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Fault Aware Energy Efficient Mechanisms in Cloud: A Comprehensive Survey

Sunila 1 , Kamaljit Kaur2

  1. Guru Nanak Dev University, Amritsar, Punjab, India.
  2. Guru Nanak Dev University, Amritsar, Punjab, India.

Section:Review Paper, Product Type: Journal Paper
Volume-6 , Issue-5 , Page no. 556-568, May-2018

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v6i5.556568

Online published on May 31, 2018

Copyright © Sunila, Kamaljit Kaur . 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.

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IEEE Style Citation: Sunila, Kamaljit Kaur, “Fault Aware Energy Efficient Mechanisms in Cloud: A Comprehensive Survey,” International Journal of Computer Sciences and Engineering, Vol.6, Issue.5, pp.556-568, 2018.

MLA Style Citation: Sunila, Kamaljit Kaur "Fault Aware Energy Efficient Mechanisms in Cloud: A Comprehensive Survey." International Journal of Computer Sciences and Engineering 6.5 (2018): 556-568.

APA Style Citation: Sunila, Kamaljit Kaur, (2018). Fault Aware Energy Efficient Mechanisms in Cloud: A Comprehensive Survey. International Journal of Computer Sciences and Engineering, 6(5), 556-568.

BibTex Style Citation:
@article{Kaur_2018,
author = {Sunila, Kamaljit Kaur},
title = {Fault Aware Energy Efficient Mechanisms in Cloud: A Comprehensive Survey},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {5 2018},
volume = {6},
Issue = {5},
month = {5},
year = {2018},
issn = {2347-2693},
pages = {556-568},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=2021},
doi = {https://doi.org/10.26438/ijcse/v6i5.556568}
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v6i5.556568}
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=2021
TI - Fault Aware Energy Efficient Mechanisms in Cloud: A Comprehensive Survey
T2 - International Journal of Computer Sciences and Engineering
AU - Sunila, Kamaljit Kaur
PY - 2018
DA - 2018/05/31
PB - IJCSE, Indore, INDIA
SP - 556-568
IS - 5
VL - 6
SN - 2347-2693
ER -

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Abstract

The energy consumption within cloud increases as fault or failure encountered within cloud computing. This paper presents the analysis of mechanisms used to decrease the energy consumption and enhances fault tolerance degree. The mechanisms which are discussed include both proactive and reactive fault tolerance. This study presents the comparatives analysis of techniques used to ensure fault tolerance and parameters which are enhanced through the application of the techniques. The modification to the existing techniques is required which is concluded through this proposed survey. VM Migration strategies are followed in order to migrate the load on the fittest virtual machine. This happens only if fault appears within the virtual machine. To preserve the VMs against the faults parametric comparison of existing techniques is required. Parametric enhancement is critical in future work.

