The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
User-priority guided Min-Min scheduling algorithm for load balancing in cloud computing
Abstract
Cloud computing is emerging as a new paradigm of large-scale distributed computing. In order to utilize the power of cloud computing completely, we need an efficient task scheduling algorithm. The traditional Min-Min algorithm is a simple, efficient algorithm that produces a better schedule that minimizes the total completion time of tasks than other algorithms in the literature [7]. However the biggest drawback of it is load imbalanced, which is one of the central issues for cloud providers. In this paper, an improved load balanced algorithm is introduced on the ground of Min-Min algorithm in order to reduce the makespan and increase the resource utilization (LBIMM). At the same time, Cloud providers offer computer resources to users on a pay-per-use base. In order to accommodate the demands of different users, they may offer different levels of quality for services. Then the cost per resource unit depends on the services selected by the user. In return, the user receives guarantees regarding the provided resources. To observe the promised guarantees, user-priority was considered in our proposed PA-LBIMM so that user's demand could be satisfied more completely. At last, the introduced algorithm is simulated using Matlab toolbox. The simulation results show that the improved algorithm can lead to significant performance gain and achieve over 20% improvement on both VIP user satisfaction and resource utilization ratio.
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Cites
Braun, T. D., Siegel, H. J., Beck, N., Bölöni, L. L., Maheswaran, M., Reuther, A. I., Robertson, J. P., Theys, M. D., Yao, B., Hensgen, D., et al. (2001) · Journal of Parallel and Distributed Computing · doi:10.1006/jpdc.2000.1714 · Cited by 1,722
QoS guided Min-Min heuristic for grid task scheduling
He, X., Sun, X., & von Laszewski, G. (2003) · Journal of Computer Science and Technology · doi:10.1007/bf02948918 · Cited by 420
Towards a Load Balancing in a three-level cloud computing network
Wang, S.-C., Yan, K.-Q., Liao, W.-P., & Wang, S.-S. (2010) · 2010 3rd International Conference on Computer Science and Information Technology · doi:10.1109/iccsit.2010.5563889 · Cited by 294
Load Balanced MinMin Algorithm for Static MetaTask Scheduling in Grid Computing
Kokilavani, T., & George Amalarethinam, D. I. (2011) · International Journal of Computer Applications · doi:10.5120/2403-3197 · Cited by 210
An Adaptive Scoring Job Scheduling algorithm for grid computing
Chang, R.-S., Lin, C., & Lin, C.-F. (2012) · Information Sciences · doi:10.1016/j.ins.2012.04.019 · Cited by 60
Cited by
Energy efficiency in cloud computing data centers: a survey on software technologies
Katal, A., Dahiya, S., & Choudhury, T. (2023) · Cluster Computing · doi:10.1007/s10586-022-03713-0 · Cited by 516
“To address the issue of energy usage, the authors of [56] proposed the Energy and Performance-Efficient Task Scheduling Algorithm (EPETTS) in a heterogeneous virtualized cloud.”
MethodInfluentialLoad balancing in cloud computing: A big picture
Mishra, S. K., Sahoo, B., & Parida, P. P. (2020) · Journal of King Saud University - Computer and Information Sciences · doi:10.1016/j.jksuci.2018.01.003 · Cited by 384
Houssein, E. H., Gad, A. G., Wazery, Y. M., & Suganthan, P. N. (2021) · Swarm and Evolutionary Computation · doi:10.1016/j.swevo.2021.100841 · Cited by 355
Kumar, M., Dubey, K., Singh, S., Kumar Samriya, J., & Gill, S. S. (2023) · Concurrency and Computation: Practice and Experience · doi:10.1002/cpe.7469 · Cited by 26
Metadata
- Authors
- Chen, H., Wang, F., Helian, N., & Akanmu, G.
- Venue
- 2013 National Conference on Parallel Computing Technologies (PARCOMPTECH)
- Publisher
- IEEE
- Published
- 2013-02
- Pages
- 1-8
- OpenAlex
- W2115495728
- Cited by
- 287
- References
- 5
- FWCI
- 17.49
- Type
- conference paper
- Language
- en
- Open access
- closed
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