Adaptive Resource Scheduling in Operating Systems (by Brett Mitchell) - bmitch26/Operating-Systems GitHub Wiki

Introduction

Adaptive resource scheduling is a critical component of modern operating systems, enabling efficient management and allocation of system resources such as CPU, memory, storage, and network bandwidth. With the rapid growth of computational demands and the diversity of workloads in contemporary systems, static scheduling approaches often fail to meet the dynamic requirements of complex environments. Adaptive scheduling, by contrast, leverages real-time data, predictive algorithms, and system feedback to adjust resource allocation on the fly, optimizing performance, scalability, and energy efficiency.

This paradigm is particularly relevant in an era dominated by cloud computing, serverless architectures, edge computing, and high-performance computing systems. Adaptive scheduling not only enhances the performance of general-purpose operating systems but also supports specialized applications in areas like AI workloads, real-time systems, and decentralized architectures. By enabling systems to respond dynamically to fluctuating workload characteristics, adaptive scheduling ensures that resources are utilized effectively while maintaining quality of service (QoS) requirements.

This wiki page explores the concepts and current research within adaptive resource scheduling, providing an overview of its foundational principles and summarizing recent advancements in the field. The focus is on key research contributions that address challenges such as energy efficiency, task prioritization, workload heterogeneity, and system scalability. By examining the current state of adaptive scheduling, this report highlights its transformative potential for shaping the future of operating systems across diverse computational domains.

Recent research/case studies:

Dynamic Priority-based Adaptive Scheduling (DPAS) for Modern Operating Systems

Dynamic Priority-based Adaptive Scheduling (DPAS) is a modern CPU scheduling algorithm designed to optimize system performance in increasingly complex and dynamic computing environments. Unlike traditional static scheduling methods, DPAS dynamically adjusts process priorities and resource allocation in real-time by leveraging feedback, machine learning, and resource awareness. This adaptability ensures that critical processes are prioritized while preventing resource monopolization.

Key features of DPAS include adaptive time quantum allocation, energy-efficient scheduling, and integrated security measures, all aimed at achieving a balance between responsiveness, energy conservation, and system security. The algorithm also employs process grouping and tagging to manage diverse workload characteristics, improving fairness and system efficiency. By continuously evaluating process behavior and resource utilization, DPAS represents a significant advancement in CPU scheduling, offering enhanced system responsiveness, resource management, and adaptability to modern workloads.

Yogi, M. K. ., Aiswarya, D. ., & Mundru, Y. . (2023). Dynamic Priority-based Adaptive Scheduling (DPAS) for Modern Operating Systems. JOURNAL OF OPERATING SYSTEMS DEVELOPMENT &Amp; TRENDS, 10(2), 6–15. Retrieved from https://stmcomputers.stmjournals.com/index.php/JoOSDT/article/view/666

Method and Algorithms for Adaptive Multiagent Resource Scheduling in Heterogeneous Distributed Computing Environments

This study introduces new methods and algorithms for adaptive resource scheduling in heterogeneous distributed computing environments, particularly focusing on cloud computing systems (CCEs). The proposed approach aims to minimize task execution time by dynamically allocating computing resources that deliver the highest performance for specific tasks. A multiagent system is employed, where each element of the CCE is managed by a software agent with detailed, real-time information about its associated computing resource. These agents collaboratively select the most appropriate tasks and subtasks based on available data, optimizing resource utilization.

The article describes the principles behind the adaptive multiagent resource manager, detailing the operational methods and algorithms used by resource agents and task schedulers. Additionally, the study evaluates the effectiveness of these algorithms through simulations using a distributed software model. This work demonstrates the potential of adaptive multiagent scheduling to enhance efficiency and performance in cloud and distributed computing environments.

Kalyaev, I.A., Kalyaev, A.I. Method and Algorithms for Adaptive Multiagent Resource Scheduling in Heterogeneous Distributed Computing Environments. Autom Remote Control 83, 1228–1245 (2022). https://doi.org/10.1134/S0005117922080069

Adaptive AI Systems for Energy-Aware Cloud Resource Scheduling

The study highlights the role of adaptive AI systems in addressing the growing energy challenges of cloud computing by optimizing resource scheduling dynamically and intelligently. These systems use machine learning and advanced analytics to predict workload demands, optimize resource allocation, and adjust power consumption in real-time. By analyzing factors such as environmental conditions, server utilization, and energy consumption patterns, adaptive AI systems minimize energy waste while ensuring high performance and service availability.

Key techniques, including reinforcement learning and neural networks, empower these systems to adapt to changing workloads and conditions, making them scalable and resilient. This paper explores the design, implementation, and benefits of adaptive AI systems for energy-aware cloud resource scheduling, emphasizing their ability to reduce operational costs, lower carbon footprints, and enhance the sustainability of cloud infrastructures.

Ade, Martins. (2024). Adaptive AI Systems for Energy-Aware Cloud Resource Scheduling.

SmartOS: Towards Automated Learning and User-Adaptive Resource Allocation in Operating Systems

SmartOS introduces a novel learning-based approach to resource allocation in modern operating systems, aiming to prioritize tasks based on user preferences dynamically. Unlike traditional one-size-fits-all scheduling strategies, SmartOS uses reinforcement learning (RL) to adjust CPU, memory, I/O, and network bandwidth allocation in real time. Implemented in Linux user space, SmartOS demonstrates the ability to learn and adapt to increasingly complex scenarios, outperforming static prioritization methods.