Key-Words / Index Term

Cloud computing, VM migration, virtual machine, data centre

References

[1] Mills, T. Znati, R. Melhem, K. B. Ferreira, R.E.G., 2011. Energy Consumption of Resilience Mechanisms in Large Scale Systems. Proc. of IEEE Int’l Conf. Comput. Design (ICCD `11.
[2] A. Beloglazov, R.B., 2010. Energy Efficient Allocation of Virtual Machines in Cloud Data Centers. 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing.
[3] A. Beloglazov, J. Abawajy, R.B., 2012. Energy-aware resource allocation heuristics for efficient management of data centers for Cloud Computing. Future generation Computer System, pp.755–768.
[4] A. Zhou, S. Wang, Z. Zheng, C. Hsu, M. Lyu, and F.Y., 2014. On cloud service reliability enhancement with optimal resource usage. IEEE Transactions on Cloud Computing,, 1(1), pp.1–1.
[5] A.Avinzienis, J.CLapne, B.randell, C. landweh., 2004. Basic Concepts and Taxonomy of dependable and secure computing. IEEE Transaction, 1, pp.11–33.
[6] Anon, INDIA’S ENERGY SCENARIO 2020,2014,IEEE.
[7] Anton Beloglazov, R.B., 2012. 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 (CCPE), Wiley Press, pp.755–768.
[8] Arvind Kumar, A.B., 2015. “Improved EDF Algorithm for Fault Tolerance with Energy Minimization.” In Computational Intelligence & Communication Technology (CICT), 2015 IEEE International Conference, pp.370–374.
[9] Asai, H., 2013. P2V Migration with Post-copy Hot Cloning for Service Downtime Reduction. IEEE Third International Conference on Cloud and Green Computing, pp.1-8.
[10] B.P. Rimal, E.C., 2009. A taxonomy and survey of Cloud Computing Systems. IEEE Fifth International Joint Conference on INC, IMS and IDC, pp.44–51.
[11] Bakhta Meroufeland, G.B., 2014. Adaptive time-based coordinated checkpointing for cloud computing workflows. IEEE Scalable Computing: Practice and Experience, pp.153–168.
[12] C. Engelmann, G. R. Vallee, T. Naughton, and S.L.S., 2009. Proactive Fault Tolerance Using Preemptive Migration.IEEEEuromicro International Conference on Parallel, Distributed and Network-based Processing,, pp.252–257.
[13] C. N. Höfer, G.K., 2011. Cloud computing services: taxonomy and comparison. Journal of Internet Services and Applications, Springer.
[14] C.C. Meixner, C.Develder, M.Tornatore, B.M., 2015. Survey on Resiliency Techniques in Cloud Computing Infrastructures and Applications. IEEE.
[15] Chua, A.D.G.K.C., 2015. Time-aware VMFlow Placement, Routing and Migration for Power Efficiency in Data Centers. IEEE Transactions on Network and Service Management,, pp.349–362.
[16] D. Boru, D. Kliazovich, F. Granelli, P. Bouvry, A.Z., 2013.IEEE, Energy-Efficient Data Replication in Cloud Computing Datacenters. CCSNA workshop at Globecom.
[17] D. Jung, S. Chin, K. Chung, and H.Y., 2013. VM migration for fault tolerance in spot instance based cloud computing. in Grid and Pervasive Computing, Springer,, pp.142–151.
[18] D. Sun, Guiran, Chang, C. Miao, X.W., 2013. Analyzing, modeling and evaluating dynamic adaptive fault tolerance strategies in cloud computing environments. Springer Science+Business Media New York.
[19] Daeyong Jung, SungHo Chin, KwangSik Chung, HeonChang Yu1, J.G.“, 2011. An Efficient Checkpointing Scheme Using Price History of Spot Instances in Cloud Computing Environment. IEEE,IFIP International Federation for Information Processing, pp.185–200.
[20] Di, Y. Robert, F. Vivien, D. Kondo, C. Wang, F.C., 2013. Optimization of Cloud Task Processing with SPRINGER,Checkpoint-Restart Mechanism. ACM.
[21] E. Feller, L. Rilling, C. Morin, R. Lottiaux, and D.L., 2010. Snooze: A Scalable, Fault-Tolerant and Distributed Consolidation Manager for Large-Scale Clusters. Proc. 2010 IEEE/ACM Int’l Conference on Green Computing and Communications & Int`l Conference on Cyber, Physical and Social Computing, (125-132).
