731 research outputs found

    A stochastic Reputation System Architecture to support the Partner Selection in Virtual Organisations

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    In recent business environments, collaborations among organisations raise an increased demand for swift establishment. Such collaborations are increasingly formed without prior experience of the other partner\u27s previous performance. The STochastic REputation system (STORE) is designed to provide swift, automated decision support for selecting partner organisations. STORE is based on a stochastic trust model and evaluated by means of multi agent simulations in Virtual Organisation scenarios

    Formulating and managing viable SLAs in cloud computing from a small to medium service provider's viewpoint: A state-of-the-art review

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    © 2017 Elsevier Ltd In today's competitive world, service providers need to be customer-focused and proactive in their marketing strategies to create consumer awareness of their services. Cloud computing provides an open and ubiquitous computing feature in which a large random number of consumers can interact with providers and request services. In such an environment, there is a need for intelligent and efficient methods that increase confidence in the successful achievement of business requirements. One such method is the Service Level Agreement (SLA), which is comprised of service objectives, business terms, service relations, obligations and the possible action to be taken in the case of SLA violation. Most of the emphasis in the literature has, until now, been on the formation of meaningful SLAs by service consumers, through which their requirements will be met. However, in an increasingly competitive market based on the cloud environment, service providers too need a framework that will form a viable SLA, predict possible SLA violations before they occur, and generate early warning alarms that flag a potential lack of resources. This is because when a provider and a consumer commit to an SLA, the service provider is bound to reserve the agreed amount of resources for the entire period of that agreement – whether the consumer uses them or not. It is therefore very important for cloud providers to accurately predict the likely resource usage for a particular consumer and to formulate an appropriate SLA before finalizing an agreement. This problem is more important for a small to medium cloud service provider which has limited resources that must be utilized in the best possible way to generate maximum revenue. A viable SLA in cloud computing is one that intelligently helps the service provider to determine the amount of resources to offer to a requesting consumer, and there are number of studies on SLA management in the literature. The aim of this paper is two-fold. First, it presents a comprehensive overview of existing state-of-the-art SLA management approaches in cloud computing, and their features and shortcomings in creating viable SLAs from the service provider's viewpoint. From a thorough analysis, we observe that the lack of a viable SLA management framework renders a service provider unable to make wise decisions in forming an SLA, which could lead to service violations and violation penalties. To fill this gap, our second contribution is the proposal of the Optimized Personalized Viable SLA (OPV-SLA) framework which assists a service provider to form a viable SLA and start managing SLA violation before an SLA is formed and executed. The framework also assists a service provider to make an optimal decision in service formation and allocate the appropriate amount of marginal resources. We demonstrate the applicability of our framework in forming viable SLAs through experiments. From the evaluative results, we observe that our framework helps a service provider to form viable SLAs and later to manage them to effectively minimize possible service violation and penalties

    Time-bounded distributed QoS-aware service configuration in heterogeneous cooperative environments

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    The scarcity and diversity of resources among the devices of heterogeneous computing environments may affect their ability to perform services with specific Quality of Service constraints, particularly in dynamic distributed environments where the characteristics of the computational load cannot always be predicted in advance. Our work addresses this problem by allowing resource constrained devices to cooperate with more powerful neighbour nodes, opportunistically taking advantage of global distributed resources and processing power. Rather than assuming that the dynamic configuration of this cooperative service executes until it computes its optimal output, the paper proposes an anytime approach that has the ability to tradeoff deliberation time for the quality of the solution. Extensive simulations demonstrate that the proposed anytime algorithms are able to quickly find a good initial solution and effectively optimise the rate at which the quality of the current solution improves at each iteration, with an overhead that can be considered negligible

