References
- Ebadifard, F., S. M. Babamir. Autonomic Task Scheduling Algorithm for Dynamic Workloads through a Load Balancing Technique for the Cloud-Computing Environment. – Cluster Computing, 2020. DOI: 10.1007/s10586-020-03177-0.
- Devi, K. L., S. Valli. Multi‐Objective Heuristics Algorithm for Dynamic Resource Scheduling in the Cloud Computing Environment. – The Journal of Supercomputing, 2020. DOI: 10.1007/s11227-020-03606-2.
- Kaur, G., A. Bala. Prediction-Based Task Scheduling Approach for Food Plain Application in Cloud Environment. – Computing, Vol. 103, 2021, pp. 895-916. DOI: 10.1007/s00607-021-00936-8.
- Kaur, G., A. Bala. OPSA: An Optimized Prediction-Based Scheduling Approach for Scientific Applications in Cloud Environment. – Cluster Computing, 2021. DOI: 10.1007/s10586-021-03232-4.
- Li, H., Y. Zhao, S. Fang. CSL‐Driven and Energy‐Efcient Resource Scheduling in the Cloud Data Center. – The Journal of Supercomputing. DOI: 10.1007/s11227-019-03036-9.
- Karimunnisa, S., Y. Pachipala. Task Classification and Scheduling Using Enhanced Coot Optimization in Cloud Computing. – International Journal of Intelligent Engineering and Systems, 2023. DOI: 10.22266/ijies2023.1031.43.
- Peng, Z., J. Lin, D. Cui, Q. Li, J. He. A Multi-Objective Trade-Off Framework for Cloud Resource Scheduling Based on the Deep Q-Network Algorithm. – Cluster Computing, 2019. DOI: 10.1007/s10586-019-03042-9.
- Shishira, S. R., A. Kandasamy. A Novel Feature Extraction Model for Large‐ Scale Workload Prediction in Cloud Environment. – SN Computer Science, 2021. DOI: 10.1007/s42979-021-00730-5.
- Tarafdar, A., M. Debnath, S. Khatua, R. K. Das. Energy and Makespan Aware Scheduling of Deadline Sensitive Tasks in the Cloud Environment. – Journal of Grid Computing, 2021. DOI: 10.1007/s10723-021-09548-0.
- Pachipala, Y., D. B. Dasari, V. V. R. M. Rao, P. Bethapudi, T. Srinivasarao. Workload Prioritization and Optimal Task Scheduling in Cloud: Introduction to Hybrid Optimization Algorithm. – Wireless Networks, 2024. DOI: 10.1007/s11276-024-03793-3.
- Nabi, S., M. Ahmed. OG‐RADL: Overall Performance‐Based Resource‐Aware Dynamic Load‐Balancer for Deadline Constrained Cloud Tasks. – The Journal of Supercomputing, 2020. DOI: 10.1007/s11227-020-03544-z.
- Singh, H., A. Bhasin, P. R. Kaveri. QRAS: Efficient Resource Allocation for Task Scheduling in Cloud Computing. – SN Appl. Sci., Vol. 3, 2021, 474. DOI: 10.1007/s42452-021-04489-5.
- Leka, H. L., Z. Fengli, A. T. Kenea, N. W. Hundera, T. G. Tohye, A. T. Tegene. PSO-Based Ensemble Meta-Learning Approach for Cloud Virtual Machine Resource Usage Prediction. – Symmetry, Vol. 15, 2023.
- Karimunnisa, S., Y. Pachipala. An AHP Based Task Scheduling and Optimal Resource Allocation in Cloud Computing. – International Journal of Advanced Computer Science and Applications, 2023. DOI: 10.14569/ijacsa.2023.0140317.
- Menon, S. M., P. Rajarajeswari. A Hybrid Machine Learning Approach for Drug Repositioning. – International Journal of Computer Science and Network Security, Vol. 20, 2020, Issue 12, pp. 217-223.
- Umbarkar, A. J., P. D. Sheth. Crossover Operators in Genetic Algorithms: A Review. – ICTACT Journal on Soft Computing, Vol. 6, October 2015, Issue 1. DOI: 10.21917/ijsc.2015.0150.
- Dickson, M. C., A. S. Bosman, K. M. Malan. Hybridised Loss Functions for Improved Neural Network Generalisation. – arXiv:2204.12244v1 [cs.LG] 26 April 2022.
- Ramadan, A., S. Kamel, M. H. Hassan, T. Khurshid, C. Rahmann. An Improved Bald Eagle Search Algorithm for Parameter Estimation of Different Photovoltaic Models. – Processes 2021, Vol. 9, 1127. DOI: 10.3390/pr9071127.
- Ilankumaran, A., S. J. Narayanan. An Energy-Aware QoS Load Balance Scheduling Using Hybrid GAACO Algorithm for Cloud. – Cybernetics and Information Technologies, Vol. 23, 2023, No 1, pp. 161-177.
- Srivastava, V., K. Dwivedi, A. K. Singh. Cryptocurrency Price Prediction Using Enhanced PSO with Extreme Gradient Boosting Algorithm. – Cybernetics and Information Technologies, Vol. 23, 2023, No 2, pp. 170-187.
- Guliashki, V., L. Kirilov, A. Nuzi. Optimization Models and Strategy Approaches Dealing with Economic Crises, Natural Disasters, and Pandemics – an Overview. – Cybernetics and Information Technologies, Vol. 23, 2023, No 4, pp. 3-25.
