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The Journal of Supercomputing
Erschienen in: The Journal of Supercomputing 9/2020

23.01.2020

An approach toward design and development of an energy-aware VM selection policy with improved SLA violation in the domain of green cloud computing

verfasst von: Riman Mandal, Manash Kumar Mondal, Sourav Banerjee, Utpal Biswas

Erschienen in: The Journal of Supercomputing | Ausgabe 9/2020

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Abstract

With the rapid demand for service-oriented computing in association with the growth of cloud computing technologies, large-scale virtualized data centers have been established throughout the globe. These huge data centers consume power at a large scale that results in a high operational cost. The massive carbon footprint from the energy generators is another great issue to deal global warming. It is essential to lower the rate of carbon emission and energy consumption as much as possible. The live-migration-enabled dynamic virtual machine consolidation results in high energy saving. But it also incurs the violation of service level agreement (SLA). Excessive migration may lead to performance degradation and SLA violation. The process of VM selection for migration plays a vital role in the domain of energy-aware cloud computing. Using VM selection policies, VMs are selected for migration. A new power-aware VM selection policy has been proposed in this research that helps in VM selection for migration. The proposed power-aware VM selection policy has been further evaluated using trace-based simulation environment.
Vorheriger Artikel Deployment techniques in wireless sensor networks: a survey, classification, challenges, and future research issues
Nächster Artikel Design and implementation of an elastic processor with hyperthreading technology and virtualization for elastic server models