    Volare Mobile Context-aware Adaptation for the Cloud

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    As the explosive growth in the proliferation and use of mobile devices accelerates, more web service providers move their premises on the Cloud under the Software as a Service (SaaS) service model. Mobile environments present new challenges that Service Discovery methods developed for non-mobile environments cannot address. The requirements a mobile client device will have from internet services may change, even at runtime, due to variable context, which may include hardware resources, environmental variables (like network availability) and user preferences. Binding to a discovered service having QoS levels different from the ones imposed by current context and policy requirements may lead to low application performance, excessive consumption of mobile resources such as battery life and service disruption, especially for long lasting foreground applications like media-streaming, navigation etc. This thesis presents the Volare approach for performing parameter adaptation for service requests to Cloud services, in SaaS architecture. For this purpose, we introduce an adaptive mobile middleware solution that performs context-aware QoS parameter adaptation. When service discovery is initiated, the middleware calculates the optimal service requests QoS levels under the current context, policy requirements and goals and adapts the service request accordingly. At runtime, it can trigger dynamic service rediscovery following significant context changes, to ensure optimal binding. The adaptation logic is built through the characteristics of the declarative domain-specific Volare Adaptation Policy Specification Language (APSL). Key characteristics of this approach include two-level policy support (providing both device specific and application specific adaptation), integration of a User Preferences Model and high behavioral (parameter adaptation) variability, by allowing multiple weighted adaptation rules to influence each QoS variable. The Volare approach supports unanticipated quantitative long term performance goals (LTPGs) with finite horizons. A use case and a proof-of-concept implementation have been developed on cloud service discovery through a cloud service provider, as well as an appropriate case study, which demonstrates significant savings in battery consumption, provider data usage and monetary cost, compared to unadapted QoS service bindings, while consistently avoiding service disruptions caused by QoS levels that the device cannot support. In addition, adaptation policies using the Volare approach tend to increase in size, in a mostly linear fashion, instead of the combinatorial increase of more conventional situation-action approaches

    Evolution of security engineering artifacts: a state of the art survey

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    Security is an important quality aspect of modern open software systems. However, it is challenging to keep such systems secure because of evolution. Security evolution can only be managed adequately if it is considered for all artifacts throughout the software development lifecycle. This article provides state of the art on the evolution of security engineering artifacts. The article covers the state of the art on evolution of security requirements, security architectures, secure code, security tests, security models, and security risks as well as security monitoring. For each of these artifacts the authors give an overview of evolution and security aspects and discuss the state of the art on its security evolution in detail. Based on this comprehensive survey, they summarize key issues and discuss directions of future research

    Organic Service-Level Management in Service-Oriented Environments

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    Dynamic service-oriented environments (SOEs) are characterised by a large number of heterogeneous service components that are expected to support the business as a whole. The present work provides a negotiation-based approach to facilitate automated and multi-level service-level management in an SOE, where each component autonomously arranges its contribution to the whole operational goals. Evaluation experiments have shown an increased responsiveness and stability of an SOE in case of changes

    Accuracy Assessment of forecasting services

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    English: A service system is a dynamic configuration of people, technologies, organisations and shared information that create and deliver value to customers and other stakeholders [1]. The following cases are examples of customers receiving a service: taking a bus to go somewhere, or going to a restaurant to have a meal, or for a small IT (information technology) company, contracting a service to a bigger one in order to save costs and time. Service-oriented architecture (SOA) has become more popular during last years. Basically, this emerging development paradigm allows service providers to offer loosely coupled services. These services are normally only owned by the providers. As a result, the service user or client does not have to worry about the development, maintenance, infrastructure, or any other issue of how the service is working. To sum up, the user just has to find and choose the proper service. On the one hand, it presents several advantages. Firstly, common functionality can be contracted as a service in order to be able to focus on the own core missions. Secondly, it decreases the cost, since it is cheaper to contract a service than creating it yourself. Thirdly, clients take benefit of provider’s latest technologies. On the other hand, there is one big drawback: lack of trust. When you contract a service, you lose the direct control, the provider has access to your own data, you depend on him, and you experiment delays since your functionality is not working in-home. That is why the user has to decide previously which service is the most appropriate for his needs. Each client has different needs: quality (it varies among services), reputation (a famous or recommended provider usually gives more confidence), speed (agreements not to break thresholds), security (contract and trust in the provider), personalisation (preferential treatment from the provider), and locality (law is not the same in all countries). Therefore, a customer needs to know about the best service(s).Among all kind of services, we concentrate on forecasting services. Forecasting services show in advance a condition or occurrence about the future. There are plenty of domains: weather forecasts, stock market prices, results in betting shops, elections… Let us see a domain which is really familiar to all of us: weather forecast. When we are planning to travel, going somewhere or just deciding what to wear first thing in the morning, we wonder about weather conditions. To make these decisions, we check the weather forecast on TV news, a thermometer, or on a web site. However, sometimes we check several predictions and they do not agree. Which one will be the most accurate? Our goal in this master thesis is to assess the accuracy of these forecasting services in order to help prospective users to choose the best one according to their needs. To do it, we are going to compare forecast predictions with actual real observations
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