Simeon Veloudis
email: sveloudis@seerc.org

Iraklis Paraskakis
email: iparaskakis@seerc.org

Christos Petsos
email: chrpetsos@seerc.org

Cloud Service Brokerage: Strengthening Service Resilience in Cloud-Based Virtual Enterprises

Keywords: virtual enterprises, cloud computing, cloud service brokerage, governance, quality control, ontologies, Linked USDL

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Introduction

Cloud computing has evolved out of Grid computing [START_REF] Vaquero | A break in the clouds: Towards a cloud definition[END_REF]2] as a result of a shift in focus from an infrastructure aiming to deliver mainly storage and compute resources, to an economy-based computing paradigm aiming to deliver a wide range of resources abstracted as services [2]. Such a shift is anticipated to impact the manner in which businesses and organisations share skills and core competencies within a distributed collaborative network [START_REF] Veloudis | Continuous Quality Assurance and Optimisation in Cloud-based Virtual Enterprises[END_REF]. More specifically, activities performed by a dynamic multi-institutional virtual enterprise (VE) may involve the use of heterogeneous, externally-sourced cloud services which span different clouds and capability levels (IaaS, PaaS, and SaaS) [4], and which are entrusted by their users with data, software, and computation; we shall term such a VE a cloud-based one.

As an example, consider the following scenario. An industrial consortium is formed to collaboratively process the data produced as part of a seismic survey. Such processing integrates software components, offered as a service, by different consortium participants (SaaS offerings). Each component may be operating on a participant's proprietary infrastructure or, alternatively, on infrastructure provisioned as a cloud service (IaaS offering). At the same time, the simulation requires the develop-ment of new specialised software components. To this end, the consortium is provisioned the necessary software platform for developing these applications as a service (PaaS offering).

Evidently, the IT environment of a cloud-based VE is transformed into a complex ecosystem of intertwined infrastructure, platform, and application services delivered remotely, over the Internet, by diverse service providers. As the number of services proliferates, it becomes increasingly difficult to keep track of when and how they evolve over time, either through intentional changes, initiated by their providers, or through unintentional changes, such as variations in their performance and availability. Moreover, it becomes increasingly difficult to accurately predict the potential repercussions that such an evolution has with respect to a service's compliance to policies and regulations, and its conformance to service level agreements (SLAs).

In order to strengthen the resilience of the services in such an ecosystem, cloudbased VEs are anticipated to increasingly rely on cloud service brokerage (CSB) [5]. In this respect, the work in [START_REF] Veloudis | Continuous Quality Assurance and Optimisation in Cloud-based Virtual Enterprises[END_REF] proposed a conceptual architecture of a framework which offers capabilities with respect to two dimensions of CSB, namely Quality Assurance Service Brokerage, and Service Customisation Brokerage. These capabilities revolve around the following themes: (i) governance and quality control; (ii) failure prevention and recovery, and (iii) optimisation. The 1 st theme is concerned with checking the compliance of services with a set of policies and regulations; it is also concerned with testing services for conformance with their expected behaviour. The 2 nd theme is concerned with the reactive and proactive detection of service failures, and the selection of suitable strategies to prevent, or recover from, such failures. The 3 rd theme is concerned with continuously identifying opportunities to optimise service consumption.

Continuing the work in [START_REF] Veloudis | Continuous Quality Assurance and Optimisation in Cloud-based Virtual Enterprises[END_REF], this paper reports on the implementation of a particular mechanism of the aforementioned framework, namely the Service Completeness-Compliance Checker (SC 3 ), which offers capabilities with respect to the governance and quality control theme. More specifically, SC 3 strengthens the resilience of cloud services by continuously evaluating their compliance with pre-specified policies concerning their business aspects of delivery. By relying on a declarative representation of policies and services, one which is based on an RDF(S) ontology, SC 3 achieves a clear separation of concerns: policies are represented independently of the code that SC 3 employs for enforcing them. SC 3 is thereby kept generic and orthogonal to any underlying cloud delivery platform employed by a cloud-based VE. Such a separation of concerns is generally absent in contemporary governance mechanisms [START_REF] Veloudis | Underpinning a Cloud Brokerage Service Framework for Quality Assurance and Optimization[END_REF], with negative repercussions on their portability, as well as on their ability to represent and reason about, policy interrelations.

The rest of this paper is structured as follows. Section 2 presents a motivating scenario. Section 3 outlines our declarative approach to policy representation and explains how SC 3 extracts from this representation the necessary information for the subsequent policy enforcement process. Section 4 describes this process, and Section 5 outlines related work. Finally, Section 6 presents conclusions and future work.

2

Motivating Scenario [START_REF] Veloudis | Continuous Quality Assurance and Optimisation in Cloud-based Virtual Enterprises[END_REF] proposed a conceptual architecture of a CSB framework offering capabilities spanning the main phases of a service's lifecycle, namely Service On-boarding, Service Operation, and Service Evolution. SC 3 offers capabilities with respect to the Service On-boarding phase 1 . Below we identify these capabilities through the prism of the example of Section 1.

Let CPx (stands for Cloud Platform x) be a cloud delivery platform that hosts various services that are potentially used by the industrial consortium. The platform houses a variety of apps developed by CPx's network of ecosystem partners. CPx also allows advanced users to develop and deploy custom applications on the platform, and to create rich compositions of applications (mash-ups) offered by third-party service providers. Suppose that an ecosystem partner offers a new service on CPx, call it StoreCloud, which provides an encrypted and versioned persistence layer for storing the results during the various phases of the seismic survey. In order for the new service to be onboarded on CPx, a number of entry-level criteria must be satisfied. These crucially capture a set of service-level objectives (SLOs) expressed in terms of restrictions on relevant service-level attributes (see Table 1). These SLOs essentially form CPx's business (or broker2 ) policy (BP) with respect to on-boarding StoreCloud.

We assume that the ecosystem partner who offers StoreCloud, hereafter referred to as the service provider (SP), submits a service description (SD) which details the manner in which StoreCloud is to be deployed on CPx. The SC 3 mechanism offers a policy evaluation capability which essentially allows the cloud-based VE to determine whether this SD is compliant with CPx's BP. Such a capability entails two kinds of evaluation: SD completeness evaluation and SD compliance evaluation. The former kind of evaluation aims at determining whether the SD specifies values for all required service-level attributes. For example, an SD which does not specify a value for the encryption attribute cannot be considered complete. The latter kind of evaluation aims at determining whether the specified attribute values fall within the corresponding ranges prescribed in the BP. For example, an SD which specifies a 64bit value for the encryption attribute cannot be considered compliant. Clearly, the aforementioned evaluations seek to determine whether StoreCloud attains the SLOs specified in CPx's BP. In this respect, they strengthen the service's resilience.

3

Parsing BPs: The SC 3 Approach

This section describes how the SC 3 mechanism parses a BP in order to extract the necessary information for evaluating the completeness and compliance of SDs. A brief description of a conceptual architecture for SC 3 is, however, first in order.

Conceptual Architecture

As depicted in Figure 1, the SP submits StoreCloud's SD through the SP-facing component -an interface which exposes an editor for facilitating the construction of the SD. The SD is then transported to the SC 3 mechanism, and also stored in the Governance Registry (GReg) depicted in Figure 1; the transportation takes place through a Publish/subscribe (Pub/sub) system. An explanation of the reasons for opting for the open-source WSO2 Carbon platform [START_REF]WSO2 Carbon -100% Open Source Middleware Platform[END_REF] (see Figure 1), as well as for advocating a Pub/sub system for transporting SDs, is omitted here due to space limitations; a relevant discussion can be found in [START_REF]Broker@Cloud project Deliverable 30[END_REF][START_REF]Broker@Cloud project Deliverable 40[END_REF]. The SC 3 exposes a callback function for subscribing to the appropriate topic of the Pub/sub system and (asynchronously) receiving the SD. More specifically, this callback function exposes the EvaluationComponentSDSubscriber class which is responsible for orchestrating all the actions required for creating connections to the Pub/sub system and subscribing to its topics. This class triggers the SC 3 mecha-nism when a fresh SD arrives. In particular, it invokes an object of the class Poli-cyCompletenessCompliance, one which is parameterised with the appropriate BP against which the evaluation will take place. The PolicyCompletenessCompliance class is one of the core classes of the SC 3 mechanism. It offers three main methods: getBrokerPolicy, completenessCheck, and compli-anceCheck. The first method extracts all the required information from the BP for the subsequent SD completeness and compliance evaluations to take place (see Section 3.2), whilst the last two implement these evaluations (see Section 4). All three methods are implemented in Java using the Apache Jena (Core and ARQ) APIs [START_REF]Error > 1 connections[END_REF].

