Host Detection and Classification using Support Vector Regression in Cloud Environment
Abstract Having the potential to provide global users with pay-per-use utility-oriented IT services across the Internet, cloud computing has become increasingly popular. These services are provided via the establishment of data centers (DCs) across the world. These data centers are growing increasingly with the growing demand for cloud, leading to massive energy consumption with energy requirement soaring by 63% and inefficient resource utilization. This paper contributes by utilizing a dynamic time series-based prediction support vector regression (SVR) model. This prediction model defines upper and lower limits, based on which the host is classified into four categories: overload, under pressure, normal, and underload. A series of migration strategies have been considered in the case of load imbalance. The proposed mechanism improves the load distribution and minimizes energy consumption and execution time by balancing the host in the data center. Also, it optimizes the execution cost and resource utilization. In the proposed framework, the energy consumption is 0.641kWh, and the execution time is 165.39sec. Experimental results show that the proposed approach outperforms other existing approaches.
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A. El-Moursy, A., Abdelsamea, A., Kamran, R., & Saad, M. (2019). Multi-dimensional regression host utilization algorithm (MDRHU) for host overload detection in cloud computing. Journal of Cloud Computing, 8(1), 1–17. 10.1186/s13677-019-0130-2
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Adhikari, M., & Koley, S. (2018). Cloud Computing: a multi-workflow scheduling algorithm with dynamic reusability. Arabian Journal for Science and Engineering, 43(2), 645–660. 10.1007/s13369-017-2739-0
Arroba, P., Moya, J. M., Ayala, J. L., & Buyya, R. (2017). Dynamic voltage and frequency scaling-aware dynamic consolidation of virtual machines for energy-efficient cloud data centers. Concurrency and Computation: Practice and Experience, 29(10), e4067. 10.1002/cpe.4067
Arunarani, A. R., Manjula, D., & Sugumaran, V. (2019). Task scheduling techniques in cloud computing: A literature survey. Future Generation Computer Systems, 91, 407–415. 10.1016/j.future.2018.09.014
Asghari, A., Sohrabi, M. K., & Yaghmaee, F. (2020). Online scheduling of dependent tasks of Cloud’s workflows to enhance resource utilization and reduce the makespan using multiple reinforcement learning-based agents. Soft Computing, 24(21), 16177–16199. 10.1007/s00500-020-04931-7
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Garg, N., Neeraj, Raj, M., Gupta, I., Kumar, V., & Sinha, G. R. (2022). Energy-efficient scientific workflow scheduling algorithm in the cloud environment. Wireless Communications and Mobile Computing, 2022, 1–12. 10.1155/2022/1637614
Gupta, S. D., Iyer, S., Agarwal, G., Poongodi, M., Algarni, A. D., Aldehim, G., & Raahemifar, K. (2022). Efficient Prioritization and Processor Selection Schemes for HEFT Algorithm: A Makespan Optimizer for Task Scheduling in Cloud Environment. Electronics, 11(16), 2557. 10.3390/electronics11162557
Hosseinzadeh, M., Ghafour, M. Y., Hama, H. K., Vo, B., & Khoshnevis, A. (2020). Multi-objective task and workflow scheduling approach in cloud computing: a comprehensive review. Journal of Grid Computing, 18(3), 327–356. 10.1007/s10723-020-09533-z
Jia, H., Peng, X., & Lang, C. (2021). Remora optimization algorithm. Expert Systems with Applications, 185, 115665. 10.1016/j.eswa.2021.115665
