Modeling a Dynamic Data Replication Strategy to Increase System Availability in Cloud Computing Environments

Sun, Dawei; Chang, Guiran; Gao, Shang; Jin, Lizhong; Wang, Xingwei

doi:10.1007/s11390-012-1221-4

Cited by 127 publications

(100 citation statements)

References 39 publications

(82 reference statements)

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“…The performance of our proposed data replication methodis compared with the existing system [2] to show that our proposed technique has better performance than the previous approaches. In [2], their system suffers to increase data availability and improve cloud system task successful execution rate.…”

Section: 3comparative Resultsmentioning

confidence: 99%

“…In [2], their system suffers to increase data availability and improve cloud system task successful execution rate.…”

Section: 3comparative Resultsmentioning

confidence: 99%

“…Here they compared with the eight existing algorithms and improve Mean Job Time and Effective Network under all of the access patterns, especially under the different random file access patterns. In [2], they discussed about the dynamic centralized data replication algorithm MinDmr. It minimizes the data access time and network load by creating and spreading replicas from the super data centers to main data centers.…”

Section: Review Of Related Workmentioning

confidence: 99%

“…In cloud, the services are conveyed on demand to external users over high speed internet with 'XaaS' where X as a services is a computing architecture which is broken down in to three segments: "infrastructure", "platforms", and "applications". It gives clients with most adaptable services in a clear manner and with less expensive and most powerful processors [2]. The IT systems sent to satisfy their business functionalities and to provide good Quality of Service (QoS) parameters such as availability, scalability and performance.…”

Section: Introductionmentioning

confidence: 99%

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An Efficient Placement Algorithm for Data Replication and To Improve System Availability in Cloud Environment

Karuppusamy¹,

Madiajagan²

2016

IJIES

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Abstract:The IT systems sent to satisfy their business functionalities and to provide good Quality of Service (QoS) parameters such as availability, scalability and performance. The failures are normal rather than exceptional in case of cloud computing. To improve system availability enhancing the system performance is an important factor. For that replicating the popular data to multiple sites is a best choice as the users can access the data from a nearby site. In this paper, we proposed a dynamic replication and placement algorithm to enhance the performance of the software system. We calculate popularity degree and replica factor to identify the suitable file to replicate. From this the most accessed file and the file need to be replicated is found and the replicate of the file is created. Then the proposed algorithm is used to place the replicas in the suitable location. We compare the performance of the technique with the existing technique.

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Section: 3comparative Resultsmentioning

confidence: 99%

“…In [2], their system suffers to increase data availability and improve cloud system task successful execution rate.…”

Section: 3comparative Resultsmentioning

confidence: 99%

Section: Review Of Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

An Efficient Placement Algorithm for Data Replication and To Improve System Availability in Cloud Environment

Karuppusamy¹,

Madiajagan²

2016

IJIES

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“…Many of them focus just on satisfying the availability SLO [10], [11]. In a typical cloud environment, where frequent queries are placed on a largescale data, having low response time is crucial for the tenants.…”

Section: Introductionmentioning

confidence: 99%

A Performance and Profit Oriented Data Replication Strategy for Cloud Systems

Tos

Mokadem

Hameurlain

et al. 2016

2016 Intl IEEE Conferences on Ubiquitous Intelligence &Amp; Computing, Advanced and Trusted Computing, Scalable Computing and C

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OATAO is an open access repository that collects the work of Toulouse researchers and makes it freely available over the web where possible. This is an author-deposited version published in : http://oatao.univ-toulouse.fr/ Eprints ID : 18792The contribution was presented at CBDCom 2016 :https://cbdcom2016.sciencesconf.org/ Abstract-In today's world, tenants of cloud systems expect timely responses to queries that process ever-increasing sizes of data. However, most cloud providers offer their services without any performance guarantees to their tenants. In this paper we propose a data replication strategy that aims to satisfy performance guarantees for the tenant while ensuring profitability of the cloud provider. Our strategy estimates the response time of the queries, as well as the expenditures that affect the profitability of the cloud provider. The decision of whether to perform replication is determined by the fulfillment of these two criteria. Validity of the proposed strategy is provided by means of a simulation study.

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An energy‐aware method for data replication in the cloud environments using a Tabu search and particle swarm optimization algorithm

Ebadi

Navimipour

2018

Concurrency and Computation

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Cloud computing is a type of parallel, configurable, and flexible system, which refers to the provision of applications on virtual data centers. However, reducing the energy consumption and also maintaining high computation capacity have become timely and important challenges. The concept of replication is used to face these challenges. By increasing the number of data replicas, the energy consumption, the performance, and also the cost of creating and maintaining new replicas also are increased. Deciding on the number of required replicas and their location on the cloud system is an NP-hard problem. In this paper, the problem is formulated as an optimization problem and a hybrid metaheuristic algorithm is offered to solve it. The algorithm uses the global search capability of the Particle Swarm Optimization (PSO) algorithm and the local search capability of the Tabu Search (TS) to get high-quality solutions. The efficiency of the method is shown by comparing it with simple PSO, TS, and Ant Colony Optimization (ACO) algorithm on different test cases. The obtained results indicate that the method outperforms all of them in terms of consumed energy and cost. KEYWORDS cloud computing, data replication, particle swarm optimization, replica INTRODUCTIONCloud computing is a deep revolution in providing the information technology-based services based on virtualization technology. 1-4 It considers the ideas of distributed and parallel computing to offer on-demand computing resources for computers or other devices. 5-9 It aims to offer shared services with high reliability, dynamicity, and scalability; although, it is facing many issues because of its complex architecture. [10][11][12] In the cloud data centers, scalability and complexity issues cause cost and energy become two key challenges that need attention and careful study. 13,14 As the carbon footprint and consumed energy of the cloud infrastructures have been increased, investigators tried to find applicable methods for decreasing energy consumption. 15 In the last few years, the main focus of investigators and engineers was on improving the performance in terms of energy, space, and cost. 1

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Modeling a Dynamic Data Replication Strategy to Increase System Availability in Cloud Computing Environments

Cited by 127 publications

References 39 publications

An Efficient Placement Algorithm for Data Replication and To Improve System Availability in Cloud Environment

An Efficient Placement Algorithm for Data Replication and To Improve System Availability in Cloud Environment

A Performance and Profit Oriented Data Replication Strategy for Cloud Systems

An energy‐aware method for data replication in the cloud environments using a Tabu search and particle swarm optimization algorithm

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