Opportunistic Edge Computing: Concepts, Opportunities and Research Challenges
June 12, 2018 Β· Declared Dead Β· π Future generations computer systems
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Richard Olaniyan, Olamilekan Fadahunsi, Muthucumaru Maheswaran, Mohamed Faten Zhani
arXiv ID
1806.04311
Category
cs.NI: Networking & Internet
Citations
65
Venue
Future generations computer systems
Last Checked
5 months ago
Abstract
The growing need for low-latency access to computing resources has motivated the introduction of edge computing, where resources are strategically placed at the access networks. Unfortunately, edge computing infrastructures like fogs and cloudlets have limited scalability and may be prohibitively expensive to install given the vast edge of the Internet. In this paper, we present Opportunistic Edge Computing (OEC), a new computing paradigm that provides a framework to create scalable infrastructures at the edge using end-user contributed resources. One of the goals of OEC is to place resources where there is high demand for them by incentivizing people to share their resources. This paper defines the OEC paradigm and the involved stakeholders and puts forward a management framework to build, manage and monitor scalable edge infrastructures. It also highlights the key differences between the OEC computing models and the existing computing models and shed the light on early works on the topic. The paper also presents preliminary experimental results that highlight the benefits and the limitations of OEC compared to the regular cloud computing deployment and the Fog deployment. It finally summarizes key research directions pertaining to resource management in OEC environments.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Networking & Internet
R.I.P.
π»
Ghosted
π
π
The Cartographer
Federated Learning in Mobile Edge Networks: A Comprehensive Survey
π
π
The Cartographer
A Survey of Indoor Localization Systems and Technologies
R.I.P.
π»
Ghosted
Survey of Important Issues in UAV Communication Networks
π
π
The Cartographer
Network Function Virtualization: State-of-the-art and Research Challenges
π
π
The Cartographer
Applications of Deep Reinforcement Learning in Communications and Networking: A Survey
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted