Timely CPU Scheduling for Computation-intensive Status Updates

2025-05-20

AI Summary

This paper addresses the joint CPU scheduling problem for minimizing Age of Information (AoI) under average power constraints in real-time status update systems. We formulate the co-optimization of sleep timing and Dynamic Voltage and Frequency Scaling (DVFS) as a constrained Semi-Markov Decision Process (SMDP) with an uncountable state space—the first such formulation. For both predictable and unpredictable task-size scenarios, we propose a unified optimal scheduling framework that integrates Dinkelbach’s fractional programming with value iteration. Theoretical analysis reveals key structural properties of the optimal policy. Experiments demonstrate that our approach reduces AoI by over 50% compared to fixed-frequency baselines, and achieves over 50% energy savings under the same AoI target. Moreover, performance gains increase significantly with larger task-size variance.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Mixed Discrete/Continuous Search
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Security and Privacy: Large-scale security measurements
• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems

Abstract

The proliferation of mobile devices and real-time status updating applications has motivated the optimization of data freshness in the context of age of information (AoI). Meanwhile, increasing computational demands have inspired research on CPU scheduling. Since prior CPU scheduling strategies have ignored data freshness and prior age-minimization strategies have considered only constant CPU speed, we formulate the first CPU scheduling problem as a constrained semi-Markov decision process (SMDP) problem with uncountable space, which aims to minimize the long-term average age of information, subject to an average CPU power constraint. We optimize strategies that specify when the CPU sleeps and adapt the CPU speed (clock frequency) during the execution of update-processing tasks. We consider the age-minimal CPU scheduling problem for both predictable task size (PTS) and unpredictable task size (UTS) cases, where the task size is realized at the start (PTS) or at the completion (UTS) of the task, respectively. To address the non-convex objective, we employ Dinkelbach's fractional programming method to transform our problem into an average cost SMDP. We develop a value-iteration-based algorithm and prove its convergence to obtain optimal policies and structural results for both the PTS and UTS systems. Compared to constant CPU speed, numerical results show that our proposed scheme can reduce the AoI by 50% or more, with increasing benefits under tighter power constraints. Further, for a given AoI target, the age-minimal CPU scheduling policy can reduce the energy consumption by 50% or more, with greater AoI reductions when the task size distribution exhibits higher variance.

Problem

• Optimize CPU scheduling to minimize data freshness (AoI) under power constraints
• Develop adaptive CPU speed strategies for update-processing tasks
• Address predictable and unpredictable task size cases for age-minimal scheduling

Innovation

• Formulates CPU scheduling as constrained SMDP problem
• Employs Dinkelbach's method for non-convex optimization
• Develops value-iteration algorithm for optimal policies