Accelerating the Computation of Multi-Objectives Scheduling Solutions for Cloud Computing

Abstract

This paper presents two practical Large Scale Multi-Objectives Scheduling (LSMOS) strategies, proposed for Cloud Computing environments. The goal is to address the problems of companies that manage a large cloud infrastructure with thousands of nodes, and would like to optimize the scheduling of several requests submitted online by users. In our context, requests submitted by users are configured according to multi-objectives criteria, as the number of used CPUs and the used memory size, to take an example. The novelty of our strategies is to select effectively, from a large set of nodes forming the Cloud Computing platform, a node that execute the user request such that this node has a good compromise among a large set of multi-objectives criteria. In this paper, first we show the limit, in terms of performance, of exact solutions. Second, we introduce approximate algorithms in order to deal with high dimensional problems in terms of nodes number and criteria number. The proposed two scheduling strategies are based on exact Kung multi-objectives decision algorithm and k-means clustering algorithm or LSH hashing (random projection based) algorithm. The experiments of our new strategies demonstrate the potential of our approach under different scenarios.