Service Migrations in Multi-Access Edge Computing Using Adaptive Migration Window DOI
Saravanan Velrajan, V. Ceronmani Sharmila

Published: Sept. 14, 2023

5G networks support a Multi-access Edge Computing (MEC) infrastructure to host many heterogeneous applications at the network's edge. MEC helps achieve high-bandwidth and low-latency targets demanded by modern applications. Applications hosted in have Service Level Agreements (SLAs) that guarantee service quality users. migrations are done prevent SLA violations caused application performance or overload conditions. While several existing research on study impact of user mobility, there is paucity studies performed during This paper proposes novel Adaptive Migration Window (ASMWA) algorithm initiate using application's resource utilisation, QoS migration duration. We compared ASMWA with state-of-the-art Exponential Weighted Moving Average (EWMA3) static threshold algorithms. Our simulations show performs better when EWMA3 algorithms minimising degradation MEC.

Language: Английский

Optimization of edge server group collaboration architecture strategy in IoT smart cities application DOI
Fangfang Gou, Jia Wu

Peer-to-Peer Networking and Applications, Journal Year: 2024, Volume and Issue: 17(5), P. 3110 - 3132

Published: June 18, 2024

Language: Английский

Citations

6
Optimizing Network Service Continuity with Quality-Driven Resource Migration DOI Open Access
Chaofan Chen, Yubo Song, Yu Jiang

et al.

Electronics, Journal Year: 2024, Volume and Issue: 13(9), P. 1666 - 1666

Published: April 25, 2024

Despite advances in security technology, it is impractical to entirely prevent intrusion threats. Consequently, developing effective service migration strategies crucial maintaining the continuity of network services. Current initiate process only upon detecting a loss functionality nodes, which increases risk interruptions. Moreover, decision-making has not adequately accounted for alignment between tasks and node resources, thereby amplifying system overload. To address these shortcomings, we introduce Quality-Driven Resource Migration Strategy (QD-RMS). Specifically, QD-RMS initiates at an opportune moment, determined through analysis quality. Subsequently, employs method combining Pareto optimality simulated annealing algorithm identify most suitable migration. This approach guarantees seamless but also ensures optimal resource distribution load balancing. The experiments demonstrate that, comparison with conventional strategies, achieves superior quality approximate 20% increase maximum task capacity. substantiates strategic superiority technological advancement proposed strategy.

Language: Английский

Citations

1
A Hyper-Heuristic Approach for Quality of Experience Aware Service Placement Scheme in 5G Mobile Edge Computing DOI Creative Commons

Safiqul Islam,

M Ahammed,

Nura Alam Siddique

et al.

IEEE Access, Journal Year: 2024, Volume and Issue: 12, P. 72746 - 72765

Published: Jan. 1, 2024

The 5 th Generation (5G) Mobile Edge Computing (MEC) addresses the problem of high end-to-end delay experienced by traditional cloud computing users ensuring fast accessible and reliable resources. However, deployment service instances in MEC resources requires migration due to user mobility. While Proactive Migration at multiple MECs increases users' Quality-of-Experience (QoE), Reactive might reduce cost expense QoE. In this paper, we have developed a framework, that distributes proactively among Nodes depending on movement trajectories ensure faster deliver higher QoE within minimum VNF considering budgets. aforementioned Service Placement (PSP) is formulated as Multi-Objective Linear Programming (MOLP) brings trade-off between these two conflicting objectives, maximizing lowering cost. For large networks, PSP proven be an NP-hard problem. Thus, artificial intelligence-based Hyper-heuristic algorithm for PSP, called HPSP, which can provide high-performing solution polynomial time. HPSP exploits Tabu Search Optimization high-level meta-heuristic selects one three lower-level algorithms-Golden Eagle Optimizer, Sine Cosine Optimization, Jellyfish situation. results numerical analysis describe system outperforms other state-of-the-art works terms QoE, cost, ratio proactive reactive placements.

Language: Английский

Citations

1
Vehicle Edge Computing Network Service Migration Strategy Based on Multi-agent Reinforcement Learning DOI
Zhongli Chen, Jichang Chen, Taoshen Li

et al.

Communications in computer and information science, Journal Year: 2024, Volume and Issue: unknown, P. 473 - 484

Published: Jan. 1, 2024

Language: Английский

Citations

0
Investigation on Dynamic Characteristics of Vibration Isolation System for Impact Resistance of Marine Container DOI
Chaochao Ma, Jin Xu, Zhengrong Jia

et al.

Published: Jan. 1, 2024

Download This Paper Open PDF in Browser Add to My Library Share: Permalink Using these links will ensure access this page indefinitely Copy URL DOI

Language: Английский

Citations

0
Dynamic service prioritization with predicted intervals for QoS-sensitive service migrations in MEC DOI
Saravanan Velrajan, V. Ceronmani Sharmila

Service Oriented Computing and Applications, Journal Year: 2024, Volume and Issue: unknown

Published: May 10, 2024

Language: Английский

Citations

0
Optimizing low-power task scheduling for multiple users and servers in mobile edge computing by the MUMS framework DOI Creative Commons
Guangxu Li, Junke Li

Heliyon, Journal Year: 2024, Volume and Issue: 10(11), P. e31622 - e31622

Published: May 23, 2024

In today's increasingly popular Internet of Things (IoT) technology, its energy consumption issue is also becoming prominent. Currently, the application Mobile Edge Computing (MEC) in IoT important, and scheduling tasks to save imperative. To address aforementioned issues, we propose a Multi-User Multi-Server (MUMS) framework aimed at reducing MEC. The starts with model definition phase, detailing multi-user multi-server systems through four fundamental models: communication, offloading, energy, delay. Then, these models are integrated construct an optimization for MUMS. final step involves utilizing proposed L1_PSO (an enhanced version standard particle swarm algorithm) solve problem. Experimental results demonstrate that, compared typical algorithms, MUMS both reasonable feasible. Notably, algorithm reduces by 4.6% Random Assignment 2.3% conventional Particle Swarm Optimization algorithm.

Language: Английский

Citations

0
Investigation of dynamic characteristics of a vibration isolation system for impact resistance of the marine container DOI
Chaochao Ma, Jin Xu, Zhengrong Jia

et al.

Ships and Offshore Structures, Journal Year: 2024, Volume and Issue: unknown, P. 1 - 13

Published: Sept. 13, 2024

Language: Английский

Citations

0
Service Migrations in Multi-Access Edge Computing Using Adaptive Migration Window DOI
Saravanan Velrajan, V. Ceronmani Sharmila

Published: Sept. 14, 2023

5G networks support a Multi-access Edge Computing (MEC) infrastructure to host many heterogeneous applications at the network's edge. MEC helps achieve high-bandwidth and low-latency targets demanded by modern applications. Applications hosted in have Service Level Agreements (SLAs) that guarantee service quality users. migrations are done prevent SLA violations caused application performance or overload conditions. While several existing research on study impact of user mobility, there is paucity studies performed during This paper proposes novel Adaptive Migration Window (ASMWA) algorithm initiate using application's resource utilisation, QoS migration duration. We compared ASMWA with state-of-the-art Exponential Weighted Moving Average (EWMA3) static threshold algorithms. Our simulations show performs better when EWMA3 algorithms minimising degradation MEC.

Language: Английский

Citations

0