A fast algorithm for online placement and reorganization of replicated data

Honicky, R.J.; Miller, Ethan L.

doi:10.1109/ipdps.2003.1213151

Cited by 58 publications

(41 citation statements)

References 25 publications

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“…The first methods with dedicated support for replication were proposed by Honicky and Miller [14] [15]. RUSH (Replication Under Scalable Hashing) maps replicated objects to a scalable collection of storage servers according to userspecified server weighting.…”

Section: B Previous Resultsmentioning

confidence: 99%

Reliable and randomized data distribution strategies for large scale storage systems

Miranda

Effert

Kang

et al. 2011

2011 18th International Conference on High Performance Computing

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Abstract-The ever-growing amount of data requires highly scalable storage solutions. The most flexible approach is to use storage pools that can be expanded and scaled down by adding or removing storage devices. To make this approach usable, it is necessary to provide a solution to locate data items in such a dynamic environment. This paper presents and evaluates the Random Slicing strategy, which incorporates lessons learned from table-based, rule-based, and pseudo-randomized hashing strategies and is able to provide a simple and efficient strategy that scales up to handle exascale data. Random Slicing keeps a small table with information about previous storage system insert and remove operations, drastically reducing the required amount of randomness while delivering a perfect load distribution.

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Section: B Previous Resultsmentioning

confidence: 99%

Reliable and randomized data distribution strategies for large scale storage systems

Miranda

Effert

Kang

et al. 2011

2011 18th International Conference on High Performance Computing

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“…So methods to improve the SAN are mainly focused on these three components. For example, many data placement algorithms, for example [1][2][3] [4] and so on, which mainly improve the storage devices are proposed to get more performance and scalability of the storage system. In this paper, we focus on the optimization of the switches which are used to connect the servers and storage devices.…”

Section: Introductionmentioning

confidence: 99%

Optimization of the Switches in Storage Networks

Yao

Xin

Hui-min

et al. 2007

Computational Science – ICCS 2007

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Abstract. In the Storage Area Network (SAN), the mature network technology is used to substitute for the IO bus. Usually, the common switch is used in the storage network especially in the iSCSI storage system. Because the common switches are not aware of the environment it is used in, it can not make optimization for the storage network. In this paper, an optimization of storage network is presented which can greatly improve the performance and resource utilization of the whole storage system. In this optimization, the IO acknowledgment commands passing through the switch which is used in storage network are transferred in highest priority. This optimization of SAN has been certified by the simulation experiments.

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“…RUSH P is described in detail in our previous work [10] so we will only briefly discuss this algorithm. The algorithm for RUSH P , using symbols described in Table 1 is shown in Figure 2.…”

Section: Rush P : Placement Using Prime Numbersmentioning

confidence: 99%

“…However, there is a finite probability of this occurring, and it indeed will always happen if there is only one sub-cluster, so the algorithm must allow for this possibility. RUSH P uses a trick to allow all of the sub-clusters except for the original subcluster to have fewer disks than the object replication factor, as described in our original paper on RUSH P [10].…”

Section: Rush P : Placement Using Prime Numbersmentioning

confidence: 99%