Key findings include the use of a Monte Carlo RL algorithm, which achieves rapid convergence with minimal overhead (tenths of milliseconds). The paper emphasizes future work, including testing SmartOS in real-world applications, conducting user studies to collect implicit feedback, and integrating more complex user contexts. Additionally, cross-platform adaptability is highlighted, with SmartOS capable of aggregating data across devices via cloud or edge computing to make more informed scheduling decisions.

SmartOS represents a significant step toward automated, user-adaptive operating systems, demonstrating the potential of AI-driven resource management to enhance system performance and user experience.

Goodarzy, Sepideh et al. “SmartOS: towards automated learning and user-adaptive resource allocation in operating systems.” Proceedings of the 12th ACM SIGOPS Asia-Pacific Workshop on Systems (2021): n. pag.

Operating Systems for Resource-adaptive Intelligent Software: Challenges and Opportunities

The paper discusses the challenges and opportunities in designing operating systems for resource-adaptive intelligent software in a ubiquitous computing environment spanning the cloud, edge, mobile devices, and IoT. As software systems increasingly require robustness and intelligence to adapt to changes in heterogeneous physical and logical resources, traditional "monolithic OS" structures become insufficient. Instead, future operating systems should dynamically compose themselves over distributed resources and flexibly adapt to changing conditions.

The proposed concept, ServiceOS, envisions a new OS abstraction inspired by Service-Oriented Architecture (SOA) and the delivery model of "Software-as-a-Service." ServiceOS operates on three key principles: resource disaggregation, resource provisioning as a service, and learning-based resource scheduling and allocation. By leveraging advanced machine learning and deep learning techniques, ServiceOS aims to derive configurations, policies, and scheduling decisions autonomously.

Rather than providing an immediately deployable system, the article highlights the challenges and potential opportunities of resource-adaptive intelligent operating systems. It provides a forward-looking framework to guide researchers and practitioners in rethinking operating system design for the rapidly evolving landscape of distributed, resource-adaptive software systems.

Xuanzhe Liu, Shangguang Wang, Yun Ma, Ying Zhang, Qiaozhu Mei, Yunxin Liu, and Gang Huang. 2021. Operating Systems for Resource-adaptive Intelligent Software: Challenges and Opportunities. ACM Trans. Internet Technol. 21, 2, Article 27 (June 2021), 19 pages. https://doi.org/10.1145/3425866

Reinforcement Learning for Adaptive Resource Scheduling in Complex System Environments

This study introduces an adaptive resource scheduling algorithm leveraging Q-learning, a reinforcement learning technique, to optimize computer system performance and manage dynamic workloads effectively. Traditional static scheduling methods, such as Round-Robin and Priority Scheduling, struggle to meet the demands of modern computing environments characterized by increasing data volumes, task complexity, and fluctuating workloads. By contrast, the proposed Q-learning-based algorithm dynamically learns and adapts to system state changes, enabling intelligent scheduling and resource allocation.

Experimental results demonstrate the approach's superiority in reducing task completion times and improving resource utilization compared to both traditional and dynamic resource allocation (DRA) algorithms. The research highlights the scalability and efficiency of AI-driven adaptive scheduling, providing a foundation for its integration into future large-scale systems. This method offers benefits such as enhanced system performance, reduced operating costs, and sustainable energy consumption, making it broadly applicable to cutting-edge computing frameworks, including edge computing, cloud computing, and IoT.

Li, Pochun & Xiao, Yuyang & Yan, Jinghua & Li, Xuan & Wang, Xiaoye. (2024). Reinforcement Learning for Adaptive Resource Scheduling in Complex System Environments. 10.48550/arXiv.2411.05346.

Conclusion

Adaptive resource scheduling has emerged as a transformative approach to addressing the complexities of modern computing environments. From optimizing system performance in traditional operating systems to enabling intelligent resource management in cloud, edge, and IoT frameworks, adaptive scheduling techniques have demonstrated significant potential in improving efficiency, scalability, and sustainability.

The case studies reviewed the diverse methodologies employed to tackle these challenges. Dynamic Priority-based Adaptive Scheduling (DPAS) and reinforcement learning-based algorithms showcase how AI can dynamically adjust to workload demands, ensuring critical processes receive priority while maximizing resource utilization. Innovations like multiagent scheduling systems and ServiceOS emphasize the importance of distributed, resource-adaptive architectures in heterogeneous environments, laying the groundwork for next-generation operating systems. Furthermore, energy-aware scheduling solutions underscore the role of machine learning in reducing energy consumption and carbon footprints, a growing concern in large-scale cloud infrastructures.

These advancements collectively illustrate a shift towards operating systems that are not only more intelligent and user-centric but also environmentally conscious and resilient to dynamic workloads. However, challenges remain in scaling these solutions for real-world applications, integrating complex user feedback, and maintaining performance parity with established scheduling algorithms.

As computing continues to evolve, adaptive resource scheduling will play a critical role in shaping the future of operating systems, bridging the gap between static policies and the demands of increasingly dynamic and diverse computational environments. By leveraging AI, resource disaggregation, and real-time adaptability, researchers and practitioners can create systems that meet the dual goals of performance optimization and sustainable resource management, driving innovation across a wide range of industries.