QoS-Aware Distributed Replica Placement in Hierarchical Data Grids

Data Grids provide services and infrastructure for distributed data-intensive applications accessing massive geographically distributed datasets. An important technique to speed access in Data Grids is replication, which provides nearby data access. Much of the work on the replica placement problem...

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Bibliographic Details
Published in2011 IEEE International Conference on Advanced Information Networking and Applications pp. 291 - 299
Main Authors Shorfuzzaman, Mohammad, Graham, P, Eskicioglu, R
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.03.2011
Subjects
Bandwidth
data grids
distributed algorithm
Distributed databases
Dynamic programming
Heuristic algorithms
Quality of service
replication
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ISBN9781612843131
1612843131
ISSN1550-445X
DOI10.1109/AINA.2011.76

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Summary:Data Grids provide services and infrastructure for distributed data-intensive applications accessing massive geographically distributed datasets. An important technique to speed access in Data Grids is replication, which provides nearby data access. Much of the work on the replica placement problem has focused on average system performance and ignored quality assurance issues. In a data grid environment, resource availability, network latency, and users' requests may change. Moreover, different sites may have different service quality requirements. In this paper, we introduce a new highly distributed and decentralized replica placement algorithm for hierarchical Data Grids that determines the positions of a minimum number of replicas expected to satisfy certain quality requirements. Our placement algorithm exploits the data access history for popular data files and computes replica locations by minimizing overall replication cost (read and update) while maximizing QoS satisfaction for a given traffic pattern. The problem is formulated using dynamic programming. We assess our algorithm using OptorSim. A comparison between our algorithm and its QoS-unconstrained counterpart shows that our algorithm can shorten job execution time greatly while consuming moderate bandwidth for data transfer.
ISBN:9781612843131
1612843131
ISSN:1550-445X
DOI:10.1109/AINA.2011.76