Dynamic replica placement and selection strategies in data grids— A comprehensive survey

doi:10.1016/j.jpdc.2013.10.009

Journal of Parallel and Distributed Computing

Volume 74, Issue 2, February 2014, Pages 2099-2108

https://doi.org/10.1016/j.jpdc.2013.10.009 Get rights and content

Highlights

•
Survey on replica placement and selection strategies in data grids is presented.
•
Parameters that are used to evaluate the grid performance are summarized.
•
Grid architectural models and simulation tools used are discussed.

Abstract

Data replication techniques are used in data grid to reduce makespan, storage consumption, access latency and network bandwidth. Data replication enhances data availability and thereby increases the system reliability. There are two steps involved in data replication, namely, replica placement and replica selection. Replica placement involves identifying the best possible node to duplicate data based on network latency and user request. Replica selection involves selecting the best replica location to access the data for job execution in the data grid. Various replica placement and selection algorithms are available in the literature. These algorithms measure and analyze different parameters such as bandwidth consumption, access cost, scalability, execution time, storage consumption and makespan. In this paper, various replica placement and selection strategies along with their merits and demerits are discussed. This paper also analyses the performance of various strategies with respect to the parameters mentioned above. In particular, this paper focuses on the dynamic replica placement and selection strategies in the data grid environment.

Section snippets

Introduction: replica placement and replica selection

A computational grid [22] is a combination of both hardware and software that provides reliable and consistent resources to execute a job in distributed environment. Data grid is a distributed collection of storage and computational resources located in different geographical locations. [1], [18], [23] describe grid is a flexible, secure and co-ordinated resource sharing environment for individuals, institutions and resources. Computationally intensive applications need large amount of data,

Dynamic replica placement techniques

Both centralized and distributed dynamic replica placement strategies are further classified according to the type of network used for implementation.

Dynamic replica selection techniques

Replica selection is one of the key components of data management in data intensive application. It decides which replica location is the best place to access the data for users. If several replicas are available for a file, the optimization algorithm determines which replica should be selected to execute the job. The optimal replica is selected based on the following parameters: access cost, access latency, bandwidth consumption, balanced workload, maintenance cost, job execution time,

Summary of replica placement and selection strategies

In this paper, the summary of various replica placement and selection strategies is done based on the following aspects:

•
parameters that are used to evaluate the grid performance;
•
architectural models;
•
assumptions that are made during replication;
•
simulation tools used.

The grid replication and selection strategies are evaluated based on certain performance parameters. Any replica placement and selection strategy tries to improve one or more of the following parameters: makespan, quality assurance,

Conclusion

This paper presents a survey on replica placement and selection strategies for dynamic data grid environment. Different replica placement and selection strategies are proposed by researchers. In dynamic grid configuration, the user can join and leave the network at any point of time. Therefore, there is no specific grid topology used for the dynamic data grid. Most of the work done in replica placement and selection is based on the hierarchical and modified hierarchical architecture. The graph

R. Kingsy Grace graduated with B.E. Computer Science and Engineering in 2003 from Noorul Islam College of Engineering, India and completed M.E. Computer Science and Engineering in 2005 from Karunya Institute of Technology, Coimbatore, India. She is currently pursuing her Ph.D. at Anna University, Chennai, India. Her area of interest includes Grid Computing and her current research focus is on Dynamic replica placement and selection in data grids. She has about 10 years of teaching experience.

References (54)

T. Amjad et al.
A survey of dynamic replication strategies for improving data availability in data grids
Future Gener. Comput. Syst.
(2012)
V. Andronikou et al.
Dynamic QoS- aware data replication in grid environments based on data importance
Future Gener. Comput. Syst.
(2012)
R.S. Chang et al.
Job scheduling and data replication on data grids
Future Gener. Comput. Syst.
(2007)
D.W. Chen et al.
Methods for replica creation in data grids using complex network
J. China Univ. Posts Telecommun.
(2010)
U. Cibej et al.
The complexity of static data replication in data grids
Parallel Comput.
(2005)
N. Mansouri et al.
A dynamic replica management strategy in data grid
J. Netw. Comput. Appl.
(2012)
J.M. Perez et al.
Branch replica scheme: a new model for data replication in large scale
Future Gener. Comput. Syst.
(2010)
X.Y. Ren et al.
Using OptorSim to efficiently simulate replica placement strategies
J. China Univ. Posts Telecommun.
(2010)
K. Sashi
Antony selvadoss thanamani, dynamic replication in a data grid using modified BHR region based algorithm
Future Gener. Comput. Syst.
(2011)
M. Tang et al.
Dynamic replication algorithm for the multi-tier data grid
Future Gener. Comput. Syst.
(2005)

J.J. Wu et al.

Optimal replica placement in hierarchical data grids with locality assurance

J. Parallel Distrib. Comput.

(2008)

W. Allcock et al.

Data management and transfer in high performance computational grid environments

Parallel Comput. J.

(2002)

W. Allcock et al.

Secure, efficient data transport and replica management for high-performance data-intensive computing

W. Allcock, I. Foster, V. Nefedova, A. Chervenak, E. Deelman, C. Kesselman, J. Lee, A. Sim, A. Shoshani, B. Drach, D....

R.M. Alumuttairi, R. Wankar, A. Negi, C.R. Rao, Smart Replica Selection for Data Grids using Rough Set Approximation...

R.M. Alumuttairi, R. Wankar, A. Negi, C.R. Rao, Replica Selection in Data Grids using Preconditioning of Decision...

R.M. Alumuttairi, R. Wankar, A. Negi, C.R. Rao, Rough Set Clustering Approach to Replica Selection in Data Grids...

