The complexity of static data replication in data grids

ibej, Uroš; Slivnik, Boštjan; Robič, Borut

doi:10.1016/j.parco.2005.04.010

Cited by 45 publications

(27 citation statements)

References 3 publications

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“…In Data Grids, the gigantic scientific data and complex scientific applications call for new replication algorithms, which have attracted lots of research recently. The most closely related work to ours is by Cibej, Slivnik, and Robic [34]. The authors study data replication on Data…”

Section: Related Workmentioning

confidence: 95%

Data Replication in Data Intensive Scientific Applications with Performance Guarantee

Nukarapu

Tang

Wang

et al. 2011

IEEE Trans. Parallel Distrib. Syst.

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Section: Related Workmentioning

confidence: 95%

Data Replication in Data Intensive Scientific Applications with Performance Guarantee

Nukarapu

Tang

Wang

et al. 2011

IEEE Trans. Parallel Distrib. Syst.

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“…In static replication, all decisions regarding the replication strategy are made before the system is operational and not changed during operation [13,18,22,32]. On the other hand, in dynamic replication, what, when, and where to replicate are decided as a response to the changing trends of the data grid [11,41,42,54].…”

Section: Static Versus Dynamic Replicationmentioning

confidence: 99%

“…fault-tolerance, security, load balancing points of view. However, from the most general perspective, data replication strategies can be divided into two categories, namely static [13,18,22,32] and dynamic [11,41,42,54] replication. In this paper, we are focusing on dynamic replication strategies, where all replication decisions are made adaptively while the system is in operation.…”

Section: Introductionmentioning

confidence: 99%

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Dynamic replication strategies in data grid systems: a survey

et al. 2015

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In data grid systems, data replication aims to increase availability, fault tolerance, load balancing and scalability while reducing bandwidth consumption, and job execution time. Several classification schemes for data replication were proposed in the literature, (i) static vs. dynamic, (ii) centralized vs. decentralized, (iii) push vs. pull, and (iv) objective function based. Dynamic data replication is a form of data replication that is performed with respect to the changing conditions of the grid environment. In this paper, we present a survey of recent dynamic data replication strategies. We study and classify these strategies by taking the target data grid architecture as the sole classifier. We discuss the key points of the studied strategies and provide feature comparison of them according to important metrics. Furthermore, the impact of data grid architecture on dynamic replication performance is investigated in a simulation study. Finally, some important issues and open research problems in the area are pointed out. B Uras Tos

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“…Hence, ensuring an efficient and fast access to such massive data is a challenge that must be addressed. The data grid, which represents a type of grid computing [15], is a solution for this problem. A data grid connects a collection of hundreds of geographically distributed computers and storage resources located in different parts of the world to facilitate sharing of data and resources [23].…”

mentioning

confidence: 99%

A new decentralized periodic replication strategy for dynamic data grids

Chettaoui¹,

Charrada²

2014

SCPE

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Abstract. Data grids provide scalable infrastructure for storage resource and data files management, which support dataintensive applications that need to access to huge amount of data stored at distributed locations around the world. The size of these data can reach the scale of terabytes or even petabytes in many applications. These applications require reaching several main goals, namely efficient accessing, storing, transferring and analyzing a large amount of data in geographically distributed locations. In this situation, replication is a general and simple technique used in data grids to achieve these goals. Indeed, it has as main purposes improving data access efficiency, providing high availability, decreasing bandwidth consumption, improving fault tolerance and enhancing scalability. In this paper, we propose a new classification of replication strategies through two complementary criteria as well as a survey of the induced categories of strategies. In addition, we introduce a new decentralized periodic replication strategy for dynamic data grids assuming limited storage for replicas, called DPRSKP, which stands for Decentralized Periodic Replication Strategy based on Knapsack Problem. This strategy takes into consideration the changing availability of sites. DPRSKP is based on two polynomial-time complexity algorithms. The first one starts by selecting the best candidate files for replication while the second places them in the best locations. The replication problem in DPRSKP is formulated according to the Knapsack problem. In addition, DPRSKP extends the well known LRU and LFU strategies. The simulation experiments were carried out using OptorSim and a dynamic period rather than a static one. The obtained results show that DPRSKP can effectively improve response time, bandwidth consumption, remote file accesses number and local file accesses number as compared with other replication strategies.

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The complexity of static data replication in data grids

Cited by 45 publications

References 3 publications

Data Replication in Data Intensive Scientific Applications with Performance Guarantee

Data Replication in Data Intensive Scientific Applications with Performance Guarantee

Dynamic replication strategies in data grid systems: a survey

A new decentralized periodic replication strategy for dynamic data grids

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