- Matoussi, W., T. Hamrouni. A New Temporal Locality-Based Workload Prediction Approach for SaaS Services in a Cloud Environment. – Journal of King Saud University – Computer and Information Sciences, 2021. DOI: 10.1016/j.jksuci.2021.04.008.
- Meyer, V. D., F. Kirchoff, M. L. Da Silva, C. A. F. De Rose. ML-Driven Classification Scheme for Dynamic Interference-Aware Resource Scheduling in Cloud Infrastructures. – Journal of Systems Architecture, Vol. 116, 2021.
- Grzegorowski, M., E. Zdravevski, A. Janusz, P. Lameski, C. Apanowicz, D. Slezak. Cost Optimization for Big Data Workloads Based on Dynamic Scheduling and Cluster-Size Tuning. – Big Data Research, Vol. 25, 2021.
- Cao, M., Y. Li, X. Wen, Y. Zhao, J. Zhu. Energy-Aware Intelligent Scheduling for Deadline-Constrained Workflows in Sustainable Cloud Computing. – Egyptian Informatics Journal, Vol. 24, 2023, Issue 2.
- Bi, J., S. Li, H. Yuan, M. C. Zhou. Integrated Deep Learning Method for Workload and Resource Prediction in Cloud Systems. – Neurocomputing, Vol. 424, 2021, No 1.
- Kumar, J., A. K. Singh, R. Buyya. Self-Directed Learning Based Workload Forecasting Model for Cloud Resource Management. – Information Sciences, Vol. 543, 2021.
- Ji, K., F. Zhang, C. Chi, P. Song, B. Zhou, A. Marahatta, Z. Liu. A Joint Energy Efficiency Optimization Scheme Based on Marginal Cost and Workload Prediction in Data Centers. – Sustainable Computing: Informatics and Systems, Vol. 32, 2021.
- Xiao, Z., B. Wang, X. Li, J. Du. Workload-Driven Coordination between Virtual Machine Allocation and Task Scheduling. – Advances in Parallel and Distributed Computing for Neural Computing, Neural Computing and Applications, 2019. DOI: 10.1007/s00521-019-04022-1.
- Chandrashekar, C., P. Krishnadoss, V. K. Poornachary, B. Ananthakrishnan, K. Rangasamy. HWACOA Scheduler: Hybrid Weighted Ant Colony Optimization Algorithm for Task Scheduling in Cloud Computing. – Applied Sciences, Vol. 13, 2023, No 6, p. 3433. DOI: 10.3390/app13063433.
- Sharma, N., S. Beniwal, P. Garg. Ant Colony Based Optimization Model for QoS-Based Task Scheduling in Cloud Computing Environment. – SSRN Electronic Journal, 2022. DOI: 10.2139/ssrn.4237751.
- Chaudhary, S., V. K. Sharma, R. N. Thakur, A. Rathi, P. Kumar, S. Sharma. Modified Particle Swarm Optimization Based on Aging Leaders and Challengers Model for Task Scheduling in Cloud Computing. – Mathematical Problems in Engineering, Vol. 2023, 2023, No 1. DOI: 10.1155/2023/3916735.
- Hosseinzadeh, M., et al. Improved Butterfly Optimization Algorithm for Data Placement and Scheduling in Edge Computing Environments. – Journal of Grid Computing, Vol. 19, March 2021, No 2, DOI: 10.1007/s10723-021-09556-0.
- Mangalampalli, S., S. K. Swain, V. K. Mangalampalli. Multi Objective Task Scheduling in Cloud Computing Using Cat Swarm Optimization Algorithm. – Arabian Journal for Science and Engineering, Vol. 47, September 2021, No 2, pp. 1821-1830. DOI: 10.1007/s13369-021-06076-7.
- Peta, J,. S. Koppu. An IoT-Based Framework and Ensemble Optimized Deep Maxout Network Model for Breast Cancer Classification. – Electronics, Vol. 11, 2022, 4137. DOI: 10.3390/electronics11244137.
- Dickson, M. C., A. S. Bosman, K. M. Malan. Hybridised Loss Functions for Improved Neural Network Generalisation. – arXiv:2204.12244v1 [cs.LG] 26 April 2022.
- Dehghani, M., T. P. Hubalovsky, P. Trojovsky. Tasmanian Devil Optimization: A New Bio-Inspired Optimization Algorithm for Solving Optimization Algorithm. – Digital Object Identifier, Vol. 10, 2022. DOI: 10.1109/Access.2022.3151641.
- Alsattar, H. A., A. A. Zaidan, B. B. Zaidan. Novelmeta-Heuristic Bald Eagle Search Optimization Algorithm. – Artificial Intelligence Review, Vol. 53, 2020, No 6. DOI: 10.1007/s10462-019-09732-5.
- Ramadan, A., S. Kamel, M. H. Hassan, T. Khurshid, C. Rahmann. An Improved Bald Eagle Search Algorithm for Parameter Estimation of Different Photovoltaic Models. – Processes, Vol. 9, 2021, 1127. DOI: 10.3390/pr9071127.
- Yang, X., J. Liu, Y. Liu, P. Xu, L. Yu, L. Zhu, H. Chen, W. Deng. A Novel Adaptive Sparrow Search Algorithm Based on Chaotic Mapping and T-Distribution Mutation. – Appl. Sci., Vol. 11, 2021, 11192. DOI: 10.3390/app112311192.
- Umbarkar, A. J., P. D. Sheth. Crossover Operators in Genetic Algorithms: A Review. – ICTACT Journal on Soft Computing, Vol. 6, 2015, Issue 1. DOI: 10.21917/ijsc.2015.0150.
- https://research.google/tools/datasets/google-cluster-workload-traces-2019/
