11 Fog Computing Realization for Big Data Analytics

Farhad Mehdipour Bahman Javadi Aniket Mahanti and Guillermo Ramirez‐Prado

11.1 Introduction

Internets of Things (IoT) deployments generate large quantities of data that need to be processed and analyzed in real time. Current IoT systems do not enable low‐latency and high‐speed processing of data and require offloading data processing to the cloud (example applications include smart grid, oil facilities, supply chain logistics, and flood warning). The cloud allows access to information and computing resources from anywhere and facilitates virtual centralization of application, computing, and data. Although cloud computing optimizes resource utilization, it does not provide an effective solution for hosting big data applications [1]. There are several issues, which hinder adopting IoT‐driven services, namely:

  • Moving large amounts of data over the nodes of a virtualized computing platform may incur significant overhead in terms of time, throughput, energy consumption, and cost.
  • The cloud may be physically located in a distant data center, so it may not be possible to service IoT with reasonable latency and throughput.
  • Processing large quantities of IoT data in real time will increase as a proportion of workloads in data centers, leaving providers facing new security, capacity, and analytics challenges.
  • Current cloud solutions lack the capability to accommodate analytic engines for efficiently processing big data.

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