Computing Offloading Strategy in Mobile Edge Computing Environment: A Comparison between Adopted Frameworks, Challenges, and Future Directions

With the proliferation of the Internet of Things (IoT) and the development of wireless communication technologies such as 5G, new types of services are emerging and mobile data traffic is growing exponentially. The mobile computing model has shifted from traditional cloud computing to mobile edge computing (MEC) to ensure QoS. The main feature of MEC is to “sink” network resources to the edge of the network to meet the needs of delay-sensitive and computation-intensive services, and to provide users with better services. Computation offloading is one of the major research issues in MEC. In this paper, we summarize the state of the art in task offloading in MEC. First, we introduce the basic concepts and typical application scenarios of MEC, and then we formulate the task offloading problem. In this paper, we analyze and summarize the state of research in the industry in terms of key technologies, schemes, scenarios, and objectives. Finally, we provide an outlook on the challenges and future research directions of computational offloading techniques and indicate the suggested direction of follow-up research work.

[1]  L. Ismail,et al.  HealthEdge: A Machine Learning-Based Smart Healthcare Framework for Prediction of Type 2 Diabetes in an Integrated IoT, Edge, and Cloud Computing System , 2023, ANT/EDI40.

[2]  IoT Based Control Networks and Intelligent Systems , 2023, Lecture Notes in Networks and Systems.

[3]  Keping Yu,et al.  DRL-Based Partial Offloading for Maximizing Sum Computation Rate of Wireless Powered Mobile Edge Computing Network , 2022, IEEE Transactions on Wireless Communications.

[4]  J. McArthur,et al.  B-SMART: A Reference Architecture for Autonomic Smart Buildings. , 2022, IOP Conference Series: Earth and Environmental Science.

[5]  Deze Zeng,et al.  Stackelberg-Game-Based Computation Offloading Method in Cloud–Edge Computing Networks , 2022, IEEE Internet of Things Journal.

[6]  Daisy Nkele Molokomme,et al.  Edge Intelligence in Smart Grids: A Survey on Architectures, Offloading Models, Cyber Security Measures, and Challenges , 2022, J. Sens. Actuator Networks.

[7]  Kuan-Ching Li,et al.  Multiobjective Optimization for Joint Task Offloading, Power Assignment, and Resource Allocation in Mobile Edge Computing , 2022, IEEE Internet of Things Journal.

[8]  J. Shuja,et al.  Computation Offloading in Mobile Cloud Computing and Mobile Edge Computing: Survey, Taxonomy, and Open Issues , 2022, Mobile Information Systems.

[9]  Lisha Hu,et al.  Game-Theory-Based Task Offloading and Resource Scheduling in Cloud-Edge Collaborative Systems , 2022, Applied Sciences.

[10]  Yan Zhang,et al.  Joint Power Control and Computation Offloading for Energy-Efficient Mobile Edge Networks , 2022, IEEE Transactions on Wireless Communications.

[11]  Weifa Liang,et al.  Maximizing User Service Satisfaction for Delay-Sensitive IoT Applications in Edge Computing , 2022, IEEE Transactions on Parallel and Distributed Systems.

[12]  Jorge Arthur Schneider Aranda,et al.  Context-aware Edge Computing and Internet of Things in Smart Grids: A systematic mapping study , 2022, Comput. Electr. Eng..

[13]  M. Ergen,et al.  Mobility-Aware Offloading Decision for Multi-Access Edge Computing in 5G Networks , 2022, Sensors.

[14]  Fei Xu,et al.  Research on computing offloading strategy based on Genetic Ant Colony fusion algorithm , 2022, Simul. Model. Pract. Theory.

[15]  K. Wolter,et al.  Energy-Efficient Offloading for DNN-Based Smart IoT Systems in Cloud-Edge Environments , 2022, IEEE Transactions on Parallel and Distributed Systems.

[16]  R. Hu,et al.  Energy Efficiency and Delay Tradeoff in an MEC-Enabled Mobile IoT Network , 2022, IEEE Internet of Things Journal.

[17]  Mohsen Guizani,et al.  Privacy-Aware Collaborative Task Offloading in Fog Computing , 2022, IEEE Transactions on Computational Social Systems.

[18]  Mohammad Hossein Rezvani,et al.  Partial offloading with stable equilibrium in fog-cloud environments using replicator dynamics of evolutionary game theory , 2022, Clust. Comput..

[19]  Chengsheng Pan,et al.  Profit Maximization Incentive Mechanism for Resource Providers in Mobile Edge Computing , 2022, IEEE Transactions on Services Computing.