W.H. Bell et al.

OptorSim: a grid simulator for studying dynamic data replication strategies

Int. J. High Perform. Comput. Appl.

(2003)

W.H. Bell et al.

Evaluation of an economy based file replication strategy for a data grid

G. Bingxiang, Y. Kui, A Global Dynamic Scheduling with Replica Selection Algorithm Using GridFTP, in: Proceedings of...

R. Buyya et al.

Z. Challal, Bouabana Tebibel, A priori replica placement strategy in data grid, in: Proceedings of International...

R. Chang, H. Chang, Y. Wang, A dynamic weighted data replication strategy in data grids, in: Proceedings of AICCSA...

K.Y. Cheng, H.H. Wang, C.H. Wen, Y.L. Lin, K.C. Li, C.L. Wang, Dynamic file replica location and selection strategy in...

A. Chervenak, E. Deelman, I. Foster, L. Guy, W. Hoschek, A. Iamnitchi, C. Kesselman, P. Kunst, M. Ripeanu, B....

A. Chervenak et al.

The data grid: towards architecture for the distributed management and analysis of large scientific datasets

J. Netw. Comput. Appl.

(2001)

B. Dhruba, The Hadoop Distributed File System: Architecture and Design....

Cited by (69)

A multi-objective optimized replication using fuzzy based self-defense algorithm for cloud computing
2020, Journal of Network and Computer Applications
Cloud computing has attracted increasing attention in data management. Data replication, which brings files closer to the data consumers, is a well-known technique that reduces access time and bandwidth consumption. This paper addresses two issues concerning replica placement process. The first is how to reduce access costs and replication costs that are two conflicting goals. To achieve this, we propose a multi-objective optimized placement algorithm based on meta-heuristic technique and fuzzy system that finds the optimal locations for replicas by balancing the trade-offs among the six optimization objectives (i.e., system availability, service time, load, energy consumption, latency, and centrality). The second issue is how to determine the optimal number of replicas since storing a great number of replicas in cloud is expensive. To solve this problem, we determine the number of replicas without excessively reducing the performance. In addition, we improve self-defense algorithm by a new prey-predator model based on a fuzzy system to simulate the interaction between prey and predator population. The superior results with ten benchmark functions demonstrate the merits of the proposed fuzzy-self-defense algorithm in solving the problems compared with seven optimization algorithms. Moreover, the extensive simulations with CloudSim prove that the proposed replication algorithm outperforms the main existing replication strategies in terms of hit ratio, number of replications, load variance, latency, average service time, availability, and energy consumption.
Keeping up with storage: Decentralized, write-enabled dynamic geo-replication
2018, Future Generation Computer Systems
Citation Excerpt :
In this paper, we focus on allowing a geo-distributed application to access a geo-distributed data source with the lowest possible latency. Kingsy Grace et al. [7] provide an extensive survey of replica placement and selection algorithms available in the literature. Among these, Chen et al. [8] propose a dissemination-tree based replication algorithm leveraging a peer-to-peer location service.
Large-scale applications are ever-increasingly geo-distributed. Maintaining the highest possible data locality is crucial to ensure high performance of such applications. Dynamic replication addresses this problem by dynamically creating replicas of frequently accessed data close to the clients. This data is often stored in decentralized storage systems such as Dynamo or Voldemort, which offer support for mutable data. However, existing approaches to dynamic replication for such mutable data remain centralized, thus incompatible with these systems. In this paper we introduce a write-enabled dynamic replication scheme that leverages the decentralized architecture of such storage systems. We propose an algorithm enabling clients to locate tentatively the closest data replica without prior request to any metadata node. Large-scale experiments on various workloads show a read latency decrease of up to 42% compared to other state-of-the-art, caching-based solutions.
Data Replication and Placement Strategies in Distributed Systems: A State of the Art Survey
2023, Wireless Personal Communications
A Fuzzy Logic-Based Method for Replica Placement in the Peer to Peer Cloud Using an Optimization Algorithm
2022, Wireless Personal Communications
Access strategies for network caching
2021, IEEE/ACM Transactions on Networking
Advanced algorithms in heterogeneous and uncertain networking environments
2020, arXiv

View all citing articles on Scopus

R. Manimegalai is presently working as a Professor and Director–Research, Department of Computer Science and Engineering, Park College Engineering and Technology, Coimbatore, India. She has published many papers in international/national journals and conferences. Her area of interest includes Reconfigurable Computing and Distributed Systems. She has to her credit 19 years of teaching, research and industry experience.

View full text

Dynamic replica placement and selection strategies in data grids— A comprehensive survey

Highlights

Abstract

Section snippets

Introduction: replica placement and replica selection

Dynamic replica placement techniques

Dynamic replica selection techniques

Summary of replica placement and selection strategies

Conclusion

Future Gener. Comput. Syst.

Future Gener. Comput. Syst.

Future Gener. Comput. Syst.

J. China Univ. Posts Telecommun.

Parallel Comput.

J. Netw. Comput. Appl.

Future Gener. Comput. Syst.

J. China Univ. Posts Telecommun.

Future Gener. Comput. Syst.

Future Gener. Comput. Syst.

J. Parallel Distrib. Comput.

Data management and transfer in high performance computational grid environments

Parallel Comput. J.

Secure, efficient data transport and replica management for high-performance data-intensive computing

OptorSim: a grid simulator for studying dynamic data replication strategies

Int. J. High Perform. Comput. Appl.

Evaluation of an economy based file replication strategy for a data grid

The data grid: towards architecture for the distributed management and analysis of large scientific datasets

J. Netw. Comput. Appl.