Machine Learning for Adaptive Power Management

Machine Learning for Adaptive Power Management

Authors: G. Theocharous et alPresenter: Guangdong Liu

April 1th, 2011

Outline

IntroductionBackgroundRelated WorkContributionThe Context-Based SolutionIncorporating Stochastic ProcessesAn Introduction to Machine Learning

Introduction

Power Management (for laptops)Motivation: mobile systems face battery life issues && high performance systems face heating issues

Objective: to maximize the battery life while minimizing the annoyance to the user

Approaches: to place a component into various power saving states

Crucial point: which component and when should it be shutdown

Introduction

Existing commercial solutions: Timeout Policies

Decide to turn off a component if the time passed since it was last used for more than some predefined threshold

Control the annoyance by varying the threshold

Widely implemented across all Operating Systems

Windows* OS has several built-in power management schemes that allow the user to choose between different levels of thresholds

Introduction

Existing commercial solutions: Timeout Policies

Advantages Simple and robust

Disadvantages Aggressiveness: the length of the timeout React too slow: waste power during the

inactivity periods React too fast: annoy the user at an

inappropriate time Non adaptive!

Introduction

Adaptive Power ManagementObjective: to make PM “autonomous”

What does “autonomous” refer to in this context?

Intelligently decide when to place a component into various power saving states given the user activity

An instance of autonomic computing systems

Background

Autonomic Computing SystemsMotivation

Current programming paradigms, methods, management tools are inadequate to handle the scale, complexity, dynamism and heterogeneity of emerging systems

Background

Without requiring our conscious involvement- when we run, it increasesour heart and breathing rate

BackgroundGoal of Autonomic Systems

Build a self-managing system with a minimum human interference

Characteristics of Autonomic SystemsSelf-ConfiguringSelf-AdaptingSelf-OptimizingSelf-HealingSelf-ProtectingHighly DecentralizedHeterogeneous Architecture

Background

Background

How Autonomic Systems Work

Managed Element

ES

Monitor

Analyze

Execute

Plan

Knowledge

Autonomic Manager

Background

Power Management

Background

APM As An Autonomic System

Background

Adaptive Power ManagementObjective: to make PM “autonomous”

What does “autonomous” refer to in this context?

Intelligently decide when to place a component into various power saving states given the user activity

Challenges: (1) better tradeoff between high power saving and low user annoyance (2) accurate modeling of real world uncertainty

BackgroundUncertainty in APM

Perception Uncertainty User context cannot be directly observed from

sensors, such as keyboard and mouse activity, or the currently active application

Action Uncertainty Turning off a component can generate

uncertainty: (1) reflected by the time it takes to turn a component on and off: placing the machine on a standby does not always take the same amount of time (2) reflected by the effects on the user’s context: if the user context is a measure of idleness … if the user context is a measure of the users’ mode of operation

Background

Adaptive Power ManagementIdea: taking into account two essential things: “Context” and “Uncertainty”

What is “Context” for an APM? What is “Uncertainty” for an APM? How can we consider the two factors

APM?

Background

How to solve the uncertainty in APMMachine Learning

Construct the programs that automatically improve with experience

The ability of a program to learn from experience — that is, to modify its execution on the basis of newly acquired information

Using machine learning algorithms, APM systems can automatically learn to map laptop usage patterns into power management actions specifically for individual users

Related Work

Machine Learning Based Prediction in APM[16] attempts to predict the length of the idle

period based on two thresholds calculated by using regression and manually obtained from data and observes that typically a long short idle time is followed by a long active time and vice versa.

[7] predicts the future idleness based on “Recency” : the future idleness is predicted as an exponentially weighted sum of recent delays

But the thresholds parameters are obtained based on non adaptive “recency”

Related Work

Simple Stochastic Process Approaches in APM

In [1][14][15], a single Markov Decision (MDP) or a Semi MDP is constructed

SMDP is a MDP where the next state does not only depend on the current state but also on how long the current state has been active

However, it is assumed that the state can be directly observed from sensors without uncertainty

No models for user context or consideration of user annoyance

Contribution

Summarize past approaches to the APM problem

Approaches To develop a context-based approach that maps user patterns to power saving actions (current work with experiment results presented in the paper)

To establish a stochastic model-based approach (future work)

The Context-Based Solution

Objective: to make a good tradeoff between the power savings and the perceived performance degradation (the annoyance)


Metrics of APM actions and annoyanceQuantify the power savings

Turn on the laptop (18.5Watts/s) Turn off the LCD (14Watts/s) Turn off the WLAN (17.5 Watts/s) Run the CPU in low frequency (15 Watts/s) Place the laptop in standby mode (0.7

Watt/s )


Metrics of APM actions and annoyanceQuantify the Annoyance (Based on interviews)

Turn down the CPU frequency by mistake (1) Turn off the WLAN by mistake (3) Turn off the LCD by mistake (7) Move to standby mode by mistake (10)

How to detect mistakesDetect it if this component is needed after it was

turned off. For example, turn off the LCD but the user opens a new application, it is a mistake!


