Multi-objective workflow scheduling in Amazon EC2

Abstract

Nowadays, scientists and companies are confronted with multiple competing goals such as makespan in high-performance computing and economic cost in Clouds that have to be simultaneously optimised. Multi-objective scheduling of scientific applications in these systems is therefore receiving increasing research attention. Most existing approaches typically aggregate all objectives in a single function, defined a-priori without any knowledge about the problem being solved, which negatively impacts the quality of the solutions. In contrast, Pareto-based approaches having as outcome a set of (nearly) optimal solutions that represent a tradeoff among the different objectives, have been scarcely studied. In this paper, we analyse MOHEFT, a Pareto-based list scheduling heuristic that provides the user with a set of tradeoff optimal solutions from which the one that better suits the user requirements can be manually selected. We demonstrate the potential of our method for multiobjective workflow scheduling on the commercial Amazon EC2 Cloud. We compare the quality of the MOHEFT tradeoff solutions with two state-of-theart approaches using different synthetic and real-world workflows: the classical HEFT algorithm for single-objective scheduling and the SPEA2* genetic algorithm used in multi-objective optimisation problems. The results demonstrate that our approach is able to compute solutions of higher quality than SPEA2*. In addition, we show that MOHEFT is more suitable than SPEA2* for workflow scheduling in the context of commercial Clouds, since the genetic-based approach is unable of dealing with some of the constraints imposed by these systems.

A Hybrid Metaheuristic for Multi-Objective Scientific Workflow Scheduling in a Cloud Environment

“…At each iteration of the algorithm, the relationship among organisms (i.e., solutions) is decided based on the desired optimization fitness function using their corresponding positions according to Equation (3). Then, the organism with the best fitness value X best is updated.…”

“…It is an NP-complete problem, so building an optimum workflow scheduler with reasonable performance and computation speed is very challenging in the heterogeneous distributed environment of clouds [3].…”

“…[2] proposed an evolutionary multi-objective scheduling for cloud (EMS-C) algorithm to solve the workflow scheduling problem on the IaaS platform. Extensions of HEFT [10], the Pareto Optimal Scheduling Heuristic (POSH) [22], and Multi-Objective Heterogeneous Earliest Finish Time (MOHEFT) [3] were designed to provide users with a set of trade-off optimal solutions for scheduling workflows in the cloud. A multi-objective heuristic algorithm, Min-min based time and cost tradeoff (MTCT), was proposed by Xu et al (2016) [23].…”

Workflow Scheduling Techniques and Algorithms in IaaS Cloud: A Survey

“…Durillo and Prodan developed the MOHEFT algorithm [37] as an extension of the classical DAG scheduling HEFT algorithm [38] for mono-objective scheduling. A Pareto-based list scheduling heuristic computes a set of tradeoff optimal solutions from which the user can select the one which suits their requirements better.…”

An Evolutionary Study of Multi-Objective Workflow Scheduling in Cloud Computing

“…[1] Multi-objective scheduling is getting growing exploration attention. Multi-objective aggregate all the objectives in a single function defined a deductive without any knowledge about the problem being solved [2].…”