Edge-centric Computing: Vision and Challenges
Open accessAbstract
In many aspects of human activity, there has been a continuous struggle between the forces of centralization and decentralization. Computing exhibits the same phenomenon; we have gone from mainframes to PCs and local networks in the past, and over the last decade we have seen a centralization and consolidation of services and applications in data centers and clouds. We position that a new shift is necessary. Technological advances such as powerful dedicated connection boxes deployed in most homes, high capacity mobile end-user devices and powerful wireless networks, along with growing user concerns about trust, privacy, and autonomy requires taking the control of computing applications, data, and services away from some central nodes (the "core") to the other logical extreme (the "edge") of the Internet. We also position that this development can help blurring the boundary between man and machine, and embrace social computing in which humans are part of the computation and decision making loop, resulting in a human-centered system design. We refer to this vision of human-centered edge-device based computing as Edge-centric Computing . We elaborate in this position paper on this vision and present the research challenges associated with its implementation.
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The major challenge for Edge Computing may be scalability, meaning designing architectures that rapidly scale to multiple users, especially in Multi-access Edge Computing.
“The major challenge may be that of scalability, i.e. designing architectures that rapidly scale to multiple users, especially in MEC [14].”
From Edge AI: A survey (2023) · cites this paper as [14]
Section 3.4, “Scalability” (page unavailable), says scaling remains a relevant challenge for edge-centric computing and discusses architectures serving millions of users. It does not specifically address Multi-access Edge Computing or say that architectures must scale rapidly; it identifies the tradeoff between computing and communication responsibilities as a major challenge. (LLM, conf=0.84)
Experimental — accuracy not yet measuredFinding and accessing appropriate nodes in an Edge Computing network, discovery, and trust must be considered together, especially for mobile devices.
“Finding and accessing the appropriate nodes in an Edge Computing network and discovery and trust are features that must be considered together, especially for mobile devices [14].”
From Edge AI: A survey (2023) · cites this paper as [14]
Edge computing is a distributed computing architecture that brings computing power closer to the data source.
“On the other hand, edge computing is a distributed computing architecture that brings computing power closer to the data source [5,6,22].”
From Edge AI for Early Detection of Chronic Diseases and the Spread of Infectious Diseases: Opportunities, Challenges, and Future Directions (2023) · cites this paper as [22]
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- Authors
- Garcia Lopez, P., Montresor, A., Epema, D., Datta, A., Higashino, T., Iamnitchi, A., Barcellos, M., Felber, P., & Riviere, E.
- Affiliations
- Universitat Rovira i Virgili, Tarragona, Spain
- University of Trento (IT)
- Delft University of Technology (NL)
- Nanyang Technological University (SG)
- Osaka University, Osaka, Japan
- University of Florida (US)
- Universidade do Vale do Rio dos Sinos (BR)
- University of Neuchâtel (CH)
- University of Neuchatel, Neuchatel, Switzerland
- ORCID
- Venue
- ACM SIGCOMM Computer Communication Review
- Published
- 2015-09-30
- Volume
- 45
- Issue
- 5
- Pages
- 37-42
- ISSN
- 0146-4833 (ISSN-L)
- OpenAlex
- W2061860832
- Cited by
- 934
- References
- 9
- Citations by year
- 2026: 16 · 2025: 44 · 2024: 39 · 2023: 48 · 2022: 59 · 2021: 109 · 2020: 128 · 2019: 162 · 2018: 167 · 2017: 116 · 2016: 46
- FWCI
- 125.70
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- ACM SIGCOMM Computer Communication Review (publishedVersion)
- Digital Access to Libraries (Université catholique de Louvain (UCL), l'Université de Namur (UNamur) and the Université Saint-Louis (USL-B)) (submittedVersion, other-oa)
- Iris (University of Trento) (submittedVersion, other-oa)
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
- Physical Sciences › Computer Science › Hardware and Architecture › Energy Efficiency in Computing
- Physical Sciences › Computer Science › Hardware and Architecture › Cloud Computing and Remote Desktop Technologies
- Keywords
- edge computing, decentralization (distributed systems), cloud computing, data centers, human-centered computing, social computing, human-machine interaction, privacy, trust, mobile devices, wireless networks, research challenges
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