Algorithms for Temperature-Aware Task Scheduling in Microprocessor Systems

TL;DR

This paper investigates temperature-aware task scheduling in microprocessors, modeling thermal dynamics and proposing algorithms to maximize task throughput while managing heat constraints, revealing NP-hardness and competitive online strategies.

Contribution

It introduces a simplified thermal management model for scheduling, proves NP-hardness of optimal scheduling, and develops a 2-competitive online algorithm with a matching lower bound.

Findings

Optimal offline scheduling is NP-hard.

A 2-competitive online algorithm is proposed.

Matching lower bound established for online strategy.

Abstract

We study scheduling problems motivated by recently developed techniques for microprocessor thermal management at the operating systems level. The general scenario can be described as follows. The microprocessor's temperature is controlled by the hardware thermal management system that continuously monitors the chip temperature and automatically reduces the processor's speed as soon as the thermal threshold is exceeded. Some tasks are more CPU-intensive than other and thus generate more heat during execution. The cooling system operates non-stop, reducing (at an exponential rate) the deviation of the processor's temperature from the ambient temperature. As a result, the processor's temperature, and thus the performance as well, depends on the order of the task execution. Given a variety of possible underlying architectures, models for cooling and for hardware thermal management, as well as types of tasks, this scenario gives rise to a plethora of interesting and never studied scheduling problems. We focus on scheduling real-time jobs in a simplified model for cooling and thermal management. A collection of unit-length jobs is given, each job specified by its release time, deadline and heat contribution. If, at some time step, the temperature of the system is t and the processor executes a job with heat contribution h, then the temperature at the next step is (t+h)/2. The temperature cannot exceed the given thermal threshold T. The objective is to maximize the throughput, that is, the number of tasks that meet their deadlines. We prove that, in the offline case, computing the optimum schedule is NP-hard, even if all jobs are released at the same time. In the online case, we show a 2-competitive deterministic algorithm and a matching lower bound.