Survey of fault management techniques for edge-enabled distributed metaverse applications

被引:1
作者
Shaikh, Shahzaib [1 ]
Jammal, Manar [1 ]
机构
[1] York Univ, Sch Informat Technol, Toronto, ON, Canada
关键词
Metaverse; Edge computing; Fault tolerance; Failure remediation; Distributed systems; Machine learning; PLACEMENT;
D O I
10.1016/j.comnet.2024.110803
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
The metaverse, envisioned as a vast, distributed virtual world, relies on edge computing for low-latency data processing. However, ensuring fault tolerance - the system's ability to handle failures - is critical for a seamless user experience. This paper analyzes existing research on fault tolerance in edge computing over the past six years, specifically focusing on its applicability to the metaverse. We identify common fault types like node failures, communication disruptions, and security issues. The analysis then explores various fault management techniques including proactive monitoring, resource optimization, task scheduling, workload migration, redundancy for service continuity, machine learning for predictive maintenance, and consensus algorithms to guarantee data integrity. While these techniques hold promise, adaptations are necessary to address the metaverse's real-time interaction requirements and low-latency constraints. This paper analyzes existing research and identifies key areas for improvement, providing valuable research guidelines and insights to pave the way for the development of fault management techniques specifically tailored to the metaverse, ultimately contributing to a robust and secure virtual world.
引用
收藏
页数:11
相关论文
共 69 条
[41]  
Samir A, 2019, 2019 FOURTH INTERNATIONAL CONFERENCE ON FOG AND MOBILE EDGE COMPUTING (FMEC), P21, DOI [10.1109/fmec.2019.8795337, 10.1109/FMEC.2019.8795337]
[42]  
Sathiaseelan A., 2017, P 2 WORKSH MIDDL EDG
[43]   The Case for VM-Based Cloudlets in Mobile Computing [J].
Satyanarayanan, Mahadev ;
Bahl, Paramvir ;
Caceres, Ramon ;
Davies, Nigel .
IEEE PERVASIVE COMPUTING, 2009, 8 (04) :14-23
[44]   ReLIEF: A Reinforcement-Learning-Based Real-Time Task Assignment Strategy in Emerging Fault-Tolerant Fog Computing [J].
Siyadatzadeh, Roozbeh ;
Mehrafrooz, Fatemeh ;
Ansari, Mohsen ;
Safaei, Bardia ;
Shafique, Muhammad ;
Henkel, Jorg ;
Ejlali, Alireza .
IEEE INTERNET OF THINGS JOURNAL, 2023, 10 (12) :10752-10763
[45]   Infrastructure Fault Detection and Prediction in Edge Cloud Environments [J].
Soualhia, Mbarka ;
Fu, Chunyan ;
Khomh, Foutse .
SEC'19: PROCEEDINGS OF THE 4TH ACM/IEEE SYMPOSIUM ON EDGE COMPUTING, 2019, :222-235
[46]  
Sowmya R., 2024, 2024 IEEE International Conference for Women in Innovation, Technology & Entrepreneurship (ICWITE), P15, DOI 10.1109/ICWITE59797.2024.10503227
[47]   Tango of Edge and Cloud Execution for Reliability [J].
Suryavansh, Shikhar ;
Bothra, Chandan ;
Chiang, Mung ;
Peng, Chunyi ;
Bagchi, Saurabh .
PROCEEDINGS OF THE 2019 4TH WORKSHOP ON MIDDLEWARE FOR EDGE CLOUDS & CLOUDLETS (MECC '19), 2019, :10-15
[48]   Joint failure recovery, fault prevention, and energy-efficient resource management for real-time SFC in fog-supported SDN [J].
Tajiki, Mohammad M. ;
Shojafar, Mohammad ;
Akbari, Behzad ;
Salsano, Stefano ;
Conti, Mauro ;
Singhal, Mukesh .
COMPUTER NETWORKS, 2019, 162
[49]   Service Placement and User Assignment in Multi-Access Edge Computing with Base-Station Failure [J].
Taka, Haruto ;
He, Fujun ;
Oki, Eiji .
2022 IEEE/ACM 30TH INTERNATIONAL SYMPOSIUM ON QUALITY OF SERVICE (IWQOS), 2022,
[50]   Satisfaction Optimization in Failure-Aware Vehicular Edge Computing [J].
Tang, Chaogang ;
Wu, Huaming ;
Zhu, Chunsheng .
2022 IEEE GLOBAL COMMUNICATIONS CONFERENCE (GLOBECOM 2022), 2022, :5783-5788