Provenance of Feedback in Cloud Services

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1 Provenance of Feedback in Cloud Services Kahina Hamadache European Projects Department Singular Logic S.A. Athens Greece Paraskevi Zerva Department of Informatics King s College London London, UK Abstract With the fast adoption of Services Computing, even more driven by the emergence of the Cloud, the need to ensure accountability for quality of service (QoS) for servicebased systems/services has reached a critical level. This need has triggered numerous researches in the fields of trust, reputation and provenance. Most of the researches on trust and reputation have focused on their evaluation or computation. In case of provenance they have tried to track down how the service has processed and produced data during its execution. If some of them have investigated credibility models and mechanisms, only few have looked into the way reputation information is produced. In this paper we propose an innovative design for the evaluation of feedback authenticity and credibility by considering the feedback s provenance. This innovative consideration brings up a new level of security and trust in Services Computing, by fighting against malicious feedback and reducing the impact of irrelevant one. Keywords provenance; feedback; reputation; credibility; cloud computing; I. INTRODUCTION With the emergence of the Cloud Computing paradigm the functionality and capabilities of cloud-based applications are exposed by their service providers as on-demand available self-contained services. For service providers, the Cloud offers an opportunity to propose more sophisticated (and thus more valuable) services to a broader audience. Furthermore, the Cloud paradigm enables them to outsource a part of their own offer in order to increase their flexibility and cost-effectiveness. For the customer, the Cloud brings numerous advantages: digitalization of its infrastructure, instead of buying expensive hardware and not being always sure of whether he has the necessity for it, a customer can simply rent virtualized infrastructure and access it remotely, being able to scale up or down its consumption to be as close as possible to its real need. The complexity of those services increases even more when dynamically aggregating third party services. This composition implies the consideration of various providers QoS where the guarantees offered for each atomic service may differ or conflict with regards to the aggregated service s QoS requirements. Therefore, service providers need to be accountable for their actions with respect to the actual QoS delivered against the expected one (described in the SLAs). As identified in [7], reputation management mechanisms and SLAs are tightly intertwined during the whole service lifecycle and can help in demonstrating the accountability of providers. We argue that This research is funded by the European Union under FP7 ITN RELATE reputation is not a separate quality factor that requires only to be monitored (e.g., performance), but represents the way the different QoS properties of a service are perceived by its current and past users (what is the actual users feedback). We conceptualize this idea by considering both objective (monitoring results) and subjective feedback (user evaluation). This approach falls in line with the proposition made in [8], unifying evaluation and monitoring under the main feedback concept by differentiating objective and subjective feedback. From the strict feedback computation, evaluation, creation, generation point of view, an important mass of literature already exists. However the creation of feedback is far from being the only issue faced in the field of trust and reputation. In order to be processed by a reputation manager, feedback has to be authenticated to ensure its origin and identity and contextualized in order to assess its applicability, reliability and relevance. This can be summarized as the authenticity and credibility of the feedback information. These two pieces of information are obviously highly critical for the consideration of reputation in a massively distributed and entangled environment as they are the ones that will condition the rating of services and ultimately their recommendation and selection. Yet, a proper formalization of these mechanisms remains to be given. In the long run towards this formalization, several paths can be foreseen. In this research we propose to explore the one relying on provenance of data. From our perspective, considering the provenance of data means considering the provenance of feedback. Hence we argue that the heart of authentication and credibility lies in the consideration of feedback origin, not simply by relying on some certificate or consolidated statistics, but by the consideration of the whole context of production of this data. In the remainder of this paper, we first give a short background description on fundamental concepts for reputation and provenance in Section. II. Section III introduces a use case scenario based on which we identify the need for a mechanism to elaborate both objective and subjective feedback as a means for the evaluation of services. Based on this scenario, in Sect. IV, we present our approach towards a provenance based feedback evaluation design. We also present our ontological design where we merge and extend two different ontologybased models/schemas to represent 1) SLA lifecycle for service evaluation [8] and 2) provenance of service-based systems [18] respectively. In Sect. V we present an evaluation of our scenario by using our approach. Finally, in Sect. VII we discuss our proposed approach and plans for future work.

