Performance Analysis of Applying Replica Selection Technology for Data Grid Environments

Lecture Notes in Computer Science

Wang

2007

Self Cite

Abstract. In this paper, we propose a dynamic file transfer scheme with coallocation architecture, called Dynamic Adjustment Strategy, a dynamic file transfer scheme with co-allocation architecture that reduce the file transfer times and improves the performance in Data Grid environments. Our approach reduces the idle time faster servers spend waiting for the slowest server, and decreases file transfer completion time. We also present a new toolkit, called Cyber-Transformer, with a friendly graphical user interface interface running on the client side that makes it easy for inexperienced users to manage replicas and download the files in Data Grid environments. We also provide an effective scheme for reducing the cost of reassembling data blocks.

Section: The Co-allocation Architecturementioning

confidence: 99%

Section: Fig 1 the Co-allocation Architecture In Data Gridsmentioning

confidence: 99%

Section: The Dynamic Adjustment Strategymentioning

confidence: 99%

Section: The Dynamic Adjustment Strategymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A Dynamic Adjustment Strategy for File Transformation in Data Grids

Lecture Notes in Computer Science

Wang

2007

Self Cite

An Anticipative Recursively Adjusting Mechanism for parallel file transfer in data grids

Concurrency and Computation

Chi

et al. 2010

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SUMMARYData Grids enable the sharing, selection, and connection of a wide variety of geographically distributed computational and storage resources for content needed by large-scale data-intensive applications such as high-energy physics, bioinformatics, and virtual astrophysical observatories. In Data Grids, co-allocation architectures were developed to enable parallel downloads of data sets from selected replica servers. As Internet is usually the underlying network of a grid, network bandwidth plays as the main factor affecting file transfers between clients and servers. In this paradigm, there are still some challenges that need to be solved, such as to reduce differences in finish times between selected replica servers, to avoid traffic congestion resulting from transferring the same blocks in different links among servers and clients, and to manage network performance variations among parallel transfers. In this paper, we propose the Anticipative Recursively Adjusting Mechanism (ARAM) scheme to adjust the workloads on selected replica servers and handle unpredictable variations in network performance by those servers. Our algorithm is based on using the finish rates for previously assigned transfers to anticipate the bandwidth status for the next section to adjust workloads, and to reduce file transfer times in grid environments. Our approach is useful in grid environments with unstable network link. It not only reduces idle time wasted waiting for the slowest server, but also decreases file transfer completion times.

RACAM: design and implementation of a recursively adjusting co‐allocation method with efficient replica selection in Data Grids

Concurrency and Computation

Chen

2010

Self Cite

SUMMARYData Grids enable the sharing, selection, and connection of a wide variety of geographically distributed computational and storage resources for addressing large-scale data-intensive scientific application needs in, for instance, high-energy physics, bioinformatics, and virtual astrophysical observatories. Data sets are replicated in Data Grids and distributed among multiple sites. Unfortunately, data sets of interest sometimes are significantly large in size, and may cause access efficiency overhead. A co-allocation architecture was developed in order to enable parallel downloading of data sets from multiple servers. Several co-allocation strategies have been coupled and used to exploit download rate by specifying among various client-server divides files into multiple blocks of equal sizes to link and address dynamic rate fluctuations. However, one major obstacle, the idle time of faster servers having to wait for the slowest server to deliver the final block, makes it important to reduce differences in finishing time among replica servers. In this paper, we propose a dynamic co-allocation method, called Recursively Adjusting Co-Allocation Method (RACAM), to improve the performance of parallel data file transfer. Our approach reduces the idle time spent waiting for the slowest server and decreases data transfer completion time. We also provide an effective scheme for reducing the cost of reassembling data blocks.