model [START_REF]ISO 19100: commission for basic systems information and services interprogramme task team on the future WMO information system[END_REF] with respect to the UML (ISO 19505) approach for modelling. On the other hand, when considering IFC, the information model is object-oriented and based on the EXPRESS approach (ISO 10303-11). As there are several fundamental differences between information modelling in ISO 191xx standard family and in ISO 10303, this section focuses on highlighting them.

Information modelling with in BIM

IFC is an object-oriented open standard initiated by buildingSMART in 1994. It has now become a formally registered international standard as ISO 16739:2013, and represents the BIM data exchange standard. According to [START_REF]industry Foundation (IFC) for data sharing in the construction and facilities management[END_REF] IFC (ISO 16739-1), data is structured according to the EXPRESS specification. Thus the structure of IFC comprises four different schema levels, as illustrated by Figure 2. These four layers define the overall EXPRESS Schema and are fully described in the IFC standard. While the lower layer contains abstract schemas for resources as involved in BIM projects (e.g. date and time, topology, geometry, costs), the core layer is limited to the definition of IFC basic concepts namely the IfcKernel schema and the core extensions schemas (e.g. IfcControlExtension 2 , IfcProductExtension 3 and IfcProcessExtension 4 ). Elements defined according to the core layer schemas can be further extended and referenced by schemas from the shared element layer, which contains elements as required for interoperability with additional services or domains (e.g. facility management, building services). Finally, the top layer contains domain specific data schemas representing entities as specialized according to industry discipline. These entities are "self-contained and cannot be referenced by any other layer 5 ". 2 c)) as well as rich semantic information: such as owner information, modification history of model, and cost and schedule of building components. BIM models based on IFC could be used in various phases of the construction, such as in feasibility studies, tendering [START_REF] Zhiliang | Application and extension of the IFC standard in construction cost estimating for tendering in China[END_REF], code checking [START_REF] Pauwels | A semantic rule checking environment for building performance checking[END_REF], and operation management [START_REF] Hassanain | Development of a maintenance management model based on IAI standards[END_REF]. In addition, IFC file have several format: .ifc, .ifcXML, .ifcZIP, .ifcSTEP, .ifcOWL. For example, an .ifc file can use the STEP serialization, as specified by ISO 10303-21 [START_REF]Industrial automation systems and integration --Product data representation and exchange --Part 21: Implementation methods: Clear text encoding of the exchange structure[END_REF]. This is the most common exchange format used is illustrated in (figure 2 d).

Figure 2: IFC geometrical representation and STEP .ifc serialization

Information modelling in GIS

A "geographic information system" (GIS) is a computer-based tool that allows you to create, manipulate, analyze, store and display information based on its location. GIS makes it possible to integrate different types of geographic information, such as digital maps, aerial photographs, satellite images and global positioning system data (GPS), along with associated tabular data base information (e.g. attributes). For example, GIS can help in answering questions such as: What exists at a given location? Where does some event occur? GIS allows one to examine and analyze geographic information at different levels of detail or from different perspectives. Then, it enables you to customize the display of your maps and analyses for presentation to particular audiences [START_REF] Sahoo | Introduction to Geographic Information System[END_REF] (figure 3 When considering geographic information, the ISO 191xx standard family is concerned. Based on model-driven architecture, each ISO standard in this family specifies a different level of abstraction. The ISO 19103 UML (Unified Modelling Language) [START_REF]Geographic information -conceptual schema language[END_REF] profile along with the ISO 19109 GFM (General Feature Model) [START_REF]Geographic information-rules for application schema[END_REF] represent the two meta-models. Thus, while in the BIM domain, information is modelled according to the EXPRESS schema, in GIS information is modelled according to UML and GFM, following four main levels of abstraction as depicted in Figure 4.

Comparison of the two modelling approaches

Given the above considerations about modelling approaches as standardized for BIM and GIS, a consistent modelling of BIM&GIS information would require defining rules and constraints aligning the concepts and models used in each domain. Notably, the IFC Core data schemas layer could be aligned with the Meta model layer in ISO 19103, while the Conceptual (abstract) schemas layer in ISO 19103 could be aligned with IFC Resource Definition data schemas layer.