2 II. CONCEPTS AND BACKGROUND In this section we present into more detail the concepts involved in our research and the background from which we build our approach. We first discuss the concept of Reputation, a widely often used term but not always clearly defined, especially when it comes to distinguish this from Trust. Secondly, we discuss the data s Provenance, which is a relatively young domain of research but has been implicitly around for some time. The latter is mentioned here as this is going to form the basis for evaluating the feedback information for cloud services for our proposed reputation approach. A. Reputation Reputation and Trust terms are often confusingly used interchangeably. While trust is the belief of someone into another to correctly perform an action, evolving over time, through different sources (e.g., user s own experience, rumors, advice, marketing etc.), the reputation is close to the sum of all the trust towards one entity. In our conception, reputation is not really the sum of trust, as trust carries a very personal and subjective characteristic (almost a feeling), while the reputation of a service should be objective and unbiased. Hence, we define the reputation as the collective opinion of a group towards an entity. For instance, the reputation of a service can be defined as the opinion of the group of parties previously involved in its consumption (users, broker, third-parties, etc.). 1) Feedback: In [8] the authors have proposed to structure and represent feedback along two types: Objective feedback: this information is obtained through the service monitoring activity. One of the key aspects of service computing is to be able to assess the respect of some predefined parameters (Quality of Service parameters). In this perspective, it is usual for a service to be monitored by its provider or by a third party. This monitoring is the source of information that will constitute the objective feedback. It is called Objective to specifically indicate that it is not subject to human evaluation, and as such is not (or less) subject to interpretation and credibility evaluation. Subjective feedback: we differentiate subjective feedback from objective one by the fact that the former relies on a human evaluation of the service/actor. Even if the human evaluation can be considered, in many cases, as more valuable than metrics measurement (as it provides a more complex and complete evaluation), it relies on a personal evaluation (or group evaluation if several persons are involved in the process, as it is expected for complex or critical services). This human factor introduces an uncertainty on the feedback information provided, and it is then required to evaluate this uncertainty to correctly consider the feedback. Feedback is not only to be considered as information that should escape the control of its subject. Indeed, feedback information, even before being used in the context of reputation, should be used for the Regulation of the system. Indeed, even if providers should set up monitoring features, it is also extremely valuable for them to have a direct feedback from their consumer. It is even more critical in the Cloud, as services can be tailored or customized to the requirements of the service consumer. 2) Credibility: This can be seen as the reliability of feedback information. This concept is critical in massively distributed systems as it gives intrinsic information on feedback, independently from the initial subject of feedback. Indeed, the credibility relates to the source of the feedback, its production context, indicating to which degree one can rely on this information. B. Provenance Provenance, i.e., the origin or source of an object, is becoming an important aspect since it offers the means to verify data products, to infer quality and to analyse the processes that led to them and to decide whether they can be trusted [12]. In this section, we give a description of the fundamental elements of provenance for services and servicebased systems and of W3C s generic PROV model [6] in particular. According to PROV [6]: Provenance covers the data about entities, activities, or people involved in the process that produced a data item or thing with the purpose to understand how data was collected, to determine ownership and rights over the data object or to verify that the process and steps used to obtain the data result complies with given requirements. We can consider provenance to represent the origin or source of a digital object [6]. PROV is W3C s specification to express provenance records, which contains descriptions of the entities and activities involved in producing and delivering or otherwise influencing a given object [6]. As people may see provenance from a different perspective, PROV specification covers different types of information that may be captured in provenance records corresponding to the primary concepts of the W3C PROV s notation; namely entities, activities and agents and events: Activities represent processes that have occurred over a period of time and act upon entities. Entities are digital, physical or conceptual things with some fixed values, that existed. Activities generated new entities and used existing entities, and one entity may have been derived from another. Agents denote something that was responsible for an activity having taken place. PROV also allows us to express the role played by an entity or agent in an activity, the time at which an entity was generated or used by an activity, the plan that was followed by an activity in execution and much more. PROV also introduces a number of expanded terms such as collection, which denotes an entity that provides a structure to some constituents (members), or instantaneous events that denote transitions in the world. Events include generation, usage, or invalidation of entities, as well as start or end of activities. W3C PROV has built PROV ontology [3], an OWL2[14] ontology which allows mapping of the PROV data model to

