计算机科学 ›› 2017, Vol. 44 ›› Issue (12): 163-168.doi: 10.11896/j.issn.1002-137X.2017.12.031
郭梅,袁栋,杨耘
GUO Mei, YUAN Dong and YANG Yun
摘要： 云计算系统强大的计算能力和存储容量,使得科学家可以在其上部署计算型和数据密集型的应用,并把大量的应用数据存储在云计算环境下。基于云服务即用即付模型,针对原有数据存储状态,考虑云服务价格变化所产生的状态调整成本,同时为降低存储大量生成的科学数据的成本,在传统最小成本基准的CTT-SP算法的基础上,提出了一种演化CTT-SP算法。 在云计算环境下针对云服务的新价格,该算法可自动决定所生成的科学数据是否需要存储,从而使计算和存储达到更佳的平衡。以亚马逊的成本模型为例,对大量随机数据集进行实验,结果表明,当云服务价格变化后,所提演化CTT-SP算法有效地降低了存储科学数据的总成本。
| [1] SINGH S,CHANA I.Cloud resource provisioning:survey,status and future research directions[J].Knowledge & Information Systems,2016,9(3):1-65. [2] Amazon Cloud Services [EB/OL].http://aws.amazon.com. [3] LI Q,ZHENG X.Research Survey of Cloud Computing[J].Computer Science,2011,8(4):32-37.(in Chinese) 李乔,郑啸.云计算研究现状综述[J].计算机科学,2011,8(4):32-37. [4] KONDO D,JAVADI B,MALECOT P,et al.Cost-benefit analysisof Cloud Computing versus desktop grids[C]∥Proceedings of the 2009 IEEE International Symposium on Parallel and Distribu-ted Processing(IPDPS 2009).Washington DC,2009:1-12. [5] CHEN C L P,ZHANG C Y.Data-intensive applications,challenges,techniques and technologies:A survey on Big Data[J].Information Sciences,2014,5(11):314-347. [6] YANG X,WALLOM D,WADDINGTON S,et al.Cloud computing in e-Science:research challenges and opportunities[J].Journal of Supercomputing,2014,0(1):408-464. [7] GUNDA P K,RAVINDRANATH L,THEKKATH C A,et al.Nectar:automatic management of data and computation in datacenters[C]∥Proceedings of the 9th USENIX Conference on Operating Systems Design and Implementation(OSDI 2010).Berkeley,2010:1-8. [8] DEELMAN E,SINGH G,LIVNY M,et al.The cost of doing science on the cloud:The Montage example[C]∥International Conference for High Performance Computing,Networking,Storageand Analysis(SC 2008).Austin:IEEE,2008:1-12. [9] ADAMS I F,LONG D D E,MILLER E L,et al.Maximizing efficiency by trading storage for computation[C]∥Proceedings of the 2009 Conference on Hot topics in Cloud Computing(HotCloud’2009).Berkeley,2009:1-5. [10] YUAN D,YANG Y,LIU X,et al.A cost-effective strategy for intermediate data storage in scientific cloud workflow systems[C]∥2010 IEEE International Symposium on Parallel & Distributed Processing (IPDPS).Atlanta,2010:1-12. [11] YUAN D,YANG Y,LIU X,et al.A data dependency based strategy for intermediate data storage in scientific cloud workflow systems[J].Concurrency and Computation:Practice & Experience,2012,4(9):956-976. [12] YUAN D,YANG Y,LIU X,et al.On-demand minimum costbenchmarking for intermediate dataset storage in scientific cloud workflow systems[J].Journal of Parallel and Distributed Computing,2011,1(2):316-332. [13] YUAN D,YANG Y,LIU X,et al.A Local-Optimisation Based Strategy for Cost-Effective Datasets Storage of Scientific Applications in the Cloud[C]∥Proceedings of the 2011 IEEE 4th International Conference on Cloud Computing(Cloud 2011).Washington DC,2011:179-186. [14] YUAN D,YANG Y,LIU X,et al.A Highly Practical Approach toward Achieving Minimum Data Sets Storage Cost in the Cloud[J].IEEE Transactions on Parallel and Distributed Systems,2013,4(6):1234-1244. [15] YUAN D,CUI L,LI W,et al.An Algorithm for Finding the Minimum Cost of Storing and Regenerating Datasets in Multiple Clouds[J].IEEE Transactions on Cloud Computing,2015(99):1. [16] YUAN D,LIU X,YANG Y.Dynamic On-the-Fly MinimumCost Benchmarking for Storing Generated Scientific Datasets in the Cloud[J].IEEE Transactions on Computers,2015,4(10):2781-2795. [17] YUAN D,YANG Y,LIU X,et al.Computation and StorageTrade-Off for Cost-Effectively Storing Scientific Datasets in the Cloud[M]∥Handbook of Data Intensive Computing.Springer New York,2011:129-153. |
| No related articles found! |
|
||
首页