An Efficient Hybrid Metaheuristic Algorithm for QoS-Aware Cloud Service Composition Problem

Abstract

Cloud computing has a great ability to store and manage remote access to services in a term of software as a service (SaaS). Recently, many organizations have moved to use outsourcing over the cloud to reduce the local resource burden. The stored services over the cloud are too scalable and complex, so an optimization method is more desirable to select appropriate services that satisfy the clients' request. To do so, the quality of service (QoS) parameters that associated with each service are the best resources for choosing and optimizing the appropriate services over the cloud. Therefore, the cloud service composition aims to select and integrate services over the cloud to satisfy the clients' request. In this work, a hybrid algorithm is introduced, which combines ant colony optimization (ACO) and genetic algorithm (GA) to efficiently compose the services over the cloud. The GA is used to tune the ACO's parameters automatically and the ACO adapts its performance based on the parameters tuning. The main contribution of this work is to help the ACO algorithm to avoid stagnation problem and enhance the performance of the ACO where this performance is affected by the value of the ACO's parameters. The experimental results on 15 different real datasets have shown the effectiveness of the proposed algorithm to search comparable solutions compared to five competitors.INDEX TERMS Cloud Services Composition (CSC); Ant Colony Optimization (ACO), Genetic Algorithm (GA).

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

“…For this, every reliable CMs were organized as sub-tasks then the robust service composition and optimal selection (rSCOS) built the expected QoS, along with this, to solve any inefficiency in the rSCOS, a guiding artificial bee colonygrey wolf optimization (gABC-GWO) algorithm (Bo Yang, et al, 2020) [62] was incorporated, which designed based on the features of GWO. 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.…”

An efficient composite cloud service model using multi-criteria decision-making techniques

“…Service selection [7,[9][10][11][12][13][14][15][16][17][18][19][20][21][22] is one of the most challenging research topics for cloud computing researchers, especially when multiple CSPs are taken into consideration. This paper reports three types of studies: the MCDM algorithms for selecting CSP, objective and subjective weighting techniques, and cloud service composition.…”

“…Dahan et al [21] have introduced a hybrid algorithm by combining two meta-heuristic algorithms, which are ant colony optimization (ACO) and genetic algorithm (GA), to compose the services of the cloud efficiently. The GA automatically tunes ACO's parameters, and its performance is adjusted based on the tuned parameters.…”

A Fast Fully Parallel Ant Colony Optimization Algorithm Based on CUDA for Solving TSP

“…These improvements are relevant to the idea of the algorithm. By improving some operations of ACO or combining ACO with other algorithms like genetic algorithms (GA) [16], the improved ACO has a better performance. However, only the CPU is responsible for executing these algorithms, bringing the excessive load to the CPU.…”