CFP last date
20 January 2025
Reseach Article

Trends towards Energy Efficient with Backfilling based Scheduling Techniques for Cloud Computing

by Amanjot Kaur, Anil Kumar
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 155 - Number 6
Year of Publication: 2016
Authors: Amanjot Kaur, Anil Kumar
10.5120/ijca2016912324

Amanjot Kaur, Anil Kumar . Trends towards Energy Efficient with Backfilling based Scheduling Techniques for Cloud Computing. International Journal of Computer Applications. 155, 6 ( Dec 2016), 18-23. DOI=10.5120/ijca2016912324

@article{ 10.5120/ijca2016912324,
author = { Amanjot Kaur, Anil Kumar },
title = { Trends towards Energy Efficient with Backfilling based Scheduling Techniques for Cloud Computing },
journal = { International Journal of Computer Applications },
issue_date = { Dec 2016 },
volume = { 155 },
number = { 6 },
month = { Dec },
year = { 2016 },
issn = { 0975-8887 },
pages = { 18-23 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume155/number6/26608-2016912324/ },
doi = { 10.5120/ijca2016912324 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:00:32.658903+05:30
%A Amanjot Kaur
%A Anil Kumar
%T Trends towards Energy Efficient with Backfilling based Scheduling Techniques for Cloud Computing
%J International Journal of Computer Applications
%@ 0975-8887
%V 155
%N 6
%P 18-23
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The OpenNebula is an open source platform which provides flexible and feature-rich cloud management solutions; one of them is Haizea which act as lease manager and it reinforce distinctive types of leases. The deadline sensitive lease is one of the supporting leases provided by Haizea. In real time scenario, the majority of the leases are deadline sensitive and these leases are scheduled by implementing the backfilling algorithm. The backfilling algorithm optimizes scheduling by sorting one of the leases from best effort queue and allocate the free resources to schedule the deadline sensitive lease. But in some cases, if there is the same kind of leases and should be in connective in sequence than backfilling algorithm does not provide an efficient platform for scheduling. AHP (Analytic Hierarchy Process) is used to enhance the backfilling algorithm, which acts as a decision maker in the backfilling algorithm to choose the possible best lease from the given best effort queue in order to schedule the deadline sensitive lease. The overall objective of this paper is to explore gaps associated with existing backfilling based scheduling techniques.

References
  1. Mell, Peter, and Tim Grance. "The NIST definition of cloud computing." (2011).
  2. Liu, Jinzhao, et al. "Aggressive resource provisioning for ensuring QoS in virtualized environments." IEEE Transactions on Cloud Computing 3.2 (2015): 119-131.
  3. Haizea.cs.uchicago.edu, 2016. Haizea – An Open Source VM-Based.
  4. Lease Manager". N.p., 2016. Web. 15 Jan. 2016.Saaty, Thomas L. "Decision making with the analytic hierarchy process." International journal of services sciences 1.1 (2008): 83-98.
  5. Saaty, Thomas L. "Decision-making with the AHP: Why is the principal eigenvector necessary." European journal of operational research 145.1 (2003): 85-91.
  6. Patel, Pradip D., et al. "Live Virtual Machine Migration Techniques in Cloud Computing: A Survey." International Journal of Computer Applications 86.16 (2014).
  7. Nathani, Amit, Sanjay Chaudhary, and Gaurav Somani. "Policy based resource allocation in IaaS cloud." Future Generation Computer Systems 28.1 (2012): 94-103.
  8. Cho, Keng-Mao, et al. "A hybrid meta-heuristic algorithm for VM scheduling with load balancing in cloud computing." Neural Computing and Applications 26.6 (2015): 1297-1309.
  9. Beloglazov, Anton, and Rajkumar Buyya. "OpenStack Neat: a framework for dynamic and energy‐efficient consolidation of virtual machines in OpenStack clouds." Concurrency and Computation: Practice and Experience 27.5 (2015): 1310-1333.
  10. Mahmoud, Aminu Abdulkadir, et al. "Multi-Criteria Strategy for Job Scheduling and Resource Load Balancing in Cloud Computing Environment." Indian Journal of Science and Technology 8.30 (2015).
  11. Liu, Chengjiang. "A Load Balancing Aware Virtual Machine Live Migration Algorithm." (2016).
  12. Boru, Dejene, et al. "Energy-efficient data replication in cloud computing datacenters." Cluster Computing 18.1 (2015): 385-402.
  13. Fiandrino, Claudio, et al. "Performance and energy efficiency metrics for communication systems of cloud computing data centers." (2015).
  14. Singh, Aarti, Dimple Juneja, and Manisha Malhotra. "Autonomous agent based load balancing algorithm in cloud computing." Procedia Computer Science 45 (2015): 832-841.
  15. Zhou, Zhou, et al. "A novel virtual machine deployment algorithm with energy efficiency in cloud computing." Journal of Central South University 22 (2015): 974-983.
  16. Enokido, Tomoya, Dilawaer Duolikun, and Makoto Takizawa. "An extended improved redundant power consumption laxity-based (EIRPCLB) algorithm for energy efficient server cluster systems." World Wide Web 18.6 (2015): 1603-1629.
  17. Farahnakian, Fahimeh, et al. "Using ant colony system to consolidate vms for green cloud computing." IEEE Transactions on Services Computing 8.2 (2015): 187-198.
  18. Li, Hongjian, et al. "Energy-efficient migration and consolidation algorithm of virtual machines in data centers for cloud computing." Computing 98.3 (2016): 303-317.
  19. Sun, Degang, et al. "SPLM: Security Protection of Live Virtual Machine Migration in Cloud Computing." Proceedings of the 4th ACM International Workshop on Security in Cloud Computing. ACM, 2016.
  20. Qian, Zhang, et al. "A Load Balancing Task Scheduling Algorithm based on Feedback Mechanism for Cloud Computing." International Journal of Grid and Distributed Computing 9.4 (2016): 41-52.
  21. Kansal, Nidhi Jain, and Inderveer Chana. "Energy-aware Virtual Machine Migration for Cloud Computing-A Firefly Optimization Approach." Journal of Grid Computing 14.2 (2016): 327-345.
  22. Sabar, Nasser R., and Andy Song. "Grammatical Evolution Enhancing Simulated Annealing for the Load Balancing Problem in Cloud Computing." Proceedings of the 2016 on Genetic and Evolutionary Computation Conference. ACM, 2016.
  23. Esposito, Flavio, and Walter Cerroni. "GeoMig: Online Multiple VM Live Migration." Cloud Engineering Workshop (IC2EW), 2016 IEEE International Conference on. IEEE, 2016.
  24. Akbari, Elham, et al. "Incorporation of weighted linear prediction technique and M/M/1 Queuing Theory for improving energy efficiency of Cloud computing datacenters." Long Island Systems, Applications and Technology Conference (LISAT), 2016 IEEE. IEEE, 2016.
  25. Chien, Nguyen Khac, Nguyen Hong Son, and Ho Dac Loc. "Load balancing algorithm based on estimating finish time of services in cloud computing." 2016 18th International Conference on Advanced Communication Technology (ICACT). IEEE, 2016.
  26. Tripathy, Nayak Chitaranjan. "Deadline Sensitive Lease Scheduling in Cloud Computing Environment Using AHP." Journal of King Saud University-Computer and Information Sciences (2016).
Index Terms

Computer Science
Information Sciences

Keywords

Haizea Backfilling Scheduling AHP live migration load balancing energy efficiency Open Nebula Deadline sensitive.