Thermal aware overall energy minimization scheduling for hard real-time systems

Abstract

As the semiconductor technology proceeds into the deep sub-micron era, the leakage and its dependency with the temperature become critical in dealing with power/energy minimization problems. In this paper, we study the problem on how to schedule a hard real-time system to achieve the minimal overall energy, including both dynamic and leakage energy consumption. We first develop an energy estimation method that can be used to accurately and efficiently calculate the overall energy consumption of a candidate schedule. Based on the proposed energy equation, we then develop two scheduling methods, i.e. an off-line and an on-line method, to minimize the overall energy consumption for real-time systems. Our experimental results demonstrate that the proposed energy estimation method can achieve up to two orders of magnitude speedup compared with an existing approach while maintaining good accuracy. In addition, with a large number of different test cases, both our off-line and on-line approaches can significantly outperform existing related works.

Introduction

The human's appetite for high-performance computing systems has driven the semiconductor technology into the deep sub-micron (DSM) era. The continued shrinking of the transistor size, together with increasingly complicated circuit architectures, have resulted in a significant increase of the power density. It is predicted that in a matter of a few years, the number of transistors that can be integrated into one 300 mm² die will reach 100 billion and its power consumption can be as high as 300 W [1]. One of the immediate consequences is the energy consumption issue which has posed tremendous challenge to digital system designers of both embedded and server platforms. For battery-driven embedded systems, given the fact that the growing of battery technology is far lagging behind the development of the semiconductor technology, unless more advanced low-power or power-aware design techniques are applied, the applicability of such devices will be severely limited. For power-rich server systems, on the other hand, the high energy consumption directly leads to high temperature, which not only increases the packaging/cooling costs, degrades the reliability/performance of the system, but also significantly increases the leakage power consumption due to the strong leakage/temperature dependency in the DSM domain. Therefore, power/energy minimization techniques are urgently demanded at every design abstraction levels.

Early research efforts are mainly focusing on how to reduce the dynamic energy consumption of a system. Dynamic voltage scaling (DVS) is well known to be one of the most effective dynamic energy reduction techniques. Due to the convex relationship between the dynamic power consumption and the processor speed level, a quadratic dynamic power reduction can be achieved at the expense of a linear performance reduction. However, as the leakage becomes more and more prominent in the DSM domain, the leakage energy component as well as its interdependence with the temperature have to be properly taken into consideration. As evidenced in [2], the leakage power will increase by 38% when chip temperature raising from 65 °C to 110 °C. In fact, thermal/temperature constraint is becoming an increasingly critical issue in the computing system deign [3]. Due to the leakage/temperature dependency, the leakage energy consumption is as important as the dynamic energy consumption. Therefore, an energy optimization design technique can become ineffective or inefficient if the interdependency between leakage and temperature is not properly addressed.

In this paper, we are interested in studying the problem of energy minimization for hard real-time scheduling on single-core systems by considering the dependency between leakage and temperature. The complexity of the problem lies in the fact that leakage energy consumption depends on both supply voltage level and temperature. For instance, two identical speed schedules may result in different energy consumptions simply because their initial temperatures are different. Therefore, how to accurately and efficiently estimate the energy consumption of various candidate speed schedules is the key to solve this problem.

We summarize the major contributions of this work below:

• First, we develop an effective closed-form energy estimation method with the leakage/temperature dependency taken into consideration.

• Secondly, based on the proposed energy estimation method, we further develop two scheduling algorithms to minimize the overall energy consumption. The first algorithm is an off-line algorithm, targeting at a real-time system consisting of a set of periodic tasks with the same period and deadline. The second algorithm is an on-line algorithm, which is intended for more general real-time systems consisting of multiple sporadic tasks.

• Finally, we conduct extensive experiments to evaluate the performance of our proposed energy estimation method as well as the online/offline energy efficient scheduling algorithms. Our experimental results show that the proposed energy estimation method can achieve a speedup up to two orders of magnitude compared with the existing approach while maintaining high accuracy. Our experimental results also show that the proposed scheduling algorithms consistently outperform the existing approaches in terms of energy reduction.

The rest of this paper is organized as follows. Section 2 discusses the related work. Section 3 introduces the system models used in this paper and Section 4 provides a motivational example. In Section 5 we show how to derive the proposed energy estimation equations, based on which we present our overall energy minimization scheduling algorithms in Section 6. Experimental results are discussed in Section 7. Section 8 concludes this paper.