Adaptive delay-energy balanced partial offloading strategy in Mobile Edge Computing networks
Shumei Liu, Yao Yu, Lei Guo, Phee Lep Yeoh, Branka Vucetic, Yonghui Li
Abstract
Mobile Edge Computing (MEC)-based computation offloading is a promising application paradigm for serving large numbers of users with various delay and energy requirements. In this paper, we propose a flexible MEC-based requirement-adaptive partial offloading model to accommodate each user's specific preference regarding delay and energy consumption. To address the dimensional differences between time and energy, we introduce two normalized parameters and then derive the computational overhead of processing tasks. Different from existing works, this paper considers practical variations in the user request patterns, and exploits a flexible partial offloading mode to minimize computation overheads subject to tolerable delay, task workload and power constraints. Since the resulting problem is non-convex, we decouple it into two convex subproblems and present an iterative algorithm to obtain a feasible offloading solution. Numerical experiments show that our proposed scheme achieves a significant improvement in computation overheads compared with existing schemes.