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Literatur
1.
Erl T (1900) Service-oriented architecture: concepts, technology, and design. Pearson Education India, Noida
2.
Fox A, Griffith R, Joseph A, Katz R, Konwinski A, Lee G, Patterson D, Rabkin A, Stoica I (2009) Above the clouds: a berkeley view of cloud computing. Dept. Electrical Eng. and Comput. Sciences, University of California, Berkeley, Rep. UCB/EECS, vol 28(13), p 2009
3.
Zhang Q, Cheng L, Boutaba R (2010) Cloud computing: state-of-the-art and research challenges. J Internet Serv Appl 1(1):7–18
4.
Buyya R, Yeo CS, Venugopal S, Broberg J, Brandic I (2009) Cloud computing and emerging it platforms: vision, hype, and reality for delivering computing as the 5th utility. Future Gener Comput Syst 25(6):599–616
5.
6.
Barham P, Dragovic B, Fraser K, Hand S, Harris T, Ho A, Neugebauer R, Pratt I, Xen AW (2003) The art of virtualization. In: Proceedings of the 19th ACM Symposium on Operating Systems Principles
7.
Ferreto TC, Netto MA, Calheiros RN, De Rose CA (2011) Server consolidation with migration control for virtualized data centers. Future Gener Comput Syst 27(8):1027–1034
8.
Corradi A, Fanelli M, Foschini L (2014) Vm consolidation: a real case based on openstack cloud. Future Gener Comput Syst 32:118–127
9.
Beloglazov A, Buyya R (2012) Optimal online deterministic algorithms and adaptive heuristics for energy and performance efficient dynamic consolidation of virtual machines in cloud data centers. Concurr Comput Pract Exp 24(13):1397–1420
10.
Yavari M, Rahbar AG, Fathi MH (2019) Temperature and energy-aware consolidation algorithms in cloud computing. J Cloud Comput 8(1):1–16
11.
Zhou Z, Abawajy J, Chowdhury M, Hu Z, Li K, Cheng H, Alelaiwi AA, Li F (2018) Minimizing sla violation and power consumption in cloud data centers using adaptive energy-aware algorithms. Future Gener Comput Syst 86:836–850
12.
Xiao H, Hu Z, Li K (2019) Multi-objective vm consolidation based on thresholds and ant colony system in cloud computing. IEEE Access 7:53441–53453
13.
Ranjbari M, Torkestani JA (2018) A learning automata-based algorithm for energy and sla efficient consolidation of virtual machines in cloud data centers. J Parallel Distrib Comput 113:55–62
14.
Mirobi GJ, Arockiam L (2015) Service level agreement in cloud computing: an overview. In: 2015 International Conference on Control, Instrumentation, Communication and Computational Technologies (ICCICCT). IEEE, pp 753–758
15.
Calheiros RN, Ranjan R, Beloglazov A, De Rose CA, Buyya R (2011) Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Softw Pract Exp 41(1):23–50
16.
Zhou Z, Hu Z, Li K (2016) Virtual machine placement algorithm for both energy-awareness and sla violation reduction in cloud data centers. Sci Program 2016:15
17.
Yadav R, Zhang W, Chen H, Guo T (2017) Mums: Energy-aware vm selection scheme for cloud data center. In: 2017 28th International Workshop on Database and Expert Systems Applications (DEXA). IEEE, pp 132–136
18.
Akhter N, Othman M, Naha RK (2018) Energy-aware virtual machine selection method for cloud data center resource allocation. arXiv preprint arXiv:​1812.​08375
19.
Liu L, Wang H, Liu X, Jin X, He WB, Wang QB, Chen Y (2009) Greencloud: a new architecture for green data center. In: Proceedings of the 6th International Conference Industry Session on Autonomic Computing and Communications Industry Session. ACM, pp 29–38
20.
Radu L-D (2017) Green cloud computing: a literature survey. Symmetry 9(12):295
21.
Ardagna D, Casale G, Ciavotta M, Pérez JF, Wang W (2014) Quality-of-service in cloud computing: modeling techniques and their applications. J Internet Serv Appl 5(1):11
22.
Beloglazov A, Buyya R (2010) Energy efficient resource management in virtualized cloud data centers. In: Proceedings of the 2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing. IEEE Computer Society, pp 826–831
23.
Goiri I, Julia F, Nou R, Berral JL, Guitart J, Torres J (2010) Energy-aware scheduling in virtualized datacenters. In: 2010 IEEE International Conference on Cluster Computing. IEEE, pp 58–67
24.
Hsu S-F, Lee C-C, Hwang S-W, Chen CH (2005) Highly efficient top-emitting white organic electroluminescent devices. Appl Phys Lett 86(25):253508
25.
Sharma V, Thomas A, Abdelzaher T, Skadron K, Lu Z (2003) Power-aware QoS management in web servers. In: RTSS 2003. 24th IEEE Real-Time Systems Symposium. IEEE, pp 63–72
26.
Deng Q, Meisner D, Ramos L, Wenisch TF, Bianchini R (2011) Memscale: active low-power modes for main memory. In: ACM SIGARCH Computer Architecture News. ACM, vol 39, pp 25–238
27.
Deng Q, Meisner D, Bhattacharjee A, Wenisch TF, Bianchini R (2012) Multiscale: memory system DVFS with multiple memory controllers. In: Proceedings of the 2012 ACM/IEEE International Symposium on Low Power Electronics and Design. ACM, pp 297–302
28.
Chaudhry MT, Ling TC, Manzoor A, Hussain SA, Kim J (2015) Thermal-aware scheduling in green data centers. ACM Comput Surv (CSUR) 47(3):39
