Hi, this is Soumi Chattopadhyay. I am a Ph.D student at Indian Statistical Institute, affiliated with the Advanced Computing and Microelectronics Unit.

Abstract: In recent times, automated business processes and web services have become ubiquitous in diverse application spaces. Efficient composition of web services in real time while providing necessary Quality of Service (QoS) guarantees is a computationally complex problem and several heuristic based approaches have been proposed to compose services optimally. In this paper, 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 solution on real benchmarks. On the other hand, we handle the case of multiple QoS parameters using aggregation techniques. As a final contribution, we explore search time versus solution quality trade-off using parameterized search algorithms that produce better quality solutions at the cost of time. We present experimental results to show the efficiency of our proposed mechanism.

Abstract : 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. Experimental results on Web Service Challenge (WSC) benchmarks show the efficiency of our proposed mechanism.

Abstract: It is common practice today for small and medium business houses to assemble and host services, than hosting everything themselves. To cater to diverse market needs, these houses often need to subscribe to different services from different information providers. The service contracts and the range of features and facilities supported and provided by the providers vary widely. A non-trivial challenge for a service assembler is in deciding the set of information providers to subscribe to, given the heterogeneity in the offerings provided, the economics of the business model, the target set of customers in the market place and most importantly, the profit margin. We present in this paper, an automated framework that addresses this challenge and aids a service assembler with a cost-feature-performance balanced recommendation of the providers that can best serve his needs. The problem gets exacerbated since there can be multiple dimensions/categories of services (e.g., hotel, flight, and local conveyance in the travel domain) and there can be multiple relevant recommendations which may be of use for the service assemblers. We examine the service subscription recommendation problem from different perspectives and present algorithms for service assembly. Experimental results on small-scale real data as well as large-scale simulation data show the efficacy of our proposal.

Abstract: In recent times, with the proliferation of Internet usage, the number of web services is increasing rapidly. Efficient service composition in real time while providing necessary Quality of Service (QoS) guarantees is therefore, a challenging problem for the researchers. Service composition is a computationally complex problem and several heuristic based approaches have been proposed for optimal service composition. In this paper, we present a scalable approach for efficient service composition based on abstraction refinement. Instead of looking at each service during composition, we propose several abstractions of the services and the composition is done by these abstract services. Abstraction reduces the search space significantly and thereby the composition can be done reasonably fast. However, we may fail to generate the composite solution with desired QoS parameters on an abstraction. Hence, we propose to refine an abstraction to generate the composite solution with desired QoS values. In conclusion, the abstraction refinement techniques give a significant speed-up compared to the traditional composition technique at the cost of preprocessing overhead. The experimental results on real benchmark shows the efficiency of our proposed mechanism in terms of speed up and memory requirement.