Open Access   Article Go Back

A Taxonomy and Survey of Energy Efficient Resource Allocation Schemes for Cloud Datacenter

Satveer 1 , Mahendra Singh2

Section:Survey Paper, Product Type: Journal Paper
Volume-6 , Issue-9 , Page no. 691-998, Sep-2018

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v6i9.691998

Online published on Sep 30, 2018

Copyright © Satveer, Mahendra Singh . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

View this paper at   Google Scholar | DPI Digital Library

How to Cite this Paper

  • IEEE Citation
  • MLA Citation
  • APA Citation
  • BibTex Citation
  • RIS Citation

IEEE Style Citation: Satveer, Mahendra Singh, “A Taxonomy and Survey of Energy Efficient Resource Allocation Schemes for Cloud Datacenter,” International Journal of Computer Sciences and Engineering, Vol.6, Issue.9, pp.691-998, 2018.

MLA Style Citation: Satveer, Mahendra Singh "A Taxonomy and Survey of Energy Efficient Resource Allocation Schemes for Cloud Datacenter." International Journal of Computer Sciences and Engineering 6.9 (2018): 691-998.

APA Style Citation: Satveer, Mahendra Singh, (2018). A Taxonomy and Survey of Energy Efficient Resource Allocation Schemes for Cloud Datacenter. International Journal of Computer Sciences and Engineering, 6(9), 691-998.

BibTex Style Citation:
@article{Singh_2018,
author = {Satveer, Mahendra Singh},
title = {A Taxonomy and Survey of Energy Efficient Resource Allocation Schemes for Cloud Datacenter},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {9 2018},
volume = {6},
Issue = {9},
month = {9},
year = {2018},
issn = {2347-2693},
pages = {691-998},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=2929},
doi = {https://doi.org/10.26438/ijcse/v6i9.691998}
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v6i9.691998}
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=2929
TI - A Taxonomy and Survey of Energy Efficient Resource Allocation Schemes for Cloud Datacenter
T2 - International Journal of Computer Sciences and Engineering
AU - Satveer, Mahendra Singh
PY - 2018
DA - 2018/09/30
PB - IJCSE, Indore, INDIA
SP - 691-998
IS - 9
VL - 6
SN - 2347-2693
ER -

VIEWS PDF XML
656 260 downloads 256 downloads
  
  
           

Abstract

The cloud computing is gaining the popularity rapidly due to its esteem services benefits and high computational demand of social, business, web and scientific applications. The cloud datacenters across the world are consuming high volume of energy thus affecting the environment also. The resource allocation in cloud is a key factor to achieve energy efficiency. In this paper, we reviewed the energy concept and different kinds of resource allocation schemes being used in cloud datacenters and went on to derive a taxonomical classification of these strategies based upon various metrics.

Key-Words / Index Term

Energy efficiency, QoS, Virtual Machine, Datacenters, Cloud Service Provider, Resource Allocation