[20]  Sajal K. Das,et al.  An Efficient Online Computation Offloading Approach for Large-Scale Mobile Edge Computing via Deep Reinforcement Learning , 2021, IEEE Transactions on Services Computing.

[21]  Handi Chen,et al.  Joint Optimization of Task Offloading and Resource Allocation Based on Differential Privacy in Vehicular Edge Computing , 2021, IEEE Transactions on Computational Social Systems.

[22]  Li Qing,et al.  QoS Driven Task Offloading With Statistical Guarantee in Mobile Edge Computing , 2020, IEEE Transactions on Mobile Computing.

[23]  Kezhi Wang,et al.  Deep Reinforcement Learning Based Dynamic Trajectory Control for UAV-Assisted Mobile Edge Computing , 2019, IEEE Transactions on Mobile Computing.

[24]  Fuliang Li,et al.  RLbR: A reinforcement learning based V2V routing framework for offloading 5G cellular IoT , 2022, IET Commun..

[25]  Sung-Uk Jung,et al.  Strategy for Creating AR Applications in Static and Dynamic Environments Using SLAM- and Marker Detector-Based Tracking , 2022, Computer Modeling in Engineering & Sciences.

[26]  A. Vladyko,et al.  Distributed Edge Computing with Blockchain Technology to Enable Ultra -Reliable Low-Latency v2x Communications , 2021 .

[27]  Danfeng Yan,et al.  Deep reinforcement learning-based computation offloading for 5G vehicle-aware multi-access edge computing network , 2021, China Communications.

[28]  Daniele Tarchi,et al.  Multi-Objective Computation Sharing in Energy and Delay Constrained Mobile Edge Computing Environments , 2021, IEEE Transactions on Mobile Computing.

[29]  Wei Huang,et al.  Collaborative Cloud-Edge-End Task Offloading in Mobile-Edge Computing Networks With Limited Communication Capability , 2021, IEEE Transactions on Cognitive Communications and Networking.

[30]  Wen Chen,et al.  Enhancing Mobile Edge Computing with Efficient Load Balancing Using Load Estimation in Ultra-Dense Network , 2021, Sensors.

[31]  Dimitrios P. Pezaros,et al.  Data quality-aware task offloading in Mobile Edge Computing: An Optimal Stopping Theory approach , 2021, Future Gener. Comput. Syst..

[32]  Shuchen Zhou,et al.  Jointly Optimizing Offloading Decision and Bandwidth Allocation with Energy Constraint in Mobile Edge Computing Environment , 2021, Computing.

[33]  Xiaofeng Wang,et al.  A High Reliable Computing Offloading Strategy Using Deep Reinforcement Learning for IoVs in Edge Computing , 2021, Journal of Grid Computing.

[34]  Harvinder Singh,et al.  QRAS: efficient resource allocation for task scheduling in cloud computing , 2021, SN Applied Sciences.

[35]  S. Sasikala,et al.  RETRACTED ARTICLE: Multi-parameter optimization for load balancing with effective task scheduling and resource sharing , 2021, Journal of Ambient Intelligence and Humanized Computing.

[36]  Huansheng Ning,et al.  A Novel Framework for Mobile-Edge Computing by Optimizing Task Offloading , 2021, IEEE Internet of Things Journal.

[37]  Md Zakirul Alam Bhuiyan,et al.  Trust-Aware Service Offloading for Video Surveillance in Edge Computing Enabled Internet of Vehicles , 2021, IEEE Transactions on Intelligent Transportation Systems.

[38]  Jiamei Shi,et al.  An Efficient Computation Offloading Strategy with Mobile Edge Computing for IoT , 2021, Micromachines.

[39]  Adel Nadjaran Toosi,et al.  Serverless Edge Computing: Vision and Challenges , 2021, ACSW.

[40]  Zheng Chang,et al.  Dynamic Resource Allocation and Computation Offloading for IoT Fog Computing System , 2020, IEEE Transactions on Industrial Informatics.

[41]  Junaid Shuja,et al.  Machine Learning-Based Offloading Strategy for Lightweight User Mobile Edge Computing Tasks , 2021, Complex..

[42]  Xiaolong Li,et al.  Privacy-Enhanced Data Collection Based on Deep Learning for Internet of Vehicles , 2020, IEEE Transactions on Industrial Informatics.

[43]  Waqas Jadoon,et al.  The partial computation offloading strategy based on game theory for multi-user in mobile edge computing environment , 2020, Comput. Networks.

