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FAULT TOLERANCE USING SELF-HEALING SLA AND LOAD BALANCED DYNAMIC RESOURCE PROVISIONING IN CLOUD COMPUTING

(Received: 13-May-2021, Revised: 22-May-2021 , Accepted: 24-May-2021)
Over the internet, application efficiency management has recently emerged as an essential service cloud computing. The Cloud Service Provider (CSP) gives various cloud services based on pay per use, which requires efficient monitoring and measuring of services delivered for management of Quality of Service (QoS) through the Internet of Things (IoT) and therefore needs to fulfil the Service Level Agreements (SLAs). However, avoiding SLA violations and ensuring a user’s dynamic demands as per QoS fulfilment are challenging in cloud computing while delivering dedicated cloud services. Cloud environment intricacy, heterogeneity and dynamism are expanding quickly, making cloud frameworks unmanageable and unreliable. Cloud systems need self-management of services to overcome these issues. Therefore, there is a need to develop a resource-provisioning scheme that automatically fulfils cloud user’s QoS requirements, thus helping the CSP accomplish the SLAs and avoid SLA violations. This paper presents a prediction-based resource management technique called Predictive Cloud Computing Systems (PCCSs). Focus is on the self-healing-based prediction that handles unexpected failures and self-configuration-based prediction of resources for applications. The Predictive Cloud Computing System (PCCS) performance is evaluated in the cloud simulator. The simulation results revealed that Predictive Cloud Computing Systems (PCCSs) achieve better results than existing techniques, in terms of execution time, cost-effectiveness, resource conflict and SLA breach while delivering reliable services.

[1] A. Quiroz, H. Kim, M. Parashar, N. Gnanasambandam and N. Sharma, "Towards Autonomic Workload Provisioning for Enterprise Grids and Clouds," Proc. of the 10th IEEE/ACM International Conference on Grid Computing, pp. 50-57, Banff, AB, Canada, Oct. 2009.

[2] T. Lorido-Botran, J. Miguel-Alonso and J. A. Lozano, "A Review of Auto-scaling Techniques for Elastic Applications in Cloud Environments," Journal of Grid Computing, vol. 12, no. 4, pp. 559-592, Oct. 2014.

[3] Md. Toukir Imam, S. F. Miskhat, R. M. Rahman and M. A. Amin, "Neural Network and Regression- based Processor Load Prediction for Efficient Scaling of Grid and Cloud Resources," Proc. of the 14th IEEE International Conference on Computer and Information Technology (ICCIT 2011), pp. 333-338, Dhaka, Bangladesh, Dec. 2011.

[4] Z. Zhou, J. Abawajy, M. Chowdhury, Z. Hu, K. Li, H. Cheng, A. A. Alelaiwi and F.-M. Li, "Minimizing SLA Violation and Power Consumption in Cloud Data Centers Using Adaptive Energy-aware Algorithms," Future Generation Computer Systems, vol. 86, pp. 836-850, 2018.

[5] J. Zhu, P. He, Z. Zheng and M. R. Lyu, "Online QoS Prediction for Runtime Service Adaptation via Adaptive Matrix Factorization," IEEE Transactions on Parallel and Distributed Systems, vol. 28, no. 10, pp. 2911-2924, Oct. 2017.

[6] Shalu and D. Singh, "Swarm Intelligence Based Virtual Machine Migration Techniques in Cloud Computing," Proc. of the International Conference on Computation, Automation and Knowledge Management (ICCAKM), pp. 120-124, Dubai, United Arab Emirates, 2020.

[7] A. M. R. AlSobeh, S. AlShattnawi, A. Jarrah and M. M. Hammad, "WEAVESIM: A Scalable and Reusable Cloud Simulation Framework Leveraging Aspect-oriented Programming," Jordanian Journal of Computers and Information Technology (JJCIT), vol. 06, no. 02, pp. 182-201, June 2020.

[8] R. Yadav, W. Zhang, K. Li et al., "Managing Overloaded Hosts for Energy-efficiency in Cloud Data Centers," Cluster Computing, vol. 2021, DOI: 10.1007/s10586-020-03182-3, Feb. 2021.

[9] D. A. Shafiq, N. Z. Jhanjhi, A. Abdullah and M. A. Alzain, "A Load Balancing Algorithm for the Data Centres to Optimize Cloud Computing Applications," IEEE Access, vol. 9, pp. 41731-41744, 2021.

[10] S. K. Pande, S. K. Panda, S. Das, K. S. Sahoo, A. K. Luhach et al., "A Resource Management Algorithm for Virtual Machine Migration in Vehicular Cloud Computing," Computers, Materials & Continua, vol. 67, no.2, pp. 2647–2663, 2021.

