Master data

Title: Adaptive Nature-Inspired Fog Architecture
Description:

During the last decade, Cloud computing has efficiently exploited the economy of scale by providing low cost computational and storage resources over the Internet, eventually leading to consolidation of computing resources into large data centers. However, the nascent of the highly decentralized Internet of Things (IoT) technologies that cannot effectively utilize the centralized Cloud infrastructures pushes computing towards resource dispersion. Fog computing extends the Cloud paradigm by enabling dispersion of the computational and storage resources at the edge of the network in a close proximity to where the data is generated. In its essence, Fog computing facilitates the operation of the limited compute, storage and networking resources physically located close to the edge devices. However, the shared complexity of the Fog and the influence of the recent IoT trends moving towards deploying and interconnecting extremely large sets of pervasive devices and sensors, requires exploration of adaptive Fog architectural approaches capable of adapting and scaling in response to the unpredictable load patterns of the distributed IoT applications. In this paper we introduce a promising new nature- inspired Fog architecture, named SmartFog, capable of providing low decision making latency and adaptive resource management. By utilizing novel algorithms and techniques from the fields of multi- criteria decision making, graph theory and machine learning we model the Fog as a distributed intelligent processing system, therefore emulating the function of the human brain.

Keywords:
Type: Registered lecture
Homepage: http://www.cloudbus.org/fog/icfec2018/program.html
Event: 2nd IEEE International Conference on Fog and Edge Computing (ICFEC 2018) (Washington)
Date: 03.05.2018
lecture status:

Participants

Assignment

Organisation Address
Fakultät für Technische Wissenschaften
 
Institut für Informationstechnologie
Universitaetsstr. 65-67
9020 Klagenfurt am Wörthersee
Austria
   martina.steinbacher@aau.at
http://itec.aau.at/
To organisation
Universitaetsstr. 65-67
AT - 9020  Klagenfurt am Wörthersee

Categorisation

Subject areas
  • 1020 - Computer Sciences
Research Cluster No research Research Cluster selected
Focus of lecture
  • Science to Science (Quality indicator: I)
Classification raster of the assigned organisational units:
Group of participants
  • Mainly international
Published?
  • Yes
working groups
  • Distributed Multimedia Systems

Cooperations

No partner organisations selected