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Adaptive Threshold Fuzzy C-Means (ATFCM) VMmigration and resource optimization based dynamic scheduling for edge-cloud computing environments

S. Supriya, K. Dhanalakshmi

Abstract


The method of delivery of information technology services has changed according to cloud computing. The latest generation of IoT applications benefits from low latency response offered by edge-cloud computing architecture. The migration procedure suffers when there is insufficient network capacity available. This further increases the difficulty of scheduling and resource monitoring. In this study, (1) Average Migration Time (AMT), Average Energy Consumption (AEC), Average Response Time (ART), and Average Service Level Agreement Violations (SLAV) evaluation parameters are minimised using Mutation Donkey and Smuggler Optimisation (MDSO). The amount of load that they can manage, data centre servers are categorised into four groups using Adaptive Threshold Fuzzy C-Means (ATFCM) clustering: extremely low load, mild load, medium load, maximum load. ATFCM moves thevirtual machine (VM) on maximum loaded or extremely low loaded hosts to very lowly loaded hosts. Utilising host information from Resource Monitoring Service (RMS), Residual Recurrent Neural Network (R2N2). Asynchronous advantage actor critical (A3C) learning is acknowledged for its ability to swiftly adapt to dynamic settings with fewer data, whereas R2N2 for rapid updating of model parameters. When contrasted to modern methods, trials done on practical applications data sets in areas of energy usage, SLA, response time, and running costs.


Keywords


deep reinforcement learning; edge computing; Residual Recurrent Neural Network (R2N2); Asynchronous Advantage Actor-Critic; Mutation Donkey and Smuggler Optimization (MDSO) algorithm

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References


1. Katal A, Dahiya S, Choudhury T. Energy efficiency in cloud computing data centers: a survey on software technologies. Cluster Computing. 2023; 26(3): 1845-1875.

2. Mohiuddin I, Almogren A. Workload aware VM consolidation method in edge/cloud computing for IoT applications.Journal of Parallel and Distributed Computing. 2019;123: 204-214.

3. Skarlat O, Nardelli M, Schulte S, et al. Optimized IoT service placement in the fog. Service Oriented Computing and Applications. 2017; 11(4): 427-443.

4. Pham XQ, Man ND, Tri NDT, et al. A cost-and performance-effective approach for task scheduling based on collaboration between cloud and fog computing. International Journal of Distributed Sensor Networks. 2017; 13(11): 1-16.

5. Mnih V, Badia AP, Mirza M, et al. Asynchronous methods for deep reinforcement learning. Proceedings of the International conference on machine learning. 2016; 1928-1937.

6. Mao H, Alizadeh M, Menache I, Kandula S. Resource management with deep reinforcement learning. Proceedings of the 15th ACM Workshop on Hot Topics in Networks. 2016; 50-56.

7. Aujla GS, Kumar N. MEnSuS: An efficient scheme for energy management with sustainability of cloud data centers in edge–cloud environment.Future Generation Computer Systems. 2018; 86: 1279-1300.

8. Nayyer MZ, Raza I, Hussain SA, et al. LBRO: Load Balancing for Resource Optimization in Edge Computing. IEEE Access. 2022; 10: 97439-97449.

9. Li C, Sun H, Tang H, Luo Y. Adaptive resource allocation based on the billing granularity in edge-cloud architecture.Computer Communications. 2019; 145: 29-42.

10. Zeng D, Gu L, Pan S, et al. Resource management at the network edge: A deep reinforcement learning approach.IEEE Network. 2019; 33(3): 26-33.

11. Tuli S, Ilager S, Ramamohanarao K, Buyya R. Dynamic scheduling for stochastic edge-cloud computing environments using a3c learning and residual recurrent neural networks.IEEE transactions on mobile computing. 2020; 21(3): 940-954.

12. Nabavi S, Wen L, Gill SS, Xu, M. Seagull optimization algorithm based multi-objective VM placement in edge-cloud data centers.Internet of Things and Cyber-Physical Systems. 2023; 3: 28-36.

13. Akbari M, Hassan R, Alizadeh SH. An enhanced genetic algorithm with new operators for task scheduling in heterogeneous computing systems. Engineering Applications of Artificial Intelligence. 2017; 61: 35-46.

14. Kim D, Son J, Seo D, et al. A novel transparent and auditable fog-assisted cloud storage with compensation mechanism. Tsinghua Science and Technology. 2019; 25(1): 28-43.

15. Shamsaldin AS, Rashid TA, Al-Rashid Agha RA, et al. Donkey and smuggler optimization algorithm: A collaborative working approach to path finding.Journal of Computational Design and Engineering. 2019; 6(4): 562-583.

16. Gosain A, Dahiya S. Performance analysis of various fuzzy clustering algorithms: a review.Procedia Computer Science. 2016; 79: 100-111.

17. Pérez-Ortega J, Rey-Figueroa CD, Roblero-Aguilar SS, et al. POFCM: A Parallel Fuzzy Clustering Algorithm for Large Datasets.Mathematics. 2023; 11(8): 1-16.

18. Beloglazov A, Buyya R. Optimal online deterministic algorithms and adaptive heuristics for energy and performance efficient dynamic consolidation of virtual machines in cloud data centers. Concurrency and Computation: Practice and Experience. 2012; 24(13): 1397-1420.

19. Shen S, van Beek V, Iosup A. Statistical characterization of business-critical workloads hosted in cloud datacenters. 15th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing. 2015; 465-474.

20. Available online: http://gwa.ewi.tudelft.nl/datasets/gwa-t-12-bitbrains (accessed on 25 December 2023).

21. Supriya S, Dhanalakshmi K. Residual Recurrent Neural Network (R2N2) and Intelligent Resource Optimization based Dynamic Scheduling for Edge-Cloud Computing Environments.International Journal of Intelligent Systems and Applications in Engineering. 2023; 12(8s): 160–172.




DOI: https://doi.org/10.32629/jai.v7i5.1554

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