A Framework for an Integrated Co-allocator for Data Grid in Multi-Sender Environment

Bhuvaneswaran, R. S.; Katayama, Yoshiaki; Takahashi, Nobutaka

doi:10.1093/ietcom/e90-b.4.742

Cited by 8 publications

(6 citation statements)

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“…Dynamic co-allocation with duplicate assignments (DCDA) The co-allocation strategies described above do not handle the shortcoming of faster servers having to wait for the slowest server to deliver its final block which, in most cases, wastes much time and decreases overall performance. Neither the prediction nor the heuristic approach, the DCDA scheme dynamically co-allocates duplicate assignments (Bhuvaneswaran et al, 2005(Bhuvaneswaran et al, , 2007 and copies nicely with changes in server speed performance, as shown in Fig. 6.…”

Section: Aggressive Load Balancingmentioning

confidence: 98%

Enhancement of anticipative recursively adjusting mechanism for redundant parallel file transfer in data grids

Yang

Chiang

2009

Journal of Network and Computer Applications

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Section: Aggressive Load Balancingmentioning

confidence: 98%

Enhancement of anticipative recursively adjusting mechanism for redundant parallel file transfer in data grids

Yang

Chiang

2009

Journal of Network and Computer Applications

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“…The dynamic approaches proposed in literature, such as the one based on duplicate assignment [5] and the recursiveadjustment co-allocation algorithm [23], do not estimate a priori the time needed for the overall content delivery and do not take into account the costs of resources usage. In [13] a performance and cost-based mechanisms to select the servers for parallel file transfer is presented.…”

Section: Related Workmentioning

confidence: 99%

“…The interval time required by RS i to transfer the assigned chunks n i , called T i , is: (4) states that the aggregate incoming throughput R par , resulting by the parallel transfer flows from each RS i averaged over the overall content transfer time T, must not exceed R a . Constraint (5) ensures that the maximum latency is not overcome. Finally, constraint (6) states that n i must be a positive integer, because of the necessity to partition a content in an integer number of chunks, whose size has to be not too large, for granting a fine striping granularity, and not too small, for limiting protocol overheads.…”

Section: Routing and Parallel Updating Algorithmmentioning

confidence: 99%

“…By increasing the budget from 0.45 € to 0.641 €, a greater number of RSs can be exploited and the content latency decreases. Figure 4 shows also the budget intervals required to exploit subsets including an increasing number of RSs, taken from the original set ordered by increasing cost {RS 1 , RS 2 , RS 3 , RS 7 , RS 6 , RS 4 , RS 5 , RS 8 }.…”

Section: Experimental Analysismentioning

confidence: 99%

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SLA-based Content Delivery in Multi-CDNs

Ranaldo

Zimeo

2011

2011 Fourth IEEE International Conference on Utility and Cloud Computing

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Broadband network technologies have improved the bandwidth of the edge of the Internet but its core is still a bottleneck for large file transfers. Content Delivery Networks (CDNs), built at the edge of the Internet, are able to reduce the workload of network backbones but their scalability and network reach is often limited, especially in case of QoS-bound delivery services. By using the emerging CDN internetworking, a CDN can dynamically exploit resources of other cooperating CDNs to face peak loads and temporary malfunctions without violating QoS levels negotiated with content providers. This paper proposes an architectural schema and an algorithm to minimize transfer time of large files taking into account the SLA constraints agreed between the CDN in charge of content delivery and the content provider, while respecting the maximum budget that the CDN can pay to peer CDNs to ensure its revenue. The experimental analysis confirms the benefits of applying the proposed algorithm in CDN internetworking to enforce SLA guarantees of CDN services.Content delivery network; parallel download; pay-per-use; content internetworking; cloud computing.

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“…• Dynamic Co-allocation with Duplicate Assignments (DCDA) [16,17]: Neither prediction nor heuristics approaches, the DCDA scheme dynamically co-allocates duplicate assignments and copes nicely with changes in server speed performance.…”

Section: Co-allocation Architecturementioning

confidence: 99%

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

Yang

Chi

et al. 2010

Concurrency and Computation

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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.

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A Framework for an Integrated Co-allocator for Data Grid in Multi-Sender Environment

Cited by 8 publications

References 0 publications

Enhancement of anticipative recursively adjusting mechanism for redundant parallel file transfer in data grids

Enhancement of anticipative recursively adjusting mechanism for redundant parallel file transfer in data grids

SLA-based Content Delivery in Multi-CDNs

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

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