References

[1] Yue Gao, “An Energy and Deadline a Aware Resource Provisioning, Scheduling and Optimization Framework for Cloud Systems”, International conference on Hardware/Software Codesign and system synthesis (CODES+ISSS), Montreal, QC, Canada 2013
[2] Arm burst M, Fox, “A view of Cloud computing”. Vol. 53, Issue.4, pp:50-58.
[3] Zoltan Adam “Rigorous results on the effectiveness of some heuristics for the consolidation of virtual machines on cloud data center” Vol 51, pages 1-6, 2015
[4] S Ali “Profit-aware DVFS enabled resource management of IaaS cloud”. Vol. 10, Issue 2, pp-237-247, 2013
[5] Nasrin Ak., “Energy aware resource allocation of cloud data center: review and open issues” Vol. 19. Issue 3, pp-1163-1182, 2016
[6] Abdul Hameed “A Survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems”. Vol. 98. Issue 7, pp-751-774, 2016
[7] Anton Beloglazov: A taxonomy and survey of energy efficientDCs and cloud computing systems.” Adv comput, Vol.82, pp :47-111, 2011
[8] Anton Beloglazov, Rajkumar,“Energy Efficient Resource Management in Virtualized Cloud Data Centers” International Conference on Cluster, Cloud and Grid Computing, Melbourne, VIC, Australia 2010
[9] S. Singh, I chana, “QoS-aware automatic cloud computing for ICT”. International conference on information and communication technology for suitable development, pp: 569-577, 2015.
[10] Marston S, “A Cloud Computing. The business perspective”, Decision support system, Vol 51, Issue 1, pp -176-189.
[11] Tarandeep Kaur “ Energy Efficiency techniques in cloud computing” ACM Computing Surveys, Vol.48, No.2, Article 22, 2015
[12] Renewable Energy Outlook. 2013, “World Energy Outlook published by International Energy Associa- tion”. Retrieved on August 5, 2014
[13] Tarandeep Kaur and Inderveer Chana, “Energy Efficiency Techniques in Cloud Computing: A Survey and Taxonomy”, Vol. 48, Issue 2, pp 46, 2015.
[14] Koomey J, “Estimating total power consumption by servers in the US and the world”. Lawrence Berkeley National Laboratory, Analytics Press, 2007
[15] Singh T, “Smart metering the clouds”. In: 18th IEEE international workshops on enabling technologies: infrastructures for collaborative enterprises, pp 66–71
[16] J Baliga, “Energy consumption in optical IP networks”. J Lightweight Technol Vol.27, Issue 13, pp:2391–2403, 2009
[17] O Tamm : “A Eco-sustainable system and network architectures for future transport networks”. Bell Labs Tech J, Vol. 14, Issue 4, pp:311-327, 2010
[18] A Vukovic “DCs network power density challenges”. J ASHRAE Vol. 47, pp:55–59, 2005
[19] J Liu “ Challenges towards elastic power management in internet datacenters". International conference on distributed systems, pp: 65–72
[20] JA Paradiso, “Energy scavenging for mobile and wireless electronics”. Pervasive Compute Vol.4, Issue, pp : 18-27
[21] Cook G, Horn J “How dirty is your data”. GreenPeace International, Amsterdam, 2011
[22] Satveer , Mahendra Singh Aswal “A Comparative Study of Resource Allocation Strategies for a Green Cloud”, International Conference on Next Generation Computing Technologies (NGCT-2016) Dehradun, India, 14-16 October 2016
[23] S Singh, I Chana, “Cloud resource provisioning: survey, status and future research directions” Knowl Inf Syst, Vol. 59, pp: 1005-1069
[24] Yousafzail,“Cloud Resource Allocation Schemes:Review, Taxonomy, and Opportunities”, Knowl Inf Syst, Vol. 50, Issue-2 pp :347–381, 2017
[25] V.P Anuradha “Surey on Resource Allocation Strategies in Cloud Computing”,InternationalConference on Information Communication and Embedded Systems (ICICES2014), Chennai, India ,IEEE.
[26] Morshedlou “Decreasing impact of SLA violations: a proactive resource alloca- tion approach for cloud computing environments”. IEEE Trans Cloud Comput Vol. 2, Issue 2, pp:156–167, 2014
[27] Hussin,“Efficient energy management using adaptive reinforcement learning-based scheduling in large-scale distributed systems”, International Conference on Parallel Processing In: ICPP, Taipei City, Taiwan, pp 385–393, 2011
[28] Lee, “Resource Allocation and Scheduling in Heterogeneous Clooud Enviroments” Ph.D. dissertation, Univ. California, Berkeley, Technical Report No UCB/EECS-2012-78, spring 2012
[29] TVT. Duy, “Performance evaluation of a green scheduling algorithm for energy savings in cloud computing”. International Symposium on Parallel and distributed processing, workshops and PhD forum (IPDPSW), Atlanta, GA, USA Atlanta, GA, USA pp 1–8, 19–23, 2010
[30] Mezmaz, “A parallel island-based hybrid genetic algorithm for precedence-constrained applications to minimize energy consumption and makespan”. International Conference on Grid Computing, Brussels, Belgium, pp 274–281, 2011
[31] Y. Chen, “Minimizing data center SLA violations and power consumption via hybrid resource provisioning”.Second international green computing conference (IGCC), pp 1–8, 2011
[32] E. Kalyvianaki,“Resource provisioning for virtualized server applications”. Technical Report UCAM-CL-TR-762, Computer Laboratory, University of Cambridge, 2009
[33] Anton Beloglazov “Adaptive Threshold-Based Approach for Energy-Efficient Consolidation of Virtual Machines in Cloud Data Centers”: MGC , Bangkore, India ISBN: 978-1-4503-0453-5 ,
[34] Mohan Raj: “Heterogeneity and thermal aware adaptive heuristics for energy efficient consolidation of virtual machines in infrastructure clouds” Journal of Computer and System Sciences, Vol.82, Issue. 2, pp: 191-212
