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Main Title: Online Replication Strategies for Distributed Data Stores
Author(s): Semmler, Niklas
Smaragdakis, Georgios
Feldmann, Anja
Type: Article
Language Code: en
Abstract: The rate at which data is produced at the network edge, e.g., collected from sensors and Internet of Things (IoT) devices, will soon exceed the storage and processing capabilities of a single system and the capacity of the network. Thus, data will need to be collected and preprocessed in distributed data stores—as part of a distributed database—at the network edge. Yet, even in this setup, the transfer of query results will incur prohibitive costs. To further reduce the data transfers, patterns in the workloads must be exploited. Particularly in IoT scenarios, we expect data access to be highly skewed. Most data will be store-only, while a fraction will be popular. Here, the replication of popular, raw data, as opposed to the shipment of partially redundant query results, can reduce the volume of data transfers over the network. In this paper, we design online strategies to decide between replicating data from data stores or forwarding the queries and retrieving their results. Our insight is that by pro ling access patterns of the data we can lower the data transfer cost and the corresponding response times. We evaluate the bene t of our strategies using two real-world datasets.
Issue Date: Aug-2019
Date Available: 4-Dec-2019
DDC Class: 000 Informatik, Informationswissenschaft, allgemeine Werke
Subject(s): database
data replication
computer networks
data transfer
online strategy
data replication
replication strategy
distributed data store
Sponsor/Funder: EC/H2020/679158/EU/Resolving the Tussle in the Internet: Mapping, Architecture, and Policy Making/ResolutioNet
Journal Title: Open Journal of Internet of Things (OJIOT)
Publisher: RonPub
Publisher Place: Lübeck
Volume: 5
Issue: 1
Page Start: 47
Page End: 57
EISSN: 2364-7108
Appears in Collections:FG Internet Measurement and Analysis (IMA) » Publications

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