Power Aware Real-time Systems

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Power AwareReal-time Systems
Rami Melhem
A joint project with profs Daniel Mosse Bruce
Childers Mootaz Elnozahy (IBM Austin)
And students Nevine Abougazaleh Cosmin Rusu Dakai
Zhu Ruibin Xu Matt Craven Sameh Gobriel
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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

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Real-time systems
Hard RT systems
Soft RT systems
Periodic
Aperiodic (frame-based)
non preemptive
non preemptive
preemptive
preemptive
parallel processors
uni-processor
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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
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Periodic, EDF scheduling

• n tasks with maximum computation times Ci and
  periods Ti, for i1,,n.

C2, T4
C3, T7

• 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

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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

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Power Management

• Why What Power Management?
• Battery operated Laptop, PDA and Cell phone
• Heating complex Servers (multiprocessors)
• Power Aware maintain QoS, reduce energy
• How?
• Power off un-used parts LCD, disk for Laptop
• Gracefully reduce the performance
• CPU dynamic power Pd CefVdd2f
  Chandrakasan-1992, Burd-1996
• Cef switch capacitance
• Vdd supply voltage
• f processor frequency ? linear related to Vdd

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Power Aware Scheduling

• Static Power Management (SPM)
• Static slack uniformly slow all tasks
  Weiser-1994, Yao-1995, Gruian-2000

fmax
Static Slack
D
E
T1
T2
idle
time
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Power Management

• Dynamic Power Management (DPM)
• Dynamic slack non-worst execution 10
  Ernst-1994
• DPM Krishna-2000, Kumar-2000, Pillai-2001,
  Shin-2001

fmax
Static Slack
D
E
T1
T2
idle
time
E/4
fmax/2
T1
T2
time

• Multi-Processor
• SPM length of schedule over deadline
• DPM ???

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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

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Speed adjustment in frame-based systems
Static speed adjustment
Assumption all tasks have the same deadline.
Smax
Smin
time
Select the speed based on worst-case execution
time,WCET, and deadline
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Dynamic Speed adjustment techniques for linear
code
WCET
time
PMP
PMP
ACET
time
PMP
Speed adjustment based on remaining WCET
Note a task very rarely consumes its estimated
worst case execution time.
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Dynamic Speed adjustment techniques for linear
code
Remaining WCET
time
PMP
PMP
Remaining time
time
PMP
Speed adjustment based on remaining WCET
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Dynamic Speed adjustment techniques for linear
code
Remaining WCET
time
PMP
PMP
Remaining time
time
PMP
Speed adjustment based on remaining WCET
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Dynamic Speed adjustment techniques for linear
code
time
PMP
PMP
time
Speed adjustment based on remaining WCET
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An alternate point of view
WCET
WCE
WCE
WCE
time
ACET
AV
WCE
time
PMP
stolen slack
Reclaimed slack
WCE
WCE
time
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Dynamic Speed adjustment techniques for
non-linear code
PMP
p1
p3
p2
min
average
max
At a
PMP

• Remaining WCET is based on the longest path
• Remaining average case execution time is based on
  the branching probabilities (from trace
  information).

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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

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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

Ti
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Slack Stealing

• Shifting Static Schedule 2-proc, D 8

D
L0
Recursive if embedded OR nodes
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Proposed Algorithms

• Greedy algorithm, two phases
• Off-line longest task first heuristic Slack
  stealing via shifting LSTi , EOi
• On-line
• Same execution order
• Claim the slack LSTi ti (ti?LSTi)
• Compute speed
• Meet timing requirement Zhu-2001

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Proposed Algorithms (cont)

• Actual Running Trace left branch, Ti use ai

f

• Possible Shortcomings
• Number of Speed change (overhead)
• Too greedy slow ? fast

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Proposed Algorithms (cont)

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

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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

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Tradeoff energy dependability

• Basic hypothesis
• Dependable systems must include redundant
  capacity in either time or space (or both)
• Redundancy can also be exploited to reduce power
  consumption

Time redundancy (checkpointing and rollbacks)
Space redundancy
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Exploring time redundancy
The slack can be used to 1) add checkpoints 2)
reserve recovery time 3) reduce processing
speed
Smax
For a given number of checkpoints, we can find
the speed that minimizes energy consumption,
while guaranteeing recovery and timeliness.
deadline
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Optimal number of checkpoints
More checkpoints more overhead less recovery
slack
r
For a given slack (C/D) and
checkpoint overhead (r/C), we can find the number
of checkpoints that minimizes energy
consumption, and guarantee recovery and
timeliness.
C
D
Energy
of checkpoints
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Non-uniform check-pointing
Observation May continue executing at Smax
after recovery.
Disadvantage increases energy consumption when a
fault occurs (a rare event)
Advantage recovery in an early section can use
slack created by execution of later sections at
Smax
Requires non-uniform checkpoints.
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Non-uniform check-pointing
Can find of checkpoints, their
distribution, and the CPU speed such that
energy is minimized, recovery is guaranteed
and deadlines are met
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Triple Modular Redundancy vs. Duplex
TMR vote and exclude the faulty result
Duplex Compare and roll-back if different
results
r
overhead of checkpoint
Load
slack in the system
p
ratio of static/dynamic power
r
r
TMR is more Energy efficient
Load0.6
Load0.7
Load0.7
0.035
Load0.5
Duplex is more Energy efficient
0.02
p
p
0.1
0.2
Energy efficiency of TMR Vs. Duplex depends on r,
p , and load
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Outline

• Introduction to real-time systems
• Introduction to power management
• Speed adjustment in frame-based systems
• Dynamic speed adjustment in multiprocessor
  environment
• Maximizing computational reward for given energy
  and deadline
• Tradeoff between energy consumption and
  reliability
• Saving power in wireless networks

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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

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Asymmetry in Transmit Power

• Instead of C sending directly to A, it can go
  through B
• Saves transmit power, but may cause some
  problems.

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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.

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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.

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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 transmit
• Increasing the power allows for higher bandwidth
• Is it useful to explore the power/bandwidth
  problem? Is it easy to characterize the problem?