EFFICIENT R-TREE BASED INDEXING SCHEME FOR SERVER-CENTRIC CLOUD STORAGE SYSTEM
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The document presents an innovative two-layer indexing scheme called rt-hcn for efficient multi-dimensional query processing in cloud storage systems. It integrates an r-tree based indexing structure with an hcn-based routing protocol, demonstrating improved query efficiency and space utilization in server-centric data centers. Experimental results validate its effectiveness in comparison to existing indexing schemes, indicating its potential for future data center implementations.
![CONTACT: PRAVEEN KUMAR. L (, +91 – 9791938249)
MAIL ID: sunsid1989@gmail.com, praveen@nexgenproject.com
Web: www.nexgenproject.com, www.finalyear-ieeeprojects.com
especially the speedup of point query of RT-HCN, depicting its potential
applicability in future data centers
CONCLUSION
In this paper, we proposed an indexing scheme named RT-HCN for multi-
dimensional query processing in data centers, which are the infrastructures for
building cloud storage systems and are interconnected using a specific data
center network (DCN). RT-HCN is a two-layer indexing scheme, which
integrates HCN-based routing protocol and the R-Tree based indexing
technology, and is portionably distributed on every server. Based on the
characteristics of HCN, we present a specialized mapping technique to
improve global index allocation in the network, resulting query-efficiency and
load-balancing for the cloud system.We also combine practical techniques in
face of data skewness and querying false positives, greatly increasing the
adaptability and querying performance of RT-HCN. We prove theoretically that
RT-HCN is both query-efficient and space-efficient, by which each server will
only maintain a constrained number of indices while a large number of users
can concurrently process queries with low routing cost. We also give an
insight into the inner relation between the HCN and other data center
topologies, and apply the two-layer indexing scheme to the DCell topology in a
generalized way. We compare our design with RT-CAN [8], a similar design
for traditional P2P network. Experiments validate the efficiency of our
proposed scheme and depict its potential implementation in data centers.
REFERENCES
[1] S. Ghemawat, H. Gobioff, and S. T. Leung. The google file system. In
ACM SIGOPS operating systems review, volume 37, pages 29–43, 2003.](https://image.slidesharecdn.com/efficientr-treebasedindexingschem-160722070621/85/EFFICIENT-R-TREE-BASED-INDEXING-SCHEME-FOR-SERVER-CENTRIC-CLOUD-STORAGE-SYSTEM-2-320.jpg)
![CONTACT: PRAVEEN KUMAR. L (, +91 – 9791938249)
MAIL ID: sunsid1989@gmail.com, praveen@nexgenproject.com
Web: www.nexgenproject.com, www.finalyear-ieeeprojects.com
[2] G. DeCandia, D. Hastorun, M. Jampani, et al. Dynamo: amazon’s highly
available key-value store. In ACM SIGOPS Operating Systems Review,
volume 41, pages 205–220, 2007.
[3] A. Lakshman and P. Malik. Cassandra: a decentralized structured storage
system. ACM SIGOPS Operating Systems Review, 44:35–40, 2010.
[4] D. Beaver, S. Kumar, H. C. Li, et al. Finding a needle in haystack:
Facebook’s photo storage. In OSDI, volume 10, pages 1–8, 2010.
[5] J. Baker, C. Bond, J. C. Corbett, et al. Megastore: Providing scalable,
highly available storage for interactive services. In CIDR, volume 11, pages
223–234, 2011.
[6] J. C. Corbett, J. Dean, M. Epstein, et al. Spanner: Googles globally
distributed database. ACM Transactions on Computer Systems (TOCS), 31:8,
2013.
[7] S. Wu and K. L. Wu. An indexing framework for efficient retrieval on the
cloud. IEEE Data Eng. Bull., 32:75–82, 2009.
[8] J. Wang, S. Wu, H. Gao, et al. Indexing multidimensional data in a cloud
system. In Proceedings of the 2010 ACM SIGMOD International Conference
on Management of data, pages 591–602, 2010.
[9] S.Wu, D. Jiang, B. C. Ooi, and K. L.Wu. Efficient b-tree based indexing for
cloud data processing. Proceedings of the VLDB Endowment, 3:1207–1218,
2010.