[44]  Anfeng Liu,et al.  A Unified Trustworthy Environment Establishment Based on Edge Computing in Industrial IoT , 2020, IEEE Transactions on Industrial Informatics.

[45]  Mostafa Ghobaei-Arani,et al.  A survey on the computation offloading approaches in mobile edge computing: A machine learning-based perspective , 2020, Comput. Networks.

[46]  Symeon Papavassiliou,et al.  Data Offloading in UAV-Assisted Multi-Access Edge Computing Systems: A Resource-Based Pricing and User Risk-Awareness Approach , 2020, Sensors.

[47]  Zhongfeng Wang,et al.  Offloading Optimization for Low-Latency Secure Mobile Edge Computing Systems , 2020, IEEE Wireless Communications Letters.

[48]  Zhiguo Shi,et al.  Latency Optimization for Cellular Assisted Mobile Edge Computing via Non-Orthogonal Multiple Access , 2020, IEEE Transactions on Vehicular Technology.

[49]  Xiaoming Tao,et al.  Latency Minimization for D2D-Enabled Partial Computation Offloading in Mobile Edge Computing , 2020, IEEE Transactions on Vehicular Technology.

[50]  Songlin Chen,et al.  Multiuser Physical Layer Authentication in Internet of Things With Data Augmentation , 2020, IEEE Internet of Things Journal.

[51]  Hao Luo,et al.  MTES: An Intelligent Trust Evaluation Scheme in Sensor-Cloud-Enabled Industrial Internet of Things , 2020, IEEE Transactions on Industrial Informatics.

[52]  Haojun Huang,et al.  P3: Privacy-Preserving Scheme Against Poisoning Attacks in Mobile-Edge Computing , 2020, IEEE Transactions on Computational Social Systems.

[53]  Qinglin Zhao,et al.  Dependency-Aware Task Scheduling in Vehicular Edge Computing , 2020, IEEE Internet of Things Journal.

[54]  Guojun Wang,et al.  Edge-based differential privacy computing for sensor-cloud systems , 2020, J. Parallel Distributed Comput..

[55]  Arun Kumar Sangaiah,et al.  Big Data Cleaning Based on Mobile Edge Computing in Industrial Sensor-Cloud , 2020, IEEE Transactions on Industrial Informatics.

[56]  Shaoyong Guo,et al.  Joint Computation Offloading and URLLC Resource Allocation for Collaborative MEC Assisted Cellular-V2X Networks , 2020, IEEE Access.

[57]  Michael Stonebraker,et al.  Pattern functional dependencies for data cleaning , 2020, Proc. VLDB Endow..

[58]  Albert Y. Zomaya,et al.  Edge Intelligence: The Confluence of Edge Computing and Artificial Intelligence , 2019, IEEE Internet of Things Journal.

[59]  Long Hu,et al.  Privacy-aware service placement for mobile edge computing via federated learning , 2019, Inf. Sci..

[60]  Frank H. P. Fitzek,et al.  Device-Enhanced MEC: Multi-Access Edge Computing (MEC) Aided by End Device Computation and Caching: A Survey , 2019, IEEE Access.

[61]  Xin Yao,et al.  Parallel Offloading in Green and Sustainable Mobile Edge Computing for Delay-Constrained IoT System , 2019, IEEE Transactions on Vehicular Technology.

[62]  Zhi Zhou,et al.  Online Orchestration of Cross-Edge Service Function Chaining for Cost-Efficient Edge Computing , 2019, IEEE Journal on Selected Areas in Communications.

[63]  Alireza Souri,et al.  Multiobjective virtual machine placement mechanisms using nature‐inspired metaheuristic algorithms in cloud environments: A comprehensive review , 2019, Int. J. Commun. Syst..

[64]  Zibin Zheng,et al.  Online Deep Reinforcement Learning for Computation Offloading in Blockchain-Empowered Mobile Edge Computing , 2019, IEEE Transactions on Vehicular Technology.

[65]  Xiong Li,et al.  Privacy Preserving Data Aggregation Scheme for Mobile Edge Computing Assisted IoT Applications , 2019, IEEE Internet of Things Journal.

[66]  Josep Domingo-Ferrer,et al.  Privacy-preserving cloud computing on sensitive data: A survey of methods, products and challenges , 2019, Comput. Commun..

[67]  R. N. Uma,et al.  Optimal Joint Scheduling and Cloud Offloading for Mobile Applications , 2019, IEEE Transactions on Cloud Computing.

