Self adaptive fruit fly algorithm for multiple workflow scheduling in cloud computing environment

• Published in: Kybernetes Vol. 50; no. 6; pp. 1704 - 1730
• Main Authors: Aggarwal, Ambika, Dimri, Priti, Agarwal, Amit, Bhatt, Ashutosh
• Format: Journal Article
• Language: English
• Published: London Emerald Publishing Limited 06.07.2021; Emerald Group Publishing Limited
• Subjects: Adaptive algorithms; Cloud computing; Computing time; Customer services; Energy consumption; Evolutionary computation; Game theory; Genetic algorithms; Optimization algorithms; Optimization techniques; Particle swarm optimization; Queuing theory; Resource utilization; Response time (computers); Schedules; Scheduling; Statistical analysis; Task scheduling; Workflow; Workloads; Self-adaptive; Task scheduling; Cloud computing; Fruit fly; Time consumption
• ISSN: 0368-492X; 1758-7883
• DOI: 10.1108/K-11-2019-0757

Purpose In general, cloud computing is a model of on-demand business computing that grants a convenient access to shared configurable resources on the internet. With the increment of workload and difficulty of tasks that are submitted by cloud consumers; “how to complete these tasks effectively and rapidly with limited cloud resources?” is becoming a challenging question. The major point of a task scheduling approach is to identify a trade-off among user needs and resource utilization. However, tasks that are submitted by varied users might have diverse needs of computing time, memory space, data traffic, response time, etc. This paper aims to proposes a new way of task scheduling. Design/methodology/approach To make the workflow completion in an efficient way and to reduce the cost and flow time, this paper proposes a new way of task scheduling. Here, a self-adaptive fruit fly optimization algorithm (SA-FFOA) is used for scheduling the workflow. The proposed multiple workflow scheduling model compares its efficiency over conventional methods in terms of analysis such as performance analysis, convergence analysis and statistical analysis. From the outcome of the analysis, the betterment of the proposed approach is proven with effective workflow scheduling. Findings The proposed algorithm is more superior regarding flow time with the minimum value, and the proposed model is enhanced over FFOA by 0.23%, differential evolution by 2.48%, artificial bee colony (ABC) by 2.85%, particle swarm optimization (PSO) by 2.46%, genetic algorithm (GA) by 2.33% and expected time to compute (ETC) by 2.56%. While analyzing the make span case, the proposed algorithm is 0.28%, 0.15%, 0.38%, 0.20%, 0.21% and 0.29% better than the conventional methods such as FFOA, DE, ABC, PSO, GA and ETC, respectively. Moreover, the proposed model has attained less cost, which is 2.14% better than FFOA, 2.32% better than DE, 3.53% better than ABC, 2.43% better than PSO, 2.07% better than GA and 2.90% better than ETC, respectively. Originality/value This paper presents a new way of task scheduling for making the workflow completion in an efficient way and for reducing the cost and flow time. This is the first paper uses SA-FFOA for scheduling the workflow.