Dynamic Task Offloading and Resource Allocation for Heterogeneous MEC-enable IoT

Abstract

With the rapid development and convergence of the mobile Internet and the Internet of Things (IoT), the diverse IoT applications with requirements of ultra-low latency and heterogeneous connectivity have been increasing sharply in recent years. In this work, we consider the problem of diverse task offloading and computation resource allocation in the dynamic and heterogeneous MEC-enable network, wherein heterogeneous characterized by the MEC servers with different computing capacities and multi-type applications with different computation requirements. Firstly, to drive the long-term system-wide effectiveness towards a near-optimum, joint task offloading and computation resource allocation is proposed by invoking the Lyapunov optimization theory, and we analyze the tradeoff between task offloading revenue and latency. Besides, a heuristic task offloading methodology based on a search tree is developed to get the optimal offloading strategy in a heterogeneous environment. Moreover, to improve processing efficiency and reduce unnecessary communication overhead, the Offloading Priority Selection Criterion (OPSC) is designed. Finally, the effectiveness and rationality of the algorithm are verified by experimental simulations.