A Mean Field Model of Work Stealing in Large-Scale Systems (ACM)
In this paper, we consider a generic model of computational grids, seen as several clusters of homogeneous processors. In such systems, a key issue when designing efficient job allocation policies is to balance the workload over the different resources.
We present a Markovian model for performance evaluation of such a policy, namely work stealing (idle processors steal work from others) in large-scale heterogeneous systems. Using mean field theory, we show that when the size of the system grows, it converges to a system of deterministic ordinary differential equations that allows one to compute the expectation of performance functions (such as average response times) as well as the distributions of these functions.
Paper available at ACM.