Automatic Statistical Evaluation of Resources for Condor

Daniel Nurmi, John Brevik, Rich Wolski

University of California, Santa Barbara

Motivation

• Distributed System/Grid applications execute on wide variety of architectures– Clusters– Large SMP systems– Interactive workstation networks

• Condor provides vast, easily accessible resource pool, but is best suited to Condor applications

Condor As Resource Pool

• Provides many required features– Resource manager

– Account manager

– Scheduler

• Resource availability very dynamic– Controlled by large number of variables including

overall load, user priority, occupancy time, owner revocation, etc.

– Resources free up and drop out frequently

• Long running apps must be checkpointed

Checkpointing Schemes

• Condor checkpointing– Standard Universe uses system call liftoff– Core file is used to capture process state for

restart

• Application-level checkpointing:– Application developer must generate

checkpoints from within the application– Disk storage may be limited (none available

locally)

Condor Checkpointing

• Checkpointing is invisible to application developer, but…– No threads– No forking– Single architecture support– Must use compiler supported by Condor (e.g.

no GMP)

Application-Level Checkpointing

• No support from Condor for checkpointing in Vanilla universe– Left to the application

• No restrictions on system calls or compilation– If it compiles it will run

• No local disk storage– Checkpoints must traverse the network to a machine

with stable storage

• Checkpoint schedule major performance concern

Checkpoint Scheduling• Given a long running application and volatile

resource, determine the amount of time perform useful computation between checkpoints such that the overhead of checkpointing is minimized

• Well studied– K. M. Chandy, C. V. Ramamoorthy. Rollback and recovery strategies for computer

systems.– M. Elnozahy, L. Alvisi, Y. M. Wang, D. B. Johnson. A survey of rollback-recovery

protocols in message passing systems.– A. Duda. The effects of checkpointing on program execution time.– N. H. Vaidya. Impact of checkpoint latency on overhead ratio of a checkpointing scheme

• We use Markov Model based approach proposed by N. H. Vaidya.

Checkpoint Interval Selection

• Model requires statistical distribution describing resource availability – Vaidya, and later Plank assume exponential distributions

What is the Availability Distribution?

• Weibull– T. Heath, P. M. Martin, T. D. Nguyen. The shape of

failure

– J. Xu, Z. Kalbarczyk, R. K. Iyer. Networked Windows NT system field failure data analysis

• Hyperexponential– M. Mutka, M. Livny. Profiling workstations’ available

capacity for remote execution.

– I. Lee, D. Tang, R. K. Iyer, M. C. Hsueh. Measurement-based evaluation of operating system fault tolerance.

Generating Statistical Models

• Network Weather Service monitoring of Condor pool over 2 year period

– 708 machines observed

• Automatic model fitting software

– Takes as input distribution type and historical Condor uptime values

– Outputs best fit parameters for given distribution

• Design experiment to test overall work efficiency of checkpointing scheme using four different distributions

Checkpoint Experiment• Test application submitted to Condor and when it

runs…– Sends resource information to central server– Model fitting software estimates model parameters

using MLE or EMpht methods– Checkpoint scheduler solves the Markov model using

tested distribution– Application uses schedule, checkpoints its memory,

and records performance

• Test different distributions• Checkpointing to disks at UCSB

Empirical Results: Execution Time

0.54

0.56

0.58

0.6

0.62

0.64

0.66

0.68

Work Efficiency

WeibullExponential2ph Hyper3ph Hyper

Empirical Results: Network Utilization

0

20000

40000

60000

80000

100000

120000

140000

160000

180000

200000

Bandwidth Consumed (Mbytes)

WeibullExponential2ph Hyper3ph Hyper

Moral

• We can determine optimal checkpoint schedules for Condor jobs automatically– Execution performance impact is about the same until

checkpoint costs get big– Network load improvements are substantial

(particularly useful in wide area)

• Software is real, but non-NWS parts are in prototype– We want to bring them into the NWS release cycle

• Paper in submission to HPDC

What’s Next

• Better Models– Brevik Method: we can predict the percentiles of

availability with provable confidence bounds using less data

– Can’t use it (yet) for Markov model

• Better Utility– Provide information to Condor itself– Automatic fault and anomaly detection

• Better Information for users– Publish availability predictions the in matchmaker

Thanks

• Rich Wolski• John Brevik• Miron Livny• NSF Next Generation Software program• VGrADS Project (NSF ITR, Ken Kennedy, PI)• NSF Middleware Initiative (NWS)• Questions?

Simulation Results: Execution Time

0.3

0.35

0.4

0.45

0.5

0.55

0.6

0.65

0.7

0.75

0.8

0 200 400 600 800 1000 1200 1400 1600

Checkpoint Time

Work Efficiency

ExponentialWeibull2ph Hyper3ph Hyper

Simulation Results: Network Utilization

0

20000

40000

60000

80000

100000

120000

0 200 400 600 800 1000 1200 1400 1600

Checkpoint Time

Bandwidth Consumed (MBytes)

ExponentialWeibull2ph Hyper3ph Hyper