Feedback and Time are Essential for the Optimal Control of Computing Systems

TL;DR

This paper argues that incorporating feedback and explicitly considering time in the design of scheduling algorithms can significantly improve the performance and efficiency of computing systems, leveraging recent advances in control theory.

Contribution

It highlights the importance of using closed-loop metrics and real-time model predictive control for developing better scheduling algorithms in computing systems.

Findings

Closed-loop metrics like the gap metric can improve scheduling validation.

Real-time model predictive control offers a promising framework for scheduling.

Explicitly considering time in scheduling algorithms enhances system performance.

Abstract

The performance, reliability, cost, size and energy usage of computing systems can be improved by one or more orders of magnitude by the systematic use of modern control and optimization methods. Computing systems rely on the use of feedback algorithms to schedule tasks, data and resources, but the models that are used to design these algorithms are validated using open-loop metrics. By using closed-loop metrics instead, such as the gap metric developed in the control community, it should be possible to develop improved scheduling algorithms and computing systems that have not been over-engineered. Furthermore, scheduling problems are most naturally formulated as constraint satisfaction or mathematical optimization problems, but these are seldom implemented using state of the art numerical methods, nor do they explicitly take into account the fact that the scheduling problem itself takes time to solve. This paper makes the case that recent results in real-time model predictive control, where optimization problems are solved in order to control a process that evolves in time, are likely to form the basis of scheduling algorithms of the future. We therefore outline some of the research problems and opportunities that could arise by explicitly considering feedback and time when designing optimal scheduling algorithms for computing systems.