3 RDF [11]. PROV-O defines a set of classes and properties along with restrictions on them to represent the provenance information which is generated or collected for different systems executing under different contexts. This ontology gives the opportunity for extensibility and specialisation of its concepts to create new domain concepts falling into specific application contexts. We have previously done so by extending this into Service Prov Ontology for representing provenance of services and more specifically composite services. The latter has formed the basis on which we have extended the ServiceProv Ontology, as this can be found on-line at https://sourceforge.net/projects/serviceprov/files/serviceprov#. The latter forms a provenance data schema for services and service-based systems taking into consideration different provenance aspects through the service life cycle such as the provenance of service execution, service discovery and selection, service orchestration and choreography, service aggregation and provenance of QoS and resources. A detailed definition of each concept and property of this ontology is provided at https: //sourceforge.net/projects/serviceprov/files/serviceprov.pdf. In the context of this research we extend the ServiceProv Ontology with concepts that allow to represent the provenance of feedback for atomic and composite services (e.g., provenance of monitoring for QoS and non-functional properties, provenance of monitoring resources etc., provenance of user profiles). We present a more detailed definition of these concepts and how these interwork and merged with concepts of the SLA ontology [8] in order to provide the means for evaluating the feedback for services in Sect. IV. III. MOTIVATING SCENARIO In this section, we introduce our motivating use case scenario. This scenario helps us in the identification of features to be handled through the use of provenance data. This identification is highlighted in the form of Aspects that will be used in the following sections of this paper. The scenario we propose relies on the consideration of a hospital using services offered by a specialised provider. The hospital H has outsourced its management system in order to save cost in terms of development, maintenance and benefit from the usual advantages of the cloud. Among the different services offered on the market, the hospital board has decided to use the services of the provider CloudHealth. CloudHealth offers a wide range of services in the medical field. Its main service ensures the complete management of a hospital, from the agenda of doctors and nurses to the handling and security of patient files. The system put in place for the hospital relies on a standard platform offered by CloudHealth and customized to the Hospital s needs. The general architecture Fig. 1. Hospital System Architecture stack is represented on Figure 1. From the Hospital point of view, the system is accessed through a web or desktop client, in the SaaS layer. Those services are running on servers of CloudHealth which are configured on virtual machines provided by underlying Infrastructure providers (IaaS layer). Due to the high criticality of the services offered, the company is not relying on a single infrastructure provider but is using two main providers and one backup. Additionally, data related to the system are encrypted and replicated on two different storage providers oriented towards security and reliability. The scenario takes place when a Doctor (Dr Malcolm) of the Hospital wishes to schedule an operation on a patient. To do this he logs on the hospital system with his tablet, selects the related patient, selects the operation scheduler and fills in the details of the operation he wishes to perform. On validation of the schedule search request, the system will collect data from different services and compute the possible schedules for the operation. Figure 2 details (at least partially) the different services that are called by the main Operation Schedule service. As it aims at automating as many tasks as possible, the system will not ask for detailed requirements but will rather benefit from a medical knowledge database to automate the process. The first service called will be used to collect all required data about the patient, and then additional requirements of the operation (logistics, drugs, blood, staff) will be collected from the medical knowledge service. Upon completion of this service call, the main service will call the Operation Requirements Availabilities to get availabilities of the different elements required. This service will in turn call multiple dedicated services, for the availability of staff (surgeon, nurses, etc.), operation rooms, necessary drugs, blood, etc. Such services may also call external services, for instance, the blood availability service can call a service of an external blood bank if stocks of the hospital are insufficient. This can also apply to other products such as drugs and equipment. Once all availabilities are collected, the main service will call the Operation Scheduling service in order to process all this information and compute potential schedules for the operation. In the same time, a Financial Handling service can be called in order to assess the viability of the operation from a financial point of view. Simultaneously to the different calls to sub-services, the main service can call a service aimed at providing advice on the operation. The latter