Dynamic Computation Offloading Based on Graph Partitioning in Mobile Edge Computing

Mobile edge computing is a new cloud computing paradigm that utilizes small-sized edge clouds to provide real-time services to users. These mobile edge clouds (MECs) are located near users, thereby enabling users to seamlessly access applications that are running on MECs and to easily access MECs. Terminal devices can transfer tasks to MEC servers nearby to improve the quality of computing. In this paper, we study multi-user computation offloading problem for mobile-edge computing in a multichannel wireless interference environment. Then, we analyze the overhead of each mobile devices, and we propose strategies for task scheduling and offloading in a multi-user MEC system. For reducing the energy consumption, we propose a server partitioning algorithm that is based on clustering. We formulate the task offloading decision problem as a multi-user game, which always has a Nash equilibrium. The simulation results demonstrate that our scheme outperforms the traditional offloading strategy in terms of energy consumption.

[1]  Laurence T. Yang,et al.  A Distributed HOSVD Method With Its Incremental Computation for Big Data in Cyber-Physical-Social Systems , 2018, IEEE Transactions on Computational Social Systems.

[2]  Z. Haitao,et al.  Mobile edge computing towards 5G: Vision, recent progress, and open challenges , 2016, China Communications.

[3]  Laurence T. Yang,et al.  A Multi-Order Distributed HOSVD with Its Incremental Computing for Big Services in Cyber-Physical-Social Systems , 2020, IEEE Transactions on Big Data.

[4]  Kalyanmoy Deb,et al.  Multiple Criteria Decision Making, Multiattribute Utility Theory: Recent Accomplishments and What Lies Ahead , 2008, Manag. Sci..

[5]  Zhao Haitao,et al.  Cross-layer framework for fine-grained channel access in next generation high-density WiFi networks , 2016 .

[6]  Khaled Ben Letaief,et al.  Dynamic Computation Offloading for Mobile-Edge Computing With Energy Harvesting Devices , 2016, IEEE Journal on Selected Areas in Communications.

[7]  Song Guo,et al.  Joint Optimization of Task Scheduling and Image Placement in Fog Computing Supported Software-Defined Embedded System , 2016, IEEE Transactions on Computers.

[8]  Zhipeng Cai,et al.  Task Scheduling in Deadline-Aware Mobile Edge Computing Systems , 2019, IEEE Internet of Things Journal.

[9]  Junhua Wu,et al.  Methods of Resource Scheduling Based on Optimized Fuzzy Clustering in Fog Computing , 2019, Sensors.

[10]  Xuyun Zhang,et al.  A Distributed Locality-Sensitive Hashing-Based Approach for Cloud Service Recommendation From Multi-Source Data , 2017, IEEE Journal on Selected Areas in Communications.

[11]  Chao Yan,et al.  Link prediction in paper citation network to construct paper correlation graph , 2019, EURASIP J. Wirel. Commun. Netw..

[12]  Xuyun Zhang,et al.  Finding All You Need: Web APIs Recommendation in Web of Things Through Keywords Search , 2019, IEEE Transactions on Computational Social Systems.

[13]  Lianyong Qi,et al.  SimHash-Based Similar Neighbor Finding for Scalable and Privacy-Preserving Service Recommendation , 2017, ICCCS.

[14]  Jinjun Chen,et al.  A two-stage locality-sensitive hashing based approach for privacy-preserving mobile service recommendation in cross-platform edge environment , 2018, Future Gener. Comput. Syst..

[15]  Lianyong Qi,et al.  Privacy-Aware Multidimensional Mobile Service Quality Prediction and Recommendation in Distributed Fog Environment , 2018, Wirel. Commun. Mob. Comput..

[16]  Wu Muqing,et al.  An Overhead-Optimizing Task Scheduling Strategy for Ad-hoc Based Mobile Edge Computing , 2017, IEEE Access.

[17]  Hao Liang,et al.  Optimal Workload Allocation in Fog-Cloud Computing Toward Balanced Delay and Power Consumption , 2016, IEEE Internet of Things Journal.

[18]  Yang Yang,et al.  MEETS: Maximal Energy Efficient Task Scheduling in Homogeneous Fog Networks , 2018, IEEE Internet of Things Journal.

[19]  Min Dong,et al.  Joint offloading decision and resource allocation for multi-user multi-task mobile cloud , 2016, 2016 IEEE International Conference on Communications (ICC).

[20]  Wenzhong Li,et al.  Efficient Multi-User Computation Offloading for Mobile-Edge Cloud Computing , 2015, IEEE/ACM Transactions on Networking.

[21]  Zhan Qiang,et al.  Fog computing dynamic load balancing mechanism based on graph repartitioning , 2016, China Communications.

[22]  Weifa Liang,et al.  Efficient Algorithms for Capacitated Cloudlet Placements , 2016, IEEE Transactions on Parallel and Distributed Systems.

[23]  Mathieu Bouet,et al.  Mobile Edge Computing Resources Optimization: A Geo-Clustering Approach , 2018, IEEE Transactions on Network and Service Management.

[24]  Ke Zhang,et al.  Energy-Efficient Offloading for Mobile Edge Computing in 5G Heterogeneous Networks , 2016, IEEE Access.

[25]  Choong Seon Hong,et al.  Decentralized Computation Offloading and Resource Allocation in Heterogeneous Networks with Mobile Edge Computing , 2018, ArXiv.

[26]  Shuai Li,et al.  Robot manipulator control using neural networks: A survey , 2018, Neurocomputing.

[27]  Xing Zhang,et al.  A Survey on Mobile Edge Networks: Convergence of Computing, Caching and Communications , 2017, IEEE Access.

[28]  Guochu Shou,et al.  Mobile Edge Computing: Progress and Challenges , 2016, 2016 4th IEEE International Conference on Mobile Cloud Computing, Services, and Engineering (MobileCloud).

[29]  Laurence T. Yang,et al.  A Cloud-Edge Computing Framework for Cyber-Physical-Social Services , 2017, IEEE Communications Magazine.

[30]  Leïla Merghem,et al.  Efficient green solution for a balanced energy consumption and delay in the IoT-Fog-Cloud computing , 2017, 2017 IEEE 16th International Symposium on Network Computing and Applications (NCA).

[31]  Tasos Dagiuklas,et al.  Multi-access edge computing: open issues, challenges and future perspectives , 2017, Journal of Cloud Computing.

[32]  Guangshun Li,et al.  Energy Consumption Optimization With a Delay Threshold in Cloud-Fog Cooperation Computing , 2019, IEEE Access.

[33]  Laurence T. Yang,et al.  A Tensor Computation and Optimization Model for Cyber-Physical-Social Big Data , 2019, IEEE Transactions on Sustainable Computing.

[34]  Yunlong Cai,et al.  Latency Optimization for Resource Allocation in Mobile-Edge Computation Offloading , 2017, IEEE Transactions on Wireless Communications.