A Trade-Off Negotiation Strategy for Pareto-Optimal Service Composition with Additive QoS-constraints

Abstract

QoS-based service composition enables the development of complex business applications. They are composed of distributed services characterized by QoS attributes representing non-functional characteristics, such as cost, execution time, and reliability. With the proliferation of services on the Internet, more candidates for each component service may be available with different QoS values. Hence, the ones satisfying QoS global constraints required for the application have to be selected. Automated negotiation is adopted to select component services in a dynamic market of services where provided QoS values may vary according to different market strategies. Nevertheless, when dealing with multiple QoS attributes, multiple service providers, and not shared information, it is difficult to guarantee the formal properties of the negotiation outcomes. In the present work, we propose a trade-off negotiation strategy that allows reaching a Pareto-optimal agreement, if it exists. The agreement consists of the QoS values of component services that are the ones selected to provide the complete application. The strategy exploits both the competition, that is due to multiple services providing the same functionality with different QoS values, and the cooperation among the providers of the different component services, that are necessary to meet the required end-to-end QoS constraints.

A Hybrid Whale Optimization Algorithm for QoS-Aware Manufacturing Cloud Service Composition

“…Xie et al [50] introduced a new algorithm that uses the differential evolution mutation operator in directional guiding ideology and combines the NSGA-II algorithm to improve the solution population distribution. Napoli et al [51] proposed a trade-off negotiation strategy that can process multiple QoS properties at the same time. NK et al [52] developed Non-dominated Sorting GA (NSGA-II) for composition service problem in IoT.…”

A Hybrid Whale Optimization Algorithm for Quality of Service-Aware Manufacturing Cloud Service Composition

“…Xie et al [49] introduced a new algorithm that uses the differential evolution mutation operator in directional guiding ideology and combines the NSGA-II algorithm to improve the solution population distribution. Napoli et al [50] proposed a trade-off negotiation strategy that can process multiple QoS properties at the same time. NK et al [51] developed a Non-dominated Sorting GA (NSGA-II) for composition service problems in IoT.…”