Simulating fog and edge computing scenarios: an overview and research challenges

The fourth industrial revolution heralds a paradigm shift in how people, processes, things, data and networks communicate and connect with each other. Conventional computing infrastructures are struggling to satisfy dramatic growth in demand from a deluge of connected heterogeneous endpoints located at the edge of networks while, at the same time, meeting quality of service levels. The complexity of computing at the edge makes it increasingly difficult for infrastructure providers to plan for and provision resources to meet this demand. While simulation frameworks are used extensively in the modelling of cloud computing environments in order to test and validate technical solutions, they are at a nascent stage of development and adoption for fog and edge computing. This paper provides an overview of challenges posed by fog and edge computing in relation to simulation

ROUTER:Fog Enabled Cloud based Intelligent Resource Management Approach for Smart Home IoT Devices

There is a growing requirement for Internet of Things (IoT) infrastructure to ensure low response time to provision latency-sensitive real-time applications such as health monitoring, disaster management, and smart homes. Fog computing offers a means to provide such requirements, via a virtualized intermediate layer to provide data, computation, storage, and networking services between Cloud datacenters and end users. A key element within such Fog computing environments is resource management. While there are existing resource manager in Fog computing, they only focus on a subset of parameters important to Fog resource management encompassing system response time, network bandwidth, energy consumption and latency. To date no existing Fog resource manager considers these parameters simultaneously for decision making, which in the context of smart homes will become increasingly key. In this paper, we propose a novel resource management technique (ROUTER) for fog-enabled Cloud computing environments, which leverages Particle Swarm Optimization to optimize simultaneously. The approach is validated within an IoT-based smart home automation scenario, and evaluated within iFogSim toolkit driven by empirical models within a small-scale smart home experiment. Results demonstrate our approach results a reduction of 12% network bandwidth, 10% response time, 14% latency and 12.35% in energy consumption

Simulating Energy Efficient Fog Computing

Nõudlus arvuti ressursside järele üha suureneb ning seega on vajadus vähendada energiakulu, et tagada arvutisüsteemide jätkusuutlikus. Praegused pilve- ja uduandmetöötlus arhitektuuride edasiarendamiseks on vaja ajajaotus- ja asetusalgoritme, mis arvestavad energiakuluga. Selles töös kirjeldatakse energiasäästlikkust pilve- ja uduandmetöötluses. Töös luuakse ajajaotus- ja asetusalgoritmid, mis maksimeerivad vabade seadmete arvu ning vähendavad seeläbi süsteemi energiakulu. Algoritme katsetatakse erinevates simulatsioonides. Simulatsioonide tulemusi analüüsitakse ja võrreldakse ning tehakse järeldused algoritmide kasulikkusest. Töö sisaldab ka lühikest ülevaadet sarnastest uurimustest.With increasing demand on computing resources, there is a need to reduce energy consumption in order to keep computer systems sustainable. Current cloud and fog computing architectures need to be improved by designing energy efficient scheduling and placement algorithms. This thesis describes power efficiency in fog computing and cloud computing. It shows a way to minimize power usage by designing scheduling and placement algorithms that maximize the number of idle hosts. Algorithms are designed to archive that goal in cloud and fog systems. The algorithms are tested in different simulation scenarios. The results are compared and analysed. The thesis also contains a brief overview of similar research that has been done on this topic