Advanced task scheduling algorithm for IoT Based FOG communication model
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Internet of Things (IoT) is hugely dependent on Cloud Computing. Cloud comput- ing uses a high degree of polymerization calculation mode and so it cannot ensure e ective use of resources like computing, storage, etc. FOG computing is a devel- oping paradigm that broadens computation, communication and storage facilities towards the edge of a network. It is used to improve e ciency along with the re- duction of transmitted data for processing to the cloud. Although the primary aim of FOG computing is to improve the processing speed of cloud computation, it has many challenges such as task scheduling, resource allocation, security, etc. Among these challenges handling incoming requests to improve latency and throughput is one of the crucial factors. As a solution, the proposed model uses a multi-layered FOG model in which tasks are scheduled on the basis of priority based on the re- quest type to increase the e ciency of the current FOG model. Firstly, the proposed model creates a rule list based on the user's request priority. While creating the rule list the model will use advance caching mechanism based on the request type in the di erent layers to improve latency and throughput. When a user sends data to the FOG, it nds its con gured layer of the FOG cloud on which the data will be processed. Stored data will be loaded in the corresponding layer based on packets' priority to make the computation faster. The proposed model has been simulated using Microsoft Azure. In the simulation, the inbound data after caching was more than 70 MB per 30 seconds wherein the traditional cloud, the inbound data rate was around 30 MB per 30 seconds. Therefore, after caching the data, the model performed twice faster than the traditional cloud.