1. Dynamic Mapping of Activation Trees Thesis Proposal January 29, 1998 Peter A. Dinda Committee David O’Hallaron (chair) Thomas Gross Peter Steenkiste Jaspal Subhlok David Bakken (BBN)

2. 2 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

3. 3 Interactive Application Model Aperiodic User Action Message Handler Feedback Activation tree mouse_click()

4. 4 Acoustic Room Modeling Physical Simulation of Wave Eqn impulse responses Frequency response plots Room model Modify model Speakers

5. 5 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)

6. 6 Responsiveness Timely feedback to individual user actions –Bound: response time  t max Jitter bound and resource usage hint –Bound: response time  t min Example: image editor drawing tool

7. 7 A Best Effort Real-time Service Execute the activation tree rooted at procedure() so that t min  t exec  t max No guarantees Responsiveness spec: bounds [t min,t max ] Performance metric: fraction of trees that meet their bounds MAP procedure() IN [t min, t max ]

8. 8 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 (<1ms)

9. 9 Execution Model 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 [t min,t max ]

10. 10 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?

11. 11 Aspects of My Approach History-based prediction Decomposition of bounds Adaptation of mapping algorithms during tree traversal

12. 12 History-based Prediction For each host, H 0 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 H0H0 H1H1 H2H2 [t’ min,t’ max ] time, duration, bounds... time, duration, bounds... time, duration, bounds... foo() bar() H0H0 foo() is executing on H 0 and calls bar(), which can be mapped to H 0, H 1, or H 2 H 0 has a local history of execution times of bar() on each of the other hosts [t min,t max ]

13. 13 Decomposition of Bounds Choice of [t’ min,t’ max ] 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 [t min,t max ] [t’ min,t’ max ]? partially executed, known unexecuted, known unexecuted, unknown foo() bar()

14. 14 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

15. 15 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.

16. 16 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

17. 17 Current Results Load trace collection and analysis Algorithms and evaluation for simplified dynamic mapping problem

18. 18 Algorithms and Evaluation for Simplified Problem Map only leaf nodes Ignore communication for I=1 to N do MAP leaf_procedure() IN [t min,t max ] end

19. 19 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: Q=Q-1 If bounds are met, increase host’s quality level by the inverse of our confidence in it: If bounds are not met, reduce host’s quality level by half: Q=Q/2

20. 20 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

21. 21 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

22. 22

23. 23

24. 24

25. 25

26. 26 Proposed Work 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 Extend current results to the full dynamic mapping problem

27. 27 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

28. 28 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

29. 29 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

30. 30 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

31. 31 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)

32. 32 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

33. 33 Thesis Timeline