Asymptotic analysis of failed recovery probability in a distributed wireless storage system with limited sum storage capacity

Hong, Bi; Choi, Wan

doi:10.1109/icassp.2014.6854848

Cited by 13 publications

(8 citation statements)

References 7 publications

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“…For probabilistic access model, we present three figures to analyze the optimal storage allocation in the interval α ∈ [1,10]. In Fig.…”

Section: B Probabilistic Accessmentioning

confidence: 99%

“…Several studies have looked into how to allocate redundant chunks of data over the storage nodes, focusing mostly on optimizing two DSS performance metrics [7]- [10]. One of 1 pei.peng@rutgers.edu; emina.soljanin@rutgers.edu them is the probability of successful data recovery when only a subset of (possibly failed) nodes are accessed, and the other is the average download time when a set of nodes from which the file can certainly be recovered is accessed.…”

Section: Introductionmentioning

confidence: 99%

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On Distributed Storage Allocations of Large Files for Maximum Service Rate

Peng

Soljanin

2018

2018 56th Annual Allerton Conference on Communication, Control, and Computing (Allerton)

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Allocation of (redundant) file chunks throughout a distributed storage system affects important performance metrics such as the probability of file recovery, data download time, or the service rate of the system under a given data access model. This paper is concerned with the service rate under the assumption that the stored data is large and its download time is not negligible. We focus on quasi-uniform storage allocations and provide a service rate analysis for two common data access models. We find that the optimal allocation varies in accordance with different system parameters. This was not the case under the assumption that the download time does not scale with the size of data, where the minimal spreading allocation was previously found to be universally optimal.

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“…For probabilistic access model, we present three figures to analyze the optimal storage allocation in the interval α ∈ [1,10]. In Fig.…”

Section: B Probabilistic Accessmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

On Distributed Storage Allocations of Large Files for Maximum Service Rate

Peng

Soljanin

2018

2018 56th Annual Allerton Conference on Communication, Control, and Computing (Allerton)

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“…Since µ s (α|k) = 1 Ts(α|k) , (8) is simply inferred from (9). While the µ s (α|k) expression in (8) looks rather complicated, it is used in the following sections to simplify the service rate analysis in the form of the following corollary.…”

Section: Analysis Of µ S (α)mentioning

confidence: 99%

On storage allocation for maximum service rate in distributed storage systems

Noori¹,

Soljanin²,

Ardakani³

2016

2016 IEEE International Symposium on Information Theory (ISIT)

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Abstract-Storage allocation affects important performance measures of distributed storage systems. Most previous studies on the storage allocation consider its effect separately either on the success of the data recovery or on the service rate (time) where it is assumed that no access failure happens in the system. In this paper, we go one step further and incorporate the access model and the success of data recovery into the service rate analysis. In particular, we focus on quasi-uniform storage allocation and provide a service rate analysis for both fixed-size and probabilistic access models at the nodes. Using this analysis, we then show that for the case of exponential waiting time distribution at individuals storage nodes, minimal spreading allocation results in the highest system service rate for both access models. This means that for a given storage budget, replication provides a better service rate than a coded storage solution.

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“…. , x N ), subject to (2), so that the probability to recover the data successfully is maximized. Assuming that the data was encoded using a maximum-distance separable (MDS) code, the data object can be recovered if the amount of data collected by the collector is above or equal to one unit.…”

Section: A System Model and State Of The Artmentioning

confidence: 99%

“…However, the problem considered in computer networks is aimed on an efficient allocation of memory volumes over the network, but without limiting the memory volume, allocated to one particular network node. For wireless DSS, the problem of limited memory has been only considered via limitation of the total amount of data, allocated to represent one information object [2]. Such an approach allows to quantify the total memory volume, occupied by a specific application in a wireless network, but doe not limit memory volume per network node.…”

Section: Introductionmentioning

confidence: 99%

On Distributed Storage Allocations for Memory-Limited Systems

Andriyanova

Olmos

2015

2015 IEEE Global Communications Conference (GLOBECOM)

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In this paper we consider distributed allocation problems with memory constraint limits. Firstly, we propose a tractable relaxation to the problem of optimal symmetric allocations, considered by Leong et al. The approximated problem is based on the Q-error function, and its solution approaches the solution of the initial problem, as the number of storage nodes in the network grows. Secondly, exploiting this relaxation, we are able to formulate and to solve the problem for storage allocations for a memory-limited distributed storage system (DSS), with an arbitrary memory profile. Finally, we discuss the extension to the case of storing multiple data objects in one DSS.

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Asymptotic analysis of failed recovery probability in a distributed wireless storage system with limited sum storage capacity

Cited by 13 publications

References 7 publications

On Distributed Storage Allocations of Large Files for Maximum Service Rate

On Distributed Storage Allocations of Large Files for Maximum Service Rate

On storage allocation for maximum service rate in distributed storage systems

On Distributed Storage Allocations for Memory-Limited Systems

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