8,952 research outputs found

    A survey of QoS-aware web service composition techniques

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    Web service composition can be briefly described as the process of aggregating services with disparate functionalities into a new composite service in order to meet increasingly complex needs of users. Service composition process has been accurate on dealing with services having disparate functionalities, however, over the years the number of web services in particular that exhibit similar functionalities and varying Quality of Service (QoS) has significantly increased. As such, the problem becomes how to select appropriate web services such that the QoS of the resulting composite service is maximized or, in some cases, minimized. This constitutes an NP-hard problem as it is complicated and difficult to solve. In this paper, a discussion of concepts of web service composition and a holistic review of current service composition techniques proposed in literature is presented. Our review spans several publications in the field that can serve as a road map for future research

    Quality-constrained routing in publish/subscribe systems

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    Routing in publish/subscribe (pub/sub) features a communication model where messages are not given explicit destination addresses, but destinations are determined by matching the subscription declared by subscribers. For a dynamic computing environment with applications that have quality demands, this is not sufficient. Routing decision should, in such environments, not only depend on the subscription predicate, but should also take the quality-constraints of applications and characteristics of network paths into account. We identified three abstraction levels of these quality constraints: functional, middleware and network. The main contribution of the paper is the concept of the integration of these constraints into the pub/sub routing. This is done by extending the syntax of pub/sub system and applying four generic, proposed by us, guidelines. The added values of quality-constrained routing concept are: message delivery satisfying quality demands of applications, improvement of system scalability and more optimise use of the network resources. We discuss the use case that shows the practical value of our concept

    An infrastructure service recommendation system for cloud applications with real-time QoS requirement constraints

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    The proliferation of cloud computing has revolutionized the hosting and delivery of Internet-based application services. However, with the constant launch of new cloud services and capabilities almost every month by both big (e.g., Amazon Web Service and Microsoft Azure) and small companies (e.g., Rackspace and Ninefold), decision makers (e.g., application developers and chief information officers) are likely to be overwhelmed by choices available. The decision-making problem is further complicated due to heterogeneous service configurations and application provisioning QoS constraints. To address this hard challenge, in our previous work, we developed a semiautomated, extensible, and ontology-based approach to infrastructure service discovery and selection only based on design-time constraints (e.g., the renting cost, the data center location, the service feature, etc.). In this paper, we extend our approach to include the real-time (run-time) QoS (the end-to-end message latency and the end-to-end message throughput) in the decision-making process. The hosting of next-generation applications in the domain of online interactive gaming, large-scale sensor analytics, and real-time mobile applications on cloud services necessitates the optimization of such real-time QoS constraints for meeting service-level agreements. To this end, we present a real-time QoS-aware multicriteria decision-making technique that builds over the well-known analytic hierarchy process method. The proposed technique is applicable to selecting Infrastructure as a Service (IaaS) cloud offers, and it allows users to define multiple design-time and real-time QoS constraints or requirements. These requirements are then matched against our knowledge base to compute the possible best fit combinations of cloud services at the IaaS layer. We conducted extensive experiments to prove the feasibility of our approach
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