According to these remarks, when aiming at BIM/GIS interoperability, modelling approaches as used in the BIM and in the GIS domain could be somehow linked or connected, notably by means of semantic vocabularies such as ontologies. Next section discusses standard approaches and levels existing for achieving interoperability.

Information Interoperability

Enterprise interoperability as an engineering discipline is not yet well defined; interoperability is still a vague concept that has many definitions and connotations in different sectors and domains. Thus, we need to define the concept of interoperability as relevant to our research problem. Interoperability barriers are numerous, and ISO 11354-1:2011 identifies three categories of interoperability barriers, namely: conceptual, technological and organizational. Interoperability barriers need to be categorized in standard ways and existing interoperability knowledge and solutions need to be related to these barriers in order to facilitate interoperability in design and implementation for industry. In computer science, we have 3 levels of interoperability as defined by IEEE 610. 12-1990 [15] [16]:

-Physical interoperability level: is related to physical interoperability. The solution to establish such interoperability, is to use basic protocols, such as computer network protocols (e.g. TCP, IP, Ethernet, etc.) -Syntactic interoperability: can be developed using syntactic formats, to exchange data such level of interoperability is achieved in the context of BIM through the IFC data exchange standard and in the context of GIS with the GML (Geography Markup language) approach -Semantic -semantic interoperability can be achieved at 3 different levels: minimal (RDF), extended (RDFS) and full (OWL family)

When considering implementing full semantic interoperability, ontologies alone do not allow reaching it and they augment heterogeneity. Thus we have to consider coupling/linking/mapping existing ontology models. The issue thus becomes how to do so. Well, ISO 14258 defines 3 ways: federation, integration, unification [START_REF]Industrial Automation Systems-concepts and rules for enterprise models[END_REF] [18].

Integrated approach

Developing interoperability through an Integrated Approach means that there exists a common format for all models. Diverse models are built and interpreted using/against the common template. This format must be as detailed as the models themselves. The common format is not necessarily an international standard but must be agreed by all parties to elaborate models and build systems. This approach is suitable when designing and implementing new systems rather than reengineering existing systems for interoperability. To some extent, the reengineering approach is more adapted to developing intra enterprise interoperability rather than inter enterprise one. Standardization at system level (not at Meta level) is a key issue to develop interoperability through integrated approach. However, in some areas such as for example enterprise model, mature standards are still missing. The integrated approach ensures the global consistency and coherence of the system. Various components of the system are designed and implemented using a common format (or standard) so that interoperability is seen as designedin quality. Interoperation between various parts can be obtained 'a priori' without any interfacing effort [START_REF] Métral | Ontology-based approached for improving the interoperability between 3D urban models[END_REF].

Unified approach

It means there is a common format but it only exists at the meta-model level. This format is not an executable entity as it is the case in integrated approach. Instead it provides a mean for semantic equivalence to allow mapping between models and applications. Using the Metamodel, a translation between the constituent models is possible even though they might encounter loss of some semantics or information. Most of research results developed in the domain of interoperability adopted the unified approach. For example, UEML (Unified Enterprise Modelling Language) aims at defining a neutral format at meta-model level to allow mapping between enterprise models and tools. The STEP initiative elaborated in ISO TC184 SC4 also defined a neutral product data format at meta-model level to allow various product data models exchanging product information. The unified approach is particularly suitable for developing interoperability for collaborative or networked enterprises. To be interoperable with networked partners, a new company just needs to map its own model/system to the neutral meta-format without the necessity to make changes on its own model/system. This approach presents the advantage to the integrated approach because of reduced efforts, time and cost in implementation [START_REF] Métral | Ontology-based approached for improving the interoperability between 3D urban models[END_REF].

Federated approach

Using the federated approach implies that no partner imposes their models, languages, and methods of work. This means that they must share an ontology. The federated approach can also make use of meta-models for mapping between diverse models/ systems. The difference to unified approach is that this meta-model is not a pre-defined one but established 'dynamically' through negotiation. Consequently, this approach is more suitable to 'Peer-to-Peer' situations rather than the cases mentioned in the unified approach. It is particularly adapted to Virtual Enterprises where diverse companies joint their resources and knowledge to manufacture a product with a limited duration. Using the federated approach to develop enterprise interoperability is most challenging and little activity has been performed in this direction. The main research area is the development of a "mapping factory" which can generate on-demand customized AAA (Anybody-Anywhere-Anytime) mapping agents among existing systems. It is worth noting that specific support for the federated approach is seen in entity profiles, which identify particular entity characteristics and properties relevant for interoperation [START_REF] Métral | Ontology-based approached for improving the interoperability between 3D urban models[END_REF] .

