Research Cell: An International Journal of Engineering Sciences

Open Access Open Access  Restricted Access Subscription Access

Power Consumption-aware Virtual Machine Placement in Cloud data Center


Affiliations
1 Department of Computer Science Engineering Global Institute of Management and Emerging technology, Amritsar, Punjab, India
 

With the speedy creating number of Cloud applications, demands for far reaching scale server ranches have raised to valid high. Cloud server ranches allow dynamic and versatile resource provisioning to suit time changing computational solicitations. Late examinations have proposed a couple of task approaches develop generally in light of vitality use of servers. Host temperature, regardless, is occasionally considered as a watching parameter. This work proposes a power and warm careful virtual machine (VM) assignment part for Cloud server ranches. The objective of the proposed segment is to reduce the general essentialness use and VM development numbers, while keeping up a vital separation from encroachment of Service Level Agreements (SLA) in Cloud Server ranches. The proposed instrument was executed and evaluated on CloudSim. Reenactment comes to fruition show that the proposed designation segment gets vital favorable circumstances terms of essentialness saving and other execution records.

Keywords

Cloud Computing, Data Centers, Energy Consumption, Thermal Aware, Virtual Machines.
User
Notifications
Font Size

  • 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.

  • Beloglazov, A., Abawajy, J. &Buyya, R., 2012. Energy-aware resource allocation heuristics for efficient management of data centers for Cloud computing. Future Generation Computer Systems, 28(5), pp.755–768. Available at: http://dx.doi.org/10.1016/j.future.2011.04.017.

  • Bhandari, A., 2016. Secure Algorithm for Cloud Computing and Its Applications. , pp.188–192.

  • Cardoso, A. et al., 2014. A Survey of Cloud Computing Migration Issues and Frameworks. , 1, pp.161–170.

  • Cui, X. et al., 2014. Shadows on the cloud: An energy-aware, profit maximizing resilience framework for cloud computing. CLOSER 2014 - Proceedings of the 4th International Conference on Cloud Computing and Services Science, pp.15–26. Available at: http://www.scopus.com/inward/record.url?eid=2-s2.0-84902334325&partnerID=tZOtx3y1.

  • Fiandrino, C. et al., 2015. Performance and Energy Efficiency Metrics for Communication Systems of Cloud Computing Data Centers. , pp.1–14.

  • Ghribi, C. et al., 2010. Energy Efficient VM Scheduling for Cloud Data Centers : Exact allocation and migration algorithms.

  • Gouasmi, T., Louati, W. &Kacem, A.H., 2017. Cost-Efficient Distributed MapReduce Job Scheduling across Cloud Federation. In 2017 IEEE International Conference on Services Computing (SCC). pp. 289–296.

  • Goudarzi, H. &Pedram, M., Energy-Efficient Virtual Machine Replication and Placement in a Cloud Computing System.

  • Hamdard, J. & Delhi, N., 2017. Cloud Computing and its Applications National Seminar on Cloud Computing and Its Applications. , 8(2).

  • Hashmi, A., 2014. Cloud Computing : VM placement & Load Balancing. , 3(11), pp.9197–9200.

  • K. et al., He, 2017. Energy-Efficient Framework for Virtual Machine Consolidation in Cloud Data Centers. , (October).

  • Jeyarani, R., Nagaveni, N. & Ram, R.V., 2012. Design and implementation of adaptive power-aware virtual machine provisioner( APA-VMP ) using swarm intelligence. Future Generation Computer Systems, 28(5), pp.811–821.

  • Kang, D. et al., 2017. Room Temperature Control and Fire Alarm / Suppression IoT Service Using MQTT on AWS.

  • Mandal, S.K., On-Demand VM Placement on Cloud Infrastructure On-Demand VM Placement on Cloud Infrastructure Master of Technology.

  • Meroufel, B. &Belalem, G., 2014. Adaptive time-based coordinated checkpointing for cloud computing workflows. Scalable Computing, 15(2), pp.153–168.

  • Nadu, T., Vasantha, S. & Nadu, T., 2015. A particle swarm optimization algorithm for power-aware virtual machine allocation.

  • Pinheiro, E., Weber, W. &Barroso, L., 2007. Failure trends in a large disk drive population. Proceedings of the 5th USENIX Conference on File and Storage Technologies (FAST 2007), (February), pp.17–29.

  • Portaluri, G. & Giordano, S., 2014. A Power Efficient Genetic Algorithm for Resource Allocation in Cloud Computing Data Centers. , pp.58–63.

  • Rajamony, E.N. (Mootaz) E.K., 2013. Energy- Efficient Server Clusters. , volume 232.

  • Rebai, S., 2017. Resource allocation in Cloud federation To cite this version : HAL Id : tel-01534528 Allocation et f ´ ed ´ eration des resources information quesdans le Cloud.

  • Srikantaiah, S., 2015. Energy Aware Consolidation for Cloud Computing.

  • Yao, L. et al., 2014. Guaranteeing Fault-Tolerant Requirement Load Balancing Scheme Based on VM Migration. ieee, 57(2).

  • Zhao, Y. & Chow, S.S.M., 2017. Updatable Block-Level Message-Locked Encryption. ACM, pp.449–460.


Abstract Views: 207

PDF Views: 0




  • Power Consumption-aware Virtual Machine Placement in Cloud data Center

Abstract Views: 207  |  PDF Views: 0

Authors

Bharti Bedi
Department of Computer Science Engineering Global Institute of Management and Emerging technology, Amritsar, Punjab, India
Meenakshi Sharma
Department of Computer Science Engineering Global Institute of Management and Emerging technology, Amritsar, Punjab, India

Abstract


With the speedy creating number of Cloud applications, demands for far reaching scale server ranches have raised to valid high. Cloud server ranches allow dynamic and versatile resource provisioning to suit time changing computational solicitations. Late examinations have proposed a couple of task approaches develop generally in light of vitality use of servers. Host temperature, regardless, is occasionally considered as a watching parameter. This work proposes a power and warm careful virtual machine (VM) assignment part for Cloud server ranches. The objective of the proposed segment is to reduce the general essentialness use and VM development numbers, while keeping up a vital separation from encroachment of Service Level Agreements (SLA) in Cloud Server ranches. The proposed instrument was executed and evaluated on CloudSim. Reenactment comes to fruition show that the proposed designation segment gets vital favorable circumstances terms of essentialness saving and other execution records.

Keywords


Cloud Computing, Data Centers, Energy Consumption, Thermal Aware, Virtual Machines.

References