1 The Challenges of Reflexive Control Systems Lui Sha [email protected].
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Transcript of 1 The Challenges of Reflexive Control Systems Lui Sha [email protected].
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Acknowledgement Many have contributed to the collaboration. In
particular, I want to thank Xue Liu, Jin Jeo at UIUC, Tarek Abdelzaher at UVA and Joe Hellerstein at IBM Research
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Web Service Performance Control
Web Servers Application ServersEnd Users
KeepAlive
TImeout
Number of
Threads
MaxClients
DB
ConnectionsFast response cache
MaxRequestsPerChild
ThreadsPerChild
Max simultan. requests
ListenBackLog
URL Cache
EJB threads
JVM heap size
Servlet reload int
Courtesy of Joe Hellerstein, IBM Research
Network based server systems, e.g., Web servers, have now become an integral part of our society.
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Overview In a queueing network, service performance control can
be viewed as a hierarchical control Setting the performance goal at each node
Rout the traffic to the nodes Performance regulation on each node
There are many interesting problems. Among them “Noise” is data Event driven control Reflexivity in control
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Some Interesting Facts Suppose we want to correct a biased coin with Prob(head)= 0.4. We begin by
soldering a small weight to the side of head, and then do some experiments and adjust the weight.
From a control perspective, the transfer function between the change of weight and the change of probability of head manifests itself clearly only when the sample size becomes large.
Fast control actions do not lead to fast convergence Low pass filter, except moving average, won’t work because there is no
noise. Event (sample size) based control action yields better results. Fixed
sample size control typically has random time intervals
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Reflexive Control Normal control system: the plant model is invariant to
control actions
Reflexive control system: the control alters the plant Genetics -> (physique, intelligence) -> mating ->
genetics Laws -> control social behaviors -> changes laws
• Queueing system control is reflexive
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Reflexivity and Uncertainty Uncertainty principle in measurement.
In quantum physics, the act of measurement distorts what we try to observe.
“Uncertainty principle” in reflexive control System. The act of control alters the model used to design
the controller
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The Bright Spot One of the worst that can happen in a network of
servers is performance failure due to congestions.
This turns out to be an easier task Heavy traffic creates long queue Long queue is persistent Low of large number kicks in Allows for fluid approximation Linear model with PI control works well
High performance regulation is reflexive. Why?
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Delay vs Control for M/M/1
1.5 2 2.5 3
5
10
15
20
25
30
delay 1
10
Keeping the model relevant
Dref
“Matching” the measured arrival rate with computed service rate e.g., Let µ= + 0.5 will “lock” the system at the point of linearization
Slope = d/dDref = - 4
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Queuing Model Based Control
Measured Delay d
d
Server Queue
Control
Request
Queueing Model
q
Ref Delay Dref
Control of Average Delay
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Experimental Results
-1
0
1
2
3
4
5
6
7
8
9
10
0 500 1000 1500 2000
time (sec)
conn
ectio
n de
lay
G/M/1
G/M/1 with PIcontroller
reference
G/M/1 with P controller
Sha, L., Li, X., Lu, Y., and; Abdelzaher, T. “Queueing model based network server performance control”, the proceedings of IEEE Real-Time Systems Symposium, 2002
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Long Shadow of Reflexivity
Alas, there are still considerable variances The queueing model is a function variances in arrival
time and service time. Good admission control smooth the input variances Good execution time control smooth the execution
time variances Control invalidates queueing model!
Feed forward over-allocates when delay turns lower
Feed forward under-allocates when delay turns higher
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Jumps in Loads In addition to over/under allocation in feed forward errors,
the jumps in arrival process creates another challenge The queue length from previous traffic load takes time
to settle to the new equilibrium queue distribution. Leads to large transients
Whatever the sources of errors, large variance in service time correlates with large variances in queue length
Adding a control term on (Actual_queue_length – expected_queue_length)
Reducing both transients induced by workload changes and variances during steady state
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Comparison of two delay regulators using web trace
)()1
( arg etedtcurrentrefq llKD
)()1
( arg etedtcurrentrefq llKD
)()1
( arg etedtcurrentrefq llKD
Queueing Model Based Feedback Control
Queue Length Model Based Feedback Control
1 arg
1( ) ( )q current t etedref
c l lD
)1
( refq D
Steady state feed forward + delay feedback + model based queue
control
2 ( )refc D d
2 ( )refk D d
Actuator parameters: K: delay control; : multiplicative noise control
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Summary – theoretical problems
Heavy traffic not only cures “reflexivity”, it also allows for fixed time interval control works as well as sample size based control
upside: simple fixed rate linear model works well downside: the fun is gone
Under moderate traffic control, we need to deal with event (sample size) driven control reflexivity
queueing model based feed forward helps but the feed forward both over allocate & under allocate
Optimized supervisory control for networked server set point adjustments
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Summary: Engineering Problems
Poor observability in actual systems: Many queues and I/O states are not visible and not controllable.
Poor actuators: Rejecting individual requests TCP window
adjustment (non-linear, coarse grain actuator in admission control)
Hard to do fine grain control in CPU cycle allocation Nearly no control of I/O bandwidth
Control OS