will interrogate the Medical Knowledge service and provide additional information for Dr Malcolm. In the end, the main service produces a set of Operation Schedules, an assessment of the Financial Sustainability and a Medical Advise. Ideally, the evaluation of the service should be done by all parties involved. However, in highly stressed and interruptionrich environments such as Hospitals, it would be utopian to consider that all staff members will have the time to give their feedback. Even if IT systems for hospitals should relieve people a bit from the pressure by automating or at least simplifying various tasks, it is extremely unlikely that every person that was involved in an operation will have time to dedicate to fill some evaluation forms. In our scenario we consider that Dr Malcolm and one of the nurses present during the operation have given feedback. The first step of the service evaluation is done just at the end of the schedule, once Dr Malcolm has chosen and validated the schedule (actual booking of the different elements is done on the final validation). The results

4 Fig. 2. Operation Schedule Service of this evaluation can be found in the second column of Table I. However, it would be insufficient to only evaluate the service just after its use. Indeed, the final outcome of the service is the operation itself, and in order to have a complete vision of service performance and impact, we need to evaluate it after the operation. Hence, once the operation completed, the system will ask both Dr Malcolm and the nurse in charge, to provide feedback. Additionally, a second Doctor, participating to the operation will be invited to give its Evaluation. This feedback is also presented in the third and fourth columns of Table I. The evaluation is intended to be fast and simply proposes to the different people involved to rate the different aspects of the service, according to a five level scale (from 1 to 5, with 1 being excellent to 5 being very bad ). TABLE I. EVALUATIONS Parameter Dr Malcolm Dr Malcolm Head Nurse Dr Cox Before Op After Op Availability 2 - Good Efficiency 2 - Good 3 - Average 5 - Very Bad Reliability 2 - Good 2 - Good 4 - Bad Responsiveness 4 - Bad Usability 4 - Bad Usefulness 2 - Good 3 - Average 2 - Good 5 - Very Bad Parameters to be evaluated before and after the evaluation are not the same. For instance it is not possible to evaluate the reliability of the service before the operation. On the opposite, it is preferable to evaluate the responsiveness of the service just after its use, in order to avoid confusion or bad recollection of the experience. The evaluation of the nurse is done on the assumption that Dr Malcolm correctly filled the information of the operation schedule. This assumption obviously introduces a biased feedback and based on the experience of the nurse with the system, this may be considered with a lower weight. (However, if the nurse is highly experienced with the system and the Doctor is not, the credibility of nurse s feedback will be higher than the doctor s and it will be indicated of higher weight.) The use of feedback provenance in this scenario is done for several purposes. The first one is identifying the source of the feedback. For Doctor Malcolm it is quite simple: he is using his own tablet, with only one user and is logged on hospital system with his personal account. As mentioned previously, the identification of the source is critical, hence the authentication of Dr Malcolm allows the system to know that the user is highly experienced both in the medical domain and in the use of the system. Aspect 1 Provenance information should be used to identify and authenticate the agent that produced the feedback From a similar aspect but with a different intent, the profile of evaluators can be used to identify overly negative or positive evaluators. In our example, Dr Cox has given significantly negative evaluations, however, has he systematically given negative feedback, it is possible for the system to consider his evaluation with a lower weight. Obviously it would be unfair to systematically consider Dr Cox evaluation with a lower weight, but we can consider that it can be useful for services with few evaluations. The evaluator is not the only aspect to be authenticated. The subject of the feedback also has to be certified as well. Hence, depending on the evaluation system implementation, it could be possible for a user to give feedback on a different service than the one he used. Aspect 2 Provenance information should be used to identify and authenticate the service related with the feedback Beyond the service itself, it is of uttermost importance to clearly identify the execution for which the feedback is given. Indeed, the service execution is characterized by an associated SLA. Indeed, as described in [7], feedback is also characterized

5 by SLA in the sense that the SLA defines the contract against which you can evaluate the service. Aspect 3 Provenance information should be used to identify and authenticate the service execution related with the feedback Sticking with our scenario, it could be interesting to determine if feedback provided by members of staff is conflicting with the monitoring information of the provider (or a third party). Hence, if all evaluations at a moment in time, point out a deficiency of the service, while monitoring information does not show any sign of such issue, this may imply either that a trouble occurred between the service and its final delivery, or that all evaluators are purposefully giving negative feedback. Yet, it can also be a symptom of unreliable monitoring information. Thus, feedback provenance should help assessing the credibility of feedback information. Aspect 4 