3.2

The getBrokerPolicy process

The getBrokerPolicy process parses the BP and places the information that it extracts in the bp object of the class BrokerPolicy. This class encompasses a number of Java HashMap objects as attributes; Table 2 depicts these attributes for the scenario of Section 2. The HashMap objects reflect our declarative framework for representing the SLOs incorporated in the BP and, effectively, the BP itself. As already mentioned, through the incorporation of this framework, SC 3 achieves a clear separation of concerns between policy representation and policy enforcement: BPs are represented independently of the code that SC 3 employs for enforcing them. SC 3 is thus kept generic and orthogonal to the underlying cloud delivery platform. The declarative framework is based upon Linked USDL [START_REF]Linked USDL[END_REF] and, in particular, upon Linked USDL's SLA schema. Linked USDL is a lightweight ontology which provides an RDF vocabulary for the description of the business aspects of policies and services; it draws upon a number of widely-adopted vocabularies such as GoodRelations, SKOS, and FOAF. The reasons for opting for Linked USDL are outlined in Section 5.1; a more complete discussion can be found in [START_REF]Broker@Cloud project Deliverable 30[END_REF]. A brief account of our framework is in order; such an account is necessary here for understanding the implementation of getBrokerPolicy. The account is based on the scenario of Section 2.

Declarative Representation of BPs and SLOs

We model the SLOs of a BP, hence the BP itself, through a specialisation process which constructs a framework of suitable subclasses and sub-properties of the Linked USDL SLA classes and properties depicted in Figure 2. These subclasses are then populated by instances specified in StoreCloud's SD. Below we outline this process for the 'gold' SLO of StoreCloud's availability attribute 3 (see Section 2); a more complete account of this process can be found in [START_REF]Broker@Cloud project Deliverable 40[END_REF][START_REF]Broker@Cloud project Deliverable 30[END_REF].

SLO Representation.

For each service-level attribute, the BP offers a subclass of the class ServiceLevel for accommodating the attribute's SLOs. For example, for accommodating the SLOs of the availability attribute (i.e. the 'gold', 'silver', and 'bronze' SLOs of Table 1), it offers the class SL-Availability (see Figure 2). SLOs appear as instances of this class -e.g., the SL-GoldAvailability instance specified in StoreCloud's SD (see Figure 2). Each SLO is defined in terms of a service-level expression (SLE) which specifies the conditions that must be satisfied in order for the SLO to be met. SLEs are modelled as instances of suitable subclasses of the ServiceLevelExpression class (see Figure 2). For example, the SLEs that correspond to the availability-related SLOs are modelled as instances of the class SLE-Availability. SLOs are associated with their corresponding SLEs through appropriate sub-properties of the hasServiceLevelExpression property. In particular, the availability-related SLOs are associated with their SLEs through the hasSLEAvailability property (see Figure 2).

Each SLE binds a variable that corresponds to a particular attribute, one which is associated with an allowable range of values. Following an approach entirely symmetrical to the one outlined above for SLOs and SLEs, variables are modelled as instances of appropriate subclacess of the class Variable, whilst value ranges are modelled as instances of appropriate subclasses of the GoodRelations class QuantitativeValueFloat 4 . Figure 2 depicts these subclasses (Var-Availability, AllowedAvailability), and instances (Var-GoldAvailability, AvailabilityValue), for the 'gold' availability attribute. SLEs are associated with their corresponding variables through subproperties of the hasVariable property (e.g. the hasVarAvailability of Figure 2), and variables are associated with their allowable values through subproperties of the hasDefaultQuantitativeValue property5 .

Service-level Profiles and BP Representation.

Above we outlined a framework for representing SLOs. We now show how a BP is associated with this framework, hence with its pertinent SLOs 6 . A BP is represented as an instance of a subclass of the ServiceModel class, e.g. as an instance of the class ServiceModelCPx of Figure 2. This instance is associated with its SLOs through one or more service-level profiles (SLPs). SLPs are groupings of SLOs whose purpose is to formulate different service deployment 'packages' that are allowable by the BP. For example, in the scenario of Section 2, the 'gold' SLP formulates a deployment package comprising the 'gold' SLOs of the attributes of Table 1. SLPs take the form of instances of appropriate subclasses of the ServiceLevelProfile class, e.g. the SLP-Gold instance of the SLP-CPx subclass of Figure 2. BPs are associated with SLPs through sub-properties of the hasServiceLevelProfile property (e.g. through hasSLPCPx of Figure 2). SLPs are associated with their constituent SLOs through appropriate sub-properties of the hasServiceLevel property -an example is hasSLAvailability of Figure 2 which associates the 'gold' SLP with the 'gold' availability SLO.

Extracting Information from BPs

Turning now back to BP parsing, the getBrokerPolicy method sets out to construct a programmatic representation of the BP framework outlined above. In this respect, it starts off by discovering, for each Linked USDL SLA class , those subclasses of that appear in the BP. It then instantiates the HashMap attributes of the bp object (see Table 2) with the corresponding subclasses. This instantiation takes place through the method getBrokerPolicyClassMap as indicated in the 1st row of Table 3 for the ServiceLevel class (the rest of the instantiations are entirely analogous and thus omitted). The getBrokerPolicy method then proceeds to construct a list of string objects holding the URIs of all QuantitativeVal-ueFloat instances found in the BP (e.g. the GoldAvailability instance depicted in Figure 2); an excerpt of the code that populates this list is shown in the 2 nd row of Table 3. Subsequently, the getBrokerPolicy method discovers all properties in the BP, along with their corresponding ranges, which have as a domain one of the subclasses ; these are effectively all the sub-properties that appear in the BP. For each subproperty, an object of the class Subproperty is constructed (see Table 4 for an excerpt of the relevant code).

SD Completeness and Compliance Checking

An SD is effectively a framework of instances that populate the subclasses of the BP framework (see Figure 2). Below we outline the processes that determine whether an SD is complete and compliant with respect to the BP.

The completenessCheck Method

The completenessCheck algorithm starts off by determining whether there is a corresponding instance for each class discovered by the getBrokerPolicy method (see Section 4.2). Then, for each object property discovered by get-BrokerPolicy such that dom ) = , it determines if is associated (via ) with exactly one instance of the class = ran ). Similarly, for each data property , such that dom ) = , it determines whether is associated via with a data value from ran ). An excerpt of the code that checks these associations is shown in Table 5. For example, let =SL-Availability (see Figure 2). The algorithm initially checks if the SD contains an instance of (in this case SL-GoldAvailability). Let now = hasSLEAvailability. The algorithm checks if SL-GoldAvailability is associated, via , with an instance of SLE-

4.2

The complianceCheck Method

The compliance checking algorithm proceeds by determining whether the values, or value ranges, specified in the SD are in accordance with the allowable values, or value ranges, specified in the BP. More specifically, the algorithm starts off by determining the number of values that are associated with a QuantitativeValueFloat instance (e.g. the instance Var-GoldAvailability of Figure 2) via each of the properties hasMinValueFloat and hasMaxValueFloat. If this number is not equal to 1, an exception is raised (see lines 1-3 in the 1 st row of Table 6). Otherwise, the algorithm proceeds to check that the range associated with the Quantita-tiveValueFloat instance is indeed subsumed by the corresponding range declared in the BP (see 2 nd row of Table 6). For example, the algorithm checks whether the range [0.99999,1) associated with Var-GoldAvailability, is subsumed by the range [0.9999,1) associated with the corresponding AvailabilityValue instance in the BP (see Figure 2).

Related Work

To the best of our knowledge, no works other than [4] address the quality assurance dimension of CSB in the context of VEs. [4] recognises the need for frameworks that guide the creation, execution, and management of services in cloud-based VEs; it does not, however, address the quality assurance aspect of such frameworks. The rest of this section outlines work related to service description languages and to ontologydriven policy-based cloud service governance and quality control.

Service Description Languages

We provide an overview of different strands of service description formalisms. More specifically we outline approaches that: focus on syntactic service descriptions; consider the underlying semantics of web services; capture business aspects of services.

Syntactic service descriptions aim, primarily, at facilitating the interoperable data exchange between service registries (notably UDDI), service providers, and service consumers. The most prominent example is, perhaps, WSDL [START_REF]W3C Recommendation[END_REF]. Nevertheless, syntactic service descriptions can only aid manual discovery, selection, and composition of services. In an attempt to automate these processes, a new breed of service description languages was introduced that enable Semantic Web Services [14]. These use ontologies in order to capture the functionality of web services in terms of an underlying, domain-specific, vocabulary. The rationale is that since both service descriptions and consumer demands rely on a common semantics, automatic service discovery and composition is, in principle, feasible. Prominent examples of standardisation efforts in this area include WSMO [START_REF]Web Service Modelling Ontology (WSMO)[END_REF], OWL-S [START_REF]Semantic Annotation s for WSDL and XML Schema[END_REF], SAWSDL [17], and SA-REST [START_REF]SA-REST: Semantic Annotations for Web Resources[END_REF].

