Publication Type : Book Chapter
Publisher : Handbook of Research on Cloud and Fog Computing Infrastructures for Data Science
Source : Handbook of Research on Cloud and Fog Computing Infrastructures for Data Science, p.175-194 (2018)
Url : https://www.igi-global.com/gateway/chapter/204270
Campus : Coimbatore
School : School of Engineering
Department : Computer Science
Year : 2018
Abstract : Mobile is getting increasingly popular and almost all applications are shifting into smartphones. Even though lots of advantages are there for smartphones, they are constrained by limitations in battery charge and the processing capacity. For running resource-intensive IoT applications like processing sensor data and dealing with big data coming from the IoT application, the capacity of existing smartphones is not enough, as the battery will be drained quickly, and it will be slow. Offloading is one of the major techniques through which mobile and cloud can be connected together and has emerged to reduce the complexity and increase the computation power of mobiles. Other than depending on the distant cloud for offloading, the extended version of cloud called fog computing can be utilized. Through offloading, the computationally intensive tasks can be shifted to the edge fog devices, and the results can be collected back at the mobile side reducing the burden. This chapter has developed mobile cloud offloading architecture for decision making using fuzzy logic where a decision is made as to whether we can shift the application to cloud or not depending on the current parameters of both cloud and the mobile side. Cloud computing introduces a number of variables depending on which offloading decision must be taken. In this chapter, the authors propose a fuzzy-logic-based algorithm which takes into consideration all the parameters at the mobile and cloud that will affect the offloading decision.
Cite this Research Publication : Dhanya N. M., Govardhanan, K., Balarksihnan, P., and Raj, P., “Fuzzy-logic-based decision engine for offloading iot application using fog computing”, in Handbook of Research on Cloud and Fog Computing Infrastructures for Data Science, 2018, pp. 175-194.