Erasure Codes for Increasing the Availability of Grid Data Storage

Pitkänen, Mikko; Swany, Martin; Niemi, Tapio

doi:10.1109/aict-iciw.2006.90

Cited by 16 publications

(5 citation statements)

References 15 publications

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“…Various solutions to the centralized version of this problem have been proposed, and are based on well known coding schemes such as Fountain codes [6] or MDS codes [16]. To distribute the information from multiple sources throughout the network so that each node stores a coded packet as if obtained by centralized LT (Luby Transform) coding [12], Lin et al [11] proposed a solution that uses random walks with traps.…”

Section: Introductionmentioning

confidence: 99%

Fountain Codes Based Distributed Storage Algorithms for Large-Scale Wireless Sensor Networks

Aly

Kong

Soljanin³

2008

2008 International Conference on Information Processing in Sensor Networks (Ipsn 2008)

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We consider large-scale networks with n nodes, out of which k are in possession, (e.g., have sensed or collected in some other way) k information packets. In the scenarios in which network nodes are vulnerable because of, for example, limited energy or a hostile environment, it is desirable to disseminate the acquired information throughout the network so that each of the n nodes stores one (possibly coded) packet and the original k source packets can be recovered later in a computationally simple way from any (1 + ǫ)k nodes for some small ǫ > 0.We developed two distributed algorithms for solving this problem based on simple random walks and Fountain codes. Unlike all previously developed schemes, our solution is truly distributed, that is, nodes do not know n, k or connectivity in the network, except in their own neighborhoods, and they do not maintain any routing tables. In the first algorithm, all the sensors have the knowledge of n and k. In the second algorithm, each sensor estimates these parameters through the random walk dissemination. We present analysis of the communication/transmission and encoding/decoding complexity of these two algorithms, and provide extensive simulation results as well 1 .

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Section: Introductionmentioning

confidence: 99%

Fountain Codes Based Distributed Storage Algorithms for Large-Scale Wireless Sensor Networks

Aly

Kong

Soljanin³

2008

2008 International Conference on Information Processing in Sensor Networks (Ipsn 2008)

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“…Our earlier work has shown how large files can be efficiently striped and erasure coded for storage both in local [20] and in Grid environments [21]. The chosen striping scheme easily works with data entities at the quite arbitrary sizes, similar to our ADUs.…”

Section: Storage In the Networkmentioning

confidence: 99%

Redundancy and distributed caching in mobile DTNs

Pitkänen

Ott²

2007

Proceedings of 2nd ACM/IEEE International Workshop on Mobility in the Evolving Internet Architecture

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Delay tolerant networking (DTN) allows endpoints to exchange information in networks where end-to-end path may not exist at any given time. In this opportunistic model, routing and forwarding functionality in intermediate nodes enables data transfer following the store, carry, and forward paradigm. Thereby, even in sparsely populated settings, node-to-node contacts can be exploited for communications where network infrastructure does not exist or is not viable to use. Beyond plain message forwarding, the increasing amount of storage in modern devices enables nodes to hold messages for an extended period of time. This feature can then be utilized to create an opportunistic cooperative storage. Having established how to leverage DTN routing nodes for distributed content retrieval, caching, and storage in previous work, in this paper, we investigate applying redundancy schemes to improve message delivery and content retrieval.

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“…Data recovery, however, is trickier than replication. Erasure codes have been examined and shown to be effective for grid systems and highly available distributed storage systems, respectively [18,19]. The storage space requirements for several file high-availability strategies are shown in Fig.…”

Section: B) New Requirements and Metricsmentioning

confidence: 99%

Untitled

2023

Adv Bioeng Biomed Sci Res

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The Terasort benchmark and the YCSB benchmark are the two most used cloud computing benchmarks. Despite the fact that these benchmarks are quite helpful, data warehouse systems and associated OLAP technologies were not the focus of their creation. We initially introduce cloud computing and data warehouse systems in this essay. Then, we contend that the TPC-H benchmark, which is the most well-known benchmark for decision support systems, conflicts with the justifications for cloud computing (scalability, elasticity, pay-per-use, fault-tolerance characteristics), and customer relationship management (end-user satisfaction, Quality of Service features). Finally, we propose updated specifications for a benchmark for cloud data warehouse systems. The suggested specifications ought to make it possible to fairly compare the products offered by various cloud system providers.

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Erasure Codes for Increasing the Availability of Grid Data Storage

Abstract: In this paper, we describe the design of a highly-

Cited by 16 publications

References 15 publications

Fountain Codes Based Distributed Storage Algorithms for Large-Scale Wireless Sensor Networks

Fountain Codes Based Distributed Storage Algorithms for Large-Scale Wireless Sensor Networks

Redundancy and distributed caching in mobile DTNs

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