Joint Optimization of Sequential Task Offloading and Service Deployment in End-Edge-Cloud System for Energy Efficiency
Meiyan Teng, Xin Li, Kun Zhu
Abstract
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 <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">terminal-edge-cloud</i> (TEC) architecture. Existing research on offloading is limited to MEC or <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">cloud-edge</i> coordination environment, and ignores the impact of sequential task ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S-Task</i> ) constraint and service constraint. To bridge the gap, our paper first formulates the jointly optimal <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S-Task</i> offloading and service deployment ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">JOTOSD</i> ) 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 ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">COD</i> ) method, including the Break-Point ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">BP</i> ) algorithm and the convex programming-based edge offloading ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CVEO</i> ) algorithm under a service deployment strategy provided by an iterative service deployment ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ISD</i> ) algorithm. Simulate results prove that the proposed method can improve by about 20% of energy utility by compared with other heuristic algorithms.