Towards Service Composition based on Hybrid Bio-Inspired Cloud-based QoS Provisioning Approach.

Abstract

It has recently become a critical issue to provide software development in a service-based conceptual style for business companies . As a powerful technology for service-oriented computing, the composition of web services is investigated. This offered great opportunities to improve IT industries and business processes by forming new value-added services that satisfy the user’s complex requirements. Unfortunately, many challenges are facing the service composition process. These include the difficulties to satisfy the user’s complex demands, maintaining the performance to be matched with the quality of service (QoS) requirements, and search space reduction for QoS missing or changeable values. Accordingly, this paper proposes a cloud-based QoS provisioning service composition (CQPC) framework to address these challenges. To prove the concept and the applicability of the CQPC framework, a Hybrid Bio-Inspired QoS provisioning (HBIQP) technique is presented for the operation of the CQPC framework modules. The solution space is reduced via utilizing skyline concepts to have faster execution time and keep only reliable and most interesting services. The CQPC framework is equipped with two proposed algorithms: (i) the modified highly accurate prediction (MHAP) algorithm to enhance the prediction of QoS values of the services participating in the composition process, (ii) the MapReduce fruit fly Particle swarm Optimization (MR-FPSO) algorithm to handle composing web services for large scale of data in the cloud environment. The experimental results demonstrate the worthiness of the HBIQP technique to meet the performance metrics more than other state-of-the-art techniques in terms of average fitness value, accuracy, and execution time.

Cloud Service Selection System Approach based on QoS Model: A Systematic Review

“…Also, to effectually provide the services across the cloud, a hybrid approach of ACO (Ant Colony optimization) with GA (Genetic Algorithm) was established (Fadl [63] to sort out and combine the optimal services over the cloud in terms of service composition. Likewise, to efficiently resolve the service composition challenges, Cloud-based QoS-Provisioning Service Composition (CQPC) framework was considered and in addition to that Hybrid Bio-Inspired QoS provisioning (HBIQP) was approached for the applicability of the service based compositions, and then to form the composite services with higher accuracy with a certain amount of duration, the MapReduce fruit fly Particle swarm Optimization (MR-FPSO) (Waleed M. Bahgat, et al, 2020) [64] yielded a massive scale of services.…”