A Study on Big Data Management Strategy Using Fog Computing

Abstract

In modern ages, data is increasing day by day using internet of things for various purposes. Data intensive analysis is the major challenge because of the ubiquitous deployment of various kinds of sensors. Traditional cloud computing infrastructure is not enough for processing these large amount, variety or velocity of data that means big data. Traditional cloud computing structure is geographically centralized. Balancing load for big data is a crucial issue. A preprocessing stage is necessary to handle these big data for real time service oriented application. In this thesis, a big data management strategy is proposed using a preprocessing stage that is fog computing. Providing real time services from cloud is too many time consuming for big data. Here, Fog Computing plays a vital role. The maximum functions of processing data are implemented outside of cloud in the case of Fog Computing and one thing considered in Fog Computing is that here memory of fog devices is very little. So, a well-organized communication system is needed for data processing. Here, in this work an affordable, robust and secure power supply or third party memory management has been suggested which is Smart Grid and Smart Local Grid. The smart grid and smart local grid or third party memory management support the customer's real time services using fog infrastructure. In this work a hierarchical architecture has been proposed for creating well-organized communication system for data processing in the case of smart grid and smart local grid or third party memory management using fog infrastructure or nodes. For task scheduling of nodes in case of fog infrastructure queue based scheduling technique is used. In this work, an effective result for big data management and providing real time services has been found. Here, different parameters such as network latency, throughput have been used for measuring performance in real time services. The overall network latency is minimized and throughput is increased in case of fog computing.