Adaptive Nature-Inspired Fog Architecture

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.

Mobility-Aware IoT Application Placement in the Cloud – Edge Continuum

“…We define the Cloud -Edge Continuum as a group of bounded computing clusters [5], consisting of Edge devices linked to the virtualized instances running in the Cloud.…”

“…Recently, Edge and Fog computing [2] emerged as extended computing paradigms that partially move lowlatency IoT applications from the Cloud closer to the data sources [3], [4]. However, the Edge computing extension of the Cloud services towards the IoT systems raises multiple placement challenges for complex applications modeled as a set of interconnected components [5], such as: 1) Increased network heterogeneity that interposes an additional Edge layer between the user and the Cloud; 2) Limited resource capacity of Edge (e.g., personal mobile) devices that cannot easily accommodate application requirements, such as processing speed, memory and storage consumption, or communication bandwidth; 3) High mobility of Edge devices with severe impact on application reliability and service quality; 4) Conflicting objective functions comprising completion time, energy consumption and economic cost; 5) Heuristic algorithms to solve this known NP-complete problem considering completion time of the precedenceconstrained components as optimization objective [6].…”

Energy Efficient Fog-Based Healthcare Monitoring Infrastructure

“…For instance, in [49] the fog layer is only used to perform preprocessing of the health data to eliminate noise from signals and to extract useful knowledge for further analysis at the cloud layer. In [21], [45]- [47], [51] the fog layer is used to perform local data processing to analyze the health data while in [44], [50] the fog layer can only perform local data processing depending on the requirement of the health application. Meanwhile, in [48], the fog layer is used to perform both local processing of the health data and filtering the analyzed data before uploading to the cloud for further analysis to reduce the redundancy.…”

“…Meanwhile, in [48], the fog layer is used to perform both local processing of the health data and filtering the analyzed data before uploading to the cloud for further analysis to reduce the redundancy. In addition, the cloud is usually used to hold the analyzed health data for either permanent storage [46] or both permanent storage and long term analysis [21], [44], [45], [47]- [51]. This section presents the proposed fog-based health monitoring system.…”

A Survey of Architecture, Framework and Algorithms for Resource Management in Edge Computing

“…It is nature-stimulated and provide a solution for multiobjective optimization problems [88]. Sharing model is used concerning various goals in a system.…”