Whilst focusing on aspects which are important for the automatic composition and invocation of web services, the aforementioned approaches neglect any pertinent business details or, at best, address them as non-functional properties. This renders service descriptions cumbersome for service consumers and third-party intermediaries who are often interested in both business details and technical specifications in order to create added value by deploying, aggregating, customising, and integrating services. A third strand of description languages has therefore emerged, one which focuses on the business aspect of services. A prominent example is the Unified Service Description Language (USDL) [START_REF] Oberle | A unified description language for human to automated services[END_REF]. USDL aims at unifying the business, operational, and technical aspects of a service in one coherent description. Although it provides a comprehensive framework, USDL has received limited adoption due mainly to its complexity and limited support for extensibility.

To overcome these limitations, Linked USDL [START_REF]Linked USDL[END_REF] has been proposed. Linked USDL is a remodelled version of USDL which offers the following advantages [START_REF] Pedrinaci | Linked USDL: a Vocabulary for Web-scale Service Trading[END_REF][START_REF] Cardoso | Foundations of Open Semantic Service Networks[END_REF]. Firstly, it uses a light-weight RDF(S) vocabulary which provides a framework of concepts and properties for modelling, comparing, and trading services and service bundles, as well as for specifying, tracking, and reasoning about the involvement of entities in service delivery chains. This framework can be easily extended through linking to other RDF(S) ontologies. Secondly, it supports largescale, efficient, multi-party interactions by: i) capitalising on widely-adopted, generalpurpose vocabularies such as Dublin Core, GoodRelations, SKOS, and FOAF; ii) embracing Linked Data as the core means for capturing facts about people, organisations, resources, and services. It is to be noted here that these advantanges facilitate the evaluation of the compliance of cloud services with pre-specified policies concerning the business aspects of service delivery and deployment. They are thus particularly significant from the standpoint of strengthening the resilience of cloud services.

Ontology-driven Policy-based Cloud Service Governance and Quality Control

Cloud service governance refers to policy-based management of cloud services with emphasis on quality assurance [START_REF] Kourtesis | Policy-driven governance in cloud application platforms: an ontology-based approach[END_REF]. Current practice [START_REF] Marks | Service-Oriented Architecture Governance for the Services Driven Enterprise[END_REF][START_REF] Zhang | CCOA: cloud computing open architecture[END_REF] focuses on the use of registry and repository systems combined with purpose-built software to check the conformance of services with relevant policies [START_REF] Kourtesis | A registry and repository system supporting cloud application platform governance[END_REF]. A major weakness in these systems is failure to achieve a separation of concerns between defining policies and evaluating data against these policies [START_REF] Kourtesis | Policy-driven governance in cloud application platforms: an ontology-based approach[END_REF][START_REF] Kourtesis | A registry and repository system supporting cloud application platform governance[END_REF]. This has a number of negative repercussions such as lack of portability and lack of explicit representation of policy interrelations. Several works have attempted to address these shortcomings [START_REF] Damianou | The Ponder Policy Specification Language[END_REF][START_REF] Uszok | KAoS Policy Management for Semantic Web Services[END_REF][START_REF] Kagal | A Policy Language for a Pervasive Computing Environment[END_REF][START_REF] Nejdl | Ontology-Based policy specification and management[END_REF]. These generally employ bespoke languages, and ontologies, for capturing policies; the policies are then enforced at run-time typically through the use of a reference monitor. Closer to our approach are the works in [START_REF] Uszok | KAoS Policy Management for Semantic Web Services[END_REF][START_REF] Kagal | A Policy Language for a Pervasive Computing Environment[END_REF][START_REF] Nejdl | Ontology-Based policy specification and management[END_REF] which embrace Semantic Web representations for capturing the knowledge encoded in policies.

In [START_REF] Uszok | KAoS Policy Management for Semantic Web Services[END_REF], the authors present KAoS -a general-purpose policy management framework which exhibits a three-layered architecture comprising: i) a human interface layer, which provides a graphical interface for policy specification; ii) a policy management layer, which uses OWL to encode and manage policy-related knowledge; iii) a policy monitoring and enforcement layer, which automatically grounds OWL policies to a programmatic format suitable for policy-based monitoring and policy enforcement. In [START_REF] Kagal | A Policy Language for a Pervasive Computing Environment[END_REF] the authors propose Rei -a policy specification language expressed in OWL-Lite. It allows the declarative representation of a wide range of policies which are purportedly understandable -hence enforceable -by a wide range of autonomous entities in open, dynamic environments. In [START_REF] Nejdl | Ontology-Based policy specification and management[END_REF], POLICYTAB is proposed for supporting trust negotiation in Semantic Web environments. POLICYTAB advocates an ontology-based approach for describing policies that drive a trust negotiation process aiming at providing controlled access to Web resources.

Whilst achieving a proper separation of concerns between policy specification and policy enforcement, the aforementioned semantically-enhanced approaches rely on bespoke, non-standards-based, ontologies for the representation of policies. Such ontologies generally lack the expressivity for addressing the business details that characterise web and cloud services. They are therefore inadequate, as they stand, for capturing the business policies on which this work reports. In this respect, in our work, we have opted for Linked USDL: a language which readily provides the necessary constructs for capturing the required business policies.

Conclusions and Future Work

We have presented SC 3 , a mechanism which offers capabilities with respect to the Quality Assurance dimension of CSB. SC 3 strengthens the resilience of services in cloud-based VE by evaluating their compliance with pre-specified policies concerning the business aspects of their delivery. By representing policies declaratively, in terms of an ontology, and not as part of the code of SC 3 , a separation of concerns between policy definition and policy enforcement is achieved. This effectively enables SC 3 to operate in a manner generic and agnostic to the underlying cloud delivery platform. This paper has focused on the policy evaluation capabilities of SC 3 during the Onboarding phase of a service's lifecycle (see Section 2). Nevertheless, SC 3 offers a number of additional relevant capabilities which have been omitted here for reasons of space. These are: (i) an SLA monitoring capability which enables SC 3 to continuously check whether a service complies with the BP during the service Operation phase (i.e. during its consumption); (ii) a standalone policy evaluation capability, which assesses the correctness of a BP. In addition, SC 3 can be used for strengthening the resilience not only of application services (as demonstrated in this paper), but also of infrastructure and platform services.

SC 3 has been successfully used, in the frame of EU's Broker@Cloud project [START_REF]Broker@Cloud project[END_REF], for evaluating the quality of CRM services that are on-boarded on an exising commercial cloud application platform -namely the CAS Open [START_REF]CAS CRM[END_REF] platform. In the future we intend to further assess the effectiveness of SC 3 by incorporating it in a number of additional cloud platforms, in particular platforms that provide infrastructure and platform services.

Fig. 1 .

 1 Fig. 1. SC3 conceptual architecture

Fig. 2 .

 2 Fig. 2. Linked USDL SLA for the gold availability SLO

Table 1 .

 1 Entry-level criteria

	Service-level Attribute	Acceptable Values	SLO	Comments
		[100,1000)	Gold storage
	storage	[10,100)	Silver storage	Size in TB
		[0,10)	Bronze storage
	availabil-ity	[0.99999,1) [0.9999,1) [0.999,1)	Gold availability Silver availability Bronze availability	Total uptime ratio
		256	Gold encryption
	encryption	192	Silver encryption	Key-length in bits
		128	Bronze encryption

Table 2 .

 2 HashMap objects

	Map<String, BrokerPolicyClass> serviceModelMap;
	Map<String, BrokerPolicyClass> serviceLevelProfileMap;
	Map<String, BrokerPolicyClass> serviceLevelMap;
	Map<String, BrokerPolicyClass> serviceLevelExpressionMap;
	Map<String, BrokerPolicyClass> expressionVariableMap;
	Map<String, BrokerPolicyClass> quantitativeValueFloatMap;

Table 3 .

 3 Storing BP subclasses and QuantitativeValueFloat instances

	bp.setServiceLevelMap(getBrokerPolicyClassMap(USDL_SLA,
	"ServiceLevel"));
	1. List<String> qvSubclassList = new ArrayList<String>();
	2. Iterator<BrokerPolicyClass> iterFl =
	bp.getQuantitativeValueFloatMap().values().iterator();
	3. while (iterFl.hasNext()) {
	4.

qvSubclassList.add((iterFl.next()).getUri());}

Table 4 .

 4 Discovering ranges of sub-properties

	1. while (riIn.hasNext()) {
	2. Resource subclassPropertyResource = riIn.next();
	3. Subproperty prop =
	new Subpropety(subclassPropertyResource.toString());
	4. prop.setDomainUri(subclassResource.toString());
	5. Property rangeProperty =
	ResourceFactory.createProperty(RDFS + "range");
	6. NodeIterator riIn2 =
	modelMem.listObjectsOfProperty(subclassPropertyResource,ran
	geProperty);
	7. prop.setRangeUri(riIn2.next().toString());}

Table 6 .

