The week's pick
Reseach Article
Enhanced Resource Provisioning Strategies for Scientific Workflows in Cloud Environment: A Survey
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 180 - Number 41 |
| Year of Publication: 2018 |
| Authors: S. Sridevi, Jeevaa Katiravan |
10.5120/ijca2018917097
|
S. Sridevi, Jeevaa Katiravan . Enhanced Resource Provisioning Strategies for Scientific Workflows in Cloud Environment: A Survey. International Journal of Computer Applications. 180, 41 ( May 2018), 27-33. DOI=10.5120/ijca2018917097
Abstract
Efficient resource provisioning is the key challenge that brings the best quality of service which is beneficial both to the users and CSPs. Since cloud computing aims at providing adaptive provisioning as pay-per-use basis, dynamic resource provisioning is a critical research issue. The on-demand provisioning and resource availability in cloud computing make it ideal for executing scientific workflow applications. To ensure better performance, there is a need for auto-scaling. The problem of assigning resources to tasks and orchestrating their execution to preserve the dependencies of workflows is NP-complete. [2]Hence, no optimal solution can be found in polynomial time. NP-complete problems are often addressed by using heuristic or meta-heuristics approaches. This survey presents the existing auto-scaling techniques and meta-heuristics approaches for VM placements of cloud workflows.
References
- P.Mell and T.Grance, “The NIST Definition of Cloud Computing,” v.15, http://csrc.nist.gov/groups/SNS/cloud-computing.
- J.D.Ullman, “NP-complete scheduling problems”, Journal of Computer and System Sciences, vol. 10, no. 3, pp. 384-393, 1975.
- E.Deelman, K.Vahi, G.Juve, M.Rynge, S.Callaghan, P.J.Maechling, R.Mayani, W.Chen, R.Ferreira da Silva, M.Livny, and K.Wenger, “Pegasus: a Workflow Management System for Science Automation”, Future Generation Computer Systems, vol. 46, pp. 17 – 35, 2015.
- Rodrigo N.Calheiros, Rajiv Ranjan, Anton Beloglazov, Cesar A.F.De Rose and RajkumarBuyya, “CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms”, Software – Practice and Experience, 2011; 41:23 – 50.
- Weiwei Chen, EwaDeelman, “WorkflowSim: A Toolkit for simulating Scientific Workflows in Distributed Environments”, IEEE International Conference; 2012: 1-8.
- ZhichengCai, Qianmu Li and Xiaoping Li, “ElasticSim: A Toolkit for simulating Workflows with Cloud Resource Runtime Auto-Scaling and Stochastic Task Execution Times”, Journal of Grid Computing (2017) 15: 257 – 272.
- ChenhaoQu, Rodrigo N.Calheiros and RajkumarBuyya, “Auto-scaling web applications in clouds: A Taxonomy and Survey”, ACM Computing Surveys, 2016.
- K.Kanagaraj and S.Swamynathan, “Structure aware resource estimation for effective scheduling and execution of data intensive workflows in cloud”, Future Generation Computer Systems (2017), http://dx.doi.org/10.1016/j.future.2017.09.001
- FengyuGuo, Long Yu, ShengweiTian and Jiong Yu, “A workflow task scheduling algorithm based on the resources’ fuzzy clustering in cloud computing environment”, Int. J. Commun. Syst. (2014), Wiley Online Library. DOI: 10.1002/dac.2743
- Xiaolong Liu, Shyan-Ming Yuan, Guo-HengLuo, Hao-Yu Huang and Paolo Bellavista, “Cloud Resource Management with Turnaround Time Driven Auto-Scaling”, IEEE Access, vol 5, 2017.
- Thanh-Phuong Pham, Juan J.Durillo, and Thomas Fahringer, “Predicting Workflow Task Execution Time in the Cloud using a Two-Stage machine learning approach”, IEEE Transactions on Cloud Computing. DOI 10.1109/TCC.2017.2732344
- Kennedy J, Eberhart R, “Particle Swarm optimization”, Proc int conf neural networks, vol. 4. IEE; 1995. 1942 – 8.
- Dorigo M, Stutzle T, “Ant colony optimization”, MIT press, 2004.
- Hamed Shah-Hosseini, “Problem Solving by Intelligent Water Drops”, IEEE Congress on Evolutionary Computation. Swissotel, The Stamford, 2007.
- Haiping Ma, Simon D, “Biogeography-based optimization: A 10-year Review”, IEEE Transactions on Emerging Topics in Computational Intelligence, Vol.1, No.5, October 2017.
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