IFFO: An Improved Fruit Fly Optimization Algorithm for Multiple Workflow Scheduling Minimizing Cost and Makespan in Cloud Computing Environments

Ambika Aggarwal,Madhushi Verma,Amit Agarwal,Mehedi Masud,Hesham A Alhumyani,Priti Dimri,Vijay Kumar

doi:10.1155/2021/5205530

Abstract

Cloud computing platforms have been extensively using scientific workflows to execute large-scale applications. However, multiobjective workflow scheduling with scientific standards to optimize QoS parameters is a challenging task. Various metaheuristic scheduling techniques have been proposed to satisfy the QoS parameters like makespan, cost, and resource utilization. Still, traditional metaheuristic approaches are incompetent to maintain agreeable equilibrium between exploration and exploitation of the search space because of their limitations like getting trapped in local optimum value at later evolution stages and higher-dimensional nonlinear optimization problem. This paper proposes an improved Fruit Fly Optimization (IFFO) algorithm to minimize makespan and cost for scheduling multiple workflows in the cloud computing environment. The proposed algorithm is evaluated using CloudSim for scheduling multiple workflows. The comparative results depict that the proposed algorithm IFFO outperforms FFO, PSO, and GA.

Highlights

Cloud is an infinite pool of configurable computing resources with some functionalities such as an on-demand pay-per-use model, high availability, scalability, and reliability [1, 2]
Yassa et al have presented a new multiobjective approach, called Dynamic Voltage and Frequency Scaling (DVFS)-MODPSO [15], for scheduling workflows on the cloud computing environment. e presented algorithm is a hybridization of PSO with Heterogeneous Earliest Finish Time (HEFT), aiming to optimize multiple objectives like makespan, cost, and energy consumption
HSGA [17] is a GA-based hybrid workflow scheduling technique adopted by Delavar et al, which utilizes the optimization characteristics of Round Robin (RR) and Best Fit (BF) scheduling algorithms

Summary

Introduction

Cloud is an infinite pool of configurable computing resources (storage, network, processor, bandwidth, etc.) with some functionalities such as an on-demand pay-per-use model, high availability, scalability, and reliability [1, 2]. It supports the distributed architecture for geographically distributed heterogeneous resources and provisioning them to clients through virtualization for hosting large-scale applications. CGA2 is compared with traditional algorithms such as PSO, HEFT, GA, and Random to demonstrate better performance in terms of meeting deadlines under strict constraints and reducing the overall workflow execution cost. The proposed technique does the priority ranking of tasks based on their dependencies, and the resource allocation is done by implementing RR and BF for appropriate VM selection. e experimental results depicted better performance of HSGA in terms of reducing makespan, lowering failure rate, and balancing the load when compared with LAGA and NGA scheduling algorithms

Objectives

Results

Conclusion