[11] M. A. Shahid, N. Islam, M. M. Alam, M. M. Su’ud and S. Musa, "A Comprehensive Study of Load Balancing Approaches in the Cloud Computing Environment and a Novel Fault Tolerance Approach," IEEE Access, vol. 8, pp. 130500-130526, 2020.

[12] Z. Chen, K. Lin, B. Lin, X. Chen, X. Zheng and C. Rong, "Adaptive Resource Allocation and Consolidation for Scientific Workflow Scheduling in Multi-cloud Environments," IEEE Access, vol. 8, pp. 190173-190183, 2020.

[13] B. Gul et al., "CPU and RAM Energy-based SLA-aware Workload Consolidation Techniques for Clouds," IEEE Access, vol. 8, pp. 62990-63003, 2020.

[14] R. Yadav, W. Zhang, K. Li et al., "An Adaptive Heuristic for Managing Energy Consumption and Overloaded Hosts in a Cloud Data Center," Wireless Networks, vol. 26, pp. 1905–1919, April 2020.

[15] W. Dargie, "Estimation of the Cost of VM Migration," Proc. of the 23rd IEEE International Conference on Computer Communication and Networks (ICCCN), pp. 1-8, Shanghai, China, 2014.

[16] S. Singh, I. Chana, M. Singh et al., "SOCCER: Self-optimization of Energy-efficient Cloud Resources," Cluster Computing, vol. 19, no. 4, pp. 1787–1800, Sep. 2016.

[17] M. Sohani and S. C. Jain, "A Predictive Priority-based Dynamic Resource Provisioning Scheme with Load Balancing in Heterogeneous Cloud Computing," IEEE Access, vol. 9, pp. 62653-62664, April 2021.

[18] F. Yao, C. Pu and Z. Zhang, "Task Duplication-based Scheduling Algorithm for Budget-constrained Workflows in Cloud Computing," IEEE Access, vol. 9, pp. 37262-37272, 2021.

[19] H. M. Khan, G. Chan and F. Chua, "An Adaptive Monitoring Framework for Ensuring Accountability and Quality of Services in Cloud Computing," Proc. of the International Conference on Information Networking (ICOIN), pp. 249-253, Kota Kinabalu, Malaysia, 2016.

[20] R. Latif, S. H. Afzaal and S. Latif, "A Novel Cloud Management Framework for Trust Establishment and Evaluation in a Federated Cloud Environment," The Journal of Supercomputing, vol. 2021, DOI: 10.1007/s11227-021-03775-8, April 2021.

[21] A. Mosallanejad, R. Atan, M. Azmi Murad and R. Abdullah, "A Hierarchical Self-healing SLA for Cloud Computing," International Journal of Digital Information and Wireless Communications (IJDIWC), vol. 4, no. 1, pp. 43-52, 2014.

[22] S. S. Gill, I. Chana, M. Singh and R. Buyya, "RADAR: Self-configuring and Self-healing in Resource Management for Enhancing Quality of Cloud Services," Concurrency and Computation: Practice and Experience, vol. 31, no. 1, DOI: 10.1002/cpe.4834, Aug. 2018.

[23] S. Banerjee, S. Roy and S. Khatua, "Efficient Resource Utilization Using Multi-step-ahead Workload Prediction Technique in Cloud," The Journal of Supercomputing, vol. 2021, DOI: 10.1007/s11227-021- 03701-y, March 2021.

[24] R. Yadav, W. Zhang, O. Kaiwartya, P. R. Singh, I. A. Elgendy and Y. Tian, "Adaptive Energy-aware Algorithms for Minimizing Energy Consumption and SLA Violation in Cloud Computing," IEEE Access, vol. 6, pp. 55923-55936, 2018.

[25] S. Sotiriadis, N. Bessis and R. Buyya, "Self-managed Virtual Machine Scheduling in Cloud Systems," Information Sciences, vol. 433-434, pp. 381–400, 2018.

[26] A. Paya and D. C. Marinescu, "Energy-aware Load Balancing and Application Scaling for the Cloud Ecosystem," IEEE Transactions on Cloud Computing, vol. 5, no. 1, pp. 15-27, 2017.

[27] I. Odun-Ayo, B. Udemezue and A. Kilanko, "Cloud Service Level Agreements and Resource Management", Advances in Science, Technology and Engineering Systems Journal, vol. 4, no. 2, pp. 228- 236, 2019.

[28] R. Yadav, W. Zhang, H. Chen and T. Guo, "MuMs: Energy-aware VM Selection Scheme for Cloud Data Center," Proc. of the 28th IEEE International Workshop on Database and Expert Systems Applications (DEXA), pp. 132-136, Lyon, France, 2017.