Provenance information should be used to help assessing the credibility of feedback Aspect 5 Provenance information should be used to help the consolidation of evaluator s profile Aspect 6 Provenance information should be used to help correlating objective and subjective feedbacks Dr Malcolm reported that the responsiveness of the service was Bad during its use. It is interesting to notice that other requests of the service around the same time have also been reported as slow or unresponsive. Identifying such pattern is very important for a provider as it can be the symptom of a malfunction in the cloud chain. For instance, in our example, this bad responsiveness may happen during a period of slowing down of the whole application, due to slowing down of the whole system, consequence of a partial failure in one of the IaaS providers of the Hospital. Thus, feedback on the Operation Scheduling Service can be related with monitoring information of IaaS providers (objective feedback). Aspect 7 Provenance information should be used to relate time frames of feedback on a service with feedback of underlying/depending services IV. APPROACH In this section we present our approach considering the provenance of feedback information with the aim to ensure feedback s authentication and credibility assessment. Our final goal is to support reliable management of both subjective and objective feedback information. This section is organised as follows: we start by presenting where the provenance of feedback is collected and used in the Cloud Service Life Cycle. This is followed by the presentation of our merged ontology, integrating concepts of the SLA-based cloud computing ontology defined in [8] and the ServiceProv Ontology as presented in [?]. A. Feedback Provenance within Cloud Service Life Cycle We base our approach for evaluating feedback through capturing the feedback s provenance on [7]. In this research the author defined a high-level service life cycle, considering the SLA as its core element. We illustrate the key points where feedback provenance has to be collected and is to be used within the evaluation phase of the proposed service life cycle (from [7]) in Fig 3. As we have previously defined, we are considering feedback of two different types: objective and subjective. Objective feedback refers to the actual monitoring results, corresponding to measurable QoS (Quality of Service) parameters (e.g. network bandwidth, CPU utilization, etc.). As measurable values, such information could and should be automatically collected through the use of different tools and mechanisms (e.g., probes, logs etc.). In Fig.3 we see that Objective feedback is created as the result of the Service Monitoring and Service Auditing phases. Subjective feedback is produced by the evaluation made by the different parties on service s execution. Usually, such evaluation is performed by consumers in order to assess the quality of their service consumption. However, feedback collected from providers and other third parties ( Service Providing phase) is also useful. Subjective feedback is created through dedicated steps, following the Service Consumption phase. In the end, both subjective and objective feedback is fed to a Reputation Management phase in order to be analysed and processed. Therefore, in Fig. 3 we illustrate the points in the service life cycle s flow where the provenance of feedback is to be collected. Objective feedbacks is not only used for reputation management. Indeed, objective feedback is the primary source of information for the service adaptation management. As depicted in Fig. 3, the service monitoring phase feeds the Service Adaptation phase with monitoring information (objective feedback) allowing the former to analyse the current state of the service and trigger adaptations if those are required. Yet, the most important part of the service life-cycle presented with regards to feedback provenance is not the collection of the related provenance information, but its use. In this perspective, Fig. 4 identifies at which internal steps of the Reputation Management and Service Adaptation, the provenance of feedback could be useful. Both phases share a similar structure in their handling of feedback and the real divergence between them only appears at the last part. This shared structure is organized in four main successive levels: 1) Feedback Authentication: This first stage having in charge the authentication of the feedback collected, is decomposed in the following three identification tasks: Source Identification Identifies the source of the feedback. This step evaluates the trustworthiness of feedback source identification and it then authenticates or not the feedback s source. In case that the source cannot be authenticated, specific handling will take place (according to the corresponding feedback management strategy adopted by the system). Usually this task does not apply for the service adaptation phase as the source of feedback is the provider itself. However, one can imagine that in a more complex scenario (for instance the monitoring activity is delegated to a third party) such task can also be useful for the service adaptation phase. It is interesting to note that provenance information about the source identification can be used in this step as a means to partly certify the feedback s authentication as a first step and the feedbacks s identity