[35] Fahimeh, “Energy Aware Consolidation Algorithm based on K-nearest Neighbor Regression for Cloud Data Centers”, International Conference on Utility and Cloud Computing, Dresden, Germany 2013
[36] Nasrin Akhter, “Energy aware resource allocation of cloud data center: Review and open issues” Cluster Compute, New York, Vol 19, Issue, 3,pp: 1163-1182 2016
[37] More,“Energy-efficiency in cloud computing environments: towards energy savings without performance degradation”. International Journal of computer Applications, Vol. 1, Issue. 1, pp:17–33, 2011
[38] C, Reid, “Coordination of energy efficiency and demand response”. Environmental Energy Technologies Division, Berkeley National Laboratory, LBNL-3044E, 2010
[39] N, Scherer, “Thermal-aware workload scheduling for energy efficient data centers”. International conference on autonomic computing (ICAC) Washington, DC, USA, pp 169–174.
[40] Heger, “Optimized Resource Allocation & Task Scheduling Challenges in Cloud Computing Environments”. 2010, dheger@ dhtusa. com
[41] I, Aida, “Applying double-sided combinational auctions to resource allocation in cloud computing”. International symposium on applications and the internet. Seoul, South Korea, pp 7–14, 2012
[42] MM. Mashayekhy “Truthful greedy mechanisms for dynamic virtual machine provisioning and allocation in clouds”. IEEE Transactions on Parallel and Distributed Systems Vol: 26 , Issue: 2 , pp: 594–603, 2015
[43] Y, Niyato, “An auction mechanism for resource allocation in mobile cloud computing systems”. International Conference on Wireless Algorithms, Systems, and Applications, pp76-87, 2013
[44] W-Y. Lin, “Dynamic auction mechanism for cloud resource allocation”. International conference on, cluster, cloud and grid computing (CCGrid),IEEE, Melbourne, VIC, Australia, pp 591–592, 2010
[45] Li Z, “An anti-cheating bidding approach for resource allocation in cloud computing environments”. Journal of Computational Information Systems, Vol 8, Issue: 4, pp:1641–1654, 2012
[46] Dharmesh Kakadia: “Network-aware Virtual Machine Consolidation for LargeDataCenters” NDM, proceding of the third international workshop on Network aware data management, 2013
[47] S. Grosu, “A combinatorial auction-based mechanism for dynamic VM provisioning and allocation in clouds”. IEEE Trans Cloud Comput, Vol 1, Issue 2, pp:129–141, 2013
[48] Ajit Singh, “Cluster Based Bee Algorithm for virtual Machine Placement in CloudDC” Journal of Theoretical and Applied Information Technology, Vol. 57, 2013.
[49] K.Mukkherjee,“Green Cloud:An Algorithmic Approach” International Journal of Computer Applications” (0975-8887) Vol. 9,2010
[50] J. Wang “An auction and league championship algorithm based resource allocation mechanism for distributed cloud”. In: Wu C, Cohen A (eds) Advanced parallel processing technologies, Vol. 8299., pp 334–346, 2013
[51] C, Wang, “A cloud resource allocation mechanism based on mean-variance optimization and double multi-attribution auction”. In: Hsu C-H, Li X, Shi X, Zheng R (eds) Network and parallel computing, vol 8147, pp 106–117, 2013
[52] W-L, Xie, “Thermal-aware task allocation and scheduling for embedded systems”. Proceedings of the conference on design, automation and test in Europe, vol 2, pp 898–899, 2005
[53] S, Kansal, “Energy aware consolidation for cloud computing”. In: Confer- ence on power aware computer and systems, San Diego, California ,2008
[54] A, Ahuja P, Neogi A (2008) “pMapper: power and migration cost aware application placement in virtualized systems”. International conference on middleware, pp 243–264, 2008
[55] R, Schwan K,“VirtualPower: coordinated power management in virtualized enterprise systems”. In: 21st ACM SIGOPS symposium on operating systems principles, Vol.41, Issue. 6, pp: 265–278, 2007.
[56] Hadi Khani, “Distributed consolidation of VMs for power efficiency in heterogeneous cloud data centers”, Journal of computers and Electrical Engineering, Vol. 47 Issue C, pp: 173-185, 2015
[57] Anton Beloglazov, “Optimal Online Deterministic Algorithms and Adaptive Heuristics for Energy and Performance Efficient Dynamic Consolidation of Virtual Machines in Cloud Data Centers” Journal of concurrency and computation, Vol. 24, Issue. 13, 2012
[58] Anton “Energy Aware resource allocation heuristics for efficient management of cloud DCs” Future generation computing system VOl. 28, pp: 755-768, 2012
[59] Alfredo Goldman “Consolidation of VMs to improve Energy Efficiency in cloud Environments” 2015 XXXIII Brazilian Symposium on Computer Networks and Distributed Systems, Vitoria,Brazil, 2015
[60] Hui Wang “Energy-aware Dynamic Virtual Machine Consolidation for CloudDCs” IEEE 7th International Conference on Cloud Computing, Anchorage, AK, USA, 2014
[61] Bruno Cesar Ribas: “On Modelling Virtual Machine Consolidation to Pseudo-Boolean Constraints” J. Pavo´n et al. (Eds.) pp. 361–370, 2012.
[62] Dabiah Ahmed, “Energy-aware Virtual Machine Consolidation for Cloud Data” International Conference on Utility and Cloud Computing Centers” London UK , 2014
[63] Sina Esfandiarpoor “Sturecture-aware online VM Consolidation for datacenter energy improvement in cloud cloud computing” Computers and Electrical Engineering, Vol. 42, pp:74-75, 2015
[64] ChaoTung, “Green Power Management with Dynamic Resource Allocation for Cloud Virtual Machines”, International Conference on High Performance Computing and Communications Banff, AB, Canada 2011