Dynamic Farm Skeleton Task Allocation Through Task Mobility

Demand for multi-process resource invariably outstrips supply and users must often share some common provision. Where batch-based, whole processor allocation proves inflexible, user programs must compete at runtime for the same resource so the load is changeable and unpredictable. We are exploring a mechanism to balance the runtime load by moving computations between processors to optimize resource use. In this paper, we present a generic algorithmic farm skeleton which is able to move worker tasks between processors in a heterogeneous architecture at runtime guided by a simple dynamic load model. Our experiments suggest that this mechanism is able to effectively compensate for unpredictable load variations.