[22] El-Sayed, Nosayba, and B.S., 2014. To checkpoint or not to checkpoint: Understanding energy-performance-I/O tradeoffs in HPC checkpointing. Understanding energy-performance-I/O tradeoffs in HPC checkpointing.", IEEE Cluster Computing (CLUSTER).
[23] Enida Sheme , Neki Frashëri , Simon Holmbacka, Sébastien Lafond, D.L., 2016. Datacenters powered by renewable energy: A case study for 60 degrees latitude north. IEEE, Software, Telecommunications and Computer Networks (SoftCOM).
[24] Eun Kyung Lee, Hariharasudhan Viswanathan, D.P., 2015. Proactive Thermal-aware Resource Management in Virtualized HPC Cloud Datacenters. IEEE Transactions on Cloud Computing,, pp.1–14.
[25] F. Ma, F. Liu, and Z.L., 2010. Virtual machine migration based on improved pre-copy approach,. In Proc. IEEE Int’l Conf. Software Engineering and Service Sciences,, pp.88–97.
[26] Fahimeh Farahnakian, Adnan Ashraf, Tapio Pahikkala, Pasi Liljeberg, Juha Plosila, Ivan Porres, and H.T., 2015. Using Ant Colony System to Consolidate VMs for Green Cloud Computing. IEEE Transactions on Services Computing,, pp.184–198.
[27] Gartner, F., 1999. Fundamentals of fault-tolerant distributed computing in asynchronous environments. ACM Computing Surveys, 31(1),IEEE pp.1–26.
[28] Goiri, F. Julia, J. Guitart, and J.T., 2010. Checkpoint-based fault tolerant infrastructure for virtualized service providers,. Proc. IEEE/IFIP Network Operations and Management Symposium (NOMS’10), pp.455–462.
[29] H. Goudarzi, M.P., 2012. Energy-Efficient Virtual Machine Replication and Placement in a Cloud Computing System. Computing, IEEE Fifth International Conference on Cloud, pp.750–759.
[30] H. Jin, L. Deng, S. Wu, X. Shi, and X.P., 2009. Live virtual machine migration with adaptive, memory compression. In Cluster Computing and Workshops, 2009. CLUSTER ’09. IEEE Inter-national Conference on, pp.1–10.
[31] I. Egwutuoha, S. Chen, D. Levy, B. Selic, R.C., 2013. Energy efficient fault tolerance for high performance computing (hpc) in the cloud. Sixth International Conference on Cloud Computing (CLOUD), IEEE, ,, pp.762–769.
[32] Ibtesham, Dewan, David DeBonis, Dorian Arnold, and K.B.F., 2014. Coarse-Grained Energy Modeling of Rollback/Recovery Mechanisms. InDependable Systems and Networks (DSN), 2014 44th Annual IEEE/IFIP International Conference, pp.708–713.
[33] J. Ansel, K. Arya, and G.C., 2009. DMTCP: Transparent checkpointing for cluster computations and the desktop,. in Proc. of the Int’l Parallel and Distributed Processing Symp. (IPDPS). Rome, Italy IEEE, pp.1–12.
[34] J. Wei, L. Rashid, K.P. and S.G., 2011.IEEE, Comparing the effects of intermittent and transient hardware faults on program. in Procs. DSN-W.
[35] Jhawar, R. and Puri, V., 2012. Fault tolerance Management in IaaS clouds. IEEE first AESS European Conference, (ESTEL), pp.pp.1–6.
[36] Johnson, J.A., 2013. Optimization of migration downtime of virtual machine in Cloud. IEEE, pp.1–5.
[37] Justin Moore , Jeffrey S. Chase, P.R., 2006. “Weatherman: Automated, Online, and Predictive Thermal Mapping and Management for Data Centers. IEEE International Conference on Autonomic Computing, pp.155–164.
[38] K. Kourai, S.C., 2007. A Fast Rejuvenation Technique for Server Consolidation with Virtual Machines. IEEE International Conference on Dependable Systems and Networks.
[39] Karanbir Singh, S.K., 2016. Energy Efficient Resource Provisioning Through Power Stability Algorithm in Cloud Computing”,. Proceedings of the International Congress on Information and Communication Technology, Springer, pp.255–263.
[40] Kim Khoa Nguyen, M.C., 2015. Environment-aware Virtual Slice Provisioning in Green Cloud Environment. IEEE Transactions on Service Computing,, pp.507–519.
[41] Kim, Changhyeon, C. Jeon, W. Lee, and S.Y., 2015. A Parallel Migration Scheme for Fast Virtual Machine Relocation on a Cloud Cluster. The Journal of Supercomputing, Springer,, pp.4623–45.
[42] L. Silva, J. Alonso, and J.T., 2009. Using Virtualization to Improve Software Rejuvenation. IEEE Trans. Computers, 58(11), pp.1525–1538.
