On decentralized proactive resource allocation in asynchronous real-time distributed systems

We present two classes of distributed algorithms called DRBA and DOBA, for decentralized, proactive resource allocation in asynchronous real-time distributed systems. The objective of the algorithms is to maximize aggregate application benefit and deadline-satisfied ratio for a user-specified future time interval. Since determining the optimal allocation is computationally intractable, the algorithms heuristically compute near-optimal allocations in polynomial-time. While the DRBA algorithms analyze subtask response times to determine allocation decisions which are computationally expensive, the DOBA algorithms analyze processor overloads to compute their decisions in a much faster way. Within each class, we present three algorithms that differ in the way they tolerate end-host failures: no reallocation upon failure, stateless reallocation, and stateful reallocation.