A Near-Optimal Approach for Online Task Offloading and Resource Allocation in Edge-Cloud Orchestrated Computing

Due to the explosion of mobile devices and the evolution of wireless communication technologies, novel applications with intensive computation demands and low-latency requirements have arisen. Edge computing has been proposed as an extension of cloud computing, which moves computation workloads from remote cloud to network edge. Cooperating edge computing and cloud computing can significantly reduce the latency of computation tasks. However, considering the heterogeneity and stochastic arrivals of tasks and the limited computation and communication resources on the edge, task offloading and resource allocation are two joint crucial problems in an edge-cloud orchestrated computing system. In this paper, we propose an online task offloading and resource allocation approach for edge-cloud orchestrated computing, with the aim to minimize the average latency of tasks over time. We first build system models to analyze the latency and energy consumption incurred under different computing modes and formally formulate the joint problem as a mixed-integer optimal decision problem. Then, we employ Lyapunov optimization and duality theory to decompose the problem into a set of subproblems, which can be solved in a semi-decentralized way. We also formally analyze that our approach can achieve near-optimal performance. Extensive simulations are conducted to verify the superiority of our approach.

Automated summary

In this paper, an online task offloading and resource allocation approach is proposed for edge-cloud orchestrated computing. By leveraging the cooperation between edge computing and cloud computing, the approach aims to reduce the latency of computation tasks and minimize the average latency over time. The approach decomposes the problem into subproblems using Lyapunov optimization and duality theory, and can achieve near-optimal performance.