WSTMOS: A Method For Optimizing Throughput, Energy, And Latency In Cloud Workflow Scheduling
Subject Areas : ICT
Arash Ghorbannia Delavar
1
*
,
Reza Akraminejad
2
,
sahar mozafari
3
1 -
2 - payam noor
3 - Payam noor
Keywords: Scheduling, Cloud Computing, Load Balancing, Energy, Cost, Batch Processing, Throughput,
Abstract :
Application of cloud computing in different datacenters around the world has led to generation of more co2 gas. In addition, energy and throughput are the two most important issues in this field. This paper has presented an energy and throughput-aware algorithm for scheduling of compressed-instance workflows in things-internet by cluster processing in cloud. A method is presented for scheduling cloud workflows with aim of optimizing energy, throughput, and latency. In the proposed method, time and energy consumption has been improved in comparison to previous methods by creating distance parameters, clustering inputs, and considering real execution time. In WSTMOS method by considering special parameters and real execution time, we managed to reach the optimized objective function. Moreover, in the proposed method parameter of time distance of tasks to virtual machines for reduction of number of migration in virtual machines was applied. In WSTMOS method by organizing the workflow inputs to low, medium and heavy groups and also by distributing appropriate load on more suitable servers for processors threshold, we accomplished to optimize energy and cost. Energy consumption was reduced by 4.8 percent while the cost was cut down by 4.4 percent using this method in comparison to studied method. Finally, average delay time, power and workload are optimized in comparison to previous methods.
[1] م. نیکسرشت، م. راجی، "یک الگوریتم زمانبندی وظیفه چندهدفه بر اساس الگوریتم ژنتیک برای طراحی سیستمهای نهفته" ، دوفصلنامه علمی فناوری اطلاعات و ارتباطات ایران، شماره 47 و 48، صفحه 186-197
[2] W. Dou, X. Xu, . S. Meng, . X. Zhang, . C. Hu, . S. Yu and . J. Yang, "An energy-aware virtual machine scheduling method for service QoS enhancement in clouds over big data." Concurr. Comput. Pract, p. Exp, vol. 29 e3909, 2016.
[3] A. Ghorbannia Delavar and . Y. Aryan, "HSGA: a hybrid heuristic algorithm for workflow scheduling in cloud systems." Cluster Comput (2014) 17, p. 129–137, 2013.
[4] N. J. Kansal and I. Chana, "Energy-aware Virtual Machine Migration for Cloud Computing .A Firefly Optimization Approach." Grid Computing, vol. 14, pp. 327-345, 2016.
[5] A. Mosa and N. W. Paton, "Optimizing virtual machine placement for energy and SLA in clouds using utility functions." Journal of Cloud Computing, vol. 5, no. 1, 2016.
[6] G. Portaluri, D. Adami, A. Gabbrielli and S. Giordano, "Power Consumption-Aware Virtual Machine Placement in Cloud Data Center." IEEE Transactions on Green Communications and Networking, vol. 1, no. 4, pp. 541-550, 2017.
[7] L. Pufahl, "Modeling and Executing Batch Activities in Business Processes (Ph.D. thesis)." 2018.
[8] L. Pufahl and D. Karastoyanova, "Enhancing Business Process Flexibility by Flexible Batch Processing." pp. 426-444, 2018.
[9] L. Pufahl, "Modeling and executing batch activities in business processes." 2018.
[10] V. Seethalakshmi, V. Govindasamy, V. Akila, G. Sivaranjini, K. Sindhuja and K. Prasanth, "A Survey Of Different Workflow Scheduling Algorithms In Cloud Computing." 2019.
[11] H. C. Y. T. S. Y. Xiaojun Ruan, "Virtual machine allocation and migration based on performance-to-power ratio in energy-efficient clouds." Future Generation Computer Systems, p. https://doi.org/10.1016/j.future.2019.05.036, 2019.
[12] M. Hussain, L.-F. Wei, A. Lakhan, S. Wali, S. Ali and A. Hussaina, "Energy and performance-efficient task scheduling in heterogeneous virtualized cloud computing." Sustainable Computing: Informatics and Systems, vol. 30, 2021.
[13] D. Sun, S. Gao, L. Xunyun, Y. Xindong and B. Rajkumar, "Dynamic redirection of real-time data streams for elastic stream." Future Generation Computer Systems, vol. 112, p. 193–208, 2020.
[14] Y. Wang, Z.-h. Jia and K. Li, "A multi-objective co-evolutionary algorithm of scheduling on parallel non-identical batch machines." Expert Systems With Applications,no. https://doi.org/10.1016/j.eswa.2020.114145, 2020.
[15] S. Taherizadeh and M. Grobelnik, "Key influencing factors of the Kubernetes auto-scaler for computingintensive microservice-native cloud-based applications." Advances in Engineering Software, vol. 140, 2020.
[16] B. Everman, M. Gao and Z. Zong, "Evaluating and reducing cloud waste and cost. A data-driven case study from Azure workloads." Sustainable Computing: Informatics and Systems, vol. 35, 2022.
[17] Yiping Wen, Zhibin Wang, Yu Zhang, Jianxun Liu, Buqing Cao and Jinjun Chen, "Energy and cost aware scheduling with batch processing for instance-intensive IoT workflows in clouds." vol. 101, pp. 39-50, 2019.
[18] R. medara and R. S. Singh, "A Review on Energy-Aware Scheduling Techniques for Workflows in IaaS Clouds." Wireless Personal Communications, vol. 116, 2022.