Step#1: The Direct ApproachTwo counterparts

Timeout-based policies Logistic regression, k-nearest-neighbors

and the C4.5 decision treeFor each APM action, a separate classifier is trained based on the given data

What is the given data? Input: the sensor measurements including

active application, keyboard and mouse activity, CPU load and network traffic

Output: whether to turn the component on/off


Step#2: Context-Based Policy LearningThe basic idea is to partition the data into contexts, for each of which a separate classifier is trained

Choose the decision threshold for each classifier such that the overall power saving is maximized

In a general case, context could be past idleness or any partitioning of the data that improves performance

Specifically, the paper defines context to be “the time since a component was last active”


Define ContextPartition the data into 30 categories

Category 1 means that the component is active in the previous step

Category 30 means that the component is idle for the last 600 time steps

The rest 28 categories are chosen between


Threshold computation for contextsUse an optimization algorithm to determine the thresholds for each context classifier

Start by setting the least annoying thresholds for all classifiers

Increase the threshold that corresponds to the maximal power savings over annoyance increase ratio subject to a global annoyance constraint



Experiment SetupTwo kinds of baselines

Timeout policyNaïve classifiers: logic regression, k-nearest-

neighbors, C4.5 decision treeA new scheme: context-based logistic

regressionData Source

There are 42 traces, which were collected for 7 users, representing the cumulative experience of 210 usage hours

The performance is compared for the same annoyance level

Results

Incorporating Stochastic ProcessesModel-Based Approaches

Capture the temporal dynamics of user and system state as well as the annoyance and power costs

Decouple the decision-making process from the problem of learning and estimating the model of the environment

Learning a model refers to the process of defining/discovering domain variables and how they relate to each other

Using the models refers to the process of computing decisions given the model

Incorporating Stochastic Processes

Markov Decision ProcessesPowerful in domains where actions change

the states stochastically and where there is usually a delayed reward signal when exercising an action

Objective Maximize its long term cumulative rewards

Markov property The true state captures all the information

needed to describe the system Given the state of the system the future is

independent of the past

Incorporating Stochastic ProcessesMarkov Decision Processes

Incorporating Stochastic ProcessesMarkov Decision Processes

Formal model <S,A,T,R> S: a finite set of states A: a finite set of actions T(s’|s, a): the transition probability from state

s to state s’ under action a R(s,a): the reward for taking action a in state s

Advantage & Disadvantage Simplicity and efficiency Cannot capture unobservable dynamics Depend on the assumption that the state of

the system can be estimated with no errors


Dynamic Bayesian NetworksSome of the variables are observed and some

are not. An agent reasons the state of the system indirectly by the observed variables.

A special DBN called Hidden Markov Model <S, T, Z, O> S: a finite set of states Z: a finite set of observations T(s’|s): the transition possibility from s to s’ O(z|s): the possibility of observing z in s

Advantage & Disadvantage Capture complex dynamics that not completely

observable Lack of support for decision making

Incorporating Stochastic ProcessesThe Pros and Cons of MDP and HMM

MDP: it is decision making process, but cannot capture unobservable dynamics

HMM: it can capture complex dynamics but lack the support for decision making

Incorporating Stochastic ProcessesPartially Observable Markov Decision

ProcessCombine the strengths of HMMs and MDPsMake decision task under uncertaintyA POMDP policy computes an action after every observation such that in the long-run the utility is maximized

Uncertainty: The true state of the world is usually

unknown Even if the state is known, some actions

may have uncertain consequences

Incorporating Stochastic ProcessesPartially Observable Markov Decision

ProcessA POMDP policy computes actions at every

stepDue to the fact that the system state is

unobserved, the POMDP maps the actions to all possible probability distributions over the states called belief states

The belief state b(s) represents the agent’s current belief that the true state is s



Model-based Approach to APMPOMDP model is the only one that is rich enough to capture the two main aspects of APM

APM includes a human user and a complex computer system that cannot be assumed perfectly

APM involves making decisions on which components to turn on and off

Formal model A: actions such as turning on or off a component S: the state space is a combination of system state

and user state T: transition possibility O: the observations including the various

sensors/features


Model-based Approach to APMMain problem

How to construct the state space It is obvious that a model would be too

complex if it fully describes the system or the user context (i.e., the way the user is interacting with the system)

Need to balance the complexity of the model with needs to be able to obtain a useful APM policy

Incorporating Stochastic ProcessesModel-based Approach to APM

Future work Develop a more complex model for user

context, especially look at automatic context construction

Different ways to measure annoyance and learning the annoyance from the user based on the individual feedback

Study the statistics of duration between changes of the values of the system and user variable

Consider the initial period where the system is initializing

Introduction to Machine Learning

K-Nearest-NeighborsGiven an object X, find the K most

similar training examples and classify X into the most common category Y among the K neighbors

Compute object similarity using Euclidean distance:

i

jiji XXXXd 2)(),(

Example: k=6 (6NN)

Government

ScienceArts

Sec.14.3



Feathered?

Volant?

Category=ratite

Carnivorous?

Category=raptor

Endothermic?

Objects to be classified

Viviparous?

… ...

YES

YES

YES YES

YES

NO

NO

NO

NO

NO

Decision Trees


Logistic RegressionAssume there are two classes y = 0 and y = 1, we want to learn the conditional distribution P(y|x)

Let py(x;w) be our estimate of P(y|x), where w is a vector of adjustable parameters

11( ; )

1p

e wxx w 0

1( ; ) 11

pe

wxx w


Logistic RegressionThis is equivalent to

That is, the log odds of class 1 is a linear function of x

Q: How to find W?

1

0

( ; )log( ; )

pp

x w wxx w


Logistic RegressionThe conditional data likelihood is the

probability of the observed Y values in the training data, conditioned on their corresponding X values. We choose parameters w that satisfy

where w = <w0,w1 ,…,wn> is the vector of parameters to be estimated, yl denotes the observed value of Y in the l th training example, and xl denotes the observed value of X in the l th training example

arg max ( | , )l l

l

P yw

w = x w

Any Questions?

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Machine Learning for Adaptive Power Management