6 Fig. 3. Provenance within Evaluation phase of Cloud Service Life Cycle as a second step. Such provenance information about the feedback s source will be both objective (e.g., by capturing the identity of the server or monitoring agent that gave a measurable type of feedback through its monitoring activity) and subjective (e.g., by capturing information on the user s profile). This step is the process associated with Aspect 1. Service Identification: In order to process the feedback, it is necessary to relate the latter to its corresponding service. This step ensures that the feedback was given for the particular service we are interested. This is done in the perspective of avoiding hijacked feedbacks (providing an evaluation or adapting a different service than the one consumed). This step is the process associated with Aspect 2. Similarly to the feedback source identification capturing provenance information about the service s identity (such as the service name, URI, provider) can be used in order to authenticate the service, which is part of the feedback authentication process as well. Executions Identification: As for the service, the identification of the service execution is a primary issue, once again in order to avoid hijacked feedbacks. Provenance data can also be used in this step to prove authentication of the service s execution. Such information may correspond for instance to the unique service s execution ID, cross-checked through execution time, and other available data. This step is the process associated with Aspect 3. 2) Context Reconstruction: This level has in charge to internally reconstruct the context of the service consumption. From a generic point of view, this part relies on the integration of monitoring information captured in different levels. Information in this level is shared by both Service Adaptation and Reputation Management tasks, as they both have to internally reconstruct the service s context, even if they will use this for different purposes. Provider Monitoring Integration: This step integrates the data strictly related with the provider s status and behaviour (e.g., from the server management point of view). Server Monitoring Integration: This step integrates monitoring information coming from the server level (server hosting). This type of information allows the identification of server malfunctioning or misuse. VM Monitoring Integration: This step integrates monitoring information on VM level. Composition Monitoring Integration: This step integrates service composition monitoring information, providing information mainly on the runtime composition of services. Contractors Monitoring Integration: This step integrates monitoring information from sub-providers involved in the service execution. This kind of information will help, on the mining level, the identification of correlations and deficiencies across different service layers. Consumer Monitoring Integration: This step integrates

7 information about the monitoring of the consumer which is of extreme importance in the context of feedback given by the consumer. The latter can be used in order to identify deceiving feedback information. 3) Context Mining: This level has in charge to infer and extrapolate additional facts and events that took place during service consumption from the context (resulting mainly from aggregated and integrated monitoring information). Provider-level Mining: It analyses the provider s context (e.g., policy, strategy, available resources, etc.). Server-level Mining: It mines server-level context in order to analyse all relevant information about the server. The mining process can for example identify overloads or inconsistent behaviours among services running on the same server. VM-level Mining: It mines collected VM-level information. This can help, for example in identifying under/over-provisioning of VM resources. Composition-level Mining: It explores the context of service-composition (not the context of each service but the interconnections of the atomic services within the composition). Contractors-level Mining: Each sub-provider s specific context is analysed to extract relevant facts, related with the main service s consumption. Consumer-level Mining: Mining consumer context is critical to identify, for instance, if the service was correctly used or if some misuses took place on the consumer s side. Mining Correlation: Once all parts have been mined, a holistic correlation of context information is realised, trying to identify patterns and extracting valuable information for this mass of data. Such correlation can lead, for instance, to the identification that a deficiency occurred during the service execution, due to a downsizing of the VM, caused by an overload of the server. The latter may be caused by an inadequate load-balancing on provider level, related to the misuse of a sub-service. This step is related with Aspects 5, 6 and 7. 4) Authenticated Feedback Processing: The final level processes the authenticated feedback. As this level is the heart of the feedback s evaluation phase, it is tightly coupled to its use and then is specific for each phase. From the reputation management point of view, this level is responsible for assessing feedback credibility, by allocating for this a weight (partly based on the credibility) and by finally integrating feedback in the reputation repository. From the adaptation point of view, it assesses the need for adaptation, selects an adaptation strategy if required and finally enforces the new adaptation strategy. Service Adaptation specific: Adaptation Need Assessment: This step assesses, based on mined context information and adaptation policies, if there is a need to make adaptations in service s behaviour in order to optimize the use of resources, to prevent SLA violation, to anticipate a change in the context, to save costs, etc. Adaptation Strategy Selection: If an adaptation of the service is required (or suggested), an adaptation strategy has to be selected (and tailored to the current context) to determine the different