Joint Optimization of Sequential Task Offloading and Service Deployment in End-Edge-Cloud System for Energy Efficiency

Intelligent terminal devices (TDs) usually request delay-sensitive and resource-demanding jobs, which are consisted of many sequential tasks. Mobile edge computing (MEC) offloads tasks to edge networks closer to TDs, making up for the lack of long delay response in the cloud, but it has a limited energy supply. Thanks to low-energy TDs also having processing capacity, it is a critical and challenging issue to offload sequential tasks for sustainable computing and reducing carbon emission in a terminal-edge-cloud (TEC) architecture. Existing research on offloading is limited to MEC or cloud-edge coordination environment, and ignores the impact of sequential task ( S-Task ) constraint and service constraint. To bridge the gap, our paper first formulates the jointly optimal S-Task offloading and service deployment ( JOTOSD ) problems objected to maximize the energy utility related to response delay, which is NP-hard and is divided into deployment and offloading sub-problems. Then, we propose a comprehensive offloading and deployment ( COD ) method, including the Break-Point ( BP ) algorithm and the convex programming-based edge offloading ( CVEO ) algorithm under a service deployment strategy provided by an iterative service deployment ( ISD ) algorithm. Simulate results prove that the proposed method can improve by about 20% of energy utility by compared with other heuristic algorithms.