Exponential Smoothing for Network-Aware Meta-scheduler in Advance in Grids

Abstract

Grid computing involves the coordinated use of disperse heterogeneous computing resources. This heterogeneity and dispersion makes Quality of Service (QoS) still an open issue requiring attention from the research community. One way of contributing to the provision of QoS in Grids is by performing meta-scheduling of jobs in advance, that is, the computing resource where a job will be executed is decided some time before jobs are actually executed. But this way of scheduling needs to do predictions about the future status of resources, including network. The main aim of this work is to provide QoS in Grid environments through network-aware job scheduling in advance. In our case, QoS means the fulfillment of a deadline for the completion of jobs. For this, predictions about future status of computing and network resources are made by using exponential smoothing functions. This paper presents a performance evaluation using a real testbed that illustrates the efficiency of this approach to meet the QoS requirements of users. This evaluation highlights the effects of using Exponential Smoothing (ES) to tune predictions in order to deliver the requested QoS.