Improving the Schedulability of Real-Time Tasks using Fog Computing


The cloud is not the best option for carrying out real-time tasks that have to be completed by a certain time because there is a significant Communication delay with user tasks. A key component of fog computing is the use of low-capability fog nodes, also known as cloudlets, which are strategically placed close to the users who are the primary generators of data. These cloudlets are perfect for carrying out responsibilities that have strict time constraints. In this paper, we propose algorithms that schedule a set of real-time tasks on an architecture similar to an embedded fog cloud. We consider tasks that are hard, firm, and easy. Processors can be embedded, in the fog, or in the cloud, and these make up the execution framework. The processing requirements of individual tasks are taken into consideration when allocating them to the appropriate machines. In general, tasks that require a hard real-time response are carried out by embedded processors, tasks that require a firm real-time response by fog processors, and tasks that require a soft real-time response by cloud processors. A sufficient schedulability condition is another one of our proposed solutions. The simulation results from the CERIT trace and the test-bed results show that the proposed algorithms provide superior performance when compared to algorithms that do not utilize fog processors. This is the case both for the proposed algorithms and for algorithms that do not utilize fog processors. When compared to scheduling tasks on the cloud by itself, utilizing an Embedded-fog-cloud architecture offers an improvement of 62.37 percent for real-time Success Ratio (SR) and of 35 percent for Average Response Time.

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