A Stochastic Scheme to Balance Power Savingand Response Time of Electronic Devices
Hua Si, Shunsuke Saruwatari, Masateru Minami, and Hiroyuki Morikawa
Research Center for Advanced Science and Technology, The University of Tokyo,
Tokyo 153-8904, Japan,{sihua, saru, minami, mori}@mlab.t.u-tokyo.ac.jp

Abstract. This paper proposes a stochastic scheme called Gynapse
which balances power saving and response time by switching electronic
devices between different power modes. Gynapse predicts the wake-up
probability of each device on the basis of a Hierarchical Hidden MarkovModel. With the predicted probability, required response time, and device power consumption as inputs, Gynapse uses our novel optimization
method to manage power mode switching, and thereby minimizes totalenergy consumption while ensuring response time does not exceed therequired level.

1

Introduction

Power consumption has become a serious concern when people use electronicdevices. However, reductions in power consumption should not be achieved at
the expense of user experience. Harris and Cahill point out the necessity of minimizing power consumption while maintaining user-perceived device performanceand focus on reducing PC power consumption based on predicted usage probability [1]. Harle and Hopper take response time as a key performance measureand investigate the power saving potential of location-awareness system for three“response time” categories of office device [2].
In this paper, we propose a stochastic scheme called Gynapse to trade off
power saving and response time by switching electronic devices between different
power modes. The idea is depicted in Fig. 1. Although the device can saveenergy in power-saving mode, the user must wait T5 − T4 after performing arequest, which is equal to the device’s wake-up time TW U . If we were able toproperly forecast the request at T4 , the device could be awoken before that, and0
the response time T5 − T4 would be shortened. If the user requires a responsetime not in excess of a maximum value TRS , the device must awoken at least
TW U − TRS before his request at T4 . This is also the optimal wake up time forthis device, where power consumption is minimized and the response time iswithin the required range.
The difficulty here is in achieving the total optimization of multiple devices
with different wake-up times. Our contribution is in solving this problem with aprediction and optimization framework: at time t, a Hierarchical Hidden MarkovService
RequestRequest 1

DeviceStatus

Busy

WK
Mode

WorkingPS
ModeWorking

Tsd

PowerwithForecast

Working

Tsd

Request 2

T1 T2

Idle

Busy

Working

Working

Pw Saving Twu
PST3

Twu

Working

Working

T4' T4 T5' T5TimeFig. 1: Power Saving Mechanism

Model (HHMM) is built to predict device i’s probability of being in a working
mode at time t+(TW U,i −TRS,i ), which is the probability that device i should be
awoken at t. While in previous research wake-up depends solely on the predictedprobability, we use the probability to filter out several “possible” scenarios, selectthe one that minimizes total power consumption as an “optimal” scenario, andthen wake up devices based on it. In this way, total power consumption can be
minimized and the response time will not exceed the required level.

2Gynapse

We will use a hypothetical environment to describe our system: a user has aTV and a DVD player in her living room and a PC her study. For brevity, 1 isused to indicate a working mode (WK), and 0 is used to indicate a power-saving
mode (PS). Therefore, in scenario 101 means TV is in WK, DVD is in PS, andPC is in WK.
A conceptual system architecture is depicted in Fig. 2 (a). The Data Aggregator receives information from different types of sensors/devices with different
data frequency, normalizes their values to a range between 0 and 1, and builds
them into a time series. The Forecaster receives these time series and required
response times to forecast the wake-up probability of each device based on athree-level HHMM. With the predicted probability, current status, and power
consumption of devices, the Optimizer handles power mode switching to maximize the total power saving while ensuring that response time does not exceed
the required level.
2.1

Forecaster

The hierarchical nature of HHMMs easily characterizes natural hierarchies inhuman activity and allows for model reusability and more efficient learning [3, 4].
Accordingly, we use a three-level Hierarchical Hidden Markov Model to represent
device usage. Fig. 2 (b) illustrates an example: one node at root level represents
scenario 110. Two nodes at the 2nd level represent the status of the living room
11 and study room 0. Three nodes at the 3rd level represent the status of theTV 1, DVD 1, and PC 0.

