Abstract
Workload consolidation is an approach to enhance the server utilization by grouping the VMs that are executing workflow tasks over multiple servers based on their server utilization. The primary objective is to optimally allocate the number of servers for executing the workflows which in turn minimize the cost and energy of data centers. This chapter consolidates the cost- and energy-aware workload consolidation approaches along with the tools and methodologies used in modern cloud data centers.
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References
Kataria D, Kumar S (2015) A study on workflow scheduling algorithms in cloud. Int J Res Appl Sci Technol 3(8):268–273
Tao J, Kunze M, Rattu D, Castellanos AC (2008) The Cumulus project: build a scientific cloud for a data center. In Cloud Computing and its Applications, Chicago
Chopra N, Singh S (2014) Survey on scheduling in Hybrid Clouds. In: 5th ICCCNT–2014 July 1113, Hefei, China
Abawajy JH (2004) Fault-tolerant scheduling policy for grid computing systems. In: Proceedings of parallel and distributed processing symposium, 2004, 18th international, IEEE, p 238
Selvarani S, Sudha Sadhasivam G (2010) Improved cost based algorithm for task scheduling in cloud computing. In: IEEE
Abrishami S, Naghibzadeh M, Epema DH (2012) Cost-driven scheduling of grid workflows using partial critical paths. IEEE Trans Parallel Distrib Syst 23(8):1400–1414
Choudhary M, Sateesh Kumar P (2012) A dynamic optimization algorithm for task scheduling in cloud environment. 2, 3, pp.2564-2568
Calheiros RN Ranjan R, De Rose CAF, Buyya R (2009) Cloudsim: a novel framework for modeling and simulation of cloud computing infrastructures and services, Arxiv preprint arXiv:0903.2525
George Amalarethinam DI, Joyce Mary GJ (2011) DAGEN–A tool to generate arbitrary directed acyclic graphs used for multiprocessor scheduling. Int J Res Rev Comput Sci (IJRRCS) 2(3):782
Jangra A, Saini T (2013) Scheduling optimization in cloud computing. Int J Adv Res Comput Sci Softw Eng 3(4)
Xiaolong Xu, Wanchun Dou, Xuyun Zhang, Jinjun Chen (2016) EnReal: an energy-aware resource allocation method for scientific workflow executions in cloud environment. IEEE Trans Cloud Comput 4(2):166–179
Zhongjin Li, Jidong Ge, Haiyang Hu, Wei Song, Hao Hu, Bin Luo Cost and energy aware Scheduling algorithm for scientific workflows with deadline constraintin clouds. In: IEEE Transactions on Services Computing. doi:10.1109/TSC.2015.2466545
Yassa S, Chelouah R, Kadima H, Granado B (2013) Multi-objective approach for energy-aware workflow scheduling in cloud computing environments. Hindawi Publishing Corporation. ScientificWorld J 2013, Article ID 350934:13. http://dx.doi.org/10.1155/2013/350934
Bouselmi K, Brahmi Z, Gammoudi MM (2016) Energy efficient partitioning and scheduling approach for scientific workflows in the cloud. In: IEEE international conference on services computing, doi:10.1109/SCC.2016.26
Peng Xiao, Zhi-Gang Hu, Yan-Ping Zhang (2013) An energy-aware heuristic scheduling for data-intensive workfows in virtualized datacenters. J Comput Sci Technol 28(6):948–961. doi:10.1007/s11390-013-1390-9
Huangke Chen, Xiaomin Zhu, Dishan Qiu, Hui Guo, Laurence T. Yang, Peizhong Lu (2016) EONS: minimizing energy consumption for executing real-time workflows in virtualized cloud data centers, 2332-5690/16 $31.00 © 2016 IEEE DOI 10.1109/ICPPW.2016.60
Guangyu Du, Hong He, Qinggang Meng (2014) Energy-efficient scheduling for tasks with Deadline in virtualized environments. Math Probl Eng 2014, Article ID 496843: 7. http://dx.doi.org/10.1155/2014/496843
Zhuo Tang, Ling Qi, Zhenzhen Cheng, Kenli Li, Samee U. Khan, Keqin Li (2015) An energy-efficient task scheduling algorithm in DVFS-enabled cloud environment, doi 10.1007/s10723-015-9334-y, © Springer Science+Business Media Dordrecht.
Yonghong Luo, Shuren Zhou (2014) Power consumption optimization strategy of cloud workflow scheduling based on SLA. WSEAS Trans Syst 13: 368–377, E-ISSN: 2224-2678
He H, Liu D (2014) Optimizing data-accessing energy consumption for workflow applications in clouds. Int J Future Gener Commun Netw 7(3):37–48
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Kousalya, G., Balakrishnan, P., Pethuru Raj, C. (2017). Workload Consolidation Through Automated Workload Scheduling. In: Automated Workflow Scheduling in Self-Adaptive Clouds. Computer Communications and Networks. Springer, Cham. https://doi.org/10.1007/978-3-319-56982-6_9
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DOI: https://doi.org/10.1007/978-3-319-56982-6_9
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