Predictive Replica Placement for Mobile Users in Distributed Fog Data Stores with Client-Side Markov Models

Mobile clients that consume and produce data are abundant in fog environments and low latency access to this data can only be achieved by storing it in their close physical proximity. To adapt data replication in fog data stores in an efficient manner and make client data available at the fog node t...

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Bibliographic Details
Main Authors: Bellmann, Malte, Pfandzelter, Tobias, Bermbach, David
Format: Article in Journal/Newspaper
Language:unknown
Published: arXiv 2021
Subjects:
Distributed, Parallel, and Cluster Computing cs.DC
FOS Computer and information sciences
DML
Online Access:https://dx.doi.org/10.48550/arxiv.2111.03395
https://arxiv.org/abs/2111.03395
id ftdatacite:10.48550/arxiv.2111.03395
record_format openpolar
spelling ftdatacite:10.48550/arxiv.2111.03395 2023-05-15T16:01:54+02:00 Predictive Replica Placement for Mobile Users in Distributed Fog Data Stores with Client-Side Markov Models Bellmann, Malte Pfandzelter, Tobias Bermbach, David 2021 https://dx.doi.org/10.48550/arxiv.2111.03395 https://arxiv.org/abs/2111.03395 unknown arXiv https://dx.doi.org/10.1145/3492323.3495595 arXiv.org perpetual, non-exclusive license http://arxiv.org/licenses/nonexclusive-distrib/1.0/ Distributed, Parallel, and Cluster Computing cs.DC FOS Computer and information sciences article-journal Article ScholarlyArticle Text 2021 ftdatacite https://doi.org/10.48550/arxiv.2111.03395 https://doi.org/10.1145/3492323.3495595 2022-03-10T13:29:17Z Mobile clients that consume and produce data are abundant in fog environments and low latency access to this data can only be achieved by storing it in their close physical proximity. To adapt data replication in fog data stores in an efficient manner and make client data available at the fog node that is closest to the client, the systems need to predict both client movement and pauses in data consumption. In this paper, we present variations of Markov model algorithms that can run on clients to increase the data availability while minimizing excess data. In a simulation, we find the availability of data at the closest node can be improved by 35% without incurring the storage and communication overheads of global replication. : Accepted for publication at 1st Workshop on Distributed Machine Learning for the Intelligent Computing Continuum (DML-ICC) (2021 IEEE/ACM 14th International Conference on Utility and Cloud Computing (UCC '21) Companion) Article in Journal/Newspaper DML DataCite Metadata Store (German National Library of Science and Technology)
institution Open Polar
collection DataCite Metadata Store (German National Library of Science and Technology)
op_collection_id ftdatacite
language unknown
topic Distributed, Parallel, and Cluster Computing cs.DC
FOS Computer and information sciences
spellingShingle Distributed, Parallel, and Cluster Computing cs.DC
FOS Computer and information sciences
Bellmann, Malte
Pfandzelter, Tobias
Bermbach, David
Predictive Replica Placement for Mobile Users in Distributed Fog Data Stores with Client-Side Markov Models
topic_facet Distributed, Parallel, and Cluster Computing cs.DC
FOS Computer and information sciences
description Mobile clients that consume and produce data are abundant in fog environments and low latency access to this data can only be achieved by storing it in their close physical proximity. To adapt data replication in fog data stores in an efficient manner and make client data available at the fog node that is closest to the client, the systems need to predict both client movement and pauses in data consumption. In this paper, we present variations of Markov model algorithms that can run on clients to increase the data availability while minimizing excess data. In a simulation, we find the availability of data at the closest node can be improved by 35% without incurring the storage and communication overheads of global replication. : Accepted for publication at 1st Workshop on Distributed Machine Learning for the Intelligent Computing Continuum (DML-ICC) (2021 IEEE/ACM 14th International Conference on Utility and Cloud Computing (UCC '21) Companion)
format Article in Journal/Newspaper
author Bellmann, Malte
Pfandzelter, Tobias
Bermbach, David
author_facet Bellmann, Malte
Pfandzelter, Tobias
Bermbach, David
author_sort Bellmann, Malte
title Predictive Replica Placement for Mobile Users in Distributed Fog Data Stores with Client-Side Markov Models
title_short Predictive Replica Placement for Mobile Users in Distributed Fog Data Stores with Client-Side Markov Models
title_full Predictive Replica Placement for Mobile Users in Distributed Fog Data Stores with Client-Side Markov Models
title_fullStr Predictive Replica Placement for Mobile Users in Distributed Fog Data Stores with Client-Side Markov Models
title_full_unstemmed Predictive Replica Placement for Mobile Users in Distributed Fog Data Stores with Client-Side Markov Models
title_sort predictive replica placement for mobile users in distributed fog data stores with client-side markov models
publisher arXiv
publishDate 2021
url https://dx.doi.org/10.48550/arxiv.2111.03395
https://arxiv.org/abs/2111.03395
genre DML
genre_facet DML
op_relation https://dx.doi.org/10.1145/3492323.3495595
op_rights arXiv.org perpetual, non-exclusive license
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
op_doi https://doi.org/10.48550/arxiv.2111.03395
https://doi.org/10.1145/3492323.3495595
_version_ 1766397587045220352

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