Dynamic Priority-based Adaptive Scheduling (DPAS) for Modern Operating Systems
Keywords:
Dynamic, priority, adaptive, scheduling, processAbstract
Dynamic Priority-based Adaptive Scheduling (DPAS) is an innovative CPU scheduling algorithm designed to optimize system performance and resource utilization in modern computing environments. As computing systems become increasingly complex and diverse, traditional static scheduling algorithms often struggle to adapt efficiently to the dynamic nature of workloads. DPAS addresses this challenge by introducing a dynamic and adaptive approach to process prioritization and resource allocation. DPAS leverages real-time feedback, machine learning, and resource awareness to assign and adjust priorities for running processes. Unlike conventional schedulers with fixed priority schemes, DPAS continuously evaluates process behavior and resource utilization, making real-time adjustments to process priorities. This adaptability ensures that critical processes receive the necessary attention while preventing resource monopolization by any single task. Furthermore, DPAS incorporates adaptive time quantum allocation, energy-efficient scheduling, and security measures to strike a balance between responsiveness, energy conservation, and system security. It utilizes process grouping and tagging to accommodate different workload characteristics and requirements, enhancing both fairness and system efficiency. The DPAS algorithm represents a significant advancement in CPU scheduling, offering improved system responsiveness, resource management, and adaptability to diverse workloads. This abstract provides an overview of DPAS's key principles, highlighting its potential to enhance the performance and reliability of modern computing systems
References
Harki N, Ahmed A, Haji L. CPU scheduling techniques: A review on novel approaches strategy and performance assessment. J Appl Sci Technol Trends. 2020 May 6; 1(2): 48–55.
Olofintuyi SS, Omotehinwa TO, Owotogbe JS. A survey of variants of round robin CPU scheduling algorithms. FUDMA J Sci. 2020; 4(4): 526–46.
Ali S, Alshahrani R, Hadadi A, Alghamdi T, Almuhsin F, Sharawy EE. A Review on the CPU Scheduling Algorithms: Comparative Study. Int J Comput Sci Netw Secur. 2021 Jan; 21(1): 19–26.
Kwok YK, Ahmad I. Static scheduling algorithms for allocating directed task graphs to multiprocessors. ACM Comput Surv (CSUR). 1999 Dec 1; 31(4): 406–71.
Ghafari R, Kabutarkhani FH, Mansouri N. Task scheduling algorithms for energy optimization in cloud environment: a comprehensive review. Cluster Comput. 2022 Apr; 25(2): 1035–93.
Bansal S, Gowtham K, Hota C. Novel adaptive scheduling algorithm for computational grid. In 2009 IEEE International Conference on Internet Multimedia Services Architecture and Applications (IMSAA). 2009 Dec 9; 1–5.
Goel N, Garg RB. A comparative study of cpu scheduling algorithms. arXiv preprint arXiv:1307.4165. 2013 Jul 16.
Zouaoui S, Boussaid L, Mtibaa A. CPU scheduling algorithms: Case & comparative study. In 2016 IEEE 17th International Conference on Sciences and Techniques of Automatic Control and Computer Engineering (STA). 2016 Dec 19; 158–164.
Arndt O, Freisleben B, Kielmann T, Thilo F. A comparative study of online scheduling algorithms for networks of workstations. Cluster Comput. 2000 Sep; 3(2): 95–112.
Parsa Saeed, Reza Entezari-Maleki. RASA: a new grid task scheduling algorithm. Int J Digit Content Technol its Appl. 2009;3(4):91–99.
Ilavarasan E, Thambidurai P. Low complexity performance effective task scheduling algorithm for heterogeneous computing environments. J Comput Sci. 2007 Feb 1; 3(2): 94–103.
Krishnadoss P, Jacob P. OCSA: Task Scheduling Algorithm in Cloud Computing Environment. Int J Intell Eng Syst. 2018 May 1; 11(3): 271–279.
Ali SA, Alam M. A relative study of task scheduling algorithms in cloud computing environment. In 2016 IEEE 2nd International Conference on Contemporary Computing and Informatics (IC3I). 2016 Dec 14; 105–111.
Ma J, Li W, Fu T, Yan L, Hu G. A novel dynamic task scheduling algorithm based on improved genetic algorithm in cloud computing. In Wireless Communications, Networking and Applications: Proceedings of WCNA 2014. India: Springer; 2016; 829–835.
