Multi-objective workflow grid scheduling using ε\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\varepsilon $$\end{document}-fuzzy dominance sort based discrete particle swarm optimization

With the rapid development of networking technology, grid computing has emerged as a source for satisfying the increasing demand of the computing power of scientific computing community. Mostly, the user applications in scientific and enterprise domains are constructed in the form of workflows in which precedence constraints between tasks are defined. Scheduling of workflow applications belongs to the class of NP-hard problems, so meta-heuristic approaches are preferred options. In this paper, ε\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\varepsilon $$\end{document}-fuzzy dominance sort based discrete particle swarm optimization (ε\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\varepsilon $$\end{document}-FDPSO) approach is used to solve the workflow scheduling problem in the grid. The ε\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\varepsilon $$\end{document}-FDPSO approach has never been used earlier in grid scheduling. The metric, fuzzy dominance which quantifies the relative fitness of solutions in multi-objective domain is used to generate the Pareto optimal solutions. In addition, the scheme also incorporates a fuzzy based mechanism to determine the best compromised solution. For the workflow applications two scheduling problems are solved. In one of the scheduling problems, we addressed two major conflicting objectives, i.e. makespan (execution time) and cost, under constraints (deadline and budget). While, in other, we optimized makespan, cost and reliability objectives simultaneously in order to incorporate the dynamic characteristics of grid resources. The performance of the approach has been compared with other acknowledged meta-heuristics like non-dominated sort genetic algorithm and multi-objective particle swarm optimization. The simulation analysis substantiates that the solutions obtained with ε\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\varepsilon $$\end{document}-FDPSO deliver better convergence and uniform spacing among the solutions keeping the computation overhead limited.