Optimal Distributed Declustering Using Replication

Frikken, Keith B.

doi:10.1007/978-3-540-30570-5_10

Cited by 9 publications

(6 citation statements)

References 16 publications

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“…Many shift schemes in two dimensions, will be scheduled in O(|Q| + N log log N) time. The shift scheme proposed in [29] schedules in O(|Q| + N log ). Heuristics, such as a simple greedy retrieval algorithm [13], has been proposed to improve the query performance using replication.…”

Section: Related Workmentioning

confidence: 99%

Efficient parallel processing of range queries through replicated declustering

Ferhatosmanoğlu

Tosun

Canahuate

et al. 2006

Distrib Parallel Databases

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A common technique used to minimize I/O in data intensive applications is data declustering over parallel servers. This technique involves distributing data among several disks so as to parallelize query retrieval and thus, improve performance. We focus on optimizing access to large spatial data, and the most common type of queries on such data, i.e., range queries. An optimal declustering scheme is one in which the processing for all range queries is balanced uniformly among the available disks. It has been shown that single copy based declustering schemes are non-optimal for range queries. In this paper, we integrate replication in conjunction with parallel disk declustering for efficient processing of range queries. We note that replication is largely used in database applications for several purposes like load balancing, fault tolerance and availability of data. We propose theoretical foundations for replicated declustering and propose a class of replicated declustering schemes, periodic allocations, which are shown to be strictly optimal for a number of disks. We propose a framework for replicated declustering, using a limited amount of replication and provide extensions to apply it on real data, which include arbitrary grids and a large number of disks. Our framework also provides an effective indexing scheme that enables fast identification of data of interest in parallel servers. In addition to optimal processing of single queries, we show that this framework is effective for parallel processing of multiple queries. We present experimental results comparing the proposed replication scheme to other techniques for both single queries and multiple queries, on synthetic and real data sets.

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Section: Related Workmentioning

confidence: 99%

Efficient parallel processing of range queries through replicated declustering

Ferhatosmanoğlu

Tosun

Canahuate

et al. 2006

Distrib Parallel Databases

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“…A maximum-flow formulation is proposed in [9] to solve this scheduling problem optimally. There are replicated declustering schemes that aim to minimize this scheduling overhead [10], [11], while minimizing I/O costs. A variation of this problem arises when replicas are assumed to be distributed over different sites, where each site hosts a parallel-disk architecture [12].…”

Section: Related Workmentioning

confidence: 99%

Query-Log Aware Replicated Declustering

Türk

Oktay

Aykanat

2013

IEEE Trans. Parallel Distrib. Syst.

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Abstract-Data declustering and replication can be used to reduce I/O times related with processing of data intensive queries. Declustering parallelizes the query retrieval process by distributing the data items requested by queries among several disks. Replication enables alternative disk choices for individual disk items and thus provides better query parallelism options. In general, existing replicated declustering schemes do not consider query log information and try to optimize all possible queries for a specific query type, such as range or spatial queries. In such schemes, it is assumed that two or more copies of all data items are to be generated and scheduling of these copies to disks are discussed. However, in some applications, generation of even two copies of all of the data items is not feasible, since data items tend to have very large sizes. In this work, we assume that there is a given limit on disk capacities and thus on replication amounts. We utilize existing query-log information to propose a selective replicated declustering scheme, in which we select the data items to be replicated and decide on their scheduling onto disks while respecting disk capacities. We propose and implement an iterative improvement algorithm to obtain a two-way replicated declustering and use this algorithm in a recursive framework to generate a multiway replicated declustering. Then we improve the obtained multiway replicated declustering by efficient refinement heuristics. Experiments conducted on realistic data sets show that the proposed scheme yields better performance results compared to existing replicated declustering schemes.

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“…Recently, replication strategies for spatial range queries [16], [23], [26], [27] and arbitrary queries [35], [39], [41] were proposed. Replication improves the worst-case additive error for declustering using multiple copies of the data.…”

Section: Introductionmentioning

confidence: 99%

Integrated Maximum Flow Algorithm for Optimal Response Time Retrieval of Replicated Data

Altiparmak

Tosun

2012

2012 41st International Conference on Parallel Processing

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Efficient retrieval of replicated data from multiple disks is a challenging problem. Traditional retrieval techniques assume that replication is done at a single site using homogeneous disk arrays having no initial load or network delay. Recently, generalized retrieval algorithms are proposed to cover heterogeneous disk arrays, initial loads, and network delays. Generalized retrieval algorithms achieve the optimal response time retrieval schedule by performing multiple runs of a maximum flow algorithm. Since the maximum flow algorithm is used as a black box technique, flow values of the previous runs cannot be conserved to speed up the process. In this paper, we propose integrated maximum flow algorithms for the generalized optimal response time retrieval problem. Our first algorithm uses Ford-Fulkerson method and the second algorithm uses Push-relabel algorithm. Besides the sequential implementations, a multi-threaded version of the push-relabel algorithm is also implemented. Proposed algorithms are investigated using various replication schemes, query types, query loads, disk specifications, and system delays. Experimental results show that the sequential integrated push-relabel algorithm runs up to 2.5X faster than the black box version. Furthermore, parallel integrated push-relabel implementation achieves up to 1.7X speed up (∼1.2X on average) over the sequential algorithm using two threads, which makes the integrated algorithm up to 4.25X (∼3X on average) faster than its black box counterpart.

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Optimal Distributed Declustering Using Replication

Cited by 9 publications

References 16 publications

Efficient parallel processing of range queries through replicated declustering

Efficient parallel processing of range queries through replicated declustering

Query-Log Aware Replicated Declustering

Integrated Maximum Flow Algorithm for Optimal Response Time Retrieval of Replicated Data

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