[10] G. Chen, H. T. Vo, S. Wu, et al. A framework for supporting dbms-like
indexes in the cloud. Proceedings of the VLDB Endowment, 4:702–713, 2011.](https://image.slidesharecdn.com/efficientr-treebasedindexingschem-160722070621/85/EFFICIENT-R-TREE-BASED-INDEXING-SCHEME-FOR-SERVER-CENTRIC-CLOUD-STORAGE-SYSTEM-3-320.jpg)
EFFICIENT R-TREE BASED INDEXING SCHEME FOR SERVER-CENTRIC CLOUD STORAGE SYSTEM
- 1. CONTACT: PRAVEEN KUMAR. L (, +91 – 9791938249) MAIL ID: sunsid1989@gmail.com, praveen@nexgenproject.com Web: www.nexgenproject.com, www.finalyear-ieeeprojects.com EFFICIENT R-TREE BASED INDEXING SCHEME FOR SERVER-CENTRIC CLOUD STORAGE SYSTEM. ABSTRACT Cloud storage system poses new challenges to the community to support efficient concurrent querying tasks for various data-intensive applications, where indexes always hold important positions. In this paper, we explore a practical method to construct a two-layer indexing scheme for multi- dimensional data in diverse server-centric cloud storage system. We first propose RT-HCN, an indexing scheme integrating R-tree based indexing structure and HCN-based routing protocol. RT-HCN organizes storage and compute nodes into an HCN overlay, one of the newly proposed sever-centric data center topologies. Based on the properties of HCN, we design a specific index mapping technique to maintain layered global indexes and corresponding query processing algorithms to support efficient query tasks. Then we expand the idea of RT-HCN onto another server-centric data center topology DCell, discovering a potential generalized and feasible way of deploying two-layer indexing schemes on other server-centric networks. Furthermore, we prove theoretically that RT-HCN is both space-efficient and query-efficient, by which each node actually maintains a tolerable number of global indexes while high concurrent queries can be processed within accepted overhead. We finally conduct targeted experiments on Amazons EC2 platforms, comparing our design with RT-CAN, a similar indexing scheme for traditional P2P network. The results validate the query efficiency,
- 2. CONTACT: PRAVEEN KUMAR. L (, +91 – 9791938249) MAIL ID: sunsid1989@gmail.com, praveen@nexgenproject.com Web: www.nexgenproject.com, www.finalyear-ieeeprojects.com especially the speedup of point query of RT-HCN, depicting its potential applicability in future data centers CONCLUSION In this paper, we proposed an indexing scheme named RT-HCN for multi- dimensional query processing in data centers, which are the infrastructures for building cloud storage systems and are interconnected using a specific data center network (DCN). RT-HCN is a two-layer indexing scheme, which integrates HCN-based routing protocol and the R-Tree based indexing technology, and is portionably distributed on every server. Based on the characteristics of HCN, we present a specialized mapping technique to improve global index allocation in the network, resulting query-efficiency and load-balancing for the cloud system.We also combine practical techniques in face of data skewness and querying false positives, greatly increasing the adaptability and querying performance of RT-HCN. We prove theoretically that RT-HCN is both query-efficient and space-efficient, by which each server will only maintain a constrained number of indices while a large number of users can concurrently process queries with low routing cost. We also give an insight into the inner relation between the HCN and other data center topologies, and apply the two-layer indexing scheme to the DCell topology in a generalized way. We compare our design with RT-CAN [8], a similar design for traditional P2P network. Experiments validate the efficiency of our proposed scheme and depict its potential implementation in data centers. REFERENCES [1] S. Ghemawat, H. Gobioff, and S. T. Leung. The google file system. In ACM SIGOPS operating systems review, volume 37, pages 29–43, 2003.
- 3. CONTACT: PRAVEEN KUMAR. L (, +91 – 9791938249) MAIL ID: sunsid1989@gmail.com, praveen@nexgenproject.com Web: www.nexgenproject.com, www.finalyear-ieeeprojects.com [2] G. DeCandia, D. Hastorun, M. Jampani, et al. Dynamo: amazon’s highly available key-value store. In ACM SIGOPS Operating Systems Review, volume 41, pages 205–220, 2007. [3] A. Lakshman and P. Malik. Cassandra: a decentralized structured storage system. ACM SIGOPS Operating Systems Review, 44:35–40, 2010. [4] D. Beaver, S. Kumar, H. C. Li, et al. Finding a needle in haystack: Facebook’s photo storage. In OSDI, volume 10, pages 1–8, 2010. [5] J. Baker, C. Bond, J. C. Corbett, et al. Megastore: Providing scalable, highly available storage for interactive services. In CIDR, volume 11, pages 223–234, 2011. [6] J. C. Corbett, J. Dean, M. Epstein, et al. Spanner: Googles globally distributed database. ACM Transactions on Computer Systems (TOCS), 31:8, 2013. [7] S. Wu and K. L. Wu. An indexing framework for efficient retrieval on the cloud. IEEE Data Eng. Bull., 32:75–82, 2009. [8] J. Wang, S. Wu, H. Gao, et al. Indexing multidimensional data in a cloud system. In Proceedings of the 2010 ACM SIGMOD International Conference on Management of data, pages 591–602, 2010. [9] S.Wu, D. Jiang, B. C. Ooi, and K. L.Wu. Efficient b-tree based indexing for cloud data processing. Proceedings of the VLDB Endowment, 3:1207–1218, 2010. [10] G. Chen, H. T. Vo, S. Wu, et al. A framework for supporting dbms-like indexes in the cloud. Proceedings of the VLDB Endowment, 4:702–713, 2011.
- 4. CONTACT: PRAVEEN KUMAR. L (, +91 – 9791938249) MAIL ID: sunsid1989@gmail.com, praveen@nexgenproject.com Web: www.nexgenproject.com, www.finalyear-ieeeprojects.com