Energy Optimal Speed Control of a Producer—Consumer Device Pair

Ravishankar Rao, Sarma Vrudhula

Research output: Contribution to journalArticle

Abstract

We propose a modular approach for minimizing the total energy consumed by a pair of generic communicating devices (producer–consumer scenario) by jointly controlling their speed profiles. Each device (like a CPU, or disk drive) is assumed to have a controllable variable called its speed (e.g., a CPU's clock frequency, a disk drive's spindle motor speed) that affects its power consumption and performance (e.g., throughput, data transfer rate). The device and task models we analyzed were inspired by applications like CD recording (hard drive to CD drive data transfer) and data processing (disk drive to CPU data transfer). The proposed solution can be used for any pair of devices with convex (for continuous speed sets) orW-convex (a discrete version of a convex function for discrete speed sets) power–speed relationships. For discrete speed sets, the method operates directly on the power–speed values and does not require an analytical relationship between power and speed. The key to solving the two-device optimization problem was the observation that it could be split into two single device parametric optimization problems, where the parameters correspond to the common task that both the devices must execute. The following divide-and-conquer approach is proposed: [divide] the optimal speed policy and energy consumption of each device is derived as an analytical function of its task parameters; [conquer] the optimal values of these parameters are found by minimizing the sum of the parameterized energy functions and plugged back into the parameterized speed profiles. The main advantage of this approach is that each device can be characterized independently and this allows system designers to mix and match manufacturer-supplied device energy curves to evaluate and optimize different application scenarios. We demonstrate our approach using three device characterization examples (for a CD drive, hard drive, and a CPU) and two application scenarios (CD recording, MD5 checksum computation).

Original languageEnglish (US)
Pages (from-to)30
Number of pages1
JournalACM Transactions on Embedded Computing Systems
Volume6
Issue number4
DOIs
StatePublished - 2007

Fingerprint

Speed control
Program processors
Data transfer
Parametric devices
Data transfer rates
Clocks
Electric power utilization
Energy utilization
Throughput

Keywords

  • disk drive
  • Energy optimization
  • Experimentation
  • joint optimization
  • Performance
  • processor
  • speed control
  • Theory

ASJC Scopus subject areas

  • Software
  • Hardware and Architecture

Cite this

Energy Optimal Speed Control of a Producer—Consumer Device Pair. / Rao, Ravishankar; Vrudhula, Sarma.

In: ACM Transactions on Embedded Computing Systems, Vol. 6, No. 4, 2007, p. 30.

Research output: Contribution to journalArticle

@article{b00436289f294238900e415b4f89b3bb,
title = "Energy Optimal Speed Control of a Producer—Consumer Device Pair",
abstract = "We propose a modular approach for minimizing the total energy consumed by a pair of generic communicating devices (producer–consumer scenario) by jointly controlling their speed profiles. Each device (like a CPU, or disk drive) is assumed to have a controllable variable called its speed (e.g., a CPU's clock frequency, a disk drive's spindle motor speed) that affects its power consumption and performance (e.g., throughput, data transfer rate). The device and task models we analyzed were inspired by applications like CD recording (hard drive to CD drive data transfer) and data processing (disk drive to CPU data transfer). The proposed solution can be used for any pair of devices with convex (for continuous speed sets) orW-convex (a discrete version of a convex function for discrete speed sets) power–speed relationships. For discrete speed sets, the method operates directly on the power–speed values and does not require an analytical relationship between power and speed. The key to solving the two-device optimization problem was the observation that it could be split into two single device parametric optimization problems, where the parameters correspond to the common task that both the devices must execute. The following divide-and-conquer approach is proposed: [divide] the optimal speed policy and energy consumption of each device is derived as an analytical function of its task parameters; [conquer] the optimal values of these parameters are found by minimizing the sum of the parameterized energy functions and plugged back into the parameterized speed profiles. The main advantage of this approach is that each device can be characterized independently and this allows system designers to mix and match manufacturer-supplied device energy curves to evaluate and optimize different application scenarios. We demonstrate our approach using three device characterization examples (for a CD drive, hard drive, and a CPU) and two application scenarios (CD recording, MD5 checksum computation).",
keywords = "disk drive, Energy optimization, Experimentation, joint optimization, Performance, processor, speed control, Theory",
author = "Ravishankar Rao and Sarma Vrudhula",
year = "2007",
doi = "10.1145/1274858.1274868",
language = "English (US)",
volume = "6",
pages = "30",
journal = "ACM Transactions on Embedded Computing Systems",
issn = "1539-9087",
publisher = "Association for Computing Machinery (ACM)",
number = "4",

