  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.

Distributed, Parallel, and Cluster Computing