Computational Offloading Strategy Based on Multi-objective Optimization in D2D Network

doi:10.3969/j.issn.1006-2475.2024.01.004

Abstract

Abstract: Abstract： Focused on the high latency and energy consumption for computational offload in mobile edge computing scenarios with device-to-device （D2D） communication technology， a computational offloading strategy based on multi-objective optimization is proposed. The strategy is based on a computing offloading model with multi-objective optimization of delay and energy consumption， introduces the analysis of excessive offloading problem， improves the NSGA-II algorithm， including genetic encoding strategy， crossover and variation methods applicable to computing offloading， and minimizes task execution time and energy consumption by solving the Pareto optimum. In addition， a data routing algorithm is proposed， which balances the transmission energy consumption of routing devices and optimizes the routing paths. Through simulation experiments， the average boosting efficiency of the algorithm is up to 41.7% and the task retransmission rate is reduced to 7.8%. The experiment results show that the proposed algorithm can significantly reduce the execution delay， energy consumption， task retransmission rate and improve the task offload success rate.

Key words: Key words： device-to-device （D2D）, mobile edge computing, multi-objective optimization, Pareto optimization

CLC Number:

TP389. 1

CHEN Qi, LI Jing-jing. Computational Offloading Strategy Based on Multi-objective Optimization in D2D Network[J]. Computer and Modernization, 2024, 0(01): 21-28.

References ［27］

［1］	ABBAS N， ZHANG Y， TAHERKORDI A， et al. Mobile edge computing： A survey［J］. IEEE Internet of Things Journal， 2018，5（1）：450-465.
［2］	田辉，范绍帅，吕昕晨，等. 面向5G需求的移动边缘计算［J］. 北京邮电大学学报， 2017，40（2）：1-10.
［3］	SUN X， ANSARI N. EdgeIoT： Mobile edge computing for the Internet of things［J］. IEEE Communications Magazine， 2016，54（12）：22-29.
［4］	MACH P， BECVAR Z. Mobile edge computing： A survey on architecture and computation offloading［J］. IEEE Communications Surveys & Tutorials， 2017，19（3）：1628-1656.
［5］	QIAN L， LUO Z G， DU Y J， et al. Cloud computing： An overview［C］// Proceedings of IEEE International Conference on Cloud Computing. 2009：626-631.
［6］	DILLON T， WU C， CHANG E. Cloud computing： Issues and challenges［C］// 2010 24th IEEE International Conference on Advanced Information Networking and Applications. 2010：27-33.
［7］	TRUONG-HUU T， THAM C， NIYATO D. A stochastic workload distribution approach for an ad hoc mobile cloud［C］// 2014 IEEE 6th International Conference on Cloud Computing Technology and Science. 2014：174-181.
［8］	STHAPIT S， THOMPSON J， ROBERTSON N M， et al. Computational load balancing on the edge in absence of cloud and fog［J］. IEEE Transactions on Mobile Computing， 2018，18（7）：1499-1512.
［9］	胡恒，金凤林，郎思琪. 移动边缘计算环境中的计算卸载技术研究综述［J］. 计算机工程与应用， 2021，57（14）：60-74.
［10］	KHALEDI M， KHALEDI M， KASERA S K. Profitable task allocation in mobile cloud computing［C］ // Proceedings of the 12th ACM Symposium on QoS and Security for Wireless and Mobile Networks. 2016：9-17.
［11］	WANG Z， ZHAO Z W， MIN G Y， et al. User mobility aware task assignment for mobile edge computing［J］. Future Generation Computer Systems， 2018，85：1-8.
［12］	ALI E B， KISHK S， ABDELHAY E H. Multidimensional auction for task allocation using computation offloading in fifth generation networks［J］. Future Generation Computer Systems， 2020，108：717-725.
［13］	李余，何希平，唐亮贵. 基于终端直通通信的多用户计算卸载资源优化决策［J］. 计算机应用， 2022，42（5）：1538-1546.
［14］	CHEN X， PU L J， GAO L， et al. Exploiting massive D2D collaboration for energy-efficient mobile edge computing［J］. IEEE Wireless Communications， 2017，24（4）：64-71.
［15］	ZHAO P T， TIAN H， QIN C， et al. Energy-saving offloading by jointly allocating radio and computational resources for mobile edge computing［J］. IEEE Access， 2017，5：11255-11268.
［16］	XIE B Y， ZHANG Q， QIN J Y. Joint optimization of cooperative communication and computation in two-way relay MEC systems［J］. IEEE Transactions on Vehicular Technology， 2020，69（4）：4596-4600.
［17］	HUYNH L N T， PHAM Q V， NGUYEN Q D， et al. Energy-efficient computation offloading with multi-MEC servers in 5G two-tier heterogeneous networks ［C］// Proceedings of the 13th International Conference on Ubiquitous Information Management and Communication （IMCOM） 2019. 2019：120-129.
［18］	KO H N， PACK S. Distributed device-to-device offloading system： Design and performance optimization［J］. IEEE Transactions on Mobile Computing， 2021，20（10）：2949-2960.
［19］	THAM C， CAO B. Stochastic programming methods for workload assignment in an Ad Hoc mobile cloud［J］. IEEE Transactions on Mobile Computing， 2018，17（7）：1709-1722.
［20］	ZHANG X M. Enhancing mobile cloud with social-aware device-to-device offloading［J］. Computer Communications， 2021，168：1-11.
［21］	LIU L Q， CHANG Z， GUO X J， et al. Multi-objective optimization for computation offloading in mobile-edge computing［C］// 2017 IEEE Symposium on Computers and Communications （ISCC）. 2017：832-837.
［22］	REN Q， LIU K， ZHANG L M. Multi-objective optimization for task offloading based on network calculus in fog environments［J］. Digital Communications and Networks， 2022，8（5）：825-833.
［23］	LUO Q Y， LI C L， LUAN T H， et al. Minimizing the delay and cost of computation offloading for vehicular edge computing［J］. IEEE Transactions on Services Computing， 2022，15（5）：2897-2909.
［24］	DEB K， PRATAP A， AGARWAL S， et al. A fast and elitist multiobjective genetic algorithm： NSGA-II［J］. IEEE Transactions on Evolutionary Computation， 2002，6（2）：182-197.
［25］	YU H， ZHAN J， JIAO L B， et al. Load balancing strategy for wireless multi-hotspot networks based on improved NSGA-II algorithm［C］// 2022 International Conference on Networking and Network Applications （NaNA）. 2022：107-112.
［26］	PENG K， ZHU M S， ZHANG Y W， et al. A Multi-objective computation offloading method for workflow applications in mobile edge computing［C］// 2019 International Conference on Internet of Things （iThings） and IEEE Green Computing and Communications （GreenCom） and IEEE Cyber， Physical and Social Computing （CPSCom） and IEEE Smart Data （SmartData）. 2019：135-141.
［27］	CHAARI R， CHEIKHROUHOU O， KOUBAA A， et al. Multi-objective computation offloading for cloud robotics using NSGA-II［C］// 2021 17th International Conference on Wireless and Mobile Computing， Networking and Communications （WiMob）. 2021：206-211.

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