Power Aware Real-time Systems A joint project with profs Daniel Mosse Bruce Childers Mootaz Elnozahy (IBM Austin) And students Nevine Abougazaleh Cosmin

Power AwarePower AwareReal-time SystemsReal-time Systems

A joint project with profsDaniel Mosse

Bruce Childers Mootaz Elnozahy (IBM Austin)

And studentsNevine Abougazaleh

Cosmin RusuDakai ZhuRuibin Xu

Matt CravenSameh Gobriel

Outline

Introduction to real-time systems

Introduction to power management

Speed adjustment in frame-based systems

Dynamic speed adjustment in multiprocessor environment

Tradeoff between energy consumption and reliability

Saving power in wireless networks

Hard RT systems

Periodic Aperiodic(frame-based)

preemptive nonpreemptive preemptive

nonpreemptive

uni-processor parallel processors

Soft RT systems

Real-time systems

REAL-TIME SYSTEMS

Hard Real Time system guarantee deadlines

• To guarantee deadlines, we need to know worst case execution times• Predictability: need to know if deadlines may be missed

Soft Real Time system try to meet deadlines

• If a deadline is missed, there is a penalty• Provides statistical guarantees (probabilistic analysis)• Need to know the statistical distribution of execution times

Applications: Safety critical systems, control and command systems, robotics, Communication, multimedia

• n tasks with maximum computation times Ci and periods Ti, for i=1,…,n.

• Dynamic priority scheduling (high priority to the task with earlier deadline)

• All tasks will meet their deadlines if utilization is not more than 1.

• Task set utilization is percentage of CPU used by all tasks:• This example: +

C=2, T=4

C=3, T=7

Periodic, EDF scheduling

24

37

Outline







Power Management

Why & What: Power Management?Battery operated: Laptop, PDA and Cell phoneHeating : complex Servers (multiprocessors)Power Aware: maintain QoS, reduce energy

How?Power off un-used parts: LCD, disk for LaptopGracefully reduce the performance

CPU: dynamic power Pd = Cef*Vdd2*f [Chandrakasan-1992, Burd-

1996] Cef : switch capacitance Vdd : supply voltage f : processor frequency linear related to Vdd

Power Aware Scheduling

T1

Dfmax

time

Static Power Management (SPM) Static slack: uniformly slow all

tasks [Weiser-1994, Yao-1995, Gruian-2000]

T2

EStatic Slack

Energy

fT1 T2

idle

timeT1

timeT2

T2

T1

0.6E

0.6E

timeT1 T2

fmax/2 E/4

Power Management

T1

Dfmax

time

time

Dynamic Power Management (DPM) Dynamic slack: non-worst execution

10% [Ernst-1994] DPM: [Krishna-2000, Kumar-2000,

Pillai-2001, Shin-2001]

T1

T2

T2

fmax/2

Static Slack

idleE

E/4

timeT1 T2

fmax/3 0.12E

timeT1

fmax/2Dynamic Slack Multi-Processor

SPM: length of schedule over deadline

DPM ???

Outline







time

Smax

Smin

Select the speed based on worst-case execution time,WCET, and deadline

CPU speed

time

deadline

Smax

Smin

WCET


Assumption: all tasks have the same deadline.

Static speed adjustment

Dynamic Speed adjustment techniques

for linear code

time

time

WCET

ACET

Speed adjustment based on remaining WCET

Note: a task very rarely consumes its estimated worst case execution time.


for linear code

time

time

Remaining WCET

Remaining time



for linear code

time

time

Remaining WCET

Remaining time



for linear code

time

time


An alternate point of view

timeWCE WCE WCE

time

WCET

ACET

time

AV

WCE WCE

WCE

Reclaimed slack

stolen slack


for non-linear code

• Remaining WCET is based on the longest path• Remaining average case execution time is based on

the branching probabilities (from trace information).