[43] Lin Wang, Fa Zhang, Jordi Arjona Aroca, Athanasios V. Vasilakos, Kai Zheng, Chenying Hou, Dan Li, and Z.L., 2014. GreenDCN: A General Framework for Achieving Energy Efficiency in Data Center Networks. IEEE Journal on Selected Areas in Communications, pp.4–15.
[44] Liu, Jialei, S. Wang, Ao Zhou, S.A P Kumar, and R.B., 2016. Using Proactive Fault - Tolerance Approach to Enhance Cloud Service Reliability. IEEE Transactions on Cloud Computing, pp.1–13.
[45] M. A. Haque, H. Aydin, D.Z., 2011. Energy-Aware Standby-Sparing "Technique for Periodic Real-Time Applications. Proc. of IEEE Int’l Conf. Comput. Design (ICCD `11.
[46] M. Castro, B.L., 2002. Practical byzantine fault tolerance and proactive recovery. ACM Transactions on Computer Systems (TOCS), 20(4), pp.398–461.
[47] M. Grottke, R. Matias, and K.T., 2008. The fundamentals of software aging. IEEE International Conference.
[48] M. Salehi, M. K. Tavana, S. Rehman, M. Shafique, A. Ejlali, and J.H., 2016. wo-State Checkpointing for Energy-Efficient Fault Tolerance in Hard Real-Time Systems. IEEE TRANSACTIONS ON VERY LARGE SCALE INTEGRATION (VLSI) SYSTEMS, pp.1–12.
[49] M. Zhao, F. DUgard, K.A. Kwait, C.A.K., 2015. “Multi-level VM replication based survivability for mission-critical cloud computing.” IEEE International Symposium on Inegrated Network Management.
[50] M.Lackovic, D.Talia, R.T. Calasanz, J.Banares, and O.R., 2010. A taxonomy for the analysis of scientific workflow faults. Proceedings of the 13th IEEE International Conference on Computational Scienceand Engineering., pp.398–403.
[51] M.R. Hines, K.G., 2009. Post-Copy Based Live Virtual Machine Migration Using Adaptive Pre-Paging and Dynamic Self-Ballooning. Proceedings of the 2009 ACM SIGPLAN/SIGOPS international conference on Virtual execution environments,, pp.51–60.
[52] Mahdi Ghamkhari, H.M.-R., Energy and Performance Management of Green Data Centers: A Profit Maximization Approach. IEEE Transactions on Smart Grid, 2014, pp.1017–1025.
[53] Mehmet Basoglu, Michael Orshansky, M.E., 2010. NBTI-Aware DVFS: A New Approach to Saving Energy and Increasing Processor Lifetime. IEEE International Symposium on Low-Power Electronics and Design, pp.253–258.
[54] P. Chi, Cong Xu, T. Zhang, X. Dong, Y.X., 2014. Using Multi-Level Cell STT-RAM for Fast and Energy-Efficient Local Checkpointing. IEEE.
[55] P. Das, P.M.K., 2013. VFT: A Virtualization and Fault Tolerance Approach for Cloud Computing. Proceedings of 2013 IEEE Conference on Information and Communication Technologies(ICT 2013), pp.473–478.
[56] P.D. Kaur, K., 2015. Fault Tolerance Techniques and Architectures in Cloud Computing-A Comparative Analysis. IEEE.
[57] Qi Zhang, Lu Cheng, R.B., 2010. Cloud computing: state-of-the-art and research challenges. J Internet Serv Appl (2010), springer, pp.7–18.
[58] R. Jhawar, V.P., 2013. Fault Tolerance and Resilience in Cloud Computing Environments. Computer and Information Security, IEEE.
[59] R. Melhem, D. Mosse, E.E., 2004. The Interplay of Power Management and Fault Recovery in Real-Time Systems. IEEE TRANSACTIONS ON COMPUTERS, 53(2).
[60] R. Rajachandrasekar, A. Venkatesh, K. Hamidouche, D.K.P., 2015. Power-Check: An Energy-Efficient Checkpointing Framework for HPC Clusters. 15th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing,, pp.261–271.
[61] S. Agarwal, R. Garg, M. S. Gupta, and J.E.M., 2004. Adaptive incremental checkpointing for massively parallel systems. In ICS ’04: Proceedings of the 18th Annual International Conference on Supercomputing ACM, pp.277–286.
[62] S. Marc, Robert W. Wisniewski, J.A. Abraham, S.V.Adve, S. Bagchi, P. Balaji, J.B. et al., 2014. Addressing failures in exascale computing. International Journal of High Performance Computing Applications, 28(2), pp.129–173.
[63] Salfner.F, Leck, M. and M., 2010. A survey of online failure prediction methods. ACM Computing Surveys(CSUR), 42(3), p.p.10.
