Proliferation of cloud computing has revolutionized 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, Microsoft Azure) and small companies (e.g. Rackspace, Ninefold), decision makers (e.g. application developers, CIOs) are likely to be overwhelmed by choices available. The decision making problem is further complicated due to heterogeneous service configurations and application provisioning Quality of Service (QoS) constraints. To address this hard challenge, in our previous work we developed a semi-automated, extensible, and ontology-based approach to infrastructure service discovery and selection based on only design time constraints (e.g., renting cost, datacentre location, service feature, etc.). In this paper, we extend our approach to include the real-time (run-time) QoS (endto- end message latency, end-to-end message throughput) in the decision making process. Hosting of next generation applications in domain of on-line interactive gaming, large scale sensor analytics, and real-time mobile applications on cloud services necessitates optimization of such real-time QoS constraints for meeting Service Level Agreements (SLAs). To this end, we present a real-time QoS aware multi-criteria decision making technique that builds over well known Analytics Hierarchy Process (AHP) 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 possible best fit combinations of cloud services at IaaS layer. We conducted extensive experiments to prove the feasibility of our approach.
There are methods proposed for network aware service composition @cite_23 @cite_15 @cite_45 considering generic web service, i.e. at the Software-as-a-Service (SaaS) and Platform-as-a-Service (PaaS) level. But the compatibility constrains at the IaaS level are different from web service. For example, generic web services are distinguished by their features, QoS and prices. It does not make sense to include 2 exact same services in one composition as one job does not need to be done twice, but using multiple quantity of an IaaS offer is perfectly valid.
@cite_15 @cite_45 @cite_23
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Web services are rapidly changing the landscape of software engineering. One of the most interesting challenges introduced by web services is represented by Quality Of Service (QoS)--aware composition and late--binding. This allows to bind, at run--time, a service--oriented system with a set of services that, among those providing the required features, meet some non--functional constraints, and optimize criteria such as the overall cost or response time. In other words, QoS--aware composition can be modeled as an optimization problem.We propose to adopt Genetic Algorithms to this aim. Genetic Algorithms, while being slower than integer programming, represent a more scalable choice, and are more suitable to handle generic QoS attributes. The paper describes our approach and its applicability, advantages and weaknesses, discussing results of some numerical simulations. Web service composition has been a prevailing research direction in recent years. There are two major challenges faced by researchers, semantic matchmaking and Quality of Service (QoS) optimisation. Semantic matchmaking aims to discover interoperable web services that can interact with each other by their resources described semantically. QoS optimisation aims to optimise the non-functional requirements of service users, such as minimum cost and maximum reliability. To meet the requirements of service users, both semantic matchmaking quality and QoS should be considered simultaneously. Most existing works on web service composition, however, focus only on one of these two aspects. Therefore, we propose a comprehensive quality model that takes both semantic matchmaking quality and QoS into account with the aim of achieving a more desirable balance of both sides. Further, we develop a PSO-based service composition approach with explicit support for the proposed comprehensive quality model. We also conduct experiments to explore the effectiveness of our PSO-based approach and the desirable balance achieved by using our comprehensive quality model. Web service composition enables seamless and dynamic integration of business applications on the web. The performance of the composed application is determined by the performance of the involved web services. Therefore, non-functional, quality of service aspects are crucial for selecting the web services to take part in the composition. Identifying the best candidate web services from a set of functionally-equivalent services is a multi-criteria decision making problem. The selected services should optimize the overall QoS of the composed application, while satisfying all the constraints specified by the client on individual QoS parameters. In this paper, we propose an approach based on the notion of skyline to effectively and efficiently select services for composition, reducing the number of candidate services to be considered. We also discuss how a provider can improve its service to become more competitive and increase its potential of being included in composite applications. We evaluate our approach experimentally using both real and synthetically generated datasets. Web service composition has become a promising technique to build powerful business applications by making use of distributed services with different functions. Due to the explosion in the volume of data, providing efficient approaches to composing data intensive services will become more and more important in the field of service-oriented computing. Meanwhile, as numerous web services have been emerging to offer identical or similar functionality, web service composition is usually performed with end-to-end Quality of Service QoS properties which are adopted to describe the non-functional properties e.g., response time, execution cost, reliability, etc. of a web service. In this paper, a hybrid approach that integrates the use of genetic programming and tabu search to QoS-aware data intensive service composition is proposed. The performance of the proposed approach is evaluated using the publicly available benchmark datasets. A full set of experimental results show that a significant improvement of our approach over that obtained by the simple genetic programming method and several traditional optimization methods, has been achieved.