Following the Usage, Not the Request: Risk-Aware Task Scheduling with Overbooking in Edge Clouds

Edge computing platforms are increasingly deployed to support delay-sensitive and resource-intensive applications. However, current task scheduling strategies, which rely on user-requested resources, often lead to low resource utilization and reduced platform profit due to users’ tendency to over-request resources. Overbooking, widely adopted in industries such as airlines and hotels, can improve utilization but introduce risk under uncertain task resource usage. This paper applies resource overbooking to edge clouds, focusing on task scheduling optimization under uncertainty. We formulate the problem as a stochastic mixed integer program, which is proven to be NP-hard. To this end, a risk evaluation scheme is proposed to accurately quantify the risk of overload without assuming specific resource usage distributions, which has an additive error guarantee. Based on this scheme, we transform the problem into a more deterministic form and prove that the objective function is submodular. This enables us to design a greedy algorithm that achieves a (1 − 1/e)-approximation ratio with lower complexity. Extensive experiments on a real-world dataset demonstrate that the proposed algorithm significantly improves profit by 0.23×-3.35× and resource utilization by 0.16×-0.75× while accurately controlling the risk associated with overbooking.