 6 Sample code for compliance checking

	1. int minMaxFloatValueCount = countQuery("{<"+instance_uri +
	">
		gr:hasMinValueFloat ?someMinValue; " +
		"gr:hasMaxValueFloat ?someMaxValue}");
	2. if (minMaxFloatValueCount != 1) {
	3. System.err.println("Erroneous range association");}
	1. QuantitativeValueInstance bpQvInstance = null;
	2. while (bpInstancesIter.hasNext()) {
	3. bpQvInstance = bpInstancesIter.next();
	4. if (((Float) vObj.getMaxValue() <
		(Float) bpQvInstance.getMaxValue()) &&
		((Float) vObj.getMinValue() >=
		(Float) bpQvInstance.getMinValue())) {
	5. System.out.println("OK -Instance ");
	6.	break;}}

SC 3 also offers capabilities with respect to the Service Operation phase and, in particular, with respect to continuously monitoring the behaviour of a service. These capabilities shall not, however, concern us in this paper.

We use the term "broker" to emphasise that, in our work, such a business policy is formulated according to the declarative approach of our brokerage framework (see Section

3.3).

Of course, an analogous account applies to the rest of the attributes and SLOs of Table1.

Or of the class QualitativeValue, in case of qualitative variables.

Or through sub-properties of the hasDefaultQualitativeValue, in case of qualitative values.

Recall from Section 2 that a BP is essentially a set of SLOs.

Acknowledgements. This research is funded by the EU 7 th Framework Programme under the Broker@Cloud project (www.broker-cloud.eu), grant agreement n°328392.

Availability (an association which exists with SLE-GoldAvailability). Analogous checks are performed for the rest of the instances in the SD framework.


Javad Jassbi
email: j.jassbi@uninova.pt

Luis M Camarinha-Matos

Jose Barata

A Framework for Evaluation of Resilience of Disaster Rescue Networks

Keywords: Resilience, Fuzzy Inference System, Disaster Rescue Network

Disasters by their very nature are unpredictable so being prepared to react in short time and with high efficiency is vital. The adoption of the collaborative networks paradigm in disaster management can lead to a new generation of rescue teams so called Disaster Rescue Network (DRN). During the first hours after a disaster, and before external help can arrive, it is the DRN implanted in the incident area that can perform the first rescue tasks. But the DRN is itself affected by the disaster and often gets its operational level drastically reduced. The main objective of this work is to propose an evaluation model, through employing experts' knowledge, for measuring a resilience index in DRNs. In this paper, a Fuzzy Inference System is used to achieve the mentioned objective. This will help in evaluating the resilience of DRNs and provide the opportunity to simulate policies to improve that resilience.

Introduction

In the first hours after a large scale disaster and before a disaster rescue team is operational, social networks play crucial roles [START_REF] Yondong | Social networks and reduction of risk in disasters: An example of Wenchuan earthquake: Economic Stress, Human Capital, and Families in Asia[END_REF]. This means that professional rescue teams implanted in the incident area are not resilient enough to recover and accomplish their mission. Recently this problem has attracted the attention of researchers as on one hand professionals are empowered to deal with crisis situations and on the other hand response time is crucial. Having an effective recovering process after being impacted could make huge difference.

Rescue networks due to their functionality and mission have to face challenges which are not predictable. In case of disasters not only we face complex and dynamic phenomena but also the multiplicity and multifunction of actors, whether individuals or organizations, needed to deal with the situation [START_REF] Camarinha-Matos | The Role of Collaborative Networks in Sustainability[END_REF]. Traditional leadership structures cannot be of much help in crisis, while a resilient response needs to be institutionalized in Disaster Rescue Networks (DRN) [START_REF] Boin | Preparing for Critical Infrastructure Breakdowns: The Limits of Crisis Management and the Need for Resilience[END_REF]. Resilience is an important decisive factor in terms of survivability of the system in unpredictable situations. It is sourced in basic systems theory concepts such as equilibrium, adoptability and stability [START_REF] Arsovski | Resilience of Automotive Sector: A Case Study[END_REF]. Both individual and collective responses to unexpected and radical changes are interesting for researchers in the area of resilience [START_REF] Bhamra | Resilience: the concept, a literature review and future directions[END_REF].

Resilience in hazard management systems has attracted the researchers' attention and many works confirm the importance of the subject when the focus is on developing strategies for building resilient societies. Macro Level is the dominant perspective in this area and achieving a resilient society the focal point. Something which is less considered is that DRNs are also affected by disasters while we are expecting them to react quickly to recover and help the victims. In all natural or human provoked disasters, the first hours are crucial and life of people depends on how the rescue networks react when the network and its members are also suffering, both as a team and as individuals, from the same disaster. Imagine there is earthquake in a big city. Most of the infrastructures could be damaged and at the same time the hierarchy and authority structure of different organizations, responsible to help in these cases, could be hardly attacked or unable to work properly due to many reasons. What is important here is the way the DRNs could recover and start to operate efficiently.

One relevant research challenge in resilient DRNs is the development of a framework for resilience assessment. It is clear that rescue networks have to be restructured to work as collaborative networks to increase the resilience of the system [START_REF] Camarinha-Matos | Collaborative Networks in Industry and the role of PRO-VE[END_REF]. The aim is to achieve a model of resilience that keeps a record of rescue networks and how they are achieving adequate levels of resilience. Having a conceptual model with the characterization of all relevant factors is a necessary but not sufficient step. To fulfill the mission, a quantitative model is also needed. A conceptual model could provide a big picture of what is essential and the strategy to be adopted. However, with no measuring system it is not possible to understand the weaknesses and strengths as the basis for improvement. The main concern of this paper is thus the measurability of DRNs' resilience in case of natural or human provoked disasters.

The paper is organized as follows: in next section the related literature is reviewed; section three deals with a conceptual model and in section four the process of developing hybrid intelligent system for evaluating resilience of DRNs is introduced. In section five the applicability of the model is presented by an illustrative example and implementing the proposed model into a real case study. Section six presents the discussion and future direction of the work.

Literature Review

It is claimed that Collaborative Networks could help achieving better operation against threats and risks and bringing a new approach to adapt to crisis situations [START_REF] Camarinha-Matos | The Role of Collaborative Networks in Sustainability[END_REF]. Typically emergency management networks have multiplex relationships, follow flat decision-making process, and involve both formal and informal groups with a combination of vertical and horizontal relations [START_REF] Kapucu | Understanding Multiplexity of Collaborative Emergency Management Networks[END_REF]. Rescue teams, seen as a form of collaborative network, need a strategic and operational plan to enhance the capacity of the system to deal with critical situations. Traditional risk-based approach is insufficient and resilience analysis should be part of all catastrophe management plans [START_REF] Park | Integrating Risk and Resilience Approaches to Catastrophe Management in Engineering Systems[END_REF]. The main difference between these two approaches is the way they look into future. In risk-based approaches the centre of attention is risk factor identification and mitigation, while in the resilience-based approach the assumption is that hazards are indefinable and internal preparedness and readiness for unexpected situations are necessary.

Adaptability is one of the most important capabilities which would guarantee the survival of a system while agility and resilience are the representatives of this capability of the system [START_REF] Noran | Collaborative Disaster Management: An Interdisciplinary approach[END_REF]. Resilience, as an emerging research field, is being analyzed from different perspectives and as a result there are different meanings and perceptions which sometimes are contested or contrasted [START_REF] Brown | Global environmental change I: A social turn for resilience?[END_REF] [START_REF] Folke | Adaptive governance of social-ecological systems[END_REF]. Resilience is used in different contexts, including ecology management, psychology, supply chain management, safety engineering, crisis management, and collaborative networks [START_REF] Bhamra | Resilience: the concept, a literature review and future directions[END_REF] [START_REF] Ponis | Supply Chain Resilience: Definition of Concept and its Formative Elements[END_REF]. Nevertheless it is important to avoid common pitfalls of synonyms and distinguish between resilience and other characteristics of the system such as flexibility, agility, and robustness [START_REF] Christopher | Building the resilient supply chain[END_REF]. In this work, a definition from [START_REF] Francis | A metric and frameworks for resilience analysis of engineered and infrastructure systems[END_REF] is taken as more appropriate to present the concept of resilience for Disaster Rescue Networks:

"Resilience can be understood as the ability of the system to reduce the chances of a shock, to absorb a shock if it occurs (abrupt reduction of performance) and to recover quickly after a shock (re-establish normal performance)" [START_REF] Francis | A metric and frameworks for resilience analysis of engineered and infrastructure systems[END_REF].

