An Adaptive Nature-inspired Fog Architecture

TL;DR

This paper proposes SmartFog, an adaptive, nature-inspired Fog computing architecture that leverages algorithms from decision making, graph theory, and machine learning to enable low-latency, scalable, and intelligent resource management at the network edge.

Contribution

It introduces a novel adaptive Fog architecture, SmartFog, utilizing interdisciplinary algorithms to emulate human brain functions for efficient edge computing.

Findings

SmartFog achieves low decision latency.

It effectively manages resources adaptively.

The architecture scales with IoT device proliferation.

Abstract

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.