details, schedule and resources of the adaptation. Enforcing Adaptation Strategy: Finally, if a strategy has been selected, it is enforced by the provider and will adapt the service and its context. Reputation Management specific: Credibility Assessment: This step assesses the credibility relative to the feedback, by taking into account its origin, the source s profile and the method of feedback collection. In this step such information about the monitoring or evaluation method used to collect the feedback or the evaluator s profile can be captured as provenance data. This step is the process associated with Aspect 4. Feedback Weighting: Feedback information is not all equally weighted (giving a feedback on a five minute s use or on a two year s one, has obviously a completely different weight for the evaluation of the service). The weighting takes also into account a number of other parameters such as the date the feedback was given, the status of the consumption when the feedback was given (in-progress, terminated, etc.), the credibility of the source, etc. Feedback Integration: Finally, after giving a weight to the feedback based to all available information, the feedback is integrated within the repository. B. Ontological Design The purpose of this section is 1) to give a definition of the concepts with which we extend the ServiceProv ontology in order to represent the provenance of objective and subjective service feedbacks and 2) to present the overall picture of our ontological design where we have merged a ServiceProv ontology extension with a SLA-based service life cycle approach [7] to support evaluation of services feedback authenticity and credibility. Fig. 5 presents the overall ontological design we propose in order to integrate provenance of services feedback, by representing the main concepts defined in the SLA-based service life cycle approach (see [8]), their association to the feedback concept, and then how the different types of feedback (subjective and objective) link to the main concepts of feedback provenance. As we have already mentioned the latter comprise the driving mechanism on which we rely for our proposed approach on the consideration of feedback. Fig. 6 depicts in more details the merging between the SLA-based cloud ontology (slac prefix) and the provenance ontology (prov prefix). The top of the figure presents the concepts directly linked to the Feedback identity and credibility in the SLAC ontology. The bottom part corresponds to a part of the PROV ontology. In between we can find concepts that are

8 Fig. 4. Regulation and Reputation Management extension of the SLAC ontology, used in the different processes related with the management of feedback provenance. Next, we discuss the concepts of our ontological design for representing the provenance of objective and subjective feedback respectively. We start by giving a detailed definition of the objective feedback related ones. 1) Provenance Concepts for Objective Feedback: : As we have already mentioned objective feedback mainly refers to feedback about measurable QoS parameters and non-functional properties (relying on specific metrics) for a given service execution that can only be measured through their corresponding service monitoring activities. For this reason, we have made an extension of the ServiceProv ontology in order to capture the provenance of those monitoring related activities, the monitoring agents that were responsible for them and the role of the latter in association to the corresponding monitoring. We also consider the time and location that those activities took place. Next, we list a detailed description of those concepts and their related properties while a version of the extended ServiceProv ontology including those concepts can be found at https://sourceforge.net/projects/serviceprov/ files/serviceprovmonitoringfeedbackextension.owl/. Monitoring Activity: is a prov:activity that monitors different properties during the service s execution such as the availability of resources (CPU, Memory, Disk, Network) or QoS attributes and non-functional properties (NFPs). We therefore introduce two subtypes of the monitoring activity: Resource Monitoring and QoSMonitoring. ResourceMonitoring Activity: is-a Monitoring Activity that monitors the resources for a particular service execution. NFPMonitoringActivity: is-a Monitoring Activity that monitors the NFPs for a particular service execution. MonitoringAgent: is a software agent that is responsible for the either a ResourceMonitoringActivity or a NFPMonitoringActivity. NFPMonitoringRole: is-a prov:role that captures the role played by a :MonitoringAgent in a qualified association with a :NFPMonitoringActivity. PerformanceMonitoringRole: is-a prov:role that captures the role played by a :MonitoringAgent in a qualified association with a :NFPMonitoringActivity that monitored the performance of a service execution. SuccessRateMonitoringRole: is-a prov:role that captures the role played by a :MonitoringAgent in a qualified association with a :NFPMonitoringActivity that monitored the number of successful invocations of a service. NonAvailabilityMonitoringRole: is-a prov:role that captures the role played by a :MonitoringAgent in a qualified association with a :NFPMonitoringActivity that monitored the number of times that a service was not available / was offline. FailureRateMonitoringRole: is-a prov:role that captures the role played by a :MonitoringAgent in a qualified association with a :NFPMonitoringActivity that monitored the number of failures of a service to be completed/invoked. ResourceMonitoringRole: is-a prov:role that captures the role played by a :MonitoringAgent in a qualified association with a :ResourceMonitoringActivity.

