Distributed storage in the plane

Eitan Altman, Konstatin Avrachenkov, Jasper Goseling

Research output: Book/ReportReportProfessional

54 Downloads (Pure)

Abstract

We consider storage devices located in the plane according to a general point process and specialize the results for the homogeneous Poisson process. A large data file is stored at the storage devices, which have limited storage capabilities. Hence, they can only store parts of the data. Clients can contact the storage devices to retrieve the data. We compare the expected cost of obtaining the complete data under uncoded as well as coded data allocation strategies. It is shown that for the general class of cost measures where the cost of retrieving data is increasing with the distance between client and storage devices, coded allocation outperforms uncoded allocation. The improvement offered by coding is quantified for two more specific classes of performance measures. Finally, our results are validated by computing the costs of the allocation strategies for the case that storage devices coincide with currently deployed mobile base stations.
Original languageUndefined
Place of PublicationEnschede
PublisherUniversity of Twente, Department of Applied Mathematics
Number of pages8
Publication statusPublished - Dec 2013

Publication series

NameMemorandum
PublisherUniversity of Twente, Department of Applied Mathematics
No.2023
ISSN (Print)1874-4850
ISSN (Electronic)1874-4850

Keywords

  • EWI-24107
  • IR-88278
  • METIS-300224

Cite this

Altman, E., Avrachenkov, K., & Goseling, J. (2013). Distributed storage in the plane. (Memorandum; No. 2023). Enschede: University of Twente, Department of Applied Mathematics.
Altman, Eitan ; Avrachenkov, Konstatin ; Goseling, Jasper. / Distributed storage in the plane. Enschede : University of Twente, Department of Applied Mathematics, 2013. 8 p. (Memorandum; 2023).
@book{6982519aba814db3b1c9f16d547bee4b,
title = "Distributed storage in the plane",
abstract = "We consider storage devices located in the plane according to a general point process and specialize the results for the homogeneous Poisson process. A large data file is stored at the storage devices, which have limited storage capabilities. Hence, they can only store parts of the data. Clients can contact the storage devices to retrieve the data. We compare the expected cost of obtaining the complete data under uncoded as well as coded data allocation strategies. It is shown that for the general class of cost measures where the cost of retrieving data is increasing with the distance between client and storage devices, coded allocation outperforms uncoded allocation. The improvement offered by coding is quantified for two more specific classes of performance measures. Finally, our results are validated by computing the costs of the allocation strategies for the case that storage devices coincide with currently deployed mobile base stations.",
keywords = "EWI-24107, IR-88278, METIS-300224",
author = "Eitan Altman and Konstatin Avrachenkov and Jasper Goseling",
year = "2013",
month = "12",
language = "Undefined",
series = "Memorandum",
publisher = "University of Twente, Department of Applied Mathematics",
number = "2023",

}

Altman, E, Avrachenkov, K & Goseling, J 2013, Distributed storage in the plane. Memorandum, no. 2023, University of Twente, Department of Applied Mathematics, Enschede.

Distributed storage in the plane. / Altman, Eitan; Avrachenkov, Konstatin; Goseling, Jasper.

Enschede : University of Twente, Department of Applied Mathematics, 2013. 8 p. (Memorandum; No. 2023).

Research output: Book/ReportReportProfessional

TY - BOOK

T1 - Distributed storage in the plane

AU - Altman, Eitan

AU - Avrachenkov, Konstatin

AU - Goseling, Jasper

PY - 2013/12

Y1 - 2013/12

N2 - We consider storage devices located in the plane according to a general point process and specialize the results for the homogeneous Poisson process. A large data file is stored at the storage devices, which have limited storage capabilities. Hence, they can only store parts of the data. Clients can contact the storage devices to retrieve the data. We compare the expected cost of obtaining the complete data under uncoded as well as coded data allocation strategies. It is shown that for the general class of cost measures where the cost of retrieving data is increasing with the distance between client and storage devices, coded allocation outperforms uncoded allocation. The improvement offered by coding is quantified for two more specific classes of performance measures. Finally, our results are validated by computing the costs of the allocation strategies for the case that storage devices coincide with currently deployed mobile base stations.

AB - We consider storage devices located in the plane according to a general point process and specialize the results for the homogeneous Poisson process. A large data file is stored at the storage devices, which have limited storage capabilities. Hence, they can only store parts of the data. Clients can contact the storage devices to retrieve the data. We compare the expected cost of obtaining the complete data under uncoded as well as coded data allocation strategies. It is shown that for the general class of cost measures where the cost of retrieving data is increasing with the distance between client and storage devices, coded allocation outperforms uncoded allocation. The improvement offered by coding is quantified for two more specific classes of performance measures. Finally, our results are validated by computing the costs of the allocation strategies for the case that storage devices coincide with currently deployed mobile base stations.

KW - EWI-24107

KW - IR-88278

KW - METIS-300224

M3 - Report

T3 - Memorandum

BT - Distributed storage in the plane

PB - University of Twente, Department of Applied Mathematics

CY - Enschede

ER -

Altman E, Avrachenkov K, Goseling J. Distributed storage in the plane. Enschede: University of Twente, Department of Applied Mathematics, 2013. 8 p. (Memorandum; 2023).