Khaleel, M. I., & Zhu, M. (2016). Energy-efficient task scheduling and consolidation algorithm for workflow jobs in cloud. International Journal of Computational Science and Engineering, 13(3), 268–284. 10.1504/IJCSE.2016.078933
Khan, M. S. A., & Santhosh, R. (2022). Task scheduling in cloud computing using hybrid optimization algorithm. Soft Computing, 26(23), 13069–13079. 10.1007/s00500-021-06488-5
Khoshkholghi, M. A., Derahman, M. N., Abdullah, A., Subramaniam, S., & Othman, М. (2017). Energy-Efficient Algorithms for dynamic virtual machine consolidation in cloud data centers. IEEE Access, 5, 10709–10722. 10.1109/access.2017.2711043
Kruekaew, B., & Kimpan, W. (2022). Multi-objective task scheduling optimization for load balancing in a cloud computing environment using a hybrid artificial bee colony algorithm with reinforcement learning. IEEE Access, 10, 17803–17818. 10.1109/ACCESS.2022.3149955
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Lee, Y. C., & Zomaya, A. Y. (2012). Energy efficient utilization of resources in cloud computing systems. The Journal of Supercomputing, 60, 268–280. 10.1007/s11227-010-0421-3
Li, C., Tang, J., Ma, T., Yang, X., & Luo, Y. (2020). Load balance-based workflow https://doi.org/10.1016/j.jnca.2019.06.006 job scheduling algorithm in a distributed cloud. Journal of Network and Computer Applications, 152, 102518. 10.1016/j.jnca.2019.102518
Li, L., Dong, J., Zuo, D., & Wu, J. (2019). SLA-aware and energy-efficient VM consolidation in Cloud data centers using the robust linear regression prediction model. IEEE Access, 7, 9490–9500. 10.1109/ACCESS.2019.2891567
Magotra, B., Malhotra, D., & Dogra, A. K. (2022). Adaptive Computational Solutions to Energy Efficiency in Cloud Computing Environment Using VM Consolidation. Archives of Computational Methods in Engineering, 30, 1–30. 10.1007/s11831-022-09852-2
Malik, S., Tahir, M., Sardaraz, M., & Alourani, A. (2022). A resource utilization prediction model for cloud data centers using evolutionary algorithms and machine learning techniques. Applied Sciences, 12(4), 2160. 10.3390/app12042160
Marahatta, A., Pirbhulal, S., Zhang, F., Parizi, R. M., Choo, K. K. R., & Liu, Z. (2019). Classification-based and energy-efficient dynamic task scheduling scheme for virtualized cloud data center. IEEE Transactions on Cloud Computing, 9(4), 1376–1390. 10.1109/TCC.2019.2918226
Mishra, S. K., Sahoo, S., Sahoo, B., & Jena, S. K. (2020). Energy-efficient service allocation techniques in the Cloud: A survey. IETE Technical Review, 37(4), 339–352. 10.1080/02564602.2019.1620648
Mohanapriya, N., Kousalya, G., Balakrishnan, P., & Pethuru Raj, C. (2018). Energy efficient workflow scheduling with virtual machine consolidation for green cloud computing. Journal of Intelligent & Fuzzy Systems, 34(3), 1561–1572. 10.3233/JIFS-169451
Nehra, P., & Nagaraju, A. (2022). Host utilization prediction using hybrid kernel-based support vector regression in cloud data centers. Journal of King Saud University – Computer and Information Sciences, 34(8), 6481–6490. 10.1016/j.jksuci.2021.04.011
Panwar, S. S., Rauthan, M. M. S., & Barthwal, V. (2022). A systematic review on effective energy utilization management strategies in cloud data centers. Journal of Cloud Computing, 11(1), 1–29. 10.1186/s13677-022-00368-5
Pirozmand, P., Hosseinabadi, A. A. R., Farrokhzad, M., Sadeghilalimi, M., Mirkamali, S. S., & Slowik, A. (2021). Multi-objective hybrid genetic algorithm for task scheduling problem in cloud computing. Neural computing and applications, 33(19), 13075–13088. (SCIE). 10.1007/s00521-021-06002-w
