International Journal of Computer Networks and Applications

Open Access Open Access  Restricted Access Subscription Access

Amended Hybrid Scheduling for Cloud Computing with Real-Time Reliability Forecasting


Affiliations
1 Department of Computer Science, Vellalar College for Women, Erode, Tamil Nadu., India
 

Cloud computing has emerged as the feasible paradigm to satisfy the computing requirements of high-performance applications by an ideal distribution of tasks to resources. But, it is problematic when attaining multiple scheduling objectives such as throughput, makespan, and resource use. To resolve this problem, many Task Scheduling Algorithms (TSAs) are recently developed using single or multi-objective metaheuristic strategies. Amongst, the TS based on a Multi-objective Grey Wolf Optimizer (TSMGWO) handles multiple objectives to discover ideal tasks and assign resources to the tasks. However, it only maximizes the resource use and throughput when reducing the makespan, whereas it is also crucial to optimize other parameters like the utilization of the memory, and bandwidth. Hence, this article proposes a hybrid TSA depending on the linear matching method and backfilling, which uses the memory and bandwidth requirements for effective TS. Initially, a Long Short-Term Memory (LSTM) network is adopted as a meta-learner to predict the task runtime reliability. Then, the tasks are divided into predictable and unpredictable queues. The tasks with higher expected runtime are scheduled by a plan-based scheduling approach based on the Tuna Swarm Optimization (TSO). The remaining tasks are backfilled by the VIKOR technique. To reduce resource use, a particular fraction of CPU cores is kept for backfilling, which is modified dynamically depending on the Resource Use Ratio (RUR) of predictable tasks among freshly submitted tasks. Finally, a general simulation reveals that the proposed algorithm outperforms the earlier metaheuristic, plan-based, and backfilling TSAs.

Keywords

Cloud Computing, Task Scheduling, TSMGWO, Meta-Learning, LSTM, Plan-Based Scheduling, Tuna Swarm Optimization, Backfilling, Vikor.
User
Notifications
Font Size

  • Schleier-Smith, J., Sreekanti, V., Khandelwal, A., Carreira, J., Yadwadkar, N. J., Popa, R. A., & Patterson, D. A. (2021). What serverless computing is and should become: the next phase of cloud computing. Communications of the ACM, 64(5), 76-84.

  • Bello, S. A., Oyedele, L. O., Akinade, O. O., Bilal, M., Delgado, J. M. D., Akanbi, L. A., ... & Owolabi, H. A. (2021). Cloud computing in construction industry: use cases, benefits and challenges. Automation in Construction, 122, 1-18.

  • Atieh, A. T. (2021). The next generation cloud technologies: a review on distributed cloud, fog and edge computing and their opportunities and challenges. ResearchBerg Review of Science and Technology, 1(1), 1-15.

  • Shafiq, D. A., Jhanjhi, N. Z., Abdullah, A., & Alzain, M. A. (2021). A load balancing algorithm for the data centres to optimize cloud computing applications. IEEE Access, 9, 41731-41744.

  • Mishra, S. K., Sahoo, B., & Parida, P. P. (2020). Load balancing in cloud computing: a big picture. Journal of King Saud University-Computer and Information Sciences, 32(2), 149-158.

  • Imran, M., Ibrahim, M., Din, M. S. U., Rehman, M. A. U., & Kim, B. S. (2022). Live virtual machine migration: a survey, research challenges, and future directions. Computers and Electrical Engineering, 103, 1-18.

  • Houssein, E. H., Gad, A. G., Wazery, Y. M., & Suganthan, P. N. (2021). Task scheduling in cloud computing based on meta-heuristics: review, taxonomy, open challenges, and future trends. Swarm and Evolutionary Computation, 62, 1-41.

  • Bittencourt, L. F., Goldman, A., Madeira, E. R., da Fonseca, N. L., & Sakellariou, R. (2018). Scheduling in distributed systems: a cloud computing perspective. Computer Science Review, 30, 31-54.

  • 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.

  • Jamil, B., Ijaz, H., Shojafar, M., Munir, K., & Buyya, R. (2022). Resource allocation and task scheduling in fog computing and internet of everything environments: a taxonomy, review, and future directions. ACM Computing Surveys, 10(1), 1-35.

  • Murad, S. A., Muzahid, A. J. M., Azmi, Z. R. M., Hoque, M. I., & Kowsher, M. (2022). A review on job scheduling technique in cloud computing and priority rule based intelligent framework. Journal of King Saud University-Computer and Information Sciences, 34, 2309-2331.

  • Al-Arasi, R., & Saif, A. (2020). Task scheduling in cloud computing based on metaheuristic techniques: a review paper. EAI Endorsed Transactions on Cloud Systems, 6(17), 1-19.

  • Alsadie, D. (2021). TSMGWO: optimizing task schedule using multi-objectives grey Wolf optimizer for cloud data centers. IEEE Access, 9, 37707-37725.

  • Abd Elaziz, M., Xiong, S., Jayasena, K. P. N., & Li, L. (2019). Task scheduling in cloud computing based on hybrid moth search algorithm and differential evolution. Knowledge-Based Systems, 169, 39-52.

