Load Shedding in Stream Databases

1
Load Shedding in Stream Databases A
Control-Based Approach

• Yicheng Tu, Song Liu, Sunil Prabhakar, and Bin
  Yao
• Department of Computer Science, Purdue University
• Presented by Chris Mayfield
• VLDB Conference, Seoul, Korea
• September 14, 2006

2
Data stream management systems

• Applications
• Financial analysis
• Mobile services
• Sensor networks
• Network monitoring
• More
• Continuous data, discarded after being processed
• Continuous query
• Data-active query-passive model

3
DSMS architecture

• Network of query operators (O1 O3)
• Each operator has its own queue (q1 q4)
• Scheduler decides which operator to execute
• Query results (Q1, Q2) pushed to clients
• Example systems
• Aurora/Borealis
• STREAM

4
Quality in DSMS data processing

• Data processing in DSMS is quality-critical
• tuple delay
• data loss
• sampling rate, window size,
• Overloading during spikes ? degraded quality
  (delay)
• Solution adjust data loss (i.e., load shedding)
• On DSMS side
• Eliminating excessive load by dropping data items
  
• The real problem is

tuple delay is the major concern results
generated from old data are useless!
How to maintain processing delays while
minimizing data loss ?
5
Related work (load shedding)

• Accuracy of aggregate queries under load shedding
  (Babcock et al., ICDE04)
• Data triage (Reiss Hellerstein, ICDE05)
• Put data into an asylum upon overloading
• LoadStar (Chi et al., VLDB05)
• QoS-driven load shedding (Tatbul et al., VLDB03)
• Key questions
• - When?
• - How much?
• - Where?
• Use a load shedding roadmap (LSRM) to decide
  where
• Intuitive algorithm to decide when and how much

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Example Limitations

• Highly dynamic environment is reality
• Bursty data input
• Variable unit processing cost
• Fails to capture current system status (queue
  length) and output (delay)
• Delay positively related to queue length
• Example 1. Unbounded increase of delay
• Example 2. Unnecessary data loss

7
Our approach

• View load shedding as a control theory problem
• Control manipulation of system behavior by
  adjusting input
• Cruise control of automobiles, room temperature
  control, etc.
• Open-loop (preset) vs. closed-loop (feedback)
  control

• The feedback control loop
• Plant
• Monitor
• Controller
• Actuator
• How it works
• Error (e) desirable output (yr) - measured
  output (y)
• Focal point controller, which maps e to control
  signal u
• Disturbances

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Challenges (theory ? practice)

• Can we model the system?
• Analytical model may not be easy to derive
• System identification experimental methods
• How to design the controller?
• Use control theoretical tools for guaranteed
  performance
• DSMS-specific problems
• Lack of real-time measurement of output signal (
  y )
• How to set control period (T)
• Real system evaluation
• we use Borealis in our study

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Modeling a DSMS

• Borealis data stream manager
• Round robin operator scheduler
• FIFO waiting queues
• For now, fix the per-tuple processing cost c
• Proposed model
• y qc
• where q is the number of outstanding data tuples
• Discrete form y(k) q(k-1) c
• Denote the input load as fi and system processing
  power as fo

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Controller design

• Design based on pole placement
• Locations tell how fast/well system responds
• Guaranteed performance targeting
• Convergence rate - responsiveness
• Damping - smoothness
• The controller (see appendix for details)

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Control period

• Provides more complete answer to the question
  when to shed load?
• Empirically set in previous studies
• Case-by-case decision with some systematic rules
• In our problem, a tradeoff between
• Sampling theory (Nyquist-Shannon Theorem) in
  order to capture the moving trends of the
  disturbances, higher (shorter) sampling frequency
  (period) is preferred
• Stochastic feature of output ( y ) and parameter
  ( c )
• more samples are needed ? longer period is
  preferred
• The first factor should be given more weight

12
Input for experiments

• Controller and load shedder implemented in
  Borealis
• Synthetic (Pareto) and real (Web) data
  streams
• Small query network with variable average
  processing cost

13
Experimental results

• Experiments for comparison
• Aurora open loop solution
• Baseline a simple feedback method
• Target delay 2 sec
• Control period 1 sec
• Total time 400 sec
• For both input types, data loss are almost the
  same for all three load shedding strategies

14
Future work

• Time-varying DSMS model
• For example, time-varying cost c
• Possible solution adaptive control
• Adaptation other than load shedding
• New disturbances?
• Model changes? (i.e. at runtime)
• Other database problems

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Summary

• Load shedding is an effective quality adaptation
  method
• Ad hoc solutions do not work well under dynamic
  load and system features
• We propose an approach to guide load shedding in
  a highly dynamic environment based on feedback
  control theory
• Initial experimental results performed in a
  real-world DSMS show promising potential of our
  approach

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Backup - 1
17
Backup - 2

• Lack of robustness of open-loop solution
• More optimistic policy adapted in Aurora
• Unstable performance
• Our solution is robust
• Under input streams with different burstiness

18
Backup - 3
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Backup - 4 (Model verification)

• Feed Borealis with synthetic streams
• Input rate step or sinusoidal function of time
• Average processing cost is fixed