Responsive Interactive Applications by Dynamic Mapping of Activation Trees February 20, 1998 Peter A. Dinda School of Computer Science Carnegie Mellon University

2 Outline Responsive interactive applications Best effort real-time service –Dynamic mapping problem History-based prediction approach Statistical properties of host load Algorithms and evaluation for simplified problem Research plan –Application and network traces

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

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

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), … Games –DIS Collaboration? –Collaborative Planning (Zinky-DUTC-95)

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 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 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 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 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 Aspects of My Approach History-based prediction Decomposition of bounds Adaptation of mapping algorithms during tree traversal Methods: Trace-driven simulation, Iterative refinement

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 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 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 aggressively 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 Statistical Properties of Load Load traces First order properties of the traces Self-Similarity Epochal Behavior Goal: modeling of load

16 Load Traces Digital Unix one minute load average 1 Hz sample rate, one week traces Wide variety of Hosts (38 machines) –Production Cluster 13 machines in the PSC’s Alpha Supercluster –Research Cluster 8 machines in CMCL cluster –Compute Servers 2 shared machines in CMCL lab –Desktops 15 machines desktop workstations owned by CMCL members

17 Self-Similarity Intuition: “looks the same” on all time scales Autocorrelations similar across time scales Power spectrum envelope decays differently from 1/f Characterization: Hurst parameter –H Ranges from 0 to 1 H<0.5 : negative near neighbor correlation - “choppy” H=0.5 : no correlations H>0.5 : positive near neighbor correlation - “smooth” Methodology: Estimate Hurst Parameter –Four different, validated, estimators

18 Hurst Parameter Estimates

19 What does Self-Similarity Tell Us? Load is complex, but not random –History matters Smoothing may be misguided Load should frighten modelers –Conventional stochastic process models are wrong –Long memory stochastic process models are desirable but may be impractical

20 Epochal Behavior Local frequency content of load signal is stable for long periods of time with abrupt transitions –“Spectrogram shows wide vertical bands” Not the same as seasonality

21 Epochal Behavior Example

22 Epoch Length Statistics

23 What Does Epochal Behavior Tell Us? Suggests decomposition of load trace modeling problem –Segmentation problem to find epochs –Modeling problem within each epoch

24 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

25 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

26 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

27 Scope of Evaluation 9 mapping algorithms 6 different groups of hosts –Chosen from 38 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

28

29

30

31

32

33

34

35

36

37 Research Plan Extend simulation environment to include communication and activation trees Trace collection (Activation trees, network) »A trace for everything Trace characterization (models, benchmarks) Simulator extension Develop algorithm Evaluate with benchmarks Incorporate into real system Extend current results to the full dynamic mapping problem

38 Activation Tree Traces Representation of real program runs Each node annotated with compute time, and what data it references Delay requirements

39 Network Traces Realistic communication times Packet traces on Ethernet with tcpdump –Simple broadcast networks seem too limiting Remos Existing trace databases