Dynamic Mapping of Activation Trees Thesis Proposal January 29, 1998

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Dynamic Mapping of Activation TreesThesis
ProposalJanuary 29, 1998

• Peter A. Dinda
• Committee
• David OHallaron (chair)
• Thomas Gross
• Peter Steenkiste
• Jaspal Subhlok
• David Bakken (BBN)

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Outline

• Responsive interactive applications
• Best effort real-time service
• Dynamic mapping problem
• History-based prediction approach
• Other approaches and related work
• Current results on simplified problem
• Proposed thesis work

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Interactive Application Model
Feedback
Message Handler
Message
mouse_click()
Aperiodic User Action
Activation tree
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Acoustic Room Modeling
Room model
impulse responses
Physical Simulation of Wave Eqn
Speakers
Modify model
Frequency response plots
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Other Applications

• Image editing
• The Adobe Photoshop universe
• Computer aided design
• Quake design optimization (Malcevic-97)
• Computational steering
• CUMULUS (Geist-96), CAVE (Disz-95), ...
• Collaboration
• Collaborative Planning (Zinky-DUTC-95), ...
• Securities trading
• (Wolfe-Lau-95)
• Games
• DIS (DIS-94)

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Responsiveness

• Timely feedback to individual user actions
• Bound response time tmax
• Jitter bound and resource usage hint
• Bound response time ³ tmin
• Example image editor drawing tool

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A Best Effort Real-time Service
MAP procedure() IN tmin, tmax

• Execute the activation tree rooted at procedure()
  so that tmintexectmax
• No guarantees
• Responsiveness spec bounds tmin,tmax
• Performance metric fraction of trees that meet
  their bounds

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Machine Model

• Hosts on a LAN
• No centralized or coordinated scheduling, or
  reservations
• Other unrelated traffic exists
• We are only a user
• Remote execution facility
• Can execute any procedure on any host
• RPC, DSM, DCE, CORBA, DCOM, ...
• Measurable - at least a good real-time clock
  exists (lt1ms)

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Execution Model
tmin,tmax

• Dynamically map nodes of the unfolding activation
  tree to the hosts
• At each procedure call, choose which host is best
  suited to execute the call in order to meet the
  bounds on the tree

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Dynamic Mapping Problem

• How do we map the nodes of the trees to the hosts
  so that the fraction of trees that satisfy their
  bounds is maximized?

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Aspects of My Approach

• History-based prediction
• Decomposition of bounds
• Adaptation of mapping algorithms during tree
  traversal

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History-based Prediction
H0
foo()
tmin,tmax
...
tmin,tmax
H1
...
H0
bar()
...
foo() is executing on H0 and calls bar(), which
can be mapped to H0, H1, or H2
H2
H0 has a local history of execution times of
bar() on each of the other hosts

• For each host, H0 predicts whether it can meet
  the bounds, based on past local history and then
  chooses one where it is possible
• Execution times include both communication for
  remote call and the actual computation

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Decomposition of Bounds
foo()
tmin,tmax
partially executed, known
tmin,tmax?
unexecuted, known
bar()
unexecuted, unknown
unexecuted, unknown

• Choice of tmin,tmax for bar() depends on
  unvisited portion of the tree
• Collect history of what fraction of time spent in
  foo() subtree was spent in bar() subtree
• Choose fraction of bounds to give to bar() based
  on that history and current time

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Adaptation of Mapping Algorithms During Tree
Traversal

• Tune strategy to how deep we are in the tree and
  how far along in the traversal
• Explore more aggresively early in the traversal,
  when the effect of a bad decision is easiest to
  overcome
• Find interesting new hosts
• Spend less time making mapping decision deep in
  the tree
• More likely to remain on single host

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Thesis Statement

• Dynamic mapping of activation trees using
  history-based prediction and traversal-based
  adaptation is an effective way to build a best
  effort real-time service for responsive
  interactive applications running in conventional
  environments.

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Other Approaches

• Distributed soft real-time system
• System modifications
• Dynamic load balancing system
• Different goals
• Even distribution of load (OS-centric)
• Minimization of exec times (app-centric)
• Resource reservation system
• System modifications
• Shared measurement system
• Information level and dissemination

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Current Results

• Load trace collection and analysis
• Algorithms and evaluation for simplified dynamic
  mapping problem

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Algorithms and Evaluation for Simplified Problem

• Map only leaf nodes
• Ignore communication
• for I1 to N do MAP leaf_procedure() IN
  tmin,tmaxend

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RangeCounter(W) A Near Optimal Algorithm

• Each host has a quality level Q and a window of
  the last W execution times (W is small)
• Choose host with highest quality level, and age
  quality levels of all hosts QQ-1
• If bounds are met, increase hosts quality level
  by the inverse of our confidence in it
• If bounds are not met, reduce hosts quality
  level by half QQ/2

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Load Trace-based Simulation

• Exec time computed from load trace using a
  simple, validated model
• Mapping algorithms are given bounds, select a
  host, then are told exec time
• Simulator computes performance of
• Algorithm under test
• Optimal (precognizant) algorithm
• Random mapping
• Individual host mappings

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Scope of Evaluation

• 9 mapping algorithms
• 6 different groups of hosts
• Chosen from 39 hosts
• 1 week, 1 Hz load trace from each host
• 648 different cases
• Combinations of nominal time and bounds
• 100,000 calls for each case

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Proposed Work
Extend current results to the full dynamic
mapping problem

• Extend simulation environment to include
  communication and activation trees
• Trace collection (Activation trees, network)
• A trace for everything
• Trace characterization
• Simulator extension
• Develop algorithm
• Evaluate with benchmarks
• Incorporate into real system

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Activation Tree Traces

• Collect activation trees where each node is
  annotated with compute time, and what data it
  references
• Goal is to instrument off-the-shelf MS Windows
  programs
• Other options exist

Contributions Instrumentation tools/methodology,
Activation tree trace database
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Network Traces

• Realistic communication times
• Packet traces on Ethernet with tcpdump
• Simple broadcast networks seem too limiting
• Remos
• Existing trace databases

Contributions Methodology, Trace database
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Trace Characterization

• Classify traces into families, from which we can
  draw benchmarks for evaluation
• Ideally, parameterized models to fit data
• Characterizing activation tree traces most
  challenging

Contributions Trace analysis, Models,
Classification scheme, Benchmark suite
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Simulator Extension

• Extend my existing simulator to support arbitrary
  activation trees and realistic communication
• Communication time model

Contributions Simulator infrastructure for
full dynamic mapping problem
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Algorithm Development

• Use approaches described earlier
• Extend RangeCounter(W) with a separate algorithm
  to recursively divide bounds among subtrees
• Iterative development using simulator and
  benchmarks

Contributions Algorithm(s)
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Evaluation

• Evaluate algorithm in simulation
• Draw connections between benchmark
  characteristics and algorithm performance
• Compare with other approaches
• Load monitor with simple heuristic
• Greater degrees of information sharing
• Incorporate into distributed object system as
  proof of concept

Contributions Evaluation, Working system
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Thesis Timeline