A Real And Accurate Real-Time Task Scheduling Architecture With Self-Feedback X-Boost Machine Learning Technique
|International Journal of Engineering Trends and Technology (IJETT)||
|© 2021 by IJETT Journal|
|Year of Publication : 2021|
|Authors : G.Kasi Reddy, Dr. D Sravan Kumar
|DOI : 10.14445/22315381/IJETT-V69I3P219|
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.
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.
 Baruah, S., Partitioned EDF scheduling: a closer look. Real-Time Systems, 49(6) (2013) 715-729.
 Chéramy, M., Déplanche, A.-M. & Hladik, P.-E., Simulation of Real-Time Multiprocessor Scheduling with Overheads. Reykjavik, Iceland, hal-00815502 , (2013) 5-14.
 Chéramy, M., Hladik, P. -E. & Déplanche, A. -M., SimSo: A Simulation Tool to Evaluate Real-Time Multiprocessor Scheduling Algorithms. In Proceedings of the 5th International Workshop on Analysis Tools and Methodologies for Embedded and Real-time Systems (WATERS), (2014) 37-42.
 Erickson, J. P., Anderson, J. H. & Ward, B. C., Fair lateness scheduling: reducing maximum lateness. Real-Time Systems, 50(1)(2013) 5-47.
 Lindh, F., Otnes, T. & Wennerstrom, J., Scheduling Algorithms for Real-Time Systems., (2010).
 Goossens, J. & Richard, P., Multiprocessor Real-Time Scheduling. Wang X. (eds) Cyber-Physical Systems: A Reference, (2017) 1-33.
 Rouhifar, M. & Ravanmehr, R., A Survey on Scheduling Approaches for Hard Real-Time Systems, International Journal of Computer Applications, 131(17) (2015).
 Saranya, N. & Hansdah, R., Dynamic Partitioning Based Scheduling of Real-Time Tasks in Multicore Processors, Auckland, New Zealand, IEEE 18th International Symposium on Real-Time Distributed Computing, (2015).
 Salam, Abdul, Sohail Abbas, Yousaf Khan, Sanaul Haq, and Saeed Ullah Jan., Developing the Best Scheduling Algorithm from Existing Algorithms for Real-Time Operating Systems, Available at SSRN 3331997 (2019).
 M. Chetto., Optimal Scheduling for Real-Time Jobs in Energy Harvesting Computing Systems, in IEEE Transactions on Emerging Topics in Computing, doi: 10.1109/TETC.2013.2296537, 2(2) (2014) 122-133.
 Zhou, J., Real-time task scheduling and network device security for complex embedded systems based on deep learning networks. Microprocessors and Microsystems, 79 (2020) 103282.
 Jr E F F, Salgado R M, Ohishi T et al., Analysis of Two-Phase Flow Pattern Identification Methodologies for Embedded Systems, IEEE Latin America Transactions, 16(3) (2018) 718-727.
 A Mallikarjuna Reddy, Vakulabharanam Venkata Krishna, Lingamgunta Sumalatha and Avuku Obulesh., Age Classification Using Motif and Statistical Features Derived On Gradient Facial Images, Recent Advances in Computer Science and Communications, .https://doi.org/10.2174/22132759 166619041715 1247 , 13(965) (2020)
 A. M. Reddy, V. V. Krishna, L. Sumalatha and S. K. Niranjan., Facial recognition based on straight angle fuzzy texture unit matrix, International Conference on Big Data Analytics And Computational Intelligence (ICBDAC), Chirala, doi: 10.1109/ICBDACI.2017.8070865, (2017) 366-372.
 A. M. Reddy, K. SubbaReddy and V. V. Krishna., Classification of child and adulthood using GLCM based on diagonal LBP, International Conference on Applied and Theoretical Computing and Communication Technology (iCATccT), Davangere, doi: 10.1109/ICATCCT.2015.7457003, (2015) 857-861.
 Bertozzi M, Bo C, Zingaretti P. Introduction to the Special Issue on Applications of Mechatronic and Embedded Systems (MESA) in ITS, IEEE Transactions on Intelligent Transportation Systems, 19(2) (2018) 530-532.
 Ebert C, Dubey A., Convergence of Enterprise IT and Embedded Systems, IEEE Software, 36(3) (2019) 92-97.
 Huang X, Su Z, Zhang Z, et al., Vibration transmission suppression for the propeller-shaft system by hub embedded damping ring under broadband propeller forces, Nonlinear Dynamics, 91(1) (2018) 1-16.
 Zheng W, Wu H, Nie C., Integrating task scheduling and cache locking for multicore real-time embedded systems, Acm Sigplan Notices, 52(4) (2017) 71-80.
 Swarajya Lakshmi V Papineni, Snigdha Yarlagadda, Harita Akkineni, A. Mallikarjuna Reddy., Big Data Analytics Applying the Fusion Approach of Multicriteria Decision Making with Deep Learning Algorithms , International Journal of Engineering Trends and Technology, doi: 10.14445/22315381/IJETT-V69I1P204, 69(1) (2021) 24-28.
 Srinivasa Reddy, K., Suneela, B., Inthiyaz, S., Kumar, G.N.S., Mallikarjuna Reddy, A., Texture filtration module under stabilization via random forest optimization methodology ,International Journal of Advanced Trends in Computer Science and Engineering, doi: 10.30534/IJATCSE/2019/20832019, 8(3) (2019).
 A.Mallikarjuna, B. Karuna Sree., Security towards Flooding Attacks in Inter-Domain Routing Object using Ad hoc Network, International Journal of Engineering and Advanced Technology (IJEAT), 8(3) (2019).
 Ghobaei?Arani, M., Souri, A., Safara, F., & Norouzi, M., An efficient task scheduling approach using moth?flame optimization algorithm for cyber?physical system applications in fog computing, Transactions on Emerging Telecommunications Technologies, 31(2) (2020) e3770.
 Yi, N., Xu, J., Yan, L., & Huang, L., Task optimization and scheduling of distributed cyber-physical systems based on improved ant colony algorithm. Future Generation Computer Systems, 109 (2020) 134-148.
 Karimi, M., & Kim, H., Energy scheduling for task execution on intermittently-powered devices, ACM SIGBED Review, 17(1) (2020) 36-41.
 Casini, D., Biondi, A., & Buttazzo, G., Timing isolation and improved scheduling of deep neural networks for real?time systems, Software: Practice and Experience, 50(9) (2020) 1760-1777.
task scheduling, real-time programming, compilation time, and machine learning