文献类型：期刊文献

英文题名：Energy-efficient offloading for DNN-based applications in edge-cloud computing: A hybrid chaotic evolutionary approach

作者：Li, Zengpeng[1];Yu, Huiqun[1];Fan, Guisheng[1];Zhang, Jiayin[1];Xu, Jin[1]

机构：[1]East China Univ Sci & Technol, Sch Informat Sci & Engn, Shanghai 200237, Peoples R China

年份：2024

卷号：187

外文期刊名：JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING

收录：;EI(收录号：20240615519414);WOS:【SCI-EXPANDED(收录号:WOS:001183485200001)】；

基金：This work was partially supported by Shanghai Municipal Natural Science Foundation (No. 21ZR1416300) , National Natural Science Foundation of China (No. 62372174) , Capacity Building Project of Local Universities Science and Technology Commission of Shanghai Municipality (No. 22010504100) , Shanghai Municipal Special Fund for Promoting High Quality Development (No. 2021-GYHLW-01007) , and Research Funding of National Engineering Laboratory for Big Data Distribution and Exchange Technologies of China.

语种：英文

外文关键词：Edge-cloud computing; Energy-efficient offloading; Constrained optimization; Deep neural networks; Evolutionary optimization

摘要：The rapid development of Deep Neural Networks (DNNs) lays solid foundations for Internet of Things systems. However, mobile devices with limited processing capacity and short battery life confront the difficulties of executing complex DNNs. To satisfy different Quality of Service requirements, a feasible solution is offloading DNN layers to edge nodes and the cloud. The energy-efficient offloading problem for DNN-based applications with the deadline and budget constraints in the edge-cloud environment is still an open and challenging issue. To this end, this paper proposes a Hybrid Chaotic Evolutionary Algorithm (HCEA) incorporating diversification and intensification strategies and a DVFS-enabled version of it (HCEA-DVFS). The Archimedes Optimization Algorithm-based diversification strategy exploits global and local guiding information to improve population diversity during the updating process and employs Metropolis acceptance rule of Simulated Annealing to avoid premature convergence. The Genetic Algorithm-based chaotic intensification strategy is designed to enhance the local search capability of HCEA. Moreover, the Dynamic Voltage Frequency Scaling-enabled adjustment strategies can be embedded into HCEA to further reduce energy consumption by resetting frequency levels and reallocating DNN layers. Experimental results over four DNN-based applications demonstrate that HCEA-DVFS can reduce more energy consumption under different deadlines, budgets, and workloads on average by 7.93, 9.68, 11.02, 11.84, and 19.38 percent in comparison with HCEA, PSO-GA, MCEA, AOA, and Greedy, respectively.