Abstract
Edge computing is considered as a scattered computing process module where the end user data can be executed at the end corner or at the last node of the network which is indirectly represented as “Edge”. It creates an sensory impact where the end user data is executed very closely to the original data center Alex Reznik has defined edge computing as “anything that’s not a traditional data center” will be an edge to the client who is the end user. Edge computing is closely connected to equipments of IoT devices that are mostly operated by mobile user which helps in the reaching to the expected constraints based on the response time for the real world applications. Edge computing can be implemented on any hardware IoT equipment using any type of software tools. The major aim of Edge computing is to reduce the latency levels and perform the task from the nearest possible data source. Edge computing performs on instant data which is real time data processed by the sensors or end user clients whereas the cloud computing works on BIG DATA generated from different sources and locations. In this paper, We try to represent few basics of edge computing along with its pros and cons and how it is related to machine learning and IoT.
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