Distributed intelligence on the Edge-to-Cloud Continuum: A systematic literature review

Rosendo, Daniel; Costan, Alexandru; Valduriez, Patrick; Antoniu, Gabriel

doi:10.1016/j.jpdc.2022.04.004

Cited by 70 publications

(20 citation statements)

References 144 publications

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“…The computing continuum (also called the digital continuum, IoT-edge-cloud continuum [ 29 ], computing fabric, or transcontinuum) is the combination of resources and services at the center of the network (cloud), at its border (edge), and in transit (fog). Data are generated and pre-processed at the edge, partially processed by intermediate nodes, and, if necessary, transferred to the cloud [ 30 ]. A node is a physical (or virtualized) device that is part of a network and has the capability to execute certain computations and communicate with other nodes.…”

Section: Terminology and Taxonomymentioning

confidence: 99%

Self-* Capabilities of Cloud-Edge Nodes: A Research Review

S-Julián

Lacalle

Vaño

et al. 2023

Sensors

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Most recent edge and fog computing architectures aim at pushing cloud-native traits at the edge of the network, reducing latency, power consumption, and network overhead, allowing operations to be performed close to data sources. To manage these architectures in an autonomous way, systems that materialize in specific computing nodes must deploy self-* capabilities minimizing human intervention across the continuum of computing equipment. Nowadays, a systematic classification of such capabilities is missing, as well as an analysis on how those can be implemented. For a system owner in a continuum deployment, there is not a main reference publication to consult to determine what capabilities do exist and which are the sources to rely on. In this article, a literature review is conducted to analyze the self-* capabilities needed to achieve a self-* equipped nature in truly autonomous systems. The article aims to shed light on a potential uniting taxonomy in this heterogeneous field. In addition, the results provided include conclusions on why those aspects are too heterogeneously tackled, depend hugely on specific cases, and shed light on why there is not a clear reference architecture to guide on the matter of which traits to equip the nodes with.

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Section: Terminology and Taxonomymentioning

confidence: 99%

Self-* Capabilities of Cloud-Edge Nodes: A Research Review

S-Julián

Lacalle

Vaño

et al. 2023

Sensors

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“…The edge-cloud continuum leverages all the resources from the edge of the network (e.g., IoT devices) to the core (e.g., cloud data centers) [50]. Specifically:…”

Section: System Architecturementioning

confidence: 99%

Edge-Cloud Continuum Solutions for Urban Mobility Prediction and Planning

et al. 2023

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In recent years, there has been an increase in the use of edge-cloud continuum solutions to efficiently collect and analyze data generated by IoT devices. In this paper, we investigate to what extent these solutions can manage tasks related to urban mobility, by combining real-time and low latency analysis offered by the edge with large computing and storage resources provided by the cloud. Our proposal is organized into three parts. The first part focuses on defining three application scenarios in which geotagged data generated by IoT objects, such as taxis, cars, and smartphones, are collected and analyzed through machine learning algorithms (i.e., next location prediction, location-based advertising, and points of interest recommendation). The second part is dedicated to modeling an edge-cloud continuum architecture capable of managing a large number of IoT devices and executing machine learning algorithms to analyze the data they generate. The third part analyzes the experimental results in which different design choices were evaluated, such as the number of devices and orchestration policies, to improve the performance of machine learning algorithms in terms of processing time, network delay, task failure, and computational resource utilization. The results highlight the potential benefits of edge and cloud cooperation in the three application scenarios, demonstrating that it significantly improves resource utilization and reduces the task failure rate compared to other widely adopted architectures, such as edge-or cloud-only architectures.INDEX TERMS Edge-cloud architecture, IoT infrastructure, Edge computing, Urban computing, Smart cities, Urban mobility

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“…It should be noted, however, that, while computation offloading at the edge and the distribution and deployment of deep learning solutions on such computing environments are still emerging topics that have gained momentum over the past five years, both have already given rise to a vast corpus of scientific articles, leading to a fairly important number of surveys, as illustrated in Table 1 . Specifically, we found sixteen papers [ 11 , 12 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 ] focused on EI that provide an extensive overview of the current state of the art in the topic space. They guide the reader through a comprehensive collection of methods and technologies designed to better leverage edge infrastructures for DNN training [ 11 , 12 , 16 , 17 , 21 , 22 , 29 ] but primarily for the execution of such DL models [ 11 , 12 , 16 , 17 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 29 ].…”

Section: Introductionmentioning

confidence: 99%

“…Specifically, we found sixteen papers [ 11 , 12 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 ] focused on EI that provide an extensive overview of the current state of the art in the topic space. They guide the reader through a comprehensive collection of methods and technologies designed to better leverage edge infrastructures for DNN training [ 11 , 12 , 16 , 17 , 21 , 22 , 29 ] but primarily for the execution of such DL models [ 11 , 12 , 16 , 17 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 29 ].…”

Section: Introductionmentioning

confidence: 99%

“…Regarding inference, an extended trend is particularly noticeable among the authors on providing an overview of architectures and workflows for enabling DNN processing at the edge, giving particular attention to (i) techniques that make DL models applicable for direct deployment and local execution on resource edge devices by creating lightweight architectures from scratch [ 11 , 12 , 16 , 23 ], i.e., naturally suitable for edge environments, or by adjusting existing DNNs to reduce their complexity and size [ 12 , 16 , 17 , 21 , 22 , 23 ]; and (ii) strategies pursuing the realization of an offloading-based collaborative inference across multiples devices located either at the same tier or in different computing levels [ 20 , 22 , 23 , 24 , 25 ]. Moreover, most authors extend (i) and (ii) by introducing core concepts [ 18 , 24 ] and providing extensive background on other EI-specific matters such as (i) the most representative application scenarios [ 12 , 18 , 21 , 27 ], (ii) the software [ 21 , 29 ] and hardware infrastructure [ 16 , 20 ] for facilitating EI, and (iii) the most relevant challenges that need to be faced for its realization, i.e., model partitioning [ 11 , 17 , 19 , 22 ], communication [ 11 , 26 ], edge coordination [ 20 , 27 ], and more AI-related challenges [ 20 ].…”

Section: Introductionmentioning

confidence: 99%

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Horizontally Distributed Inference of Deep Neural Networks for AI-Enabled IoT

Rodriguez-Conde

Campos

Fdez-Riverola

2023

Sensors

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Motivated by the pervasiveness of artificial intelligence (AI) and the Internet of Things (IoT) in the current “smart everything” scenario, this article provides a comprehensive overview of the most recent research at the intersection of both domains, focusing on the design and development of specific mechanisms for enabling a collaborative inference across edge devices towards the in situ execution of highly complex state-of-the-art deep neural networks (DNNs), despite the resource-constrained nature of such infrastructures. In particular, the review discusses the most salient approaches conceived along those lines, elaborating on the specificities of the partitioning schemes and the parallelism paradigms explored, providing an organized and schematic discussion of the underlying workflows and associated communication patterns, as well as the architectural aspects of the DNNs that have driven the design of such techniques, while also highlighting both the primary challenges encountered at the design and operational levels and the specific adjustments or enhancements explored in response to them.

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Distributed intelligence on the Edge-to-Cloud Continuum: A systematic literature review

Cited by 70 publications

References 144 publications

Self-* Capabilities of Cloud-Edge Nodes: A Research Review

Self-* Capabilities of Cloud-Edge Nodes: A Research Review

Edge-Cloud Continuum Solutions for Urban Mobility Prediction and Planning

Horizontally Distributed Inference of Deep Neural Networks for AI-Enabled IoT

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