PROJECT TITLE :

Improving the Schedulability of Real-Time Tasks using Fog Computing

ABSTRACT:

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.


Did you like this research project?

To get this research project Guidelines, Training and Code... Click Here


PROJECT TITLE : Smartphone based Indoor Path Estimation and Localization without Human Intervention ABSTRACT: Many different kinds of indoor positioning systems have been developed as a result of the growing market interest in
PROJECT TITLE : Robust Fuzzy Learning for Partially Overlapping Channels Allocation in UAV Communication Networks ABSTRACT: The emerging cellular-enabled unmanned aerial vehicle (UAV) communication paradigm poses significant challenges
PROJECT TITLE : Prediction of Traffic Flow via Connnected Vehicles ABSTRACT: We propose a framework for short-term traffic flow prediction (STP) so that transportation authorities can take early actions to control flow and prevent
PROJECT TITLE : Passenger Demand Prediction with Cellular Footprints ABSTRACT: An accurate forecast of the demand for passengers across the entire city enables providers of online car-hailing services to more efficiently schedule
PROJECT TITLE : NCF: A Neural Context Fusion Approach to Raw Mobility Annotation ABSTRACT: Improving business intelligence in mobile environments requires a thorough comprehension of human mobility patterns on a point-of-interest

Ready to Complete Your Academic MTech Project Work In Affordable Price ?

Project Enquiry