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Job Scheduling for Computational Grids

Grid computing is a method to execute computational jobs requiring a significant amount of computing resources and/or large sets of data. Contrary to large heterogeneous distributed systems, a Computational Grid has many independent resource providers with different access policies. In addition to the size of such a Grid, the diversity of those policies leads to a very complex allocation task that cannot be manually handled by the user. This task does not only include the search for suitable resources but also the coordination of the actual job execution on the selected set of resources. Therefore, an efficient and flexible Grid scheduling system is required to manage the job requests of the users. Further, the independent resource providers typically want to maintain control of their Grid resources by use of local management systems. This increases the complexity of the Grid allocation task as those local management systems usually do not provide all system information due to their architecture or due to policy restrictions. Similarly, it is difficult to consider the requirements of individual jobs with complex workflows or complex scheduling objectives. These scheduling objectives are a consequence of the differences in cost and quality of service among the resources. Therefore, Grid scheduling significantly differs from conventional job scheduling on parallel computer systems which has been addressed frequently in the past. In this paper, we analyze the requirements for scheduling methods in a Grid environment. This leads to implications for the architecture of a Grid scheduling system. Particularly, we discuss several scheduling algorithms with respect to their suitability for Grids. First, we describe how conventional scheduling strategies for parallel systems can be extended to include the Grid paradigm. As resource and job management problems in a Grid can be modelled as a utility market, it seems appropriate to also consider economic and market-oriented methods. Therefore, we discuss those methods and present a market-oriented scheduling strategy and its exemplary implementation. The different strategies are evaluated by use of simulations with trace based workloads for two scenarios. As a result, it is concluded that conventional and economic strategies are both suitable in general for Grid scheduling. Depending on the actual environment constraints in a real Grid system, both strategies have their advantages and their drawbacks which are described in detail. Due to its higher flexibility, the economic approach seems to be the solution of choice for Grid scheduling in the long term.