The benefit of a holistic perspective is the relation-hood view, so needless to say that the collective capability of a system is not just the additive composition of individuals, but more related to interactions and interrelations between individuals. To take advantage of the synergies in DRNs as collaborative networks, we are aiming resilience at both individual and collective levels [START_REF] Lengnick-Hall | Developing a capacity for organizational resilience through strategic human resource management[END_REF]. This brings complexity and dynamism to the resilience evaluation model. Resilience is more than just capacity to provide sufficient response to uncertainty, which is the minimum expectation. It is a process of learning from doing and building a knowledge repository from tough experiences [START_REF] Lengnick-Hall | Adaptive Fit Versus Robust Transformation: How Organizations Respond to Environmental Change[END_REF]. The nature of all systems is imperfect and, as such, resilient networks need to adopt a process of improvement through learning from events [START_REF] Vogus | Organizational Resilience: Towards a Theory and Research Agenda[END_REF]. Furthermore, and unlike traditional views, resilience is not any more limited to the concept of distinctive and discontinuous events but it is recognized as a capability or capacity of organizations related to ordinary adoptive practices that lead the system to higher levels of efficiency [START_REF] Akgün | Organisational resilience capacity and firm product innovativeness and performance[END_REF]. Resilience in disaster management could be discussed at different levels, while most research works are focused on the resilience of the society or citizens especially in the first hours and days after large scale disasters [START_REF] Boin | Preparing for Critical Infrastructure Breakdowns: The Limits of Crisis Management and the Need for Resilience[END_REF].

Measurement systems associated to resilience indicators could be classified in two categories, pre-event or post-event models [START_REF] Arsovski | Resilience of Automotive Sector: A Case Study[END_REF]. Pre-event models endeavor to use indicators to present the estimated level of resilience and post-event models show the actual level of resilience after analyzing the reaction of the system to real cases when a catastrophe actually happens [START_REF] Rose | Economic resilience to natural and man-made disasters: Multidisciplinary origins and contextual dimensions[END_REF]. Developing a model for pre-event estimation is a hard task that includes designing process, measurement of specific factors, and an aggregation model.

Planning, benchmarking, or a strategic move to enhance the resilience of any type of organization need an assessment model, including clear indicators. The aimed model should present the resilience of the system in a way to enable decision makers to have a clear picture of what the current situation is and the gap to meet desired state. It is difficult to develop a general mathematical model due to the variety of classes of affecting variables, while dealing with imprecise data is also indispensable [START_REF] Aleksić | An assessment of organizational resilience potential in SMEs of the process industry, a fuzzy approach[END_REF].

The number of proposed models in the literature is still limited when most of the works concentrate on meaning, and on the conceptual part of the resilience model. There are also some attempts to use fuzzy logic, or statistical methods by employing questionnaires [START_REF] Arsovski | Resilience of Automotive Sector: A Case Study[END_REF][20] [START_REF] Azusa | Organizational Resilience: An Investigation of Key Factors that Promote the Rapid Recovery of Organizations[END_REF][22] [START_REF] Azadeh | Assessment of resilience engineering factors in high-risk environments by fuzzy cognitive maps: A petrochemical plant[END_REF]. The scope of our work, different from previous approaches, can be summarized as follows: First, it considers the concept of resilience at micro-level (comparing with society level), targeting Disaster Rescue Networks in case of large scale catastrophes and second, developing an intelligent decision-support system to measure the level of resilience in DRNs.

Conceptual Model for Resilience Evaluation

There are several evaluation frameworks offering different methodologies for resilience evaluation in an organization or network [START_REF] Francis | A metric and frameworks for resilience analysis of engineered and infrastructure systems[END_REF] [START_REF] Lengnick-Hall | Developing a capacity for organizational resilience through strategic human resource management[END_REF]. Some of them are more focused on organizations, while some others try to propose a wider perspective to make it appropriate to cover from individuals to collective systems, such as teams, organizations, or societies. The main challenge in the proposed models is the measurability of the adopted criteria. This requires a trade-off analysis, as on one hand we need a comprehensive model with enough criteria covering all aspects and domains, while on the other hand non-measurable criteria are tricky. If we cannot measure it, it could not help us in reality to evaluate the level of resilience and thus it is useless for strategy selection.

In this paper, the conceptual model introduced in [START_REF] Lengnick-Hall | Resilience Capacity and Strategic Agility: Prerequisites for Thriving in a Dynamic Environment[END_REF]] is adopted and used as a basis to develop a quantitative model. The mentioned model is well accepted by experts in the area and provides a comprehensive framework for understanding resilience. In fact, in several other research works, the same model was employed on different occasions. As a result, there is an adequate number of empirical studies and satisfactory guidelines for measurability of the used criteria, which is very important for quantitative models [START_REF] Arsovski | Resilience of Automotive Sector: A Case Study[END_REF][15] [START_REF] Lengnick-Hall | Adaptive Fit Versus Robust Transformation: How Organizations Respond to Environmental Change[END_REF][18] [START_REF] Lengnick-Hall | Resilience Capacity and Strategic Agility: Prerequisites for Thriving in a Dynamic Environment[END_REF]. Fig. 1 presents the mentioned model. The value of each main criterion is a result of aggregation of sub-criteria values which could make the process of measurement applicable. Table 1 presents all criteria in the model and their description. OI encompasses the core values and beliefs of an organization that its members deem to be the most central, distinctive, and symbolic, through which an organization reveals its identity to stakeholders [START_REF] Zellweger | Why Do Family Firms Strive for Nonfinancial Goals? An Organizational Identity Perspective[END_REF].

Mission, core values, Common vocabulary, Group commitment

Sense making (SM)

The process through which individuals work to understand novel, unexpected, or confusing events [START_REF] Maitlis | Sensemaking in Organizations: Taking Stock and Moving Forward[END_REF].

Balance of confidence and scepticism, Positive perception, Experience interpretation Resource Fullness (RF)

The ability to identify problems, establish priorities, and mobilize resources when disaster occurs; resourcefulness can be further regarded as flexible plans, strategies and procedures to apply or coordinate resources [START_REF] Zhong | Developing an Evaluation Framework for Hospital Disaster Resilience: Tertiary Hospitals of Shandong Province, China[END_REF].

Creativity, Balance of originality and initiative

Preparedness (BP)

BP is taking actions and making investments before they are needed to ensure that an organization is able to benefit from situations that emerge [START_REF] Lengnick-Hall | Resilience Capacity and Strategic Agility: Prerequisites for Thriving in a Dynamic Environment[END_REF].

Prepared infrastructure, Cooperation in noncritical situations, Training Counterintuitive Moves (CM)

The ability to follow a dramatically different course of action from the one which is the norm [START_REF] Lengnick-Hall | Developing a capacity for organizational resilience through strategic human resource management[END_REF].

Variety of strategies, Scenario planning, Non-aligned activities, Learning from events Useful Habits (UH)

Useful, practical habits, especially repetitive, over-learned routines that provide the first response to any unexpected threat [START_REF] Lengnick-Hall | Developing a capacity for organizational resilience through strategic human resource management[END_REF].

Flat decision process, Power of experts

Deference to Expertise (DE)

DE in a mindful organization manifests itself in the under specification of structures, relaxation and departure from formal hierarchical decision structures to one that gives the flow of authority to people who possess the required expertise to deal with the problem at hand [START_REF] Khan | Top Management Support, Collective Mindfulness, and Information Systems Performance[END_REF].

Flat decision process, Power of experts

Deep Social Capital (DS)

DS is attained through well-maintained interpersonal relationships within an organizational community. It focuses on long term partnership that benefits parties beyond immediate transaction interests [START_REF] Dubrin | Handbook of Research on Crisis leadership in Organizations[END_REF].

Member communication & cooperation, Resource sharing

Broad Resource Network (BR)

Resilient firms are able to utilize relationships with supplier contacts, loyal customers, and strategic alliance partners to secure needed resources to support adaptive initiatives [START_REF] Lengnick-Hall | Resilience Capacity and Strategic Agility: Prerequisites for Thriving in a Dynamic Environment[END_REF].

Resource network, Meta organizational resources

Developing a Hybrid Intelligent System for Evaluation

One of the main problems in social or human based systems is the lack of tools to map from an input space to an output space. This is due to the complexity of the system and shortage of knowledge about its components and their relations. It is a challenging mission when there is neither sufficient historical data nor knowledge about the structure of the system. To tackle these types of problems, the knowledge of experts could be the only source of information.

Fuzzy Inference System (FIS) is a powerful tool to deal with expert's knowledge and for approximate reasoning [START_REF] Jassbi | Transformation of a Mamdani FIS to first order Sugeno FIS[END_REF]. To develop a FIS, knowledge is extracted and represented in a set of rules that express the relations between components of the system. For the case of resilience measurement, due to the lack of information and unclear equation between the criteria, FIS seems to be more effective comparing with other possible methods. Most of the other methods need historical behavior (information) or clear relation for aggregating. In this work, FIS is employed to develop an evaluation model for DRNs' resilience. As it can been inferred from Fig. 1, we need to have a hybrid system including four FISs to aggregate the criteria in two steps. In the first step, three FISs are built to aggregate the sub-criteria to determine the three main criteria, Cognitive Resilience, Behavioral Resilience, and Contextual Resilience. In a second step, the result will be the output of criteria fusion from first step. To do that, and for each FIS, we follow the procedure presented in Fig. 2.