Ranjan, R., Thakur, I. S., Aujla, G. S., Kumar, N., & Zomaya, A. Y. (2020). Energy-Efficient workflow scheduling using Container-Based virtualization in Software-Defined data centers. IEEE Transactions on Industrial Informatics, 16(12), 7646–7657. 10.1109/tii.2020.2985030
Safari, M., & Khorsand, R. (2018). Energy-aware scheduling algorithm for time-constrained workflow tasks in DVFS-enabled cloud environment. Simulation Modelling Practice and Theory, 87, 311–326. 10.1016/j.simpat.2018.07.006
Sardaraz, M., & Tahir, M. (2019). A hybrid algorithm for scheduling scientific workflows in cloud computing. IEEE Access, 7, 186137–186146. 10.1109/access.2019.2961106
Singh, S., Kumar, R., & Rao, U. P. (2022). Multi-Objective adaptive Manta-Ray foraging optimization for workflow scheduling with selected virtual machines using Time-Series-Based prediction. International Journal of Software Science and Computational Intelligence, 14(1), 1–25. 10.4018/ijssci.312559
Stavrinides, G. L., & Karatza, H. D. (2019). An energy-efficient, QOS-aware and cost-effective scheduling approach for real-time workflow applications in cloud computing systems utilizing DVFS and approximate computations. Future Generation Computer Systems, 96, 216–226. 10.1016/j.future.2019.02.019
Wang, S., Li, K., Mei, J., Xiao, G., & Li, K. (2017). A reliability-aware task scheduling algorithm based on replication on heterogeneous computing systems. Journal of Grid Computing, 15(1), 23–39. 10.1007/s10723-016-9386-7
Zhu, Z., Peng, J., Zhou, Z., Zhang, X., & Huang, Z. (2016). PSO-SVR-Based Resource Demand Prediction in Cloud Computing. Journal of Advanced Computational Intelligence and Intelligent Informatics, 20(2), 324–331. 10.20965/jaciii.2016.p0324
Zolfaghari, R., Sahafi, A., Rahmani, A. M., & Rezaei, R. (2021). Application of virtual machine consolidation in cloud computing systems. Sustainable Computing: Informatics and Systems, 30, 100524. (19). 10.1016/j.suscom.2021.100524
Abdullahi, M., Ngadi, M. A., & Abdulhamid, S. M. (2016). Symbiotic organism search optimization based task scheduling in cloud computing environment. Future Generation Computer Systems, 56, 640–650. 10.1016/j.future.2015.08.006
Abualigah, L., & Alkhrabsheh, M. (2022). Amended hybrid multi-verse optimizer with genetic algorithm for solving task scheduling problem in cloud computing. The Journal of Supercomputing, 78(1), 740–765. 10.1007/s11227-021-03915-0
Adhikari, M., & Koley, S. (2018). Cloud Computing: a multi-workflow scheduling algorithm with dynamic reusability. Arabian Journal for Science and Engineering, 43(2), 645–660. 10.1007/s13369-017-2739-0
Arroba, P., Moya, J. M., Ayala, J. L., & Buyya, R. (2017). Dynamic voltage and frequency scaling-aware dynamic consolidation of virtual machines for energy-efficient cloud data centers. Concurrency and Computation: Practice and Experience, 29(10), e4067. 10.1002/cpe.4067
Arunarani, A. R., Manjula, D., & Sugumaran, V. (2019). Task scheduling techniques in cloud computing: A literature survey. Future Generation Computer Systems, 91, 407–415. 10.1016/j.future.2018.09.014
Asghari, A., Sohrabi, M. K., & Yaghmaee, F. (2020). Online scheduling of dependent tasks of Cloud’s workflows to enhance resource utilization and reduce the makespan using multiple reinforcement learning-based agents. Soft Computing, 24(21), 16177–16199. 10.1007/s00500-020-04931-7