At a

p3p2p1

min average max

Outline







Multiprocs and AND/OR Applications

Real-Time Application Set of tasks Single Deadline

Directed Acyclic Graph (DAG) Comp. (ci, ai) AND (0,0) OR (0,0): probabilities

T1

T2 T3 T4 T5

(1,2/3)

(2,1) (1,1) (4,2) (3,2)

T7

T6

(1,1)

(1,1)

60% 40%

The Example

Ti

Slack Stealing

Shifting Static Schedule: 2-proc, D = 8

0 1 2 3 4 5 6 7 8 time

f

T1

T4

T5

T7

D

L0

T1

T4

T5

T7

f

0 1 2 3 4 5 6 7 8 time

Shifting

D

L0

T3

T2 T6L1

Recursive if embedded OR nodes

T3

T2 T6

T1T7

`L1

Proposed Algorithms

Greedy algorithm, two phases:Off-line: longest task first heuristic; Slack

stealing via shifting: LSTi , EOi

On-line:Same execution orderClaim the slack: LSTi – ti (tiLSTi)Compute speed:

Meet timing requirement [Zhu-2001]

ii

iig Slackc

cff

max

Proposed Algorithms (cont)

Actual Running Trace: left branch, Ti use ai

Possible ShortcomingsNumber of Speed change (overhead)Too greedy: slow fast

T7

f

0 1 2 3 4 5 6 7 8 time

D

T6

T1

L0

T3

T2

L1

Optimal for uniprocessor: Single speed Energy – Speed: Concave Minimal Energy when all tasks SAME speed

Speculation: statistical information about ApplicationStatic Speculation

All tasks fi = max ( fss, fg

i)

Adaptive Speculation Remaining tasks fi = max ( fas, fg

i)

D

wf

allaverage

ss

r

remainingaverage

as time

wf

Proposed Algorithms (cont)

Outline







Basic hypothesis:Dependable systems must include redundant capacity

in either time or space (or both)Redundancy can also be exploited to reduce power

consumption

Tradeoff: energy & dependability

Time redundancy(checkpointing and rollbacks) Space redundancy

Exploring time redundancy

deadline

The slack can be used to

1) add checkpoints

2) reserve recovery time

3) reduce processing speed

For a given number of checkpoints,we can find the speed that minimizes

energy consumption, while guaranteeing recovery and timeliness.

Smax

More checkpoints = more overhead + less recovery slack

D

C

r

Optimal number of checkpoints

For a given slack (C/D) and checkpoint overhead (r/C),we can find the number of checkpointsthat minimizes energy consumption, and guarantee recovery and timeliness.

# of checkpoints

Energy

Non-uniform check-pointing

Observation: May continue executing at Smax after recovery.

Advantage: recovery in an early section can use slack created by execution of later sections at Smax

Disadvantage: increases energy consumption when a fault occurs (a rare event)

Requires non-uniform checkpoints.

Non-uniform check-pointing

Can find # of checkpoints, their distribution, and the CPU speedsuch that energy is minimized, recovery is guaranteed and deadlines are met

Triple Modular Redundancy vs. Duplex

TMR: vote and exclude the faulty result

Duplex: Compare and roll-back if different results

Load=0.7

Load=0.5

Load=0.6

Energy efficiency of TMR Vs. Duplex depends on , and load

Duplex is more Energy efficient

TMR is more Energy efficient

0.02

0.035Load=0.7

Load

0.1 0.2

: overhead of checkpoint

: ratio of static/dynamic power

: slack in the system

Outline





Maximizing computational reward for given energy and deadline



Saving Power

Power is proportional to the square of the distance

The closer the nodes, the less power is needed

Power-aware Routing (PARO) identifies new nodes “between” other nodes and re-routes packets to save energy

Nodes decide to reduce/increase their transmit power

Asymmetry in Transmit Power

Instead of C sending directly to A, it can go through B

Saves transmit power, but may cause some problems.

AB

C

AB

C

Problems due to one-way links.

Collision avoidance (RTS/CTS) scheme is impaired Even across bidirectional links!

Unreliable transmissions through one-way link. May need multi-hop Acks at Data Link Layer.

Link outage can be discovered only at downstream nodes.

A B C

RTSCTS XCTS

MSG MSG MSG

Problems for Routing Protocols

Route discovery mechanism.Cannot reply using inverse path of route request.Need to identify unidirectional links. (AODV)

Route Maintenance.Need explicit neighbor discovery mechanism.

Connectivity of the network.Gets worse (partitions!) if only bidirectional links

are used.

Wireless bandwidth and Power savings

In addition to transmit power, what else can we do to save energy?

Power has a direct relation with signal to noise ratio (SNR)The higher the power, the higher the signal, the less noise, the

less errors, the more data a node can transmitIncreasing the power allows for higher bandwidth

Is it useful to explore the power/bandwidth problem? Is it easy to characterize the problem?

Documents

Power Aware Real-time Systems A joint project with profs Daniel Mosse Bruce Childers Mootaz Elnozahy (IBM Austin) And students Nevine Abougazaleh Cosmin