Service Composition

QoS aware Automatic Web Service Composition with Multiple Objectives


Automatic web service composition has received a significant research attention in service-oriented computing over decades of research. With increasing number of web services, providing an end-to-end Quality of Service (QoS) guarantee in responding to user queries is becoming an important concern. Multiple QoS parameters (e.g., response time, latency, throughput, reliability, availability, success rate) are associated with a service, thereby, service composition with a large number of candidate services is a challenging multi-objective optimization problem. In this paper, we study the multi-constrained multi-objective QoS aware web service composition problem and propose three different approaches to solve the same, one optimal, based on Pareto front construction and two other based on heuristically traversing the solution space. We compare the performance of the heuristics against the optimal, and show the effectiveness of our proposals over other classical approaches for the same problem setting, with experiments on WSC-2009 and ICEBE-2005 datasets.


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Towards Scalable Semantic Service Composition


State-of-the-art approaches in web services research have used semantics of web services for different purposes, for service discovery, composition, selection and execution. In this paper, our main focus is on semantics driven Quality of Service (QoS) aware service composition. Most of the contemporary approaches on service composition have used the semantic information to combine the services appropriately to generate the composition solution. However, in this paper, our objective is to use the semantic information to expedite the service composition

algorithm. Here, we present a service composition framework that uses semantic information of a web service to generate different clusters, where the services are semantically related within a cluster. Our final aim is to construct a composition solution using these clusters that can efficiently scale to large service spaces, while ensuring solution quality. Experimental results show the efficiency of our proposed method.


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A Variation Aware Composition Model for Dynamic Web Service Environments

Contemporary approaches for automated web service composition mostly deal with static web services. The underlying assumption here is that the web services participating in resolving a query are static and thereby, their functional and non-functional parameters change very infrequently or do not change at all. However, in reality, this assumption does not hold. New services are added to the repository, existing unpopular services are removed from the repository, service interfaces change due to changes in the specification. Classical service composition approaches, therefore, fall short to handle the dynamic behavior of a web service during composition. In this work, we present a stochastic model of the web service composition problem to capture the dynamic behavior of web services from the functional perspective.

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QoS Constrained Large Scale Web Service Composition Using Abstraction Refinement

Efficient service composition in real time, while satisfying desirable Quality of Service (QoS) guarantees for the composite solution has been one of the topmost research challenges in the domain of services computing. On one hand, optimal QoS aware service composition algorithms, that come with the promise of solution optimality, are inherently compute intensive, and therefore, often fail to generate the optimal solution in real time for large scale web services. On the other hand, heuristic solutions that have the ability to generate solutions fast and handle large and complex service spaces, settle for sub-optimal solution quality. The problem of balancing the trade-off between computation efficiency and optimality in service composition has alluded researchers since quite some time, and several proposals for taming the scale and complexity of web service composition have been proposed in literature. In this work, we present a new perspective towards this trade-off in service composition based on abstraction refinement, which can be seamlessly integrated on top of any off-the-shelf service composition method to tackle the space complexity, thereby, making it more time and space efficient.

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A Fast and Scalable Mechanism for Web Service Composition

Efficient composition of web services in real time while providing necessary QoS guarantees is a computationally complex problem and several heuristic based approaches have been proposed to compose services optimally. In this work, we present the design of a scalable QoS-aware service composition mechanism which balances the computational complexity of service composition with the QoS guarantees of the composed service and achieves scalability. On one hand, we handle the case of a single QoS parameter using an intelligent search and pruning mechanism in the composed service space and show that our methodology yields near optimal solutions on real benchmarks. On the other hand, we handle the case of multiple QoS parameters using aggregation techniques.

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QSCAS: QoS aware web service Composition Algorithms with Stochastic parameters

In recent times, automated business processes and web service technologies have become popular and ubiquitous for catering to diverse user needs. While providing a service, the service providers are typically expected to furnish promised QoS values for the services they deliver.  However, when the services are physically deployed and invoked during a query resolution, these parameter values vary largely depending on different factors like network load, number of applications running in the server etc. In this work, we present a stochastic model of the web service composition problem.

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