Asynchronous Mobile-Edge Computation Offloading: Energy-Efficient Resource Management

TL;DR

This paper develops energy-efficient resource management strategies for asynchronous mobile-edge computation offloading, optimizing data partitioning and transmission timing to prolong mobile battery life in heterogeneous scenarios.

Contribution

It introduces a novel optimization framework for asynchronous MECO systems, including solutions for general and special case scenarios with different data arrival and deadline orders.

Findings

Optimal resource management reduces mobile energy consumption.

Time sharing balances effective computing power among mobiles.

Transformation-and-scheduling approach effectively handles reverse order cases.

Abstract

Mobile-edge computation offloading (MECO) is an emerging technology for enhancing mobiles' computation capabilities and prolonging their battery lives, by offloading intensive computation from mobiles to nearby servers such as base stations. In this paper, we study the energy-efficient resource-management policy for the asynchronous MECO system, where the mobiles have heterogeneous input-data arrival time instants and computation deadlines. First, we consider the general case with arbitrary arrival-deadline orders. Based on the monomial energy-consumption model for data transmission, an optimization problem is formulated to minimize the total mobile-energy consumption under the time-sharing and computation-deadline constraints. The optimal resource-management policy for data partitioning (for offloading and local computing) and time division (for transmissions) is shown to be computed by using the block coordinate decent method. To gain further insights, we study the optimal resource-management design for two special cases. First, consider the case of identical arrival-deadline orders, i.e., a mobile with input data arriving earlier also needs to complete computation earlier. The optimization problem is reduced to two sequential problems corresponding to the optimal scheduling order and joint data-partitioning and time-division given the optimal order. It is found that the optimal time-division policy tends to balance the defined effective computing power among offloading mobiles via time sharing. Furthermore, this solution approach is extended to the case of reverse arrival-deadline orders. The corresponding time-division policy is derived by a proposed transformation-and-scheduling approach, which first determines the total offloading duration and data size for each mobile in the transformation phase and then specifies the offloading intervals for each mobile in the scheduling phase.