Prediction of hotspot in data centric storage

Amiri, Zeynab; Sabae, Masoud

doi:10.1109/icdim.2011.6093346

Cited by 1 publication

(3 citation statements)

References 12 publications

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“…In one application environment, the value of α in Zipf's Law is a constant, and according to formula (2) and Section 3.1, we have In this paper, α is a constant to be determined. α can be modified by the real I/O access condition.…”

Section: Hotspot Data Prediction Modelmentioning

confidence: 99%

“…Heish and Kuo [18] discussed the hotspot identification in flash memory storage systems, and proposed a highly efficient method for on-line hotspot identification with limited space requirements. Amiri and Sabae [2] proposed method to distribute storage nodes in suitable locations by preferring the quality of service for saving and retrieving data and predicting from the Hotspots problem. They will show how new method increases lifetime of the network in comparing of other methods.…”

Section: Related Workmentioning

confidence: 99%

“…The hotspot data queue length T , kT, T Three random time intervals, k is a rational number (k > 0) t, t 1 , t 2 Three random time (t, t 1 ,…”

Section: Hotspot Data Prediction Modelmentioning

confidence: 99%

See 2 more Smart Citations

A study of hotspot data prediction model in I/O workloads

Yang

Tan

Xie

et al. 2013

International Journal of Computer Mathematics

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There is a notable characteristic of the data access pattern: 80% I/O requests only access 20% data. This feature brings about the concept of hotspot data, which refer to the data in the most frequent requested area. The access to these hotspot data has direct influence upon the performance of the storage system's applications. Therefore, how to predict hotspot data is a critical research focus in the optimization of the storage system. In this paper, we propose a hotspot data prediction model based on a Zipf-like distribution, which can estimate and dynamically adjust parameters according to the present statistics of I/O access. We classify the hotspot data from every trace, and analyse the prediction rate through the classified hotspot data's characteristic. We synthesize the analysis results in different time granularities and hotspot data prediction queue lengths. Finally, we use block I/O traces to discuss the effectiveness of this model. The discussion and analysis results indicate that this model can predict the hotspot data efficiently.

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Section: Hotspot Data Prediction Modelmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%