Fig. 2. The process of Developing FIS for Evaluation of Resilience in DRN

The first step is to determine input and output, for which two important decisions should be taken: the number of fuzzy sets and the membership function. In this work we selected five fuzzy sets and a Gaussian membership function. This membership function is more complex in comparison with a triangle-shaped function, but with better results in most cases [START_REF] Markowski | Fuzzy risk matrix[END_REF]. Afterwards, the experts' knowledge, in a form of a set of rules, is extracted. This is based on the experts' experience, using questionnaires/interviews. The final rule set can be refined using an experts' panel. Below some sample rules for Cognitive Resilience FIS are presented: a) If "Organization Identity" is very high and "Sense making" is very high then the "Cognitive

Resilience" is very high. b) If "Organization high" is high and "Sense making" is medium then the "Cognitive Resilience" is Medium. c) If "Organization Identity" is very how and "Sense making" is very low then the "Cognitive

Resilience" is Medium. d) If "Organization Identity" is high and "Sense making" is Low then the "Cognitive Resilience" is Medium. e) If "Organization Identity" is very Low and "Sense making" is very Low then the "Cognitive

Resilience" is very Low.

The last three steps comprise an iterative process to make sure that the model will work properly.

In order to confirm the validity of the model, a typical three-step process was used which includes face validity, extreme conditions test, and behavior analysis. In "face validity" the rules are rechecked to make sure they are acceptable and satisfy the logic of relationships between the variables. Extreme conditions test is used to make sure that the model could respond properly to critical situations which are usually on the border, such as extreme conditions of variables. Finally, behavioral analysis is a way to analyze the behavior of the system when values of input variables are increasing, at a constant rate, from their minimum to their maximum (or from max to min) one by one while the others remain constant. This can provide detailed information about the system and its performance so any deviation from expected situation or illogical behavior should be analyzed and by imposing new rules the model could be corrected to be acceptable. Fig. 3 shows the high-level view of the model. As mentioned above, four FISs are developed and all should be analyzed one by one, while the hybrid system also needs to be verified as it could be an erroneous infusion model. Fig. 4 depicts the rules and output surface for "Behavioral Resilience", presenting the relation between "Preparedness" and "Resourcefulness". This is an example and by analyzing each relation unacceptable conditions/rules could be recognized. Fig. 5 shows the example of behavioral analysis which is used in this work. As it can be seen, the behavior of the system when one of the indicators (preparedness) is increasing from minimum to maximum value (0 to 10) and the others are fixed ( 5) is presented. Similarly, different combinations should be analyzed to verify the model. The key benefits of the introduced model, more than measuring the level of resilience, is the ability to simulate different scenarios to recognize which indicator could affect more the resilience of the network in each condition to be investigated. This can help managers to select appropriate combinations of solutions to enhance the resilience of the network in an efficient way.

Case Study

In this section, the applicability of the proposed model is demonstrated by using an illustrative example and real case study. For numerical example four types of DRNs with the following information are considered. By employing a hybrid decision-support system, the result is as below: As discussed in previous section, the advantage of the proposed model is the ability to simulate different scenarios to investigate which indicator affects more the resilience in each case and should be in priority of investment to increase the level of resilience of the network. To show this ability DRN2 and DRN4 are selected and in each iteration one of the indicators is increased by 30% to see the behavior of the system. This can help managers of the networks to check possibilities of enhancing the level of resilience and find an optimal solution as limited sources should be invested wisely. Table 2 presents the result of the illustrated test to show the impact of increasing each indicator (one by one). As it can be seen, for DRN2, the best strategies are investment on Deference to Expertise (DE) and Deep Social Capital (DS), while for DRN4 Sense Making (SM) is the best investment target.

After demonstrating the capability of the introduced model by an illustrative example, a real case study was implemented. Red Crescent/ Red Cross organizations due to their nature have to be prepared for prompt action to disasters. They are usually the first formal group to respond in natural disasters and increasing the level of resilience is part of their strategic plan. They include a combination of professionals and volunteers while, at the same time, they have several organizations to support their activities. For the case study, a Red Crescent organization from a Middle Eastern country was selected. The selected country has a long history in natural disasters such as earth quakes and floods. In several cases the rescue network was affected while as a rescue team they had to keep operation going on. The first step was to measure the level of criteria and process them to feed the model. Each criterion from Fig. 1 (details in Table 1) was "measured" by questions which were representing the subcriteria. Seven selected managers who are experts in the organization answered questionnaires. The final scores to be used by model resulted from the aggregation of information in the questionnaires using the average of sub-criteria (which were expressed in the form of questions).

For instance, Organizational Identity (OI) had five sub-criteria which were turned into questions. Each person had to answer all questions and give a number between 0 and 100 (percentage). Below you can find an example of question: "How strong is the sense of visions, goals and values in the organization?"

Then the average of all five criteria/ answers is calculated.

OIQi =Average(Cj) wheere Cj is representing the jth criterion, j= [1:5] & i= [1:7] .

In the last step information from all questionnaires should be aggregated and here average of the values were used. Final value, OItotal is the average of OIQi where max " i" is the number of filled-in questionnaires: OItotal = Average (OIQi), where i= [1:7].

Table 5 presents the result of this measurement process. Table 6 presents the level of resilience using introduced model: Finally, for scenario planning, the value of each criterion is increased 30% and the impact on total resilience is calculated. Table .7 shows the result and, as it can be seen, "Broad Resource Network" is the most relevant criteria for investment in this case.

Discussion and Future Work

Collaborative Networks, as an emerging discipline, open a new window to enhance our capability to confront complex problems. One of the most difficult cases is to work under stressful conditions of post hazards situation. Rescue Networks have to work in critical situations while, as a member of society, they are also under the influence of the event. Disasters are unpredictable and it is crucial to make sure DRNs work as resilient structures able to recover immediately from any crisis and to start their mission with minimum delay. In this paper a conceptual model based on contextual, behavioral, and cognitive resilience was employed to develop an intelligent hybrid decision system for assessing the level of resilience of DRNs. Face validity of the rules, extreme condition test, and behavioral analysis of the system were used to verify the model. At the end, the applicability of the model was illustrated by using a case study. The model not only helps to quantify the concept of resilience for DRNs but it also gives us a tool for scenario planning/testing in a virtual environment. The most important reason to select the "Fuzzy Inference System" was its ability to deal with imprecise data and the capability to make a bridge between knowledge of experts (as the only available source of data in this case) and decision support system. Other alternatives such as MCDM or statistical methods have their own strengths and weaknesses for information fusion but are not suitable for this type of problem which has characteristics such as unclear relation between variables, no knowledge about synergies or redundancies between them and lack of historical data to recognize the pattern.

As future work, the plan is to implement the introduced model in more real cases to improve the system. Further we are planning to improve the capability of the model by considering the role of time and interrelation between resilience indicators.

Fig. 1 -

 1 Fig. 1 -Resilience Evaluation Model (adapted from [24])

Fig. 3 .

 3 Fig. 3. Architecture of Resilience Evaluation Model (Hybrid Intelligent system)

Fig. 4 .

 4 Fig. 4. Graphical representation of the rules for Behavioral Resilience

Fig. 5 .

 5 Fig. 5. The behavior of the system in case of change in preparedness while all other indicators remain constant

Table 1 .

 1 Explanation of Resilience Evaluation Model

	Criteria	Definition
	Organization
	Identity (OI)

Table 2 .

 2 Initial information for a numerical example

	DRNi	OI	SM	RF	PP	CM	UH	DE	DS	BR
	DRN1	1	1	1	1	1	1	1	1	1
	DRN2	4	8	8	4	4	3	4	6	7
	DRN3	3	5	6	5	6	6	6	4	5
	DRN4	7	7	7	7	7	7	7	7	7

Table 3 .

 3 Result of Numerical example

	DRN1	DRN2	DRN3	DRN4
	Resilience	Resilience	Resilience	Resilience
	0.9084	2.6323	3.7788	7.5883

Table 4 .

 4 Result for the illustrative example with 30% increase in each indicator

		OI	SM	RF	PP	CM	UH	DE	DS	BR
		30%	30%	30%	30%	30%	30%	30%	30%	30%
	DRN2	2.6324 2.6323 2.6323 2.6323 2.6323 2.6323 6.736 7.3716 2.6323
	DRN4	7.5883 8.5746 7.5883 7.5883 7.5883 7.5883 7.5883 7.5883 7.5886
	Resilience

Table 5 .

 5 Information for the case study in percentage

	DRN	OI	SM	RF	PP	CM	UH	DE	DS	BR
	DRNRed	62%	60%	63%	50%	56%	53%	58%	55%	59%
	Crescent

Table 6 .