Bal, P. K., Mohapatra, S. K., Das, T. K., Srinivasan, K., & Hu, Y. C. (2022). A joint resource allocation, security with efficient task scheduling in cloud computing using hybrid machine learning techniques. Sensors, 22(3), 1242. 10.3390/s22031242
Garg, N., Neeraj, Raj, M., Gupta, I., Kumar, V., & Sinha, G. R. (2022). Energy-efficient scientific workflow scheduling algorithm in the cloud environment. Wireless Communications and Mobile Computing, 2022, 1–12. 10.1155/2022/1637614
Gupta, S. D., Iyer, S., Agarwal, G., Poongodi, M., Algarni, A. D., Aldehim, G., & Raahemifar, K. (2022). Efficient Prioritization and Processor Selection Schemes for HEFT Algorithm: A Makespan Optimizer for Task Scheduling in Cloud Environment. Electronics, 11(16), 2557. 10.3390/electronics11162557
Hosseinzadeh, M., Ghafour, M. Y., Hama, H. K., Vo, B., & Khoshnevis, A. (2020). Multi-objective task and workflow scheduling approach in cloud computing: a comprehensive review. Journal of Grid Computing, 18(3), 327–356. 10.1007/s10723-020-09533-z
Jia, H., Peng, X., & Lang, C. (2021). Remora optimization algorithm. Expert Systems with Applications, 185, 115665. 10.1016/j.eswa.2021.115665
Khaleel, M. I., & Zhu, M. (2016). Energy-efficient task scheduling and consolidation algorithm for workflow jobs in cloud. International Journal of Computational Science and Engineering, 13(3), 268–284. 10.1504/IJCSE.2016.078933
Khan, M. S. A., & Santhosh, R. (2022). Task scheduling in cloud computing using hybrid optimization algorithm. Soft Computing, 26(23), 13069–13079. 10.1007/s00500-021-06488-5
Khoshkholghi, M. A., Derahman, M. N., Abdullah, A., Subramaniam, S., & Othman, М. (2017). Energy-Efficient Algorithms for dynamic virtual machine consolidation in cloud data centers. IEEE Access, 5, 10709–10722. 10.1109/access.2017.2711043
Kruekaew, B., & Kimpan, W. (2022). Multi-objective task scheduling optimization for load balancing in a cloud computing environment using a hybrid artificial bee colony algorithm with reinforcement learning. IEEE Access, 10, 17803–17818. 10.1109/ACCESS.2022.3149955
Kumar, M., Sharma, S. C., Goel, A., & Singh, S. P. (2019). A comprehensive survey for scheduling techniques in cloud computing. Journal of Network and Computer Applications, 143, 1–33. 10.1016/j.jnca.2019.06.006
Lee, Y. C., & Zomaya, A. Y. (2012). Energy efficient utilization of resources in cloud computing systems. The Journal of Supercomputing, 60, 268–280. 10.1007/s11227-010-0421-3
Li, C., Tang, J., Ma, T., Yang, X., & Luo, Y. (2020). Load balance-based workflow https://doi.org/10.1016/j.jnca.2019.06.006 job scheduling algorithm in a distributed cloud. Journal of Network and Computer Applications, 152, 102518. 10.1016/j.jnca.2019.102518
Li, L., Dong, J., Zuo, D., & Wu, J. (2019). SLA-aware and energy-efficient VM consolidation in Cloud data centers using the robust linear regression prediction model. IEEE Access, 7, 9490–9500. 10.1109/ACCESS.2019.2891567
Magotra, B., Malhotra, D., & Dogra, A. K. (2022). Adaptive Computational Solutions to Energy Efficiency in Cloud Computing Environment Using VM Consolidation. Archives of Computational Methods in Engineering, 30, 1–30. 10.1007/s11831-022-09852-2
Malik, S., Tahir, M., Sardaraz, M., & Alourani, A. (2022). A resource utilization prediction model for cloud data centers using evolutionary algorithms and machine learning techniques. Applied Sciences, 12(4), 2160. 10.3390/app12042160
Marahatta, A., Pirbhulal, S., Zhang, F., Parizi, R. M., Choo, K. K. R., & Liu, Z. (2019). Classification-based and energy-efficient dynamic task scheduling scheme for virtualized cloud data center. IEEE Transactions on Cloud Computing, 9(4), 1376–1390. 10.1109/TCC.2019.2918226
