Adaptive Allocation of Independent Tasks to Maximize Throughput

In this paper, we consider the task allocation problem for computing a large set of equal-sized independent tasks on a heterogeneous computing system where the tasks initially reside on a single computer (the root) in the system. This problem represents the computation paradigm for a wide range of applications such as SETI@home and Monte Carlo simulations. We consider the scenario where the systems have a general graph-structured topology and the computers are capable of concurrent communications and overlapping communications with computation. We show that the maximization of system throughput reduces to a standard network flow problem. We then develop a decentralized adaptive algorithm that solves a relaxed form of the standard network flow problem and maximizes the system throughput. This algorithm is then approximated by a simple decentralized protocol to coordinate the resources adaptively. Simulations are conducted to verify the effectiveness of the proposed approach. For both uniformly distributed and power law distributed systems, a close-to-optimal throughput is achieved, and improved performance over a bandwidth-centric heuristic is observed. The adaptivity of the proposed approach is also verified through simulations.