Conclusion

The choice depends on the context and requirements. If the need for interoperability comes from a merger of enterprises, the integrated approach seems to be the most adapted one. In this case, there is only one common format for all partners, and all models are built and interpreted according to this one. If the need for interoperability concerns a long term based collaboration, the unified approach seems a possible solution. For that, a common meta-model across partners' models provides a means for establishing semantic equivalence allowing mapping between diverse models. Finally, for a need of interoperability originated from the short-term collaboration project, the federated approach can be used. To interoperate partners must dynamically adapt to achieve an agreement. All the three approaches allow developing interoperability between enterprises systems. In [START_REF] Panetto | Information systems for enterprise integration, interoperability and networking: theory and applications[END_REF] federation itself can be further strongcoupled (links are explicitly and formally defined at the level of the ontologies themselves, and the sum of all links is an integrated ontology) or light-coupled (no global ontology defined, higher degree of adaptability -problem: today's approaches do not take into consideration schema heterogeneity, and only define concept level mappings). Thus we'd like to explore federation, and federation addressing schema heterogeneity. So we consider two future axis for work -horizontal federation, and vertical federation.

BIM/GIS interoperability

As concluded in the previous section we are going to use federate approach to build the operability between BIM and GIS because no partner imposes their models, language or methods, plus the ability to map between diverse models/systems and finally the meta-model is not a pre-defined one but established 'dynamically' through negotiation. To be able to do so we need to convert IFC, CityGML and GIS to Resource Description Framework (RDF).

IFC-to-RDF

[21] [22] [23] introduce the process providing a semantic model from the IFC schema by using an IFC-to-RDF-Converter to get a semantic repository. They apply filtration (geometrical and semantic pre-processor) to get an RDF equivalent compact triplet. Over these triples, they build a high-level vocabulary by using SPARQL rules (e.g., highest store concept) and SWRL. [START_REF] Pauwels | From full ifcOWL graphs to simplified building graphs[END_REF] mention an approach to simplify ifcOWL building data by releasing of geometrical and (re)presentation data, and correct mapping of IFCRelationship. [START_REF] Roxin | A Rule Based System for Semantical Enrichment of Building Information Exchange[END_REF] proposed another version of the ifcOWL ontology that contains two main changes: first the EXPRESS collections (e.g.

LIST) are mapped as OWL properties, second the ifcOWL ontology is simplified, as IFC defined types are not directly converted into OWL classes. This approach presents the following advantages: it facilitates the data access and improves the query execution time since we have fewer triples to match and the ontology has a fewer class than the buildingSMART IFCOWL.

In [START_REF] De Farias | Semantically Adapting IFC Model Relations into OWL Properties[END_REF] , authors present the IfcWoD (Web of Data) ontology that correctly adapts the IFC relationships and property sets into OWL, without applying direct mapping of all EXPRESS constructs. ifcOWL is used as a meta-model for IfcWoD, without redefining all concepts and relationships. This approach presents multiple advantages such as enhance reasoning and easier query writing and relation understanding which contributes to better performances. Authors from [START_REF] Zalamea | From a CityGML to an ontology-based approach to support preventive conservation of built cultural heritage[END_REF] used a Java program based on Apache Jena libraries for automatically translating IFC to RDF, and generating a mapping between the so-generated IFC ontology and the CityGML 2.0 ontology.

CityGML-to-RDF

[27] have developed a Geotools API for translating CityGML to RDF (http://docs.geotools.org/latest/javadocs/), and generate a mapping between IFC ontology and CityGML ontology. Authors from [START_REF] Métral | Ontology-based approached for improving the interoperability between 3D urban models[END_REF] propose that the UML diagram of the transportation model of CityGML can be translated into the OWL language with the ontology editor Protégé.

The UML classes and relations of CityGML can be directly translated into OWL classes and properties. The attributes can be either translated into datatype properties or object properties.

