Edge Data Repositories - The design of a store-process-send system at the Edge

Nicolaescu, Adrian-Cristian; Ascigil, Onur; Psaras, Ioannis

doi:10.1145/3359993.3366644

Cited by 4 publications

(2 citation statements)

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“…If the user moves to other places, the repository is allowed to move with the user and eventually get processed by intelligent applications. Based on [92], Nicolaescu et al propose a feedback algorithm to evaluate the service quality and resource allocation of edge data repositories in different scenarios [93]. Liu et al propose to use embedded storage device as data repository and study the impact of storage space and the number of data repository on data availability [94].…”

Section: Edge Cachingmentioning

confidence: 99%

Edge Intelligence: Empowering Intelligence to the Edge of Network

et al. 2021

Proc. IEEE

126

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Edge intelligence refers to a set of connected systems and devices for data collection, caching, processing, and analysis proximity to where data is captured based on artificial intelligence. Edge intelligence aims at enhancing data processing and protect the privacy and security of the data and users. Although recently emerged, spanning the period from 2011 to now, this field of research has shown explosive growth over the past five years. In this paper, we present a thorough and comprehensive survey on the literature surrounding edge intelligence. We first identify four fundamental components of edge intelligence, i.e. edge caching, edge training, edge inference, and edge offloading based on theoretical and practical results pertaining to proposed and deployed systems. We then aim for a systematic classification of the state of the solutions by examining research results and observations for each of the four components and present a taxonomy that includes practical problems, adopted techniques, and application goals. For each category, we elaborate, compare and analyse the literature from the perspectives of adopted techniques, objectives, performance, advantages and drawbacks, etc. This article provides a comprehensive survey to edge intelligence and its application areas. In addition, we summarise the development of the emerging research fields and the current stateof-the-art and discuss the important open issues and possible theoretical and technical directions.

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Section: Edge Cachingmentioning

confidence: 99%

Edge Intelligence: Empowering Intelligence to the Edge of Network

et al. 2021

Proc. IEEE

126

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“…Psaras et al performed an initial exploration of the benefits and challenges of data storage at the edge to alleviate the stress that the massive amounts of data generated by IoT devices put on the core network for the transmission of these data to cloud storage [2]. Nicolaescu et al built on top of this concept by developing and assessing a preliminary design for the management of popular data at the edge [22]. Liu et al investigated the impact of the size and number of data storage units on the data availability and operational cost [23].…”

Section: Storage and Data Placement At The Edgementioning

confidence: 99%

Store Edge Networked Data (SEND): A Data and Performance Driven Edge Storage Framework

Nicolaescu

Mastorakis

Psaras

2021

IEEE INFOCOM 2021 - IEEE Conference on Computer Communications

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The number of devices that the edge of the Internet accommodates and the volume of the data these devices generate are expected to grow dramatically in the years to come. As a result, managing and processing such massive data amounts at the edge becomes a vital issue. This paper proposes "Store Edge Networked Data" (SEND), a novel framework for in-network storage management realized through data repositories deployed at the network edge. SEND considers different criteria (e.g., data popularity, data proximity from processing functions at the edge) to intelligently place different categories of raw and processed data at the edge based on system-wide identifiers of the data context, called labels. We implement a data repository prototype on top of the Google file system, which we evaluate based on real-world datasets of images and Internet of Things device measurements. To scale up our experiments, we perform a network simulation study based on synthetic and real-world datasets evaluating the performance and trade-offs of the SEND design as a whole. Our results demonstrate that SEND achieves data insertion times of 0.06ms-0.9ms, data lookup times of 0.5ms-5.3ms, and on-time completion of up to 92% of user requests for the retrieval of raw and processed data.Index Terms-Edge computing, Internet of Things (IoT), Data storage at the edge, Data management

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