Multiagent Resource Allocation for Dynamic Task Arrivals with Preemption

Abstract

In this article, we present a distributed algorithm for allocating resources to tasks in multiagent systems, one that adapts well to dynamic task arrivals where new work arises at short notice. Our algorithm is designed to leverage preemption if it is available, revoking resource allocations to tasks in progress if new opportunities arise that those resources are better suited to handle. Our multiagent model assigns a task agent to each task that must be completed and a proxy agent to each resource that is available. Preemption occurs when a task agent approaches a proxy agent with a sufficiently compelling need that the proxy agent determines the newcomer derives more benefit from the proxy agent’s resource than the task agent currently using that resource. Task agents reason about which resources to request based on a learning of churn and congestion. We compare to a well-established multiagent resource allocation framework that permits preemption under more conservative assumptions and show through simulation that our model allows for improved allocations through more permissive preemption. In all, we offer a novel approach for multiagent resource allocation that is able to cope well with dynamic task arrivals.