An adaptive service selection approach based on service density in multiple quality aspects

Abstract

In a task-oriented service computing framework in which services are composed together to accomplish a task goal of a user, appropriate component services need to be dynamically selected and bound to the task. When there are many candidate services that provide similar functionality, it is essential to consider quality of service (QoS) factors such as the response time, cost, availability, and reliability to determine which component services to use. Finding a service composite that meets the optimal quality is a well-known NP-hard problem, as the time complexity for the global optimization increases exponentially as the number of services or the number of QoS attributes increases. Although it is possible to use a heuristic approach that shows a reasonable response time with a certain level of service quality, this strategy often fails when the global QoS constraints become tight. In this paper, we propose an adaptive means of dividing quality ranges in which candidate services are sampled and their QoS values are evaluated. The sub-regions of the quality ranges are dynamically determined based on the distribution of the candidate component services on each QoS attribute and the tightness of the constraint requirement within a task. Evaluation results show that the proposed approach can successfully reduce the failure rate of the service composition while keeping the computation time reasonably low and ensuring the QoS optimality of composite services.