Gynapse
ResponseTime

EnergyForecaster

Optimizer

Data
Aggregator

R

RootP

ProductionI

Internal

EEndR0Device( S, Twu, E )StudyRoomPUser

11LivingRoomI0

Ubiquitous Computing EnvironmentSensors

110EIE

11PPTV

DVDEDevices
PC(a) System Architecture(b) 3-Level HHMM
Fig. 2: Gynapse

We divide the whole process into two phases: Learning and Predicting. In
the Learning phase, the HHMM parameters are estimated from historical data.
Then, based on these learned parameters, the Forecaster predicts pi , the wake-up
probability of device i. The problem of devices having different wake-up times
can be solved by multiplying different times of transition matrix. The Forecasterthen sends the respective values of pi to the Optimizer, such as pT V = 0.90,pDV D = 0.55, and pP C = 0.20.
2.2

Optimizer

After receiving the predicted probability, the Optimizer first calculates the probability of all scenarios. For instance, the probability of scenario 110 is pT V ×pDV D
×(1−pP C ) = 0.90×0.55×(1−0.20) = 0.396, and the probability of 100 is 0.324.
These two with the highest probabilities are selected as “possible” scenarios, andtheir total power consumptions are computed as:
E = 1 × eT V + 1 × eDV D + 0 × eP CE = 1 × eT V + 0 × eDV D + 0 × eP C
(1)where ei is the power consumption of device i. The Optimizer selects the scenario with the minimum power consumption as “optimal”. Because no forecast
algorithm guarantees 100% correctness, this method does not depend merely on
probability. Instead, it takes a different perspective: since the user will be satisfied as long as response time requirements are fulfilled, we can minimize total
power consumption under this constraint with respect to user requirements.
Then, the Optimizer compares the current scenario with the selected optimalscenario. For instance, if the current scenario is 101, while the selected scenario
is 100, the Optimizer will transition the PC into power-saving mode to reduce
total energy consumption.

3Preliminary Results

Light_L

We used sensor data from MIT PlaceLab [5] to evaluate our system, which has
totally 37 current sensors. We used the four current sensors in living/study room
to collect power consumption data of five devices: Light L in the living room,
Light S in the study, TV and DVD in the living room, and PC in the study.
Another 21 sensors in these two rooms are used as points of observation, which
include two light sensors, three humidity sensors, seven switch sensors, one pressure sensor, and eight temperature sensors. For each time step, Gynapse forecasts
the wake-up probability of five devices, and optimizes power mode switching tominimize total power consumption. Fig. 3 shows the real and optimized results
for these devices. Total energy savings is 3.4%, and respective energy savings
from each device are: Light L 11.2%, Light S 10.9%, TV 0.6%, DVD 0.0%, andPC 1.2%.
WKPS

00.5

11.5

22.5

3

Light_S

4x 10
WKPS

00.5

11.5

22.534TVx 10WKPS

00.5

11.5

22.5

34DVDx 10
WKPS

00.5

11.5

22.5

34

PCx 10
WK
PS

00.5

11.5

22.5

34

Real

Optimized

x 10

Fig. 3: Real and Optimized States for Respective Devices

4Conclusion

In this paper, we have presented a stochastic scheme called Gynapse to tradeoff power saving and response time by switching electronic devices between
different power modes, and have explained the details of forecaster and optimizer. Although the total energy saving are not large, the preliminary results
demonstrated that power saving can be achieved especially for devices with large
wattage difference between working and power saving modes. We look forward toexperimenting with all 37 current sensors in PlaceLab and believe more energy
savings can be achieved.
Acknowledgement This work is supported by Ministry of Internal Affairs
and Communications, Japan (Ubiquitous ServicePlatform)

References
1. Harris, C., Cahill, V.: An Empirical Study of the Potential for Context-Aware PowerManagement. In: UbiComp 2007: Ubiquitous Computing. LNCS, vol. 4717, pp. 235–252. Springer, Heidelberg (2007)

2. Harle, R. K., Hopper, A.: The Potential for Location-aware Power Management. In:
10th Intl. Conference on Ubiquitous Computing, pp. 302–311. ACM, NYC (2008)
3. Fine, S., Singer, Y., Tishby, N.: The Hierarchical Hidden Markov Model: Analysis
and Applications. Machine Learning. vol. 32, pp. 41–62. Kluwer, Boston (1998)
4. Theocharous, G.: Hierarchical Learning and Planning in Partially Observable
Markov Decision Processes. PhD thesis, Michigan State University (2002)
5. PlaceLab, http://architecture.mit.edu/house n/data/PlaceLab/PlaceLab.htm