Architecturing elastic edge storage services for data-driven decision making

Ivan Lujic*, Hong Linh Truong

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

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Abstract

In the IoT era, a massive number of smart sensors produce a variety of data at unprecedented scale. Edge storage has limited capacities posing a crucial challenge for maintaining only the most relevant IoT data for edge analytics. Currently, this problem is addressed mostly considering traditional cloud-based database perspectives, including storage optimization and resource elasticity, while separately investigating data analytics approaches and system operations. For better support of future edge analytics, in this work, we propose a novel, holistic approach for architecturing elastic edge storage services, featuring three aspects, namely, (i) data/system characterization (e.g., metrics, key properties), (ii) system operations (e.g., filtering, sampling), and (iii) data processing utilities (e.g., recovery, prediction). In this regard, we present seven engineering principles for the architecture design of edge data services.

Original languageEnglish
Title of host publicationSoftware Architecture - 13th European Conference, ECSA 2019, Proceedings
EditorsTomas Bures, Laurence Duchien, Paola Inverardi
Pages97-105
Number of pages9
DOIs
Publication statusPublished - 1 Jan 2019
MoE publication typeA4 Article in a conference publication
EventEuropean Conference on Software Architecture - Paris, France
Duration: 9 Sep 201913 Sep 2019
Conference number: 13
https://ecsa2019.univ-lille.fr/home

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11681 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

ConferenceEuropean Conference on Software Architecture
Abbreviated titleECSA
CountryFrance
CityParis
Period09/09/201913/09/2019
Internet address

Keywords

  • Adaptation
  • Architectural design
  • Edge computing
  • Edge data service
  • Engineering
  • IoT
  • Service computing

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