Dynamic edge clustering and task scheduling for edge assisted metaverse system in the field of remote work and collaboration

• Published in: Concurrency and computation Vol. 36; no. 18
• Main Authors: Renugadevi, R., Kalaivani, C. T., Arul Edwin Raj, A., Gracewell, Jeffin
• Format: Journal Article
• Language: English
• Published: Hoboken, USA John Wiley & Sons, Inc 15-08-2024; Wiley Subscription Services, Inc
• Subjects: Algorithms; Clustering; Collaboration; Cooperation; Deep learning; Edge computing; Machine learning; metaverse; Performance evaluation; reinforcement learning; Response time (computers); Task scheduling; User experience; Work at home
• ISSN: 1532-0626; 1532-0634
• DOI: 10.1002/cpe.8139

Summary
The metaverse, a future digital world for living, working, learning, and interacting, is rapidly gaining significance, particularly in the domain of remote work and collaboration. This emerging digital landscape demands high‐performance, low‐latency, and scalable services to provide an immersive user experience. The current technology used in metaverse systems has some limitations, which emphasize the importance of adopting emerging technologies like Edge Computing (EC). However, as the number of users and data volume increases, it can impact both system performance and scalability of the edge‐assisted metaverse system. Additionally, the uneven distribution of edge servers can cause inconsistencies and result in high latency. To overcome these challenges, this paper proposes a dynamic edge clustering and task scheduling approach for edge‐assisted metaverse systems (DTAM) in the field of remote work and collaboration. The proposed approach addresses the challenges of high user volume and uneven resource distribution by incorporating dynamic clustering and edge server assistance to improve clustering performance. Furthermore, a Prioritized Experience Replay‐based Deep Q‐learning algorithm with state augmentation (PERDQSA) for task scheduling is introduced to improve sample efficiency and performance. The performance of the proposed DTAM is evaluated against existing techniques, and experimental results demonstrate its significant superiority in terms of specific metrics such as bandwidth, task response time, energy efficiency, and latency. The experiments demonstrated that DTAM outperforms Transformation‐based Edge Computing Deep Q‐Learning (TransEC‐DQL) in several key metrics. Specifically, DTAM achieves 28.5% reduction in latency, 13.7% reduction in response time, and 6.4% improvement in bandwidth compared to TransEC‐DQL. These results signify that DTAM can deliver a significantly enhanced user experience in the metaverse, particularly in the context of remote work and collaboration.