The cardinality restrictions can be represented by formulas in descriptive logic. In [START_REF] Falquet | Towards semantically enriched 3D city models an ontology-based approach[END_REF] authors define the ontology of CityGML as the following: UML classes will be translated into concepts, associations/roles will be translated into semantic relations; association, cardinalities will be expressed as restrictions relatively to relations, aggregation/composition will be expressed "as part" of links, generalisation will be expressed as "is a" links, UML attributes will be translated either into concepts attributes or into relations between concepts

GIS-to RDF

In [START_REF] Patroumpas | TripleGeo: an ETL Tool for Transforming Geospatial Data into RDF Triples[END_REF], authors introduce TripleGeo, an open-source ETL utility that can extract geospatial features from various sources and transform them into triples for subsequent loading into RDF stores. The author aims to bridge the gap between typical geographic representations from a variety of proprietary files and the geo-reference system with the demand of geospatiallyenabled RDF store. TripleGeo provide multiple advantages such as: Directly access the geographic formats (oracle, PostGIS, shape files), Recognize many geometric data types (points, line-strings, multi-line-strings, polygons, multi-polygons), extract thematic attributes (identifiers, names, types, etc.), Allow on-the-fly projection between coordinate reference systems, export triples into various notations (RDF/XML, TTL, etc.). In [START_REF] Hamdi | GeomRDF: A Geodata Converter with a Fine-Grained Structured Representation of Geometry in the Web[END_REF] the authors present GeomRDF, as a tool that helps users to convert spatial data from traditional GIS formats to RDF model easily. It generates geometries represented as GeoSPARQL WKT literal but also as structured geometries that can be exploited by using only the RDF query language.

FOWLA approach

Federated architecture for ontologies (FOWLA) is defined as an architecture based on autonomous ontologies (including TBox and ABox) with sharing described as a rule-based format controlled by inference mechanisms (e.g. SWRL). The architecture contains two main components: The Federal Descriptor (FD) and the Federal Controller (FC). FD component is responsible for describing ontology alignments, and the FC model is executed at query time and allows exchanging data among ontologies according to the FD alignments. The main contribution of FOWLA architecture to interoperate numerous ontologies. This proposal provides several advantages: it allows for inferring new ontology alignments, it avoids data redundancy, it allows for modularizing the maintainability, through preserving the autonomy among ontology-based systems, it allows for querying with vocabulary terms issued from different ontologies and it improves the query execution time [START_REF] De Farias | A Federated Architecture for Ontologies[END_REF].

Contextual Levels Approach

[32] presents a modelling process which built the ontology and define the context and the mechanism of Contextual Levels of details that aim at improving the management level of data.

The ontology is based on C-DMF (Contextual Model Framework and Data Model Framework) the SIGA3A extends C-DMF to defined new relational items and resources for the geographic world. The model data Framework aims to define a data model. It can model semantic information as well as geometric and spatial-temporal entities. CMD consists in defining a context for the DMF graphs to simplify the management of the evolution of integrated information. This approach is a crossroads between building modeling and geographic information system, where a model is created for all information in the city, including urban proxy element, network, building, etc. into an ontology.

Conclusion

In this section we have present the previous work done to transform IFC, CityGML and GIS into RDF. In addition, we have introduced FOWLA, and contextual level that have mapped and combine different ontologies. As we aim at BIM/GIS interoperability, modelling approaches of these different domain could be linked or connected, notably by means of semantic vocabularies such as ontologies. The section 4.1, 4.2, 4.3, 4.4, and 4.5 could be used to helps us in our work but doesn't solve the interoperability between BIM and GIS.

Regulation for compliance checking

As we have transformed information model to RDF and federated them into one meta model.

Local Urban Plain (PLU) defines the urban planning rules, the different zones and the architectural prescriptions. A conversion of textual regulation to SPARQL is needed to query, and check the information contained in the generated meta model. As, PLU is divided to multiple level (Area, zone, district, construction rule) and the Meta-model contain multiple LOD. We aim to create a textTordf conversion method and an alignment between each PLU level and LOD.

Future Work

When considering a more global semantic interoperability between BIM and GIS domains, one has to take into consideration what specific use case is addressed. Reaching semantic interoperability between GIS and BIM would imply considering coupling several approaches: first an alignment or mapping should be defined for the different syntactical standards (data formats) as used in the two domains, and the second one could investigate coupling the models behind EXPRESS and GFM through model federation. By achieving BIM and GIS interoperability and aligning meta-model LOD to regulation level we can navigate between different levels (BIM, CIM, LIM) while conserving semantic and geometric information and applying SPARDL query representing regulation (building, city regulation, etc.) on multiple scale to enable the compliance checking of different objects.