Mishra, S. K., Sahoo, S., Sahoo, B., & Jena, S. K. (2020). Energy-efficient service allocation techniques in the Cloud: A survey. IETE Technical Review, 37(4), 339–352. 10.1080/02564602.2019.1620648
Mohanapriya, N., Kousalya, G., Balakrishnan, P., & Pethuru Raj, C. (2018). Energy efficient workflow scheduling with virtual machine consolidation for green cloud computing. Journal of Intelligent & Fuzzy Systems, 34(3), 1561–1572. 10.3233/JIFS-169451
Nehra, P., & Nagaraju, A. (2022). Host utilization prediction using hybrid kernel-based support vector regression in cloud data centers. Journal of King Saud University – Computer and Information Sciences, 34(8), 6481–6490. 10.1016/j.jksuci.2021.04.011
Panwar, S. S., Rauthan, M. M. S., & Barthwal, V. (2022). A systematic review on effective energy utilization management strategies in cloud data centers. Journal of Cloud Computing, 11(1), 1–29. 10.1186/s13677-022-00368-5
Pirozmand, P., Hosseinabadi, A. A. R., Farrokhzad, M., Sadeghilalimi, M., Mirkamali, S. S., & Slowik, A. (2021). Multi-objective hybrid genetic algorithm for task scheduling problem in cloud computing. Neural computing and applications, 33(19), 13075–13088. (SCIE). 10.1007/s00521-021-06002-w
Ranjan, R., Thakur, I. S., Aujla, G. S., Kumar, N., & Zomaya, A. Y. (2020). Energy-Efficient workflow scheduling using Container-Based virtualization in Software-Defined data centers. IEEE Transactions on Industrial Informatics, 16(12), 7646–7657. 10.1109/tii.2020.2985030
Safari, M., & Khorsand, R. (2018). Energy-aware scheduling algorithm for time-constrained workflow tasks in DVFS-enabled cloud environment. Simulation Modelling Practice and Theory, 87, 311–326. 10.1016/j.simpat.2018.07.006
Sardaraz, M., & Tahir, M. (2019). A hybrid algorithm for scheduling scientific workflows in cloud computing. IEEE Access, 7, 186137–186146. 10.1109/access.2019.2961106
Singh, S., Kumar, R., & Rao, U. P. (2022). Multi-Objective adaptive Manta-Ray foraging optimization for workflow scheduling with selected virtual machines using Time-Series-Based prediction. International Journal of Software Science and Computational Intelligence, 14(1), 1–25. 10.4018/ijssci.312559
Stavrinides, G. L., & Karatza, H. D. (2019). An energy-efficient, QOS-aware and cost-effective scheduling approach for real-time workflow applications in cloud computing systems utilizing DVFS and approximate computations. Future Generation Computer Systems, 96, 216–226. 10.1016/j.future.2019.02.019
Wang, S., Li, K., Mei, J., Xiao, G., & Li, K. (2017). A reliability-aware task scheduling algorithm based on replication on heterogeneous computing systems. Journal of Grid Computing, 15(1), 23–39. 10.1007/s10723-016-9386-7
Zhu, Z., Peng, J., Zhou, Z., Zhang, X., & Huang, Z. (2016). PSO-SVR-Based Resource Demand Prediction in Cloud Computing. Journal of Advanced Computational Intelligence and Intelligent Informatics, 20(2), 324–331. 10.20965/jaciii.2016.p0324
Zolfaghari, R., Sahafi, A., Rahmani, A. M., & Rezaei, R. (2021). Application of virtual machine consolidation in cloud computing systems. Sustainable Computing: Informatics and Systems, 30, 100524. (19). 10.1016/j.suscom.2021.100524
Srivastava, V., & Kumar, R. (2023). Host Detection and Classification using Support Vector Regression in Cloud Environment. ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal, 12(1), e31485. https://doi.org/10.14201/adcaij.31485
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