Lowest Data Replication Storage of Binary Vote Assignment Data Grid

Ahmad, Nesar; Fauzi, Ainul Azila Che; Sidek, Roslina Mohd; Zin, Noriyani Mat; Beg, Abul Hashem

doi:10.1007/978-3-642-14306-9_46

Cited by 16 publications

(6 citation statements)

References 10 publications

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“…In this case, the root server decides to replicate data into other sites considering the consistency among replicas. More information can be found in [14][15]. Another issue is that in distributed systems the location of data items required for jobs is not always the same site that performs the jobs.…”

Section: Amentioning

confidence: 97%

Evaluation of energy consumption and data access time in data fetching in grid-based data-intensive applications

Izadpanah

Pawlikowski

Davoli

et al. 2013

2013 22nd ITC Specialist Seminar on Energy Efficient and Green Networking (SSEEGN)

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Data-intensive applications that involve large amounts of data generation, processing and transmission, have been operated with little attention to energy efficiency. Issues such as management, movement and storage of huge volumes of data may lead to high energy consumption. Replication is a useful solution to decrease data access time and improve performance in these applications, but it may also lead to increase the energy spent in storage and data transmission, by spreading large volumes of data replicas around the network. Thus, utilizing effective strategies for energy saving in these applications is a very critical issue from both the environmental and economical aspects. In this paper, at first we review the current data replication and caching approaches and energy saving methods in the context of data replication. Then, we propose a model for energy consumption during data replication and, finally, we evaluate two schemes for data fetching based on the two critical metrics in Grid environments: energy consumption and data access time. We also compare the gains based on these metrics with the no-caching scenario by using simulation.

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Section: Amentioning

confidence: 97%

Evaluation of energy consumption and data access time in data fetching in grid-based data-intensive applications

Izadpanah

Pawlikowski

Davoli

et al. 2013

2013 22nd ITC Specialist Seminar on Energy Efficient and Green Networking (SSEEGN)

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“…Data replication, a well-known technique from distributed systems, is the main mechanism used in the cloud for reducing user waiting time, increasing data availability and minimizing cloud system bandwidth consumption by offering the user different replicas with a coherent state of the same service [19] . With the advancement and development of various technologies, data replication and replica management in distributed systems have been studied in many works, which are referenced and adopted in cloud data replication.…”

Section: Cloud Data Replicationmentioning

confidence: 99%

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

Sun

Chang

Gao

et al. 2012

J. Comput. Sci. Technol.

127

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Failures are normal rather than exceptional in the cloud computing environments. To improve system availability, replicating the popular data to multiple suitable locations is an advisable choice, as users can access the data from a nearby site. This is, however, not the case for replicas which must have a fixed number of copies on several locations. How to decide a reasonable number and right locations for replicas has become a challenge in the cloud computing. In this paper, a dynamic data replication strategy is put forward with a brief survey of replication strategy suitable for distributed computing environments. It includes: 1) analyzing and modeling the relationship between system availability and the number of replicas; 2) evaluating and identifying the popular data and triggering a replication operation when the popularity data passes a dynamic threshold; 3) calculating a suitable number of copies to meet a reasonable system byte effective rate requirement and placing replicas among data nodes in a balanced way; 4) designing the dynamic data replication algorithm in a cloud. Experimental results demonstrate the efficiency and effectiveness of the improved system brought by the proposed strategy in a cloud.

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“…A data grid is composed of hundreds of geographically distributed computers and storage resources usually located under different places and enables users to share data and other resources. The required for data grids because of the data is being produced at a tremendous rate and volume especially from scientific experiments in the fields of high-energy physics, molecular docking, computer micro-tomography and many others (Ahmad et al, 2010a;2010b). The grid computing requirements are more complex than distributed computing even though it is quite similar to normal distributed computing.…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, replication can reduce access latency; improve data locality, increase robustness, scalability and performance for distributed applications (Radi et al, 2008). Organizations need to provide current data to users who may be geographically remote and request distributed data around multiple sites in data grid (Ahmad et al, 2010a). A data grid is composed of hundreds of geographically distributed computers and storage resources usually located under different places and enables users to share data and other resources.…”

Section: Introductionmentioning

confidence: 99%

Solving Two Deadlock Cycles through Neighbor Replication on Grid Deadlock Detection Model

Zin¹,

Adzhar²,

Abdalla³

et al. 2012

Journal of Computer Science

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Abstract:A data grid is compose of hundreds of geographically distributed computers and storage resources usually locate under different places and enables users to share data and other resources. Problem statement: Data replication is one of the mechanisms in managing data grid architecture that receive particular attention since it can provide efficient access to data, fault tolerance, reduce access latency and also can enhance the performance of the system. However, during transaction deadlock may occur that can reduce the throughput by minimizing the available resources, so it becomes an important resource management problem in distributed systems. Approach: The Neighbor Replication on Grid Deadlock Detection (NRGDD) transaction model has been developed to handle two deadlock cycle problems on grid. By deploying this method, the transactions communicate with each other by passing the probe messages. The victim message has been used to detect the deadlock when the number of waiting resource by other transaction is highest and become as the cause of deadlock occurs. In addition, this transaction must be aborted to solve the problem. Results: NRGDD transaction model are able to detect and solve more than one cycle of deadlocks. Conclusion: NRGDD has resolve the deadlock problem by sending the minimum number of probes message to detect the deadlock and it can resolve the deadlock to ensure the transaction can be done smoothly.

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Lowest Data Replication Storage of Binary Vote Assignment Data Grid

Cited by 16 publications

References 10 publications

Evaluation of energy consumption and data access time in data fetching in grid-based data-intensive applications

Evaluation of energy consumption and data access time in data fetching in grid-based data-intensive applications

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

Solving Two Deadlock Cycles through Neighbor Replication on Grid Deadlock Detection Model

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