  • Xu, J., Hao, Z., Zhang, R., & Sun, X. (2019). A method based on the combination of laxity and ant colony system for cloud-fog task scheduling. IEEE Access, 7, 116218-116226.

  • Chen, X., Cheng, L., Liu, C., Liu, Q., Liu, J., Mao, Y., & Murphy, J. (2020). A WOA-based optimization approach for task scheduling in cloud computing systems. IEEE Systems Journal, 14(3), 3117-3128.

  • Jia, L., Li, K., & Shi, X. (2021). Cloud computing task scheduling model based on improved whale optimization algorithm. Wireless Communications and Mobile Computing, 2021, 1-13.

  • Wang, Y., & Zuo, X. (2021). An effective cloud workflow scheduling approach combining PSO and idle time slot-aware rules. IEEE/CAA Journal of Automatica Sinica, 8(5), 1079-1094.

  • Dubey, K., & Sharma, S. C. (2021). A novel multi-objective CR-PSO task scheduling algorithm with deadline constraint in cloud computing. Sustainable Computing: Informatics and Systems, 32, 1-20.

  • Ajmal, M. S., Iqbal, Z., Khan, F. Z., Ahmad, M., Ahmad, I., & Gupta, B. B. (2021). Hybrid ant genetic algorithm for efficient task scheduling in cloud data centers. Computers and Electrical Engineering, 95, 1-15.

  • Calzarossa, M. C., Della Vedova, M. L., Massari, L., Nebbione, G., & Tessera, D. (2021). Multi-objective optimization of deadline and budget-aware workflow scheduling in uncertain clouds. IEEE Access, 9, 89891-89905.

  • Kumar, M. S., Tomar, A., & Jana, P. K. (2021). Multi-objective workflow scheduling scheme: a multi-criteria decision making approach. Journal of Ambient Intelligence and Humanized Computing, 12(12), 10789-10808.

  • Oudaa, T., Gharsellaoui, H., & Ahmed, S. B. (2021). An agent-based model for resource provisioning and task scheduling in cloud computing using DRL. Procedia Computer Science, 192, 3795-3804.

  • Sharma, N., & Garg, P. (2022). Ant colony based optimization model for QoS-based task scheduling in cloud computing environment. Measurement: Sensors, 24, 1-9.

  • Mahmoud, H., Thabet, M., Khafagy, M. H., & Omara, F. A. (2022). Multiobjective task scheduling in cloud environment using decision tree algorithm. IEEE Access, 10, 36140-36151.

  • Kruekaew, B., & Kimpan, W. (2022). Multi-objective task scheduling optimization for load balancing in cloud computing environment using hybrid artificial bee colony algorithm with reinforcement learning. IEEE Access, 10, 17803-17818.

  • Xie, L., Han, T., Zhou, H., Zhang, Z. R., Han, B., & Tang, A. (2021). Tuna swarm optimization: a novel swarm-based metaheuristic algorithm for global optimization. Computational Intelligence and Neuroscience, 2021, 1-22.


Abstract Views: 167

PDF Views: 0




  • Amended Hybrid Scheduling for Cloud Computing with Real-Time Reliability Forecasting

Abstract Views: 167  |  PDF Views: 0

Authors

Ramya Boopathi
Department of Computer Science, Vellalar College for Women, Erode, Tamil Nadu., India
E. S. Samundeeswari
Department of Computer Science, Vellalar College for Women, Erode, Tamil Nadu., India

Abstract


Cloud computing has emerged as the feasible paradigm to satisfy the computing requirements of high-performance applications by an ideal distribution of tasks to resources. But, it is problematic when attaining multiple scheduling objectives such as throughput, makespan, and resource use. To resolve this problem, many Task Scheduling Algorithms (TSAs) are recently developed using single or multi-objective metaheuristic strategies. Amongst, the TS based on a Multi-objective Grey Wolf Optimizer (TSMGWO) handles multiple objectives to discover ideal tasks and assign resources to the tasks. However, it only maximizes the resource use and throughput when reducing the makespan, whereas it is also crucial to optimize other parameters like the utilization of the memory, and bandwidth. Hence, this article proposes a hybrid TSA depending on the linear matching method and backfilling, which uses the memory and bandwidth requirements for effective TS. Initially, a Long Short-Term Memory (LSTM) network is adopted as a meta-learner to predict the task runtime reliability. Then, the tasks are divided into predictable and unpredictable queues. The tasks with higher expected runtime are scheduled by a plan-based scheduling approach based on the Tuna Swarm Optimization (TSO). The remaining tasks are backfilled by the VIKOR technique. To reduce resource use, a particular fraction of CPU cores is kept for backfilling, which is modified dynamically depending on the Resource Use Ratio (RUR) of predictable tasks among freshly submitted tasks. Finally, a general simulation reveals that the proposed algorithm outperforms the earlier metaheuristic, plan-based, and backfilling TSAs.

Keywords


Cloud Computing, Task Scheduling, TSMGWO, Meta-Learning, LSTM, Plan-Based Scheduling, Tuna Swarm Optimization, Backfilling, Vikor.

References





DOI: https://doi.org/10.22247/ijcna%2F2023%2F221887