9 Fig. 5. Overall Ontology wasmonitoredby is an object property that has as a domain the :NFP or :Resource class and ranges to the :MonitoringActivity class. :NFP and :Resource concepts are concepts already defined in the initial version of ServiceProv ontology where a particular :Service or :ServiceExecution :hadnfp some :NFP or :usedresource some :Resource. Of course we use concepts of the PROV ontology such as prov: atlocation or prov:attime to represent the when and where a monitoring agent acted or a monitoring activity took place. While we have used the empty namespace for our concepts in the definitions given above, the actual namespace IRI is https://sourceforge.net/projects/serviceprov/files/servicep rovmonitoringfeedbackextension.owl/. 2) Provenance Concepts for Subjective Feedbacks: In this section we discuss the concepts related to the provenance of subjective feedback. As mentioned before we consider subjective feedback as the kind of evaluation made mainly by the service consumers after the service s execution. This is mainly quantified information performed during an evaluation related activity where an evaluator (e.g., service consumer) used some web service to log in and contribute his feedback. The consumer of the service has his own profile containing details about its status, identity, type and past history data about his previous feedback activity. Therefore, we introduce the following concepts to represent the provenance for subjective type of feedback: Evaluation Activity: is a prov:activity that evaluates different aspects of the service s execution such as the availability of resources or QoS attributes (e.g., efficiency, responsiveness, reliability etc.) based on quantitative scale metrics. Evaluator: is a prov:person that evaluates some NFP or resource property with regards to a particular :Service and/or :ServiceExecution. Profile: is a prov:entity that described the evaluator s user profile. It may contain past history data about his previous evaluations of a service, his characterization as an experienced or frequent user etc. We may look at when a profile prov:wasgenerated by using the existing data time properties of PROV model. EvaluatorWebService: is a prov:entity that represents a web service used by the :EvaluatingActivity to facilitate the :Evaluator in contributing his feedback evaluation about various service properties. V. EXHIBITING PROPOSED APPROACH In order to show the use of provenance for evaluating feedback information (correlating both objective and subjective feedback as described in Aspect 6) in practice, let us consider again provenance Aspects defined for our motivation scenario as mentioned in III. Aspect 6 also envisages looking into whether the feedback provided by some members of staff using CloudHealth management system is conflicting with the monitoring information of the provider (or a third party that acts as a monitoring agent) for a given time frame. This should in the end help the evaluation of feedbacks credibility (Aspect 4). In order to find out whether this is the case or not we will first have to look into the subjective feedback provided by the evaluators/users of the service for a specific time frame, corresponding to a particular service execution. In this initial step we should also look into the provenance

10 Fig. 6. Authentication and Credibility Ontology Fragment of the evaluators profiles (past history data user profile) in order to check whether these imply repeatedly negative or positive feedback evaluators (Aspect 5). To be more specific we assume that Dr Malcolm has given his feedback that the responsiveness of the service was Bad during its use. Looking back to the provenance information of his subjective feedback (past history data of his user profile exhibiting Aspects 1 and 5) we realize that Dr Malcolm is generally considered as i) a positive feedback evaluator and ii) an experienced user of the CloudHealth management system. As a second step we need to query over the provenance information captured, relevant to the particular monitoring activity that took place for the hospital s service execution (Aspects 2 and 3), within a specific time frame we are interested. To be more specific in our example the bad responsiveness of the hospital management service may be caused by delays in executing the main application (namely the Operation Request Handling service) or an underlying application (namely the Operation Requirements Availability service). To find out what may cause the problem we would need to capture provenance information such as the response time for both kinds of operations and information about who (:MonitoringAgent) was associated with the particular (:MonitoringActivity) that captured the response time of the corresponding services, where (prov:atlocation) and when (prov:started, prov:ended) the monitoring took place and what was the role of the particular monitoring agent (prov:role). A snippet of the provenance information captured for the monitoring activity related to the corresponding services is shown in Fig. 7. In this example we realise that the monitoring agent of the Operating Requirements Availability service shows bad response time value (1.2 sec) while the Operation Request Handling service s response time shows an acceptable value (0.2 sec), smaller than the one of it s underlying application. From these results we can easily draw conclusions about the reason of the bad responsiveness of CloudHealth, which was also pointed out by Dr. Malcolm s subjective feedback. Therefore, in this case the provenance acts as a means to evaluate subjective feedback by correlating the former to objective feedbacks results. VI. RELATED WORK The research community is already aware about the importance of provenance for the Cloud. Here we list a number of works and the problems these works tackle. In [1] Abbadi et. Lyle analyse the cloud computing structure to come up with a list of challenges where provenance data collection may be beneficial for the Cloud paradigm. They particularly focus on the problems of cloud logging and auditing for the different types of resources used in the cloud (e.g., physical,

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