Metascheduling Multiple Resource Types using the MMKP

Abstract

Grid computing involves the transparent sharing of computational resources of many types by users across large geographic distances. The altruistic nature of many current grid resource contributions does not encourage efficient usage of resources. As grid projects mature, increased resource demands coupled with increased economic interests will introduce a requirement for a metascheduler that improves resource utilization, allows administrators to define allocation policies, and provides an overall quality of service to the grid users. In this work we present one such metascheduling framework, based on the multichoice multidimensional knapsack problem (MMKP). This strategy maximizes overall grid utility by selecting desirable options of each task subject to constraints of multiple resource types. We present the framework for the MMKP metascheduler and discuss a selection of allocation policies and their associated utility functions. The MMKP metascheduler and allocation policies are demonstrated using a grid of processor, storage, and network resources. In particular, a data transfer time metric is incorporated into the utility function in order to prefer task options with the lowest data transfer times. The resulting schedules are shown to be consistent with the defined policies