[64] Shangguang Wang, Ao Zhou, Ching-Hsien Hsu, Xuanyu Xiao, F.Y., 2016. Provision of Data-intensive Services through Energy- and QoS-aware Virtual Machine Placement in National Cloud Data Centers. IEEE Transactions on Emerging Topics in Computing, pp.290–300.
[65] Su, W. Chen, G. Li, Z.W., 2015. RPFF: A Remote Page-fault Filter for Post-copy Live Migration. IEEE International Conference on Smart City/SocialCom/SustainCom together with DataCom, pp.936–943.
[66] T. Mastelic , A. Oleksiak , H. Claussen , I. Brandic , J.M. Pierson, A.V.V., 2015. Cloud Computing: Survey on Energy Efficiency. ACM Computing Surveys (CSUR), 47(2), pp.1–36.
[67] Vaibhav Sundriyal, M.S., 2013. Initial Investigation of a Scheme to Use Instantaneous CPU Power Consumption for Energy Savings Format. E2SC ’13 Proceedings of the 1st International Workshop on Energy Efficient Supercomputing.
[68] Vijaykumar, F.A. and T.N., 2010. Joint optimization of idle and cooling power in data centers while maintaining response time. ACM SIGPLAN,, pp.48–55.
[69] W. Lang, J.M. Patel, J.F.N., 2009. On Energy Management, Load Balancing and Replication. ACM SIGMOD Record, pp.35–42.
[70] Wei Deng1, Fangming Liu1, Hai Jin1, Bo Li, D.L., 2014. Harnessing renewable energy in cloud datacenters: opportunities and challenges. IEEE Network, pp.48–55.
[71] X. Cui, T. Znati, R.M., 2016. Adaptive and Power-Aware Resilience for Extreme-Scale Computing. Intl IEEE Conferences, pp.1–9.
[72] X. You, L. Zhou, J. Huang, J. Zhang, C.J. and J.W., 2013. E2ARS : An Energy-Effective Adaptive Replication Strategy in Cloud Storage System,. Applied Mathematics & Information Sciences An International Journal IEEE, pp.2409–2419.
[73] X. Zhang, Z. Huo, Jie Ma, D.M., 2010. Exploiting Data Deduplication to Accelerate Live Virtual Machine Migration. IEEE International Conference on Cluster Computing,, pp.88–97.
[74] Y. Abe, R. Geambasu, K. Joshi, M.S., 2016. Urgent Virtual Machine Eviction with Enlightened Post-Copy. 12th ACM SIGPLAN/SIGOPS International Conference on Virtual Execution Environments,, pp.51–64.
[75] Y. Lin, H.S., 2017. EAFR: An Energy-Efficient Adaptive File Replication System in Data-Intensive Clusters. IEEE Transactions on Parallel and Distributed Systems,, pp.1017–1030.
[76] Y. Ma, H. Wang, J. Dong, Y. Li, and S.C., 2012. ME2: efficient live migration of virtual machine with memory exploration and encoding. IEEE International Conference on Cluster Computing, pp.610–613.
[77] Yeo, R.B. and C.S., 2009. Cloud Computing and Emerging IT Platforms: Vision, Hype, and Reality for Delivering Computing as the 5th Utility. Future Generation Computer Systems, 25(6), pp.599–616.
[78] Yi Ren, Junichi Suzuki, Chonho Lee, Athanasios V. Vasilakos, S.O., 2014. Balancing Performance, Resource Efficiency and Energy Efficiency for Virtual Machine Deployment in DVFS-enabled Clouds: An Evolutionary Game Theoretic Approach. GECCO Comp ’14: Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation.
[79] Zhang, Z. Zheng, and M.R.L., 2011. BFTCloud: A byzantine fault tolerance framework for voluntary-resource cloud computing. In Cloud Computing (CLOUD), 2011 IEEE International Conference, pp.444–451.
[80] Zhu, Dakai, Rami Melhem, and D.M., 2004. The effects of energy management on reliability in real-time embedded systems.". IEEE/ACM International Conference on Computer Aided Design, ICCAD. [1]
[81] Z. Tang, L. Qi, Z. Cheng, K. Li, S. U. Khan, and K. Li, “An Energy-Efficient Task Scheduling Algorithm in DVFS-enabled Cloud Environment,” 2015.
[82] A. Najari, “Optimization of Dynamic Virtual Machine Consolidation in Cloud Computing Data Centers,” vol. 7, no. 9, pp. 202–208, 2016.
[83] H. He and D. Liu, “Optimizing Data-Accessing Energy Consumption for Workflow Applications in Clouds,” vol. 7, no. 3, pp. 37–48, 2014.
[84] Y. Liu, B. Gong, C. Xing, and Y. Jian, “A Virtual Machine Migration Strategy Based on Time Series Workload Prediction Using Cloud Model,” vol. 2014, 2014.