 6 Level of Resilience in the Case Study (from 0 to 10)

	Resilience	Contextual Resilience	Behavioral Resilience	Cognitive Resilience
	3.7920	3.2353	7.4051	5.0017

Table 7 .

 7 Red Crescent Resilience in case of 30% increase in each indicator (From 0 to 10) DRNRed Crescent 3.7920 3.7920 3.7924 3.6640 3.7942 3.7942 3.7920 3.7920 7.2976

	30% increase in	OI	SM	RF	PP	CM	UH	DE	DS	BR
	main criteria

Acknowledgements. This work was partially funded by the Center of Technology and Systems and FCT-PEST program UID/EEA/00066/2013 (Impactor project).


Jyotirmoyee Bhattacharjya
email: jyotirmoyee.bhattacharjya@sydney.edu.au

Adrian B Ellison
email: adrian.ellison@sydney.edu.au

Building Business Resilience with Social Media in B2B Environments: The Emergence of Responsive Customer Relationship Management Processes on Twitter

Keywords: business-to-business services, social media, Twitter, customer relationship management, brand resilience, Shopify

Companies operating in the business-to-consumer (B2C) space have been increasingly using the social media platform, Twitter, to conduct customer relationship management activities. Similar practices are also gradually emerging in business-to-business (B2B) environments. However, B2B service providers are yet to incorporate social media into their overall business strategy to the extent that this has become common practice amongst B2C companies. Engaging with customers over social media poses both risks and opportunities for companies. This paper examines the customer relationship management activities of a B2B service provider, Shopify, to identify opportunities for improving brand perception and business resilience.

Introduction

In recent years, there has been a rapid growth in the adoption of social media platforms for conducting B2C interactions. This is largely because companies around the world have recognized the need to build and protect their brands in the virtual environments in which consumers express their opinions and engage with their peer groups. The microblogging site, Twitter, has become the social media platform most widely adopted by Fortune 500 companies [START_REF] Culnan | How large U.S. companies can use Twitter and other social to gain business value[END_REF]. Previous research undertaken by the authors [START_REF] Bhattacharjya | Creation Using Social Media in a Virtual Business Model: How Amazon Approaches Customer Service on Twitter[END_REF] has demonstrated that these companies are not only using Twitter for promotional purposes but for the provision of customer service as well. However, companies operating in the B2B space have not as yet adopted social media as extensively as their B2C counterparts and there is a lack of understanding amongst these firms regarding how social media could help the resilience of their brands [START_REF] Michaelidou | Usage, barriers and measurement of social media marketing: An exploratory investigation of small and medium brands[END_REF] [START_REF] Swani | Should tweets differ for B2B and B2C? An analysis of Fortune 500 companies' Twitter communications[END_REF].

Nevertheless, some B2B service providers are beginning to take a strategic approach to their social media. This paper presents findings in relation to the customer relationship management (CRM) activities of a B2B company. The data presented here is part of a larger ongoing project initiated in December 2014 which has now collected over 5 million tweets (microblogs) from B2B and B2C companies. The uniqueness of the project lies in the fact that, unlike previous studies, it is designed to extract entire conversations rather than just tweets on a large scale.

Background and Literature Review

Social CRM or CRM 2.0, is a term coined by president of The 56 Group and leading CRM thinker, Paul Greenberg describes "a philosophy and a business strategy, supported by a technology platform, business rules, workflow, processes and social characteristics, designed to engage the customer in a collaborative conversation in order to provide mutually beneficial value in a trusted and transparent business environment. It's the company's response to the customer's ownership of the conversation" ( [START_REF] Greenberg | CRM at the speed of light: social CRM strategies, tools, and techniques for engaging your customers[END_REF], [START_REF] Pgreenblog | [END_REF]). This definition highlights the need for a shift in focus from customer management to customer engagement in an era where the customer is in increasing control of the narrative due to the proliferation of social media platforms.

The combination of rapid technological changes and regulatory and geopolitical upheavals over the past decade have led companies to recognize the need for resilient strategies that allow them to adapt their business models as well as their marketing approaches as their local or global contexts evolve ( [START_REF] Hamel | The quest for resilience[END_REF], [START_REF] Day | Closing the Marketing Capabilities Gap[END_REF]). As an increasing number consumers around the world build their presence on social media platforms, companies have recognized that news travels fast through social networking sites and complacency is not an option in ensuring the resilience of their brands [START_REF] Copulsky | Protecting your brand from saboteurs in a high-speed world[END_REF]. Dell, for instance, has a Social Media Listening Command Center to monitor customer conversations about its brand on social media platforms [START_REF] Menchacha | Dell's next step: The Social Media Command Center[END_REF]. Online retailer Amazon has also been at the forefront of customer engagement via social media and attempts to address customer complaints through its Twitter based channel as quickly as possible [START_REF] Bhattacharjya | Creation Using Social Media in a Virtual Business Model: How Amazon Approaches Customer Service on Twitter[END_REF]. PepsiCo and Gap are also amongst other big brand owners and retailers engaging customers in conversations about their products in order to keep abreast of brand perceptions and ensure brand resilience ( [START_REF] Bauerlein | Gatorade's mission: sell more drinks[END_REF], [START_REF] Morales | The Impact of Twitter on Customer Relationship Management within Mexican Value Chains[END_REF]).

However, as previously stated, while B2C companies have begun using social media sites to their advantage, B2B companies have been slow on the uptake ( [START_REF] Michaelidou | Usage, barriers and measurement of social media marketing: An exploratory investigation of small and medium brands[END_REF], [START_REF] Swani | Should tweets differ for B2B and B2C? An analysis of Fortune 500 companies' Twitter communications[END_REF]). The business software industry is the leading B2B sector in terms of social media activity, with Intel, Oracle and IBM being key players in the sector. The medical and pharmaceutical sector, with leaders such as Pfizer, Merck and GlaxoSmithKlein is the second most active sector. Other sectors, such as the B2B logistics sector are much less active on social media with only a few companies in each sector making a noticeable effort. For instance, the container shipping company, Maersk, is one of the few companies in the B2B logistics space that has been recognized for building its presence across social media channels [START_REF]Maersk Social[END_REF]. It has over 34,000 followers on Twitter and over 2.2 million likes on Facebook. However, while B2B companies are beginning to building their presence on social media, few are using these platforms to provide customer service and build closer ties with customers. This is in significant contrast to the growing number of B2C companies who have dedicated customer service accounts on Twitter and are taking a proactive approach to engaging in conversations with customers to manage satisfaction and brand perception.

Research Method

Twitter allows users to post microblogs or tweets which are limited to 140 characters in length. A user's timeline consists of tweets from accounts the user has chosen to follow on Twitter. These tweets can be accompanied by links, photos and videos and may be retweeted or replied to by the user. Tweets, retweets and responses are generally visible to the general public unless a direct message (DM) is sent by one user to another. The public nature of most of these interactions has led a diverse range of companies to monitor discussions about themselves and reach out to customers using this platform. The limitations posed by the Twitter application programming interface (API) on the extraction of tweets along a user's timeline along with a lack of tweet extraction tools with appropriate capabilities has led to most research till date being focused on the sentiment analysis of individual tweets ( [START_REF] Jansen | Twitter power: Tweets as electronic word of mouth[END_REF], [START_REF] Liau | Gaining customer knowledge in low cost airlines through text mining[END_REF]) rather than the conversations between interacting parties on this platform. For this research, a custom application package was developed to extract tweets from the timelines of companies of interest along with the conversations associated with these tweets. Each tweet on Twitter is allocated a unique identification number. The package developed for this research allows the tweets to be written to a MySQL database allocates identification numbers for the conversations thus allowing the subsequent analysis of not just tweets but conversations as well. Since December 2014, the package has been used to collect over 5 million tweets from companies and their customers. The data collection methodology is discussed in more detail by the authors in another paper [START_REF] Bhattacharjya | An exploration of logistics related customer service provision on Twitter: The case of e-tailers[END_REF]. The conversations are subsequently extracted from Twitter and uploaded to the text mining tool for coding and analysis.

Findings

One company that has both undergone business model transformation and adapted its marketing strategies to the proliferation of social media is the billion dollar Canadian ecommerce solution provider Shopify. The company started out as an online store for selling snowboards. It later developed its own e-commerce solution and developed into an e-commerce platform because of interest from other online retailers. As an ecommerce company, it allows businesses to set up online stores organize products, accept payments, and track orders. Its ecosystem comprises of small businesses, developers and end consumers. Although, its accounts on Twitter are oriented towards business customers, the platform allows it to get exposure to end consumers as well, thus giving it wider brand recognition. Its site received about 15-20 million visits per month in the last six months [START_REF] Similarweb | [END_REF]. Around 10.5% of the visits in the last three months have been from Twitter. It not only encourages customers to use the power of social media to create greater awareness of their offerings but has also been making a concerted effort to build its own presence on social media. As outlined in Table 1, it maintains a number of accounts on Twitter. By way of comparison, Amazon, which has been on Twitter since February 2009, has over 13,400 tweets on its main account and over 1.84 million followers. Its customer service account, operational since October 2009, has over 242,000 tweets and 24,000 followers. The differences in the numbers are indicators of the different levels of activity on Twitter in B2C and B2B contexts but both companies appear to take their presence on Twitter seriously as suggested by their verified accounts on Twitter. A Twitter verified account has a blue verified badge associated with the account [START_REF]Twitter[END_REF]. Twitter does not accept verification requests but prioritizes highly sought users as part of their ongoing verification process.

