A Real And Accurate Real-Time Task Scheduling Architecture With Self-Feedback X-Boost Machine Learning Technique
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International Journal of Engineering Trends and Technology (IJETT) |  |
| © 2021 by IJETT Journal | ||
| Volume-69 Issue-3 |
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| Year of Publication : 2021 | ||
| Authors : G.Kasi Reddy, Dr. D Sravan Kumar |
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| DOI : 10.14445/22315381/IJETT-V69I3P219 |
Citation
MLA Style: G.Kasi Reddy, Dr. D Sravan Kumar "A Real And Accurate Real-Time Task Scheduling Architecture With Self-Feedback X-Boost Machine Learning Technique" International Journal of Engineering Trends and Technology 69.3(2021):118-126.
APA Style:G.Kasi Reddy, Dr. D Sravan Kumar. A Real And Accurate Real-Time Task Scheduling Architecture With Self-Feedback X-Boost Machine Learning Technique International Journal of Engineering Trends and Technology, 69(3),118-126.
Abstract
The real-time performance analysis of software applications such as multiprocessors, uni-processors, and real-time operating systems can increase their demand. A Hard-Real-Time embedded, satellite, industrial, telecommunication, and robotics systems are widely used in RTOS applications. In these, applications, their performance is mainly depending on-time deadline, logical outcomes, the correctness of relies upon, and accuracy. Many scheduling algorithms are designed, but those are specifically workout on available deadlines as well as facing the above limitations. Therefore an advanced real-time task scheduling in the operating system is necessary to crossover the limitations and for improvement. In this research work, an advanced X-boosting machine learning technique is proposed for real-time task scheduling multi programming purposes. This work is providing fault-free, attribute, deadline, and un-restricted execution at final calculating the performance measures like as fault detection, sensitivity, worst-case performance ratio, accuracy, Arrival Time, Compilation Time, Deadline time Energy and f1-score. This self-feedback X-boosting machine-learning technique is outperformance the methodology and competes with present technology.
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Keywords
task scheduling, real-time programming, compilation time, and machine learning