Multi-objective Optimization of Clustering-based Scheduling for Multi-workflow On Clouds Considering Fairness

TL;DR

This paper introduces a multi-objective optimization model and a clustering-based scheduling strategy for multi-workflow cloud computing, balancing makespan, cost, and fairness to improve resource allocation.

Contribution

It proposes a novel multi-objective model and a global clustering-based scheduling method specifically for multi-workflow cloud environments, emphasizing fairness.

Findings

Outperforms existing algorithms in balancing makespan, cost, and fairness.

Maintains comparable makespan and cost while improving fairness.

Provides effective guidance for cloud workflow scheduling.

Abstract

Distributed computing, such as cloud computing, provides promising platforms to execute multiple workflows. Workflow scheduling plays an important role in multi-workflow execution with multi-objective requirements. Although there exist many multi-objective scheduling algorithms, they focus mainly on optimizing makespan and cost for a single workflow. There is a limited research on multi-objective optimization for multi-workflow scheduling. Considering multi-workflow scheduling, there is an additional key objective to maintain the fairness of workflows using the resources. To address such issues, this paper first defines a new multi-objective optimization model based on makespan, cost, and fairness, and then proposes a global clustering-based multi-workflow scheduling strategy for resource allocation. Experimental results show that the proposed approach performs better than the compared algorithms without significant compromise of the overall makespan and cost as well as individual fairness, which can guide the simulation workflow scheduling on clouds.