Resource-centric task allocation in grids with artificial danger model support

摘要

This paper addresses the problem of scheduling bag-of-tasks (BoT) applications in grids and presents a novel heuristic, called the most suitable match with danger model support algorithm (MSMD) for these applications. Unlike previous approaches, MSMD is capable of efficiently dealing with BoT applications regardless of whether they are computationally or data intensive, or a mixture of both; this strength of MSMD is achieved by making scheduling decisions based on the suitability of resource-task matches, instead of completion time. MSMD incorporates an artificial danger model - based on the danger model in immunology - which selectively responds to unexpected behaviors of resources and applications, in order to increase fault-tolerance. The results from our thorough and extensive evaluation study confirm the superior performance of MSMD, and its generic applicability compared with previous approaches that only consider one or the other of the task requirements.