Distributed frameworks and parallel algorithms for processing large-scale geographic data
Parallel Computing - Special issue: High performance computing with geographical data
An agent-based approach for dynamic adjustment of scheduled jobs in computational grids
Journal of Computer and Systems Sciences International
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Efficient and robust metacomputing requires the decom-position of complex jobs into tasks that must be scheduled on distributed processing nodes. There are various ways of creating a schedule and implementing it efficiently, depend-ing upon global system state knowledge. Many computa-tions may be structured as process networks, where data is either pushed from the source node to the target node where it will be used, or is pulled from source to target at the in-stigation of the target. We have developed a metacomput-ing infrastructure to investigate this idea, which employs the concept of a rich data pointer, the DISCWorld Remote Ac-cess Mechanism (DRAM)[5], which can point to either data or services, and can be traded in a client/multiple-server model.We present an extension of the DRAM concept and im-plementation to represent and describe data that has not yet been created, the "DRAM Future" (DRAMF). We show how the use of the DRAMF facilitates efficient metacomputing scheduling and runtime optimisation on high performance distributed systems. We present a recursive algorithm for determining the optimal placement of a job's components in the presence of partial system state information. This algo-rithm uses only a selected subset of all available processing nodes, and we implement it using DRAMFs.There are many research issues to consider when design-ing a robust and general algorithm for scheduling and pro-cess placement on distributed systems. We address some of these issues in our Distributed Information Systems Control World project[6] as do other research projects [2].