Proactive Self-Healing Approaches in Mobile Edge Computing: A Systematic Literature Review

被引:4
作者
Adeniyi, Olusola [1 ]
Sadiq, Ali Safaa [2 ]
Pillai, Prashant [1 ]
Taheir, Mohammed Adam [3 ]
Kaiwartya, Omprakash [2 ]
机构
[1] Univ Wolverhampton, Sch Engn Comp & Math Sci, Wolverhampton WV1 1LY, England
[2] Nottingham Trent Univ, Dept Comp Sci, Clifton Lane, Nottingham NG11 8NS, England
[3] Zalingei Univ, Fac Technol Sci, POB 6, Zalingei, Sudan
关键词
mobile edge computing; proactive self-healing; autonomous computing; fault tolerance; cyber security; RESOURCE-ALLOCATION; OUTAGE DETECTION; NETWORK; MANAGEMENT; DIAGNOSIS; SECURITY; STRATEGY; MODEL; LOCALIZATION; OPTIMIZATION;
D O I
10.3390/computers12030063
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
The widespread use of technology has made communication technology an indispensable part of daily life. However, the present cloud infrastructure is insufficient to meet the industry's growing demands, and multi-access edge computing (MEC) has emerged as a solution by providing real-time computation closer to the data source. Effective management of MEC is essential for providing high-quality services, and proactive self-healing is a promising approach that anticipates and executes remedial operations before faults occur. This paper aims to identify, evaluate, and synthesize studies related to proactive self-healing approaches in MEC environments. The authors conducted a systematic literature review (SLR) using four well-known digital libraries (IEEE Xplore, Web of Science, ProQuest, and Scopus) and one academic search engine (Google Scholar). The review retrieved 920 papers, and 116 primary studies were selected for in-depth analysis. The SLR results are categorized into edge resource management methods and self-healing methods and approaches in MEC. The paper highlights the challenges and open issues in MEC, such as offloading task decisions, resource allocation, and security issues, such as infrastructure and cyber attacks. Finally, the paper suggests future work based on the SLR findings.
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页数:40
相关论文
共 122 条
  • [41] The vision of autonomic computing
    Kephart, JO
    Chess, DM
    [J]. COMPUTER, 2003, 36 (01) : 41 - +
  • [42] Khalil K, 2019, MIDWEST SYMP CIRCUIT, P622, DOI [10.1109/mwscas.2019.8885235, 10.1109/MWSCAS.2019.8885235]
  • [43] Automated diagnosis for UMTS networks using Bayesian network approach
    Khanafer, Rana M.
    Solana, Beatriz
    Triola, Jordi
    Barco, Raquel
    Moltsen, Lars
    Altman, Zwi
    Lazaro, Pedro
    [J]. IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, 2008, 57 (04) : 2451 - 2461
  • [44] Diagnosis Based on Genetic Fuzzy Algorithms for LTE Self-Healing
    Khatib, Emil J.
    Barco, Raquel
    Gomez-Andrades, Ana
    Serrano, Inmaculada
    [J]. IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, 2016, 65 (03) : 1639 - 1651
  • [45] Graph neural network-based virtual network function deployment optimization
    Kim, Hee-Gon
    Park, Suhyun
    Lange, Stanislav
    Lee, Doyoung
    Heo, Dongnyeong
    Choi, Heeyoul
    Yoo, Jae-Hyoung
    Hong, James Won-Ki
    [J]. INTERNATIONAL JOURNAL OF NETWORK MANAGEMENT, 2021, 31 (06)
  • [46] Korhonen T., 2013, IEEE COMMUN MAG, V51, P97
  • [47] Ktari S, 2017, IEEE SYMP COMP COMMU, P1245, DOI 10.1109/ISCC.2017.8024695
  • [48] A Model-based Approach for Self-healing IoT Systems Position Paper
    Kuhn, Franziska
    Hellbruck, Horst
    Fischer, Stefan
    [J]. PROCEEDINGS OF THE 7TH INTERNATIONAL CONFERENCE ON SENSOR NETWORKS (SENSORNETS), 2018, : 135 - 140
  • [49] Kumar Y, 2017, INT WIREL COMMUN, P1090, DOI 10.1109/IWCMC.2017.7986437
  • [50] Deep Reinforcement Learning for Autonomous Internet of Things: Model, Applications and Challenges
    Lei, Lei
    Tan, Yue
    Zheng, Kan
    Liu, Shiwen
    Zhang, Kuan
    Shen, Xuemin
    [J]. IEEE COMMUNICATIONS SURVEYS AND TUTORIALS, 2020, 22 (03): : 1722 - 1760