PL EN


Preferences help
enabled [disable] Abstract
Number of results
2016 | 41 | 55-61
Article title

A Survey on Resource Provisioning Heuristics

Content
Title variants
Languages of publication
EN
Abstracts
EN
Cloud Computing allow the users to dynamically provide computing resource to meet their information technology needs. Cloud providers are able to rent resources from cloud for many computational purposes using any one of the provisioning model static/dynamic the company can pay bills as per the model. One of the major drawbacks in cloud computing is linked to step up the dataflow model with better resources. Resource allocation is performed with the aim of minimizing the costs and to get high throughput. The added challenges in resource allocation is satisfying the customer demands and application necessities. In this paper, we have presented an extensive survey on various resource allocation strategies for various dataflow model and their challenges are discussed in detail.
Year
Volume
41
Pages
55-61
Physical description
Contributors
author
  • Department of Computer Science and Engineering, Avinashilingam Institute of Home Science and Higher Education for Women, Coimbatore, India
author
  • Department of Computer Science and Engineering, Avinashilingam Institute of Home Science and Higher Education for Women, Coimbatore, India
References
  • [1] J. Dean and S. Ghemawat, MapReduce: Simplified data processing on large clusters. ACM Commun. Vol. 51, no. 1, pp. 107-113, 2008
  • [2] M. Zaharia, T. Das, H. Li, S. Shenker, and I. Stoica, Discretized streams: an efficient and fault-tolerant model for stream processing on large clusters, in Proc. USENIX Conf. Hot Topics Cloud Comput. 2012, p. 10.
  • [3] L. Neumeyer, B. Robbins, A. Nair, and A. Kesari, S4: Distributed stream computing platform, in Proc. IEEE Int. Conf. Data Min. Workshops, 2010, pp. 170-177
  • [4] B. Satzger, W. Hummer, P. Leitner, and S. Dustdar, Esc: Towards an elastic stream computing platform for the cloud, in Proc. IEEE Int. Conf. Cloud Comput., Jul. 2011, pp. 348-355
  • [5] M. Maheswaran, S. Ali, H. J. Siegel, D. Hensgen, and R. F. Freund, Dynamic mapping of a class of independent tasks onto heterogeneous computing systems, in J. Parallel Distrib. Comput. Vol. 59, no. 2, pp. 107-131, 1999
  • [6] Z. Wu, X. Liu, Z. Ni, D. Yuan, and Y. Yang, A market-oriented hierarchical scheduling strategy in cloud workflow systems, J. Supercomput. Vol. 63, no. 1, pp. 256-293, 2013
  • [7] F. Zamfirache, M. Frincu, and D. Zaharie, Population-based metaheuristics for tasks scheduling in heterogeneous distributed systems, in Proc. 7th Int. Conf. Numerical Methods Appl. 2011, Vol. 6046, pp. 321-328
  • [8] R. Castro Fernandez, M. Migliavacca, E. Kalyvianaki, and P. Pietzuch,Integrating scale out and fault tolerance in stream processing using operator state management, in Proc. ACM SIGMOD Int. Conf. Manage. Data, 2013, pp. 725-736
  • [9] R. Tolosana-Calasanz, J. Angel Ba~nares, C. Pham, and O. Rana, End-to-end qos on shared clouds for highly dynamic, large-scalesensing data streams, in Proc. IEEE/ACM Int. Symp. Cluster, Cloud Grid Comput 2012, pp. 904-911
  • [10] A. Quiroz, H. Kim, M. Parashar, N. Gnanasambandam and N. Sharma, Towards autonomic workload provisioning for enterprise Grids and clouds, 2009 10th IEEE/ACM International Conference on Grid Computing, 2009, pp. 50-57, doi: 10.1109/GRID.2009.5353066
  • [11] Ye Hu, Johnny Wong, Gabriel Iszlai and Marin Litoiu, Resource Provisioning for Cloud Computing, Proceedings of the 2009 Conference of the Center for Advanced Studies on Collaborative Research, Pages 101-111, 2009
  • [12] Saeid Abrishami, Mahmoud Naghibzadeh, Dick H.J. Epema, Deadline-constrained workflow scheduling algorithms for Infrastructure as a Service Clouds, Future Generation Computer Systems, Volume 29, Issue 1, January 2013, Pages 158-169
  • [13] W. Dawoud, I. Takouna, and C. Meinel, Infrastructure as a Service Security: Challenges and Solutions, in Proc the 7th International Conference on Informatics and Systems 2010 (INFOS’10), Cairo, March 2010, pp. 1-8
  • [14] M. Mao and M. Humphrey, Auto-scaling to minimize cost and meet application deadlines in cloud workflows, SC '11: Proceedings of 2011 International Conference for High Performance Computing, Networking, Storage and Analysis, 2011, pp. 1-12
  • [15] K. Tsakalozos, H. Kllapi, E. Sitaridi, M. Roussopoulous, D. Paparas, and A. Delis, Flexible Use of Cloud Resources through Profit Maximization and Price Discrimination, in Proc of the 27th IEEE International Conference on Data Engineering (ICDE 2011), April 2011, pp.75-86.
  • [16] R. Jeyarani, N. Nagaveni, R. Vasanth Ramc, Design and implementation of adaptive power-aware virtual machine provisioner (APA- VMP) using swarm intelligence, Journal of Future Generation Computer Systems, Volume 28, Issue 5, May 2012, pp. 811-821, DOI:/10.1016/j.future.2011.06.002
  • [17] J. Kennedy and R. Eberhart, Particle swarm optimization, in Proc. IEEE Int. Conf. Neural Netw. 1995, Vol. 4, pp. 1942–1948.
  • [18] I. De Falco, R. Del Balio, E. Tarantino, and R. Vaccaro, Improving search by incorporating evolution principles in parallel tabu search, in 1994 IEEE Conference on Evolutionary Computation, Vol. 2, pp. 823-828, 1994
Document Type
article
Publication order reference
Identifiers
YADDA identifier
bwmeta1.element.psjd-52c9905e-ace9-4598-8be8-888e8c958914
JavaScript is turned off in your web browser. Turn it on to take full advantage of this site, then refresh the page.