A Framework of Fog Computing: Architecture, Challenges and Optimization
Liu, Y; Fieldsend, JE; Min, G
Date: 26 October 2017
Journal
IEEE Access
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Publisher DOI
Abstract
Fog Computing (FC) is an emerging distributed computing platform aimed at bringing computation close to its data sources, which can reduce the latency and cost of delivering data to a remote cloud. This feature and related advantages are desirable for many Internet-of-Things applications, especially latency sensitive and mission intensive ...
Fog Computing (FC) is an emerging distributed computing platform aimed at bringing computation close to its data sources, which can reduce the latency and cost of delivering data to a remote cloud. This feature and related advantages are desirable for many Internet-of-Things applications, especially latency sensitive and mission intensive services. With comparisons to other computing technologies, the definition and architecture of FC are presented in this article. The framework of resource allocation for latency reduction combined with reliability, fault tolerance, privacy, and underlying optimization problems are also discussed. We then investigate an application scenario and conduct resource optimization by formulating the optimization problem and solving it via a Genetic Algorithm. The resulting analysis generates some important insights on the scalability of FC systems.
Computer Science
Faculty of Environment, Science and Economy
Item views 0
Full item downloads 0
Related items
Showing items related by title, author, creator and subject.
-
Dominance Measures for Multi-Objective Simulated Annealing
Smith, Kevin I.; Everson, Richard M.; Fieldsend, Jonathan E. (Institute of Electrical and Electronics Engineers (IEEE), 3 September 2004)Simulated annealing (SA) is a provably convergent optimiser for single-objective (SO) problems. Previously proposed MO extensions have mostly taken the form of an SO SA optimising a composite function of the objectives. ... -
Adiabatic graph-state quantum computation
Antonio, B.; Markham, D.; Anders, Janet (Institute of Physics Publishing, 26 November 2014)Measurement-based quantum computation (MBQC) and holonomic quantum computation (HQC) are two very different computational methods. The computation in MBQC is driven by adaptive measurements executed in a particular order ... -
Quantifying the relative contributions of divisive and subtractive feedback to rhythm generation.
Tabak, J; Rinzel, J; Bertram, R (Public Library of Science, 21 April 2011)Biological systems are characterized by a high number of interacting components. Determining the role of each component is difficult, addressed here in the context of biological oscillations. Rhythmic behavior can result ...