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The Application of Bayesian Heuristic for Scheduling in Real-Time Private Clouds

Authors: Sahar Sohrabi


The emergence of Cloud data centers has revolutionized the IT industry. Private Clouds in specific provide Cloud services for certain group of customers/businesses. In a real-time private Cloud each task that is given to the system has a deadline that desirably should not be violated. Scheduling tasks in a real-time private CLoud determine the way available resources in the system are shared among incoming tasks. The aim of the scheduling policy is to optimize the system outcome which for a real-time private Cloud can include: energy consumption, deadline violation, execution time and the number of host switches. Different scheduling policies can be used for scheduling. Each lead to a sub-optimal outcome in a certain settings of the system. A Bayesian Scheduling strategy is proposed for scheduling to further improve the system outcome. The Bayesian strategy showed to outperform all selected policies. It also has the flexibility in dealing with complex pattern of incoming task and has the ability to adapt.

Keywords: Cloud Computing, Scheduling, Bayesian, real-time private cloud

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[1] Kyong Hoon Kim, Rajkumar Buyya, and Jong Kim. Power aware scheduling of bag-of-tasks applications with deadline constraints on dvs-enabled clusters. In CCGRID, volume 7, pages 541–548, 2007.
[2] Dan Tsafrir, Yoav Etsion, and Dror G Feitelson. Backfilling using system-generated predictions rather than user runtime estimates. Parallel and Distributed Systems, IEEE Transactions on, 18(6):789–803, 2007.
[3] Chuan-Feng Chiu, Steen J Hsu, Sen-Ren Jan, and Jyun-An Chen. Task scheduling based on load approximation in cloud computing environment. In Future Information Technology, pages 803–808. Springer, 2014.
[4] Sahar Sohrabi and Irene Moser. Energy-aware deadline-based scheduling in IaaS cloud with regard to the available memory. In Proceedings of International Conference on Advanced Computing and Services. World IT Congress, 2015.
[5] Shekhar Srikantaiah, Aman Kansal, and Feng Zhao. Energy aware consolidation for cloud computing. In Proceedings of the 2008 conference on Power aware computing and systems, volume 10. San Diego, California, 2008.
[6] Nikzad Babaii Rizvandi, Javid Taheri, Albert Y Zomaya, and Young Choon Lee. Linear combinations of DVFS-enabled processor frequencies to modify the energy-aware scheduling algorithms. In Cluster, Cloud and Grid Computing (CCGrid), 2010 10th IEEE/ACM International Conference on, pages 388–397. IEEE, 2010.
[7] Ming Mao and Marty Humphrey. Auto-scaling to minimize cost and meet application deadlines in cloud workflows. In Proceedings of 2011 International Conference for High Performance Computing, Networking, Storage and Analysis, page 49. ACM, 2011.
[8] Jia Yu and Rajkumar Buyya. Scheduling scientific workflow applications with deadline and budget constraints using genetic algorithms. Scientific Programming, 14(3):217–230, 2006.
[9] Shu-Ching Wang, Kuo-Qin Yan, Wen-Pin Liao, and Shun-Sheng Wang. Towards a load balancing in a three-level cloud computing network. In Computer Science and Information Technology (ICCSIT), 2010 3rd IEEE International Conference on, volume 1, pages 108–113. IEEE, 2010.
[10] Linan Zhu, Qingshui Li, and Lingna He. Study on cloud computing resource scheduling strategy based on the Ant Colony Optimization Algorithm. IJCSI International Journal of Computer Science Issues, 9(5):1694–0814, 2012.
[11] Anton Beloglazov and Rajkumar Buyya. Optimal online deterministic algorithms and adaptive heuristics for energy and performance efficient dynamic consolidation of virtual machines in cloud data centers. Concurrency and Computation: Practice and Experience, 24(13):1397–1420, 2012.
[12] Aameek Singh, Madhukar Korupolu, and Dushmanta Mohapatra. Server-storage virtualization: integration and load balancing in data centers. In Proceedings of the 2008 ACM/IEEE conference on Supercomputing, page 53. IEEE Press, 2008.
[13] Sanjaya K Panda and Prasanta K Jana. A multi-objective task scheduling algorithm for heterogeneous multi-cloud environment. In Electronic Design, Computer Networks & Automated Verification (EDCAV), 2015 International Conference on, pages 82–87. IEEE, 2015.
[14] Amandeep Verma and Sakshi Kaushal. Bi-criteria priority based particle swarm optimization workflow scheduling algorithm for cloud. In Engineering and Computational Sciences (RAECS), 2014 Recent Advances in, pages 1–6. IEEE, 2014.
[15] Amandeep Verma and Sakshi Kaushal. Cost minimized pso based workflow scheduling plan for cloud computing. pages 37–43, 2015.
[16] Wei Zheng and Rizos Sakellariou. Budget-deadline constrained workflow planning for admission control. Journal of grid computing, 11(4):633–651, 2013.
[17] Haluk Topcuoglu, Salim Hariri, and Min-you Wu. Performance-effective and low-complexity task scheduling for heterogeneous computing. Parallel and Distributed Systems, IEEE Transactions on, 13(3):260–274, 2002.
[18] Ying Yidu Xiong and Yan Yan Wu. Cloud computing resource schedule strategy based on pso algorithm. In Applied Mechanics and Materials, volume 513, pages 1332–1336. Trans Tech Publ, 2014.
[19] M Sridhar and G Babu. Hybrid particle swarm optimization scheduling for cloud computing. In Advance Computing Conference (IACC), 2015 IEEE International, pages 1196–1200. IEEE, 2015.
[20] Jiayin Li, Meikang Qiu, Zhong Ming, Gang Quan, Xiao Qin, and Zonghua Gu. Online optimization for scheduling preemptable tasks on iaas cloud systems. Journal of Parallel and Distributed Computing, 72(5):666–677, 2012.
[21] Harmeet Kaur and Rama Krishna Challa. A new hybrid virtual machine scheduling scheme for public cloud. In Advanced Computing & Communication Technologies (ACCT), 2015 Fifth International Conference on, pages 495–500. IEEE, 2015.
[22] Young Choon Lee and Albert Y Zomaya. Energy efficient utilization of resources in cloud computing systems. The Journal of Supercomputing, 60(2):268–280, 2012.
[23] Jiandun Li, Junjie Peng, Zhou Lei, and Wu Zhang. An energy-efficient scheduling approach based on private clouds. Journal of Information & Computational Science, 8(4):716–724, 2011.
[24] Ahmed Sallam and Kenli Li. A multi-objective virtual machine migration policy in cloud systems. The Computer Journal, 2013.
[25] Qiang Guan, Ziming Zhang, and Song Fu. Ensemble of bayesian predictors for autonomic failure management in cloud computing. In Computer Communications and Networks (ICCCN), 2011 Proceedings of 20th International Conference on, pages 1–6. IEEE, 2011.
[26] Qiang Guan, Ziming Zhang, and Song Fu. Ensemble of bayesian predictors and decision trees for proactive failure management in cloud computing systems. Journal of Communications, 7(1):52–61, 2012.
[27] Xianbin Wang, Guangjie Han, Xiaojiang Du, and Joel JPC Rodrigues. Mobile cloud computing in 5g: Emerging trends, issues, and challenges
[guest editorial]. Network, IEEE, 29(2):4–5, 2015.
[28] Wei Wang, Guosun Zeng, Daizhong Tang, and Jing Yao. Cloud-DLS: Dynamic trusted scheduling for cloud computing. Expert Systems with Applications, 39(3):2321–2329, 2012.
[29] J Michael Harrison. Dynamic scheduling of a multiclass queue: Discount optimality. Operations Research, 23(2):270–282, 1975.
[30] Rodrigo N Calheiros, Rajiv Ranjan, C´esar AF De Rose, and Rajkumar Buyya. CloudSim: A novel framework for modeling and simulation of cloud computing infrastructures and services. arXiv preprint arXiv:0903.2525, 2009.
[31] Ching-Hsien Hsu, Kenn Slagter, Shih-Chang Chen, and Yeh-Ching Chung. Optimizing energy consumption with task consolidation in clouds. Information Sciences, 258:452–462, 2014.
[32] Rodrigo N Calheiros, Rajiv Ranjan, Anton Beloglazov, C´esar AF De Rose, and Rajkumar Buyya. CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and Experience, 41(1):23–50, 2011.
[33] KyoungSoo Park and Vivek S Pai. CoMon: a mostly-scalable monitoring system for planetlab. ACM SIGOPS Operating Systems Review, 40(1):65–74, 2006.