Conclusion

In our future work, we aim at creating a multi-scale digital mode (building, district, city, and region) combining GIS and BIM features and relying on Semantic Web technologies for semantic interoperability between the different components. In our approach, we do not seek to merge BIM and GIS, neither to promote one over the other. As discussed in previous sections, when considering model-driven approaches for interoperability, one can do model union, model fusion, or model federation. As a first approach we will investigate a federation approach, and consider tow axis for such federation: a) Horizontal federation will be implemented by defining semantic links among terms/concepts in different ontologies/vocabularies from the domains considered (Ontologies for BIM ontologies for GIS), b) Vertical federation will be addressed by conceiving a meta-model for BIM/GIS. Such meta-model will provide the means necessary to switch between different levels of abstraction.

Figures

Figure 1 :

 1 Figure 1: The four layers of data schemas as described in the IFC standard (ISO 16739-1) [4] and modelled in the EXPRESS Schema (ISO 10303-11) [5] IFC supports a wide range of geometric representations [6] (Boundary Representation (BRep) (figure 2 a), Swept Solid (figure 2 b), and Constructive Solid Geometry (CSG) (figure2 c)) as well as rich semantic information: such as owner information, modification history of model, and cost and schedule of building components. BIM models based on IFC could be used in various phases of the construction, such as in feasibility studies, tendering[START_REF] Zhiliang | Application and extension of the IFC standard in construction cost estimating for tendering in China[END_REF], code checking[START_REF] Pauwels | A semantic rule checking environment for building performance checking[END_REF], and operation management[START_REF] Hassanain | Development of a maintenance management model based on IAI standards[END_REF]. In addition, IFC file have several format: .ifc, .ifcXML, .ifcZIP, .ifcSTEP, .ifcOWL. For example, an .ifc file can use the STEP serialization, as specified by ISO 10303-21[START_REF]Industrial automation systems and integration --Product data representation and exchange --Part 21: Implementation methods: Clear text encoding of the exchange structure[END_REF]. This is the most common exchange format used is illustrated in (figure2 d).

  a). In addition, Geographical features are often stored in Raster or in Vector format. There are numerous formats available for both raster and vector data. It is necessary to consider the file format of GIS data because software programs rarely support all file types. Example of Raster GIS file format: ADRG, binary file, digital raster graphic (DRG), etc. Example of vector GIS file format: GeoJSON, DGN, Keyhole Markup Language (KML), MapInfo TAB format, Shapefile (figure 3 b) [12]. One of the most GIS used tools is City Geography Markup Language (CityGML). It defines basic entities, attributes, and relations present in a 3D city model. It is an open data model based on XML for storage and exchange of 3D city models. A CityGML model thus contains a description of urban elements and components [33].

Figure 3 :

 3 Figure 3: GIS information (a) and format (b)

Figure 4 :

 4 Figure 4: Layers of abstraction as defined by ISO/TC 211 (ISO 19103)

Figure 5 :

 5 Figure 5: Potential links among the layers of the information models considered

Figure 6 :

 6 Figure6: Standard approaches to achieve interoperability[START_REF] Métral | Ontology-based approached for improving the interoperability between 3D urban models[END_REF]

Figure 7 :

 7 Figure 7: Mapping between BIM and GIS through semantic interoperability

Figure 1 : 4 Figure 2 : 4 Figure 3 : 5 Figure 4 : 6 Figure 5 : 7 Figure 6 : 9 Figure 7 :

 1424354657697 Figure 1: The four layers of data schemas as described in the IFC standard (ISO 16739-1) [4] and modelled in the EXPRESS Schema (ISO 10303-11) [5] .................................................... 4 Figure 2: IFC geometrical representation and STEP .ifc serialization ....................................... 4 Figure 3: GIS information (a) and format (b)............................................................................. 5 Figure 4: Layers of abstraction as defined by ISO/TC 211 (ISO 19103) ................................. 6 Figure 5: Potential links among the layers of the information models considered .................... 7 Figure 6: Standard approaches to achieve interoperability [19]................................................. 9 Figure 7: Mapping between BIM and GIS through semantic interoperability ........................ 12

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