Comparison of multi-objective evolutionary approaches for task scheduling in distributed computing systems

Abstract

Parallel and distributed systems play an important part in the improvement of high performance computing. In these type of systems task scheduling is a key issue in achieving high performance of the system. In general, task scheduling problems have been shown to be NP-hard. As deterministic techniques consume much time in solving the problem, several heuristic methods are attempted in obtaining optimal solutions. This paper presents an application of Elitist Non-dominated Sorting Genetic Algorithm (NSGA-II) and a Non-dominated Sorting Particle Swarm Optimization Algorithm (NSPSO) to schedule independent tasks in a distributed system comprising of heterogeneous processors. The problem is formulated as a multi-objective optimization problem, aiming to obtain schedules achieving minimum makespan and flowtime. The applied algorithms generate Pareto set of global optimal solutions for the considered multi-objective scheduling problem. The algorithms are validated against a set of benchmark instances and the performance of the algorithms evaluated using standard metrics. Experimental results and performance measures infer that NSGA-II produces quality schedules compared to NSPSO.

A Comprehensive Review on NSGA-II for Multi-Objective Combinatorial Optimization Problems

“…Lastly, the scheduling problems which is the most discussed category of MOCOPs consists of open shop scheduling problem [125], [126], job shop scheduling problem (JSSP) [127], [128], [129], [130], [131], [132], FSP [133], [134], [135], [136], [137], [138], project scheduling problem (PSP) [139], resource constrained PSP (RCPSP) [140], [141], [142], [143], [144], [145], timetabling problem [146], cross-docking scheduling problem [147], task scheduling problem [148], [149], [150], [151], [152], [153], [154], machine scheduling problem [155], [156], [157], [158], [159], [160], [161], satellite range scheduling problem [162], multi-objective satellite data transmission scheduling problem [163], satellite scheduling of large areal tasks [164], operating room scheduling [165], [166], harvest scheduling problem [167], energy-efficiency scheduling problem…”

“…In [154], the researchers compared NSGA-II and NSPSO for Distributed heterogeneous computing systems on benchmark instances using standard performance metrics. The compromised optimal schedules obtained by NSGA-II have better quality than the other approach.…”

A Simulation-Based Multi-Objective Optimization Framework for the Production Planning in Energy Supply Chains

“…NSGA-II as a type of GA is flexible and easy to apply to the optimization problem in our ESC. The general advantages of NSGA-II are listed as follows [69]: 1. The non-dominated sorting technique is used to get close to the optimal solution.…”

A decision-making methodology for low-carbon electronic product design

“…At the same time, the analytical process of a MOGA is very easy to understand. In facing more complicated bi-or multi-objective problems, a MOGA can sufficiently employ the strong global searching abilities to obtain high quality solutions [37]. With regard to the weaknesses of the MOGA, the parameters of the MOGA need to be decided early.…”