Task offloading in fog computing: A survey of algorithms and optimization techniques

被引:61
作者
Kumari, Nidhi [1 ]
Yadav, Anirudh [1 ]
Jana, Prasanta K. [1 ]
机构
[1] Indian Inst Technol ISM Dhanbad, Dept Comp Sci & Engn, Dhanbad 826004, Jharkhand, India
关键词
Fogcomputing; Taskoffloading; Delay&latency; Energy&powerconsumption; Cost; Machinelearning; PARTICLE SWARM OPTIMIZATION; SOFTWARE-DEFINED NETWORKING; QUASI-NEWTON MATRICES; MOBILE EDGE; RESOURCE-ALLOCATION; SENSITIVE APPLICATIONS; IOT; ENERGY; CLOUD; DELAY;
D O I
10.1016/j.comnet.2022.109137
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
The exponential growth in Internet of Things (IoT) devices and the limitations of cloud computing in terms of latency and quality of service for time-sensitive applications have led to the unfolding of the efficient middleware technology called fog. Fog computing circumvents the limitations of the cloud by creating a seamless continuum between the things/IoT/end-user devices and the cloud and reducing the effective distance. However, fog computing faces challenges for offloading tasks for their remote computation at some level. Hence, the optimality of task offloading is the primary research area in fog computing. Several contemporary papers exist on this important subject. The research gap in reviewing all these task offloading algorithms has motivated us for their presentation in the form of a detailed survey in this paper. There exist some survey papers which deal with the task offloading. However, none of them has covered the basics of optimization techniques and their solution approaches. The primary objective of this paper is to provide the readers with a complete overview of the journey from a task offloading idea to its mathematical problem formulation and finally to its solution with all details of optimization techniques. We begin by introducing fog computing, and task offloading process followed by several task offloading factors governing decision-making process and their surveys. A section is fully dedicated to the survey of offloading objectives with examples. We also present several optimization approaches used in task offloading. Finally, the last section dedicates to the challenges and future direction in fog computing. The outcomes of the survey will benefit readers in learning the optimization used in task offloading, and it will also provide them a systematic design of offloading scheme with specific objectives.
引用
收藏
页数:24
相关论文
共 195 条
[1]   Offloading in fog computing for IoT: Review, enabling technologies, and research opportunities [J].
Aazam, Mohammad ;
Zeadally, Sherali ;
Harras, Khaled A. .
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE, 2018, 87 :278-289
[2]   Fog Computing Architecture, Evaluation, and Future Research Directions [J].
Aazam, Mohammad ;
Zeadally, Sherali ;
Harras, Khaled A. .
IEEE COMMUNICATIONS MAGAZINE, 2018, 56 (05) :46-52
[3]   Mobile Edge Computing: A Survey [J].
Abbas, Nasir ;
Zhang, Yan ;
Taherkordi, Amir ;
Skeie, Tor .
IEEE INTERNET OF THINGS JOURNAL, 2018, 5 (01) :450-465
[4]   A Review of Fog Computing and Machine Learning: Concepts, Applications, Challenges, and Open Issues [J].
Abdulkareem, Karrar Hameed ;
Mohammed, Mazin Abed ;
Gunasekaran, Saraswathy Shamini ;
Al-Mhiqani, Mohammed Nasser ;
Mutlag, Ammar Awad ;
Mostafa, Salama A. ;
Ali, Nabeel Salih ;
Ibrahim, Dheyaa Ahmed .
IEEE ACCESS, 2019, 7 :153123-153140
[5]   DPTO: A Deadline and Priority-Aware Task Offloading in Fog Computing Framework Leveraging Multilevel Feedback Queueing [J].
Adhikari, Mainak ;
Mukherjee, Mithun ;
Srirama, Satish Narayana .
IEEE INTERNET OF THINGS JOURNAL, 2020, 7 (07) :5773-5782
[6]   Application Offloading Strategy for Hierarchical Fog Environment Through Swarm Optimization [J].
Adhikari, Mainak ;
Srirama, Satish Narayana ;
Amgoth, Tarachand .
IEEE INTERNET OF THINGS JOURNAL, 2020, 7 (05) :4317-4328
[7]  
Ahmed E, 2017, IEEE ICC
[8]   Mobile Edge Computing: Opportunities, solutions, and challenges [J].
Ahmed, Ejaz ;
Rehmani, Mubashir Husain .
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE, 2017, 70 :59-63
[9]   SDN-Enabled Adaptive and Reliable Communication in IoT-Fog Environment Using Machine Learning and Multiobjective Optimization [J].
Akbar, Aamir ;
Ibrar, Muhammad ;
Jan, Mian Ahmad ;
Bashir, Ali Kashif ;
Wang, Lei .
IEEE INTERNET OF THINGS JOURNAL, 2021, 8 (05) :3057-3065
[10]  
Akioyamen P., 2018, Medium