[68]  Pan Hui,et al.  Dandelion: A Unified Code Offloading System for Wearable Computing , 2019, IEEE Transactions on Mobile Computing.

[69]  Yuan Wu,et al.  Deep reinforcement learning-based joint task offloading and bandwidth allocation for multi-user mobile edge computing , 2019, Digit. Commun. Networks.

[70]  Zhangdui Zhong,et al.  Joint Job Partitioning and Collaborative Computation Offloading for Internet of Things , 2019, IEEE Internet of Things Journal.

[71]  F. Richard Yu,et al.  Integrated Blockchain and Edge Computing Systems: A Survey, Some Research Issues and Challenges , 2019, IEEE Communications Surveys & Tutorials.

[72]  Parmeet Kaur,et al.  Efficient computation offloading using grey wolf optimization algorithm , 2019 .

[73]  Xu Chen,et al.  In-Edge AI: Intelligentizing Mobile Edge Computing, Caching and Communication by Federated Learning , 2018, IEEE Network.

[74]  Dario Pompili,et al.  Joint Task Offloading and Resource Allocation for Multi-Server Mobile-Edge Computing Networks , 2017, IEEE Transactions on Vehicular Technology.

[75]  Hong Wen,et al.  Security Enhancement for Mobile Edge Computing Through Physical Layer Authentication , 2019, IEEE Access.

[76]  Rui L. Aguiar,et al.  Network Functions Virtualization: The Long Road to Commercial Deployments , 2019, IEEE Access.

[77]  Giancarlo Fortino,et al.  Autonomic computation offloading in mobile edge for IoT applications , 2019, Future Gener. Comput. Syst..

[78]  Jianghua Feng,et al.  Comparative analysis of variable flux reluctance machines with double- and single-layer concentrated armature windings , 2019, 2018 Thirteenth International Conference on Ecological Vehicles and Renewable Energies (EVER).

[79]  Haibin Zhang,et al.  Double Auction-Based Resource Allocation for Mobile Edge Computing in Industrial Internet of Things , 2018, IEEE Transactions on Industrial Informatics.

[80]  Huan Zhou,et al.  V2V Data Offloading for Cellular Network Based on the Software Defined Network (SDN) Inside Mobile Edge Computing (MEC) Architecture , 2018, IEEE Access.

[81]  Vinod Vokkarane,et al.  A New Deep Learning-Based Food Recognition System for Dietary Assessment on An Edge Computing Service Infrastructure , 2018, IEEE Transactions on Services Computing.

[82]  Anfeng Liu,et al.  A Three-Layer Privacy Preserving Cloud Storage Scheme Based on Computational Intelligence in Fog Computing , 2018, IEEE Transactions on Emerging Topics in Computational Intelligence.

[83]  Joonhyuk Kang,et al.  Mobile Edge Computing via a UAV-Mounted Cloudlet: Optimization of Bit Allocation and Path Planning , 2016, IEEE Transactions on Vehicular Technology.

[84]  Zhigang Wen,et al.  Joint Offloading and Computing Design in Wireless Powered Mobile-Edge Computing Systems With Full-Duplex Relaying , 2018, IEEE Access.

[85]  Russell J. Clark,et al.  Advancing Software-Defined Networks: A Survey , 2017, IEEE Access.

[86]  Ilsun You,et al.  Computational Offloading for Efficient Trust Management in Pervasive Online Social Networks Using Osmotic Computing , 2017, IEEE Access.

[87]  Nirwan Ansari,et al.  Toward Hierarchical Mobile Edge Computing: An Auction-Based Profit Maximization Approach , 2016, IEEE Internet of Things Journal.

[88]  Dario Sabella,et al.  Mobile-Edge Computing Architecture: The role of MEC in the Internet of Things , 2016, IEEE Consumer Electronics Magazine.

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

[90]  Jeongho Kwak,et al.  DREAM: Dynamic Resource and Task Allocation for Energy Minimization in Mobile Cloud Systems , 2015, IEEE Journal on Selected Areas in Communications.

[91]  Athanasios V. Vasilakos,et al.  Information centric network: Research challenges and opportunities , 2015, J. Netw. Comput. Appl..

[92]  Sergio Barbarossa,et al.  Joint Optimization of Radio and Computational Resources for Multicell Mobile-Edge Computing , 2014, IEEE Transactions on Signal and Information Processing over Networks.

[93]  Bharat K. Bhargava,et al.  A Survey of Computation Offloading for Mobile Systems , 2012, Mobile Networks and Applications.