29.
Banerjee A, Mukherjee T, Varsamopoulos G, Gupta SK (2010) Cooling-aware and thermal-aware workload placement for green HPC data centers. In: International Conference on Green Computing. IEEE, pp 245–256
30.
Yun B, Shin KG, Wang S (2011) Thermal-aware scheduling of critical applications using job migration and power-gating on multi-core chips. In: 2011 IEEE 10th International Conference on Trust, Security and Privacy in Computing and Communications. IEEE, pp 1083–1090
31.
Tan H, Ranka S (2014) Thermal-aware scheduling for data parallel workloads on multi-core processors. In: 2014 IEEE Symposium on Computers and Communications (ISCC). IEEE, pp 1–7
32.
Van Damme T, De Persis C, Tesi P (2018) Optimized thermal-aware job scheduling and control of data centers. IEEE Trans Control Syst Technol 27(2):760–771
33.
Kusic D, Kephart JO, Hanson JE, Kandasamy N, Jiang G (2009) Power and performance management of virtualized computing environments via lookahead control. Cluster Comput 12(1):1–15
34.
Bishop G, Welch G et al (2001) An introduction to the kalman filter. In: Proceeding of SIGGRAPH, Course 8(27599–23175), p 41
35.
Srikantaiah S, Kansal A, Zhao F (2008) Energy aware consolidation for cloud computing. In: Proceedings of the 2008 Conference on Power Aware Computing and Systems. USENIX Association, pp 10–10
36.
Cardosa M, Korupolu MR, Singh A (2009) Shares and utilities based power consolidation in virtualized server environments. In: 2009 IFIP/IEEE International Symposium on Integrated Network Management. IEEE, pp 327–334
37.
Jung G, Joshi KR, Hiltunen MA, Schlichting RD, Pu C (2008) Generating adaptation policies for multi-tier applications in consolidated server environments. In: 2008 International Conference on Autonomic Computing. IEEE, pp 23–32
38.
Jung G, Joshi KR, Hiltunen MA, Schlichting RD, Pu C (2009) A cost-sensitive adaptation engine for server consolidation of multitier applications. In: Proceedings of the 10th ACM/IFIP/USENIX International Conference on Middleware. Springer, p 9
39.
Nathuji R, Schwan K (2007) Virtualpower: coordinated power management in virtualized enterprise systems. In: ACM SIGOPS Operating Systems Review. ACM, vol 41, pp 265–278
40.
Nadjar A, Abrishami S, Deldari H (2017) Load dispersion-aware vm placement in favor of energy-performance tradeoff. J Supercomput 73(4):1547–1566
41.
Nadjar A, Abrishami S, Deldari H (2015) Hierarchical VM scheduling to improve energy and performance efficiency in IAAS cloud data centers. In: 2015 5th International Conference on Computer and Knowledge Engineering (ICCKE). IEEE, pp 131–136
42.
Theja PR, Babu SK (2016) Evolutionary computing based on QoS oriented energy efficient VM consolidation scheme for large scale cloud data centers. Cybern Inf Technol 16(2):97–112MathSciNet
43.
Thaman J, Singh M (2017) SLA conscious VM migration for host consolidation in cloud framework. Int J Commun Netw Distrib Syst 19(1):46–64
44.
Li Z, Yan C, Yu X, Yu N (2017) Bayesian network-based virtual machines consolidation method. Future Gener Comput Syst 69:75–87
45.
Rahmani S, Khajehvand V, Torabian M (2019) Burstiness-aware virtual machine placement in cloud computing systems. J Supercomput pp 1–26
46.
Zhou X, Li K, Liu C, Li K (2019) An experience-based scheme for energy-SLA balance in cloud data centers. IEEE Access 7:23500–23513
47.
Braiki K, Youssef H (2019) Fuzzy-logic-based multi-objective best-fit-decreasing virtual machine reallocation. J Supercomput pp 1–28
48.
Yadav R, Zhang W, Kaiwartya O, Singh PR, Elgendy IA, Tian Y-C (2018) Adaptive energy-aware algorithms for minimizing energy consumption and sla violation in cloud computing. IEEE Access 6:55923–55936
49.
Moghaddam SM, Piraghaj SF, O’Sullivan M, Walker C, Unsworth C (2018) Energy-efficient and sla-aware virtual machine selection algorithm for dynamic resource allocation in cloud data centers. In: 2018 IEEE/ACM 11th International Conference on Utility and Cloud Computing (UCC). IEEE, pp 103–113
50.
Arockia RA, Arun S (2019) Virtual machine consolidation framework for energy and performance efficient cloud data centers. In: 2019 IEEE International Conference on System, Computation, Automation and Networking (ICSCAN). IEEE, pp 1–7
51.
Cleveland WS (1993) Visualizing data. Hobart press, New Jersey
52.
53.
Park K, Pai VS (2006) Comon: a mostly-scalable monitoring system for planetlab. ACM SIGOPS Oper Syst Rev 40(1):65–74
Metadaten
Titel
An approach toward design and development of an energy-aware VM selection policy with improved SLA violation in the domain of green cloud computing
verfasst von
Riman Mandal
Manash Kumar Mondal
Sourav Banerjee
Utpal Biswas
Publikationsdatum
23.01.2020
Verlag
Springer US
Erschienen in
The Journal of Supercomputing / Ausgabe 9/2020
Print ISSN: 0920-8542
Elektronische ISSN: 1573-0484
DOI
https://doi.org/10.1007/s11227-020-03165-6

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