Survey of Big Data Management on a Distributed Cloud
Keywords:
Big data management, Distributed cloud, Cloud computing, techniques, computing environmentsAbstract
In todays world more and more users share data and analysis results with each other to save the user cost of big data analytics. Communication and collaboration are increasingly important to modern big data applications. Big data management in a distributed cloud is a major challenge, which is to determine the efficient location of users’ big data and other dynamic application data, so that as many users as possible can be served. Fair allocation of cloud resources to different users is crucial, otherwise, unfair allocation may result in unsatisfied users no longer using the service, the service provider may then fall into disrepute, and its revenue will be significantly reduced. There are numerous difficulties in the load balancing techniques, problem with sharing of resources such as security, fault tolerance etc. in cloud computing environments. Many researchers have been proposed several techniques to enhance the Big data management in a distributed cloud. This paper portrays presents a review of the current big data research, exploring applications, opportunities and challenges, as well as the state-of-the-art techniques and underlying models that exploit cloud computing technologies.
References
A. Beloglazov, and R. Buyya, Energy efficient resource management in virtualized cloud data centers, Proc. 10th IEEE/ACM international conference on cluster, cloud and grid computing, 2010, 826-831.
Klaithem Al Nuaimi, Nader Mohamed, Mariam Al Nuaimi and Jameela Al-Jaroodi” A Survey of
Load Balancing in Cloud Computing: Challenges and Algorithms” 2012 IEEE Second Symposium on Network Cloud Computing and Applications.
. J. Srinivas, K.V.S.Reddy and A.M. Qyser, “Cloud Computing Basics”, International Journal of
Advanced Research in Computer and Communication Engineering, 1(5), July 2012.
. S. Ray and A.D. Sarkar, “Execution Analysis of Load Balancing Algorithms in Cloud Computing Environment”, International Journal on Cloud Computing Services and Architecture,
(5), October 2012.
http://searchcloudcomputing.techtarget.com/definition/cloud-computing
Tharam Dillon, Chen Wu and Elizabeth Chang," Cloud Computing: Issues and Challenges", 24th IEEE International Conference on Advanced Information Networking and Applications, 2010.
Parallel sysplex, http://www-03.ibm.com/systems/z/ advantages/pso/.
http://www.scl.ameslab.gov/Projects/parallel_computing/cluster_examples.html
http://searchdatacenter.techtarget.com/definition/sysplex-and-Parallel-Sysplex
M. Alicherry and T.V. Lakshman. Network aware resource allocation in distributed clouds. Proc. of INFOCOM, IEEE, 2012.
. S. Agarwal, J. Dunagan, N. Jain, S. Saroiu, A. Wolman, and H. Bhogan. Volley: automated data placement for geo-distributed cloud services. Proc. of NSDI, USENIX, 2010.
. I. Baev, R. Rajaraman, and C. Swamy. Approximation algorithms for data placement problems. SIAM J. on Computing, Vol. 38, No. 4, pp.1411-1429, SIAM, 2008.
. L. Golab, M. Hadjieleftheriou, H. Karloff, and B. Saha. Distributed data placement to minimize communication costs via graph partitioning. Proc. of SSDBM, ACM, 2014.
. L. Gu, D. Zeng, P. Li, and S. Guo. Cost minimization for big data processing in geo-distributed data centers. Trans. on Emerging Topics in Computing, Vol. 2, No. 3,pp.314-323, IEEE, 2014.
. L. Jiao, J. Li, W. Du, and X. Fu. Multi-objective data placement for multi-cloud socially aware services. Proc. of INFOCOM, IEEE, 2014.
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