Customer Service Interactions

As shown in Table 2, customers generally post tweets to make Shopify aware of problems with their system that are interrupting or slowing down business processes. Such tweets may also be accompanied by photos that communicate the problem. Monitoring the Twitter accounts allows Shopify to respond quickly on a public platform, thus allowing it to demonstrate its responsiveness to a wider customer base. The company also uses its Twitter accounts to provide information or direct customers to websites where the information may be available. In the previous example Shopify was able to inform the customer that it was working on the problem. In other instances the platform allows the company to build goodwill by providing information that resolves a perceived problem (Table 3). Like companies in the B2C space [START_REF] Bhattacharjya | Creation Using Social Media in a Virtual Business Model: How Amazon Approaches Customer Service on Twitter[END_REF], Shopify uses Twitter in conjunction with other traditional channels for providing customer service such as email and phone. However, there are some risks associated with a social media presence. Customers may contact the company via Twitter if satisfactory customer service is not received promptly via other less public media but it may not be possible for the company to provide a resolution via the platform (e.g., Table 4). Thus while the conversation may suggest that the company hasn't been able to resolve problems promptly, someone (e.g., a potential customer) visiting its Twitter account may be left wondering whether the query was resolved to the customer's satisfaction. As with B2C companies, customers are found to rarely return to thank a company via the Twitter platform if the query is resolved via a more private channel. Even if a conversation starts on Twitter, Shopify may still divert the query to email (Table 5). Unlike B2C companies, Shopify makes very limited use of Twitter's direct messaging (DM) service to address problems that cannot be resolved in public.

Collaborative Approach to Marketing

The company appears to recognize the fact that the resilience of its ecosystem which comprises of a large number of small businesses is essential for its own resilience. It takes a collaborative approach to its marketing activities and uses its @ShopifyPicks account on Twitter to highlight products sold by companies that use Shopify as their ecommerce solution (Table 1). This allows small companies with modest resources, such as Stitch & Locke, to focus on their core businesses and provide customers with links to Shopify's main Twitter account instead of maintaining their own. There is both a downside and an upside to this approach. End consumers may approach Shopify directly with queries regarding products and services provided by Shopify's customers. The upside is that if it is handled well, this may create a positive impression of its brand beyond the B2B environment in which it operates (Table 6).

In the conversation below, the customer adds a hashtag #brandPlus to show his appreciation for the prompt response. Hashtags are searchable on Twitter and allow users to easily find comments on particular topics. The company also engages with developers via Twitter in two ways. On the one hand it keeps developers informed about changes to its API using the @ShopifyDevs and @ShopifyPartners accounts so they can continue to support and build solutions for their customers. On the other hand it encourages them to earn money by developing apps, designing themes or referring clients to the Shopify platform. The @ShopifyPartners account helps to highlight successful activities of partners and create a community atmosphere.

Discussions

In the B2B environment there continues to be lower uptake of social media platforms for customer service and promotional activities that might help these companies build wider brand recognition and resilience. Those B2B companies who are present on social media are largely engaged in a one-way communication where the provision of news and updates seems to be the only purpose for maintaining the Twitter accounts. However, even though their levels of activity may be noticeably lower than those of the more active B2C companies on Twitter (e.g. Amazon), a small number of B2B companies such as Shopify are beginning to take a more strategic approach to this platform. In Shopify's case, this involves structuring its engagement with the members of its ecosystem using a number of dedicated accounts for specific purposes. Adequate resourcing of a social media strategy is essential for its success. For instance, Shopify was generally found to respond to customer and developer queries within the hour. The company's two-way engagement with small business customers, developers and consumers and its attempts to approach benefits to its ecosystem holistically through its accounts on Twitter makes it an exemplar amongst B2B companies. The ongoing longitudinal data collection project will examine the emerging trends in the practices of B2B companies around the world and provide further insights into how these practices can help to nurture resilient business ecosystems.

Table 1 .

 1 Shopify's accounts on Twitter.

	Account	Purpose	Tweets Followers
	@Shopify	Main account which also addresses	16,900 157,000
		customer service queries
	@ShopifySupport Dedicated customer service account 2,051	3,276
	@ShopifyPicks	Product stream curated from over	4,542	28,100
		160,000 Shopify powered stores
		around the world
	@ShopifyDevs	News for developers about the	687	3897
		Shopify API
	@ShopifyMasters Advice on growing store traffic and	3552	5697
		sales based on success stories of
		Shopify customers
	@ShopifyPartners Account for the Shopify Partners	4602	7165
		Program

Table 2 .

 2 A conversation with a customer following a system problem.

	User	Tweet	UTC Date, Time
	@notrab	Just spent 30 minutes editing a file with @Shopify	5:22 am, Feb 20, 2015
		templates and then it errors on save. Doh
	@Shopify @notrab Oh no! So sorry about that Jamie. We are	5:35 am, Feb 20, 2015
		working on getting everything back up as soon as
		possible!
	@notrab	@Shopify That's ok! :)	5:35 am, Feb 20, 2015

Table 3 .

 3 A conversation with a customer following a system problem.

	User	Tweet	UTC Date, Time
	@Misc_ Goods_Co	@Shopify y?! [Image of popup error	7:27 am, Dec
		message]	19, 2014
	@Misc_ Goods_Co @Shopify @ShopifySupport I'm very	8:43 am, Dec
		frustrated about this new POS fee. I only need	19, 2014
		to use POS a few times a year.
	@Misc_ Goods_Co @Shopify @ShopifySupport I've been sitting	8:44 am, Dec
		here at my last minute pop up like a bozo	19, 2014
		trying to figure out why it's not working.
	@Misc_ Goods_Co @Shopify @Shopifysupport very bad taste in	8:45 am, Dec
		my mouth right now	19, 2014
	@Shopify	@Misc_Goods_Co Thank you for the	8:51 am, Dec
		feedback and very sorry for any frustration. It	19, 2014
		is free to process on Shopify Mobile.
	@Misc_ Goods_Co @Shopify well that's cool. went from	9:00 am, Dec
		frustrated to pretty chill. wasn't aware of that	19, 2014
		change
	@Shopify	@Misc_Goods_Co Thanks for letting us	9:49 am, Dec
		know. We are happy to help with other	19, 2014
		questions.

Table 4 .

 4 A conversation with a customer following a system problem that was originally communicated through a traditional channel.

	User	Tweet	UTC Date, Time
	@gooderdle @ShopifySupport -as an early supporter of	8:15 am, Jan 16, 2015
		Shopify, we'd like to upgrade but keep our
		grandfathered "No transaction fee"… cc @Shopify
	@Shopify	@gooderdle We'd be happy to look into that. Can	8:52 am, Jan 16, 2015
		you please contact our billing team -
		billing[at]shopify[dot]com..
	@Shopify	What is your open ticket number?	8:54 am, Jan 16, 2015
	@gooderdle No open ticket number -been going back and forth	8:58 am, Jan 16, 2015
		over email and phone for a week with a "Sales
		Lead"

Table 5 .

 5 A conversation that starts on Twitter but has to be diverted to a different channel.

	User	Tweet	UTC Date,
			Time
	@Schappi @Shopify heads up that I'm getting multiple reports	8:40 pm, Dec
		of shipping methods not loading at checkout	10, 2014
	@Shopify @Schappi Apologies for the delay. Are you still	6:56 am, Dec
		experiencing issues?	11, 2014
	@Schappi @Shopify yup we are still experiencing issues… 3	8:04 pm, Dec
		people today complained. Just migrated all stores to	11, 2014
		responsive to see if that fixes it.
	@Shopify @Schappi Thanks for getting back to us. Please email	8:34 am, Dec
		support[at]shopify[dot]com with more info and	12, 2014
		respond with the ticket number.

Table 6 .

 6 Shopify's exposure to the B2C interface via Twitter.

	User	Tweet	UTC Date,
			Time
	@Marcus	@Shopify Hi there. I bought from stich and locke. My	9:13 am, Dec
	Kourie	delivery address needs to be changed though, how do I	11, 2014
		go about this?
	@Shopify	@MarcusKourie Please try contacting the store	9:24 am, Dec
		through their about us page or the original order	11, 2014
		notification.
	@Marcus	@Shopify Thank you! Appreciate you prompt	3:19 am, Dec
	Kourie	response. #brandPlus	12, 2014