Offloading Decision in Edge Computing for Continuous Applications Under Uncertainty

• Published in: IEEE transactions on wireless communications Vol. 19; no. 9; pp. 6196 - 6209
• Main Authors: Chang, Wei, Xiao, Yang, Lou, Wenjing, Shou, Guochu
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.09.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Central processing units; chance constrained programming; Computation offloading; Constraints; CPUs; Delays; dynamic programming; Edge computing; Energy conservation; Energy consumption; Games; multi-dimensional knapsack problem; Optimization; Programming; Response time; Response time (computers); Uncertainty; Upper bounds
• ISSN: 1536-1276; 1558-2248
• DOI: 10.1109/TWC.2020.3001012

Edge computing (EC) is an emerging paradigm to push sufficient computation resources towards the network edge, improving application performance significantly by offloading applications to the edge computing node. We investigate continuous application offloading decision in EC, for which it is uncertain how users operate continuous applications and how long continuous applications last before completion. That means some characteristics of continuous applications, e.g., the number of user operations, the uploading and downloading data size for offloading computation of each user operation, and the number of central processing unit (CPU) cycles required to execute computation of each user operation, are unknown when making offloading decision. In this scenario, an energy consumption constrained average response time minimization problem among multiple users for continuous applications under uncertainty is formulated. To tackle this problem, we propose the Response Time-Improved Offloading algorithm with Energy Constraint (RTIOEC) to make offloading decision with fewer characteristics of applications. The evaluation results show that the RTIOEC algorithm achieves comparatively short average response time of continuous applications while satisfying the energy consumption constraint with a predefined upper bound of violation probability. Our results demonstrate the practicality of the RTIOEC algorithm in offloading decision in EC for continuous applications under uncertainty.