[29] M. Dabbagh, B. Hamdaoui, M. Guizani and A. Rayes, "Toward Energy-efficient Cloud Computing: Prediction, Consolidation and Over-commitment," IEEE Network, vol. 29, no. 2, pp. 56-61, 2015.

[30] E. Torre, J. J. Durillo, V. de Maio, P. Agrawal, S. Benedict, N. Saurabh and R. Prodan, "A Dynamic Evolutionary Multi-objective Virtual Machine Placement Heuristic for Cloud Data Centers," Information and Software Technology, vol. 128, DOI: 10.1016/j.infsof.2020.106390, 2020.

[31] F. Farahnakian, T. Pahikkala, P. Liljeberg, J. Plosila, N. T. Hieu and H. Tenhunen, "Energy-aware VM Consolidation in Cloud Data Centers Using Utilization Prediction Model," IEEE Transactions on Cloud Computing, vol. 7, no. 2, pp. 524-536, 2019.

[32] D. Abdulkareem Shafiq, N. Z. Jhanjhi and A. Abdullah, "Load Balancing Techniques in Cloud Computing Environment: A Review," Journal of King Saud University - Computer and Information Sciences, DOI: 10.1016/j.jksuci.2021.02.007, 2021.

[33] M. Balaji, Ch. Aswani Kumar and G. Subrahmanya V. R. K. Rao, "Predictive Cloud Resource Management Framework for Enterprise Workloads," Journal of King Saud University - Computer and Information Sciences, vol. 30, no. 3, pp. 404-415, 2018.

[34] F. Ebadifard and S. M. Babamir, "Autonomic Task Scheduling Algorithm for Dynamic Workloads through a Load Balancing Technique for the Cloud-computing Environment," Cluster Computing, vol. 24, pp. 1075-1101, June 2021.

[35] N. Chaurasia, M. Kumar, R. Chaudhry et al., "Comprehensive Survey on Energy-aware Server Consolidation Techniques in Cloud Computing," The Journal of Supercomputing, vol. 2021, DOI: 10.1007/s11227-021-03760-1, March 2021.

[36] B. K. Dewangan, A., M., V. Agarwal and A. Pasricha, "Energy-aware Autonomic Resource Scheduling Framework for Cloud," International Journal of Mathematical, Engineering and Management Sciences, vol. 4, no. 1, pp. 41-55, 2019.

[37] A. A. Hassan, B. M. Bai and T. J. Gandomani, "An Integrated Model for Secure-on-Demand Resource Provisioning Based on Service Level Agreement (SLA) in Cloud Computing," Journal of Theoretical and Applied Information Technology, vol. 65, no. 2, July 2014.

[38] M. Sohani and S. C. Jain, "State-of-the-art Survey on Cloud Computing Resource Scheduling Approaches," Proc. of Ambient Communications and Computer Systems, Part of the Advances in Intelligent Systems and Computing Book Series, vol. 696, pp. 629-639, March 2018.

[39] W. Lin, J. Z. Wang, C. Liang and D. Qi, "A Threshold-based Dynamic Resource Allocation Scheme for Cloud Computing," Procedia Engineering, vol. 23, pp. 695-703, 2011.

[40] S. S. Gill, I. Chana, M. Singh et al., "CHOPPER: An Intelligent QoS-aware Autonomic Resource Management Approach for Cloud Computing," Cluster Computing, vol. 21, pp. 1203–1241, 2018.

[41] H. Alhussian et al., "Investigating the Schedulability of Periodic Real-time Tasks in Virtualized Cloud Environment," IEEE Access, vol. 7, pp. 29533-29542, 2019.

[42] M. Azaiez and W. Chainbi, "A Multi-agent System Architecture for Self-healing Cloud Infrastructure," Proceedings of the International Conference on Internet of Things and Cloud Computing (ICC’16), pp. 1-6, DOI: 10.1145/2896387.2896392, March 2016.

[43] S. Talwani and I. Chana, "Fault Tolerance Techniques for Scientific Applications in Cloud," Proc. of the 2nd International Conference on Telecommunication and Networks (TEL-NET), pp. 1-5, Noida, India, 2017.

[44] R. N. Calheiros, R. Ranjan, A. Beloglazov, C. A. F. De Rose and R. Buyya, "CloudSim: A Toolkit for Modeling and Simulation of Cloud Computing Environments and Evaluation of Resource Provisioning Algorithms," Software – Practice and Experience, vol. 41, no. 1, pp. 23–50, August 2010.