Logo image
Back
Task Offloading with Uncertain Processing Cycles
Conference proceeding

Task Offloading with Uncertain Processing Cycles

Shaoran Li, Chengzhang Li, Yan Huang, Brian A. Jalaian, Y. Thomas Hou and Wenjing Lou
Proceedings of the Twenty-second International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile Computing, pp.51-60
ACM Conferences
MobiHoc '21: The International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile Computing, 22nd (Shanghai, China, 07/26/2021–07/29/2021)
07/26/2021
DOI: https://doi.org/10.1145/3466772.3467034

Metrics

3 Record Views

Abstract

Mobile Edge Computing (MEC) has emerged to be an integral component of 5G infrastructure due to its potential to speed up task processing and reduce energy consumption for mobile devices. However, a major technical challenge in making offloading decisions is that the number of required processing cycles of a task is usually unknown in advance. Due to this processing uncertainty, it is difficult to make offloading decisions while providing any guarantee on task deadlines. To address this challenge, we propose EPD---Energy-minimized solution with Probabilistic Deadline guarantee to task offloading problem. The mathematical foundation of EPD is Exact Conic Reformulation (ECR), which is a powerful tool that reformulates a probabilistic constraint for task deadline into a deterministic one. In the absence of distribution knowledge of processing cycles, we use the estimated mean and variance of processing cycles and exploit ECR to the fullest extent in the design of EPD. Simulation results show that EPD successfully guarantees the probabilistic deadlines while minimizing the energy consumption of mobile users, and can achieve significant improvement in energy saving when compared to a state-of-the-art approach.

Details

Logo image