}

TY - JOUR

T1 - Energy Optimal Speed Control of a Producer—Consumer Device Pair

AU - Rao, Ravishankar

AU - Vrudhula, Sarma

PY - 2007

Y1 - 2007

N2 - We propose a modular approach for minimizing the total energy consumed by a pair of generic communicating devices (producer–consumer scenario) by jointly controlling their speed profiles. Each device (like a CPU, or disk drive) is assumed to have a controllable variable called its speed (e.g., a CPU's clock frequency, a disk drive's spindle motor speed) that affects its power consumption and performance (e.g., throughput, data transfer rate). The device and task models we analyzed were inspired by applications like CD recording (hard drive to CD drive data transfer) and data processing (disk drive to CPU data transfer). The proposed solution can be used for any pair of devices with convex (for continuous speed sets) orW-convex (a discrete version of a convex function for discrete speed sets) power–speed relationships. For discrete speed sets, the method operates directly on the power–speed values and does not require an analytical relationship between power and speed. The key to solving the two-device optimization problem was the observation that it could be split into two single device parametric optimization problems, where the parameters correspond to the common task that both the devices must execute. The following divide-and-conquer approach is proposed: [divide] the optimal speed policy and energy consumption of each device is derived as an analytical function of its task parameters; [conquer] the optimal values of these parameters are found by minimizing the sum of the parameterized energy functions and plugged back into the parameterized speed profiles. The main advantage of this approach is that each device can be characterized independently and this allows system designers to mix and match manufacturer-supplied device energy curves to evaluate and optimize different application scenarios. We demonstrate our approach using three device characterization examples (for a CD drive, hard drive, and a CPU) and two application scenarios (CD recording, MD5 checksum computation).

AB - We propose a modular approach for minimizing the total energy consumed by a pair of generic communicating devices (producer–consumer scenario) by jointly controlling their speed profiles. Each device (like a CPU, or disk drive) is assumed to have a controllable variable called its speed (e.g., a CPU's clock frequency, a disk drive's spindle motor speed) that affects its power consumption and performance (e.g., throughput, data transfer rate). The device and task models we analyzed were inspired by applications like CD recording (hard drive to CD drive data transfer) and data processing (disk drive to CPU data transfer). The proposed solution can be used for any pair of devices with convex (for continuous speed sets) orW-convex (a discrete version of a convex function for discrete speed sets) power–speed relationships. For discrete speed sets, the method operates directly on the power–speed values and does not require an analytical relationship between power and speed. The key to solving the two-device optimization problem was the observation that it could be split into two single device parametric optimization problems, where the parameters correspond to the common task that both the devices must execute. The following divide-and-conquer approach is proposed: [divide] the optimal speed policy and energy consumption of each device is derived as an analytical function of its task parameters; [conquer] the optimal values of these parameters are found by minimizing the sum of the parameterized energy functions and plugged back into the parameterized speed profiles. The main advantage of this approach is that each device can be characterized independently and this allows system designers to mix and match manufacturer-supplied device energy curves to evaluate and optimize different application scenarios. We demonstrate our approach using three device characterization examples (for a CD drive, hard drive, and a CPU) and two application scenarios (CD recording, MD5 checksum computation).

KW - disk drive

KW - Energy optimization

KW - Experimentation

KW - joint optimization

KW - Performance

KW - processor

KW - speed control

KW - Theory

UR - http://www.scopus.com/inward/record.url?scp=85025397843&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85025397843&partnerID=8YFLogxK

U2 - 10.1145/1274858.1274868

DO - 10.1145/1274858.1274868

M3 - Article

AN - SCOPUS:85025397843

VL - 6

SP - 30

JO - ACM Transactions on Embedded Computing Systems

JF - ACM Transactions on Embedded Computing Systems

SN - 1539-9087

IS - 4

ER -