A new fuzzy based dynamic data replication algorithm in data grids

Beigrezaei, Mahsa; Kanan, Hamidreza Rashidy; Haghighat, AbolfaziToroghi

doi:10.1109/ifsc.2013.6675676

Cited by 7 publications

(5 citation statements)

References 16 publications

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“…It also uses a migration method to improve workload. Beigrezaei et al presented a fuzzy-based replication method named Fuz-zy_Rep [23]. It utilizes a fuzzy inference system with three input parameters, bandwidth, the last access time, and the number of accesses, to select a tailored place for replication.…”

Section: Related Workmentioning

confidence: 99%

Improve Performance by a Fuzzy-Based Dynamic Replication Algorithm in Grid, Cloud, and Fog

Beigrezaei

Haghighat

Mirtaheri

2021

Mathematical Problems in Engineering

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The efficiency of data-intensive applications in distributed environments such as Cloud, Fog, and Grid is directly related to data access delay. Delays caused by queue workload and delays caused by failure can decrease data access efficiency. Data replication is a critical technique in reducing access latency. In this paper, a fuzzy-based replication algorithm is proposed, which avoids the mentioned imposed delays by considering a comprehensive set of significant parameters to improve performance. The proposed algorithm selects the appropriate replica using a hierarchical method, taking into account the transmission cost, queue delay, and failure probability. The algorithm determines the best place for replication using a fuzzy inference system considering the queue workload, number of accesses in the future, last access time, and communication capacity. It uses the Simple Exponential Smoothing method to predict future file popularity. The OptorSim simulator evaluates the proposed algorithm in different access patterns. The results show that the algorithm improves performance in terms of the number of replications, the percentage of storage filled, and the mean job execution time. The proposed algorithm has the highest efficiency in random access patterns, especially random Zipf access patterns. It also has good performance when the number of jobs and file size are increased.

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Section: Related Workmentioning

confidence: 99%

Improve Performance by a Fuzzy-Based Dynamic Replication Algorithm in Grid, Cloud, and Fog

Beigrezaei

Haghighat

Mirtaheri

2021

Mathematical Problems in Engineering

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“…This would result in better performance compared with the original BHR. Beigrezaei et al 31 propose a fuzzy-based data replication algorithm, named Fuzzy_rep. This algorithm determines the best replica location using a fuzzy inference system with bandwidth, the last access time, and the numbers of file access.…”

Section: Data Replication Algorithmsmentioning

confidence: 99%

“…Fuzzy_rep method outperforms the previous methods such as LRU, LFU, BHR, and MBHR. 31 The tendency of this method to store in sites with higher bandwidths can increase the workload on several specific sites with high bandwidth. Furthermore, a QoS-aware replication algorithm (QoS) was proposed in the Data Grid to reduce response time and increase the security.…”

Section: Data Replication Algorithmsmentioning

confidence: 99%

Minimizing data access latency in data grids by neighborhood‐based data replication and job scheduling

Beigrezaei

Haghighat

Mirtaheri

2020

Int J Communication

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Summary In Data Grid systems, quick data access is a challenging issue due to the high latency. The failure of requests is one of the most common matters in these systems that has an impact on performance and access delay. Job scheduling and data replication are two main techniques in reducing access latency. In this paper, we propose two new neighborhood‐based job scheduling strategies and a novel neighborhood‐based dynamic data replication algorithm (NDDR). The proposed algorithms reduce the access latency by considering a variety of practical parameters for decision making and the access delay by considering the failure probability of a node in job scheduling, replica selection, and replica placement. The proposed neighborhood concept in job scheduling includes all the nodes with low data transmission costs. Therefore, we can select the best computational node and reduce the search time by running a hierarchical and parallel search. NDDR reduces the access latency through selecting the best replica by performing a hierarchical search established based on the access time, storage queue workload, storage speed, and failure probability. NDDR improves the load balancing and data locality by selecting the best replication place considering the workload, temporal locality, geographical locality, and spatial locality. We evaluate our proposed algorithms by using Optorsim Simulator in two scenarios. The simulations confirm that the proposed algorithms improve the results compared with similar existing algorithms by 11%, 15%, 12%, and 10% in terms of mean job time, replication frequency, mean data access latency, and effective network usage, respectively.

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“…In [2], Beigrezaei et al propose a replication strategy called fuzzy rep improving the modified BHR method [42]. According to the fuzzy rep strategy, when there is not enough storage space to replicate the new requested file, the potential candidates for replacement will be chosen according to their weight.…”

Section: Non Periodic Strategiesmentioning

confidence: 99%

“…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]. Biomedical Informatics Research Network (BIRN) 1 , Large Hadron Collider (LHC) 2 and, the European DataGrid Project (EDG) 3 are some examples of existing data grids. In this paper, we focus on data grids.…”

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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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A new fuzzy based dynamic data replication algorithm in data grids

Cited by 7 publications

References 16 publications

Improve Performance by a Fuzzy-Based Dynamic Replication Algorithm in Grid, Cloud, and Fog

Improve Performance by a Fuzzy-Based Dynamic Replication Algorithm in Grid, Cloud, and Fog

Minimizing data access latency in data grids by neighborhood‐based data replication and job scheduling

A new decentralized periodic replication strategy for dynamic data grids

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