Predictive Operating System Scheduling: Forecasting Future Process Execution Demand and Proactively Preparing System-Level Computational Resources
Year: 2026
Type: preprint
Abstract: Modern operating systems manage increasingly complex computational environments characterized by concurrent processes, heterogeneous execution patterns, multicore processors, shared memory hierarchies, dynamic resource contention, and fluctuating application workloads. Conventional operating system scheduling mechanisms primarily make execution decisions according to the current state of runnable processes, observed scheduling history, predefined priorities, and available processor resources. Although existing scheduling algorithms provide mechanisms for fairness, responsiveness, deadline management, and processor utilization, their operational decisions remain predominantly reactive to process execution events. This paper introduces Predictive Operating System Scheduling (POSS), a conceptual framework that investigates whether future process execution behavior and system-level resource requirements can be anticipated and selectively prepared before corresponding scheduling demands materialize. Rather than waiting until a process becomes runnable or a resource conflict becomes observable, POSS attempts to forecast future CPU burst characteristics, process state transitions, resource contention, processor affinity requirements, and multicore workload distributions. Its architecture integrates predictive process behavior modeling, CPU demand forecasting, resource contention prediction, proactive scheduling decision preparation, multicore resource coordination, and adaptive scheduling control. A central component is a predictive scheduling mechanism that generates candidate execution plans according to estimated future process behavior while retaining conventional runtime scheduling mechanisms as the authoritative mechanism for actual execution decisions. The framework further investigates the predictive preparation of CPU time allocation, processor selection, scheduling priorities, context-switch opportunities, cache-affinity decisions, and resource allocation policies. A unified optimization formulation is developed to characterize the expected benefits of predictive scheduling while accounting for prediction uncertainty, scheduling overhead, execution fairness, resource contention, migration costs, cache effects, and process deadlines. Particular attention is given to the distinction between predicting future CPU demand and predicting the scheduling decisions that can improve subsequent execution efficiency. An experimental methodology is proposed to evaluate average waiting time, response time, turnaround time, scheduling overhead, CPU utilization, context-switch frequency, cache behavior, processor migration, fairness, and prediction accuracy. The proposed framework does not assume that predictive scheduling is universally superior to conventional operating system scheduling; rather, it investigates the conditions under which advance knowledge of process behavior can improve scheduling efficiency without compromising fairness, responsiveness, or execution correctness. By extending operating system scheduling from exclusively reactive decision-making toward predictive preparation of system-level computational resources, this research explores a potential new direction for intelligent operating system resource management. Its practical effectiveness remains subject to empirical validation.
Author: Zhang, Jincheng
Language: English
Topic: Real-Time Systems Scheduling
Subfield: Hardware and Architecture
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7225451317