Artemis: Practical Runtime Monitoring of Applications for

Execution Anomalies

Long Fei and Samuel P. MidkiffSchool of Electrical and Computer Engineering

Purdue University, West LafayettePLDI 2006

Subproject of PROBE

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Motivation

• Bugs are expensive!• Cost in 2002: 60 billion dollars, 0.6% GDP

• Debugging Approaches• Manual debugging – inefficient, impossible• Extensive testing – inefficient, path explosion• Static analysis – conservative, false alarms

• Manual annotation – inefficient, not scalable

• Runtime debugging – high overhead

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What is Artemis?• Is not a bug detection tool• Makes existing tools more efficient in bug detection

program execution

runtime analysis

program execution

Artemis

runtime analysisExisting schemes: a few times slowdown is common, can be up to 2 orders of magnitude

With Artemis, much less data is examined by runtime analysis, reducing overhead to

<10% in long-running programs

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Outline for the Rest of the Talk

• Bird’s eye view of related work

• Artemis framework

• Experimental results

• Conclusions

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Bird’s Eye View of Compiler-Aided Debugging Techniques

compilertechniques

softwaredebugging

static

dynamic

runtimeoverhead

faster

parallel

selective

no programinformation

use programinformation

samplingrandom

adaptive

Artemis

More efficient design•Problem specific•Usually involves assumptions about OS, compiler, or hardware

Exploit parallelism•Shadow checking process (Patil SPE’97)•Thread-level speculation (Oplinger ASPLOS’02)

Perform fewer checks

Liblit PLDI’03

Chilimbi ASPLOS’04

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Artemis Design Goals• General

• Work with multiple pre-existing debugging schemes• Pure software approach that works with general

hardware, OS and compiler

• Effective• Improve overhead in general• Have low asymptotic overhead in long-running

programs

• Adaptive• Adjust coverage of monitoring to system load

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Key Idea

• Because runtime monitoring is expensive

… want to monitor only when a bug occurs

• Our goal is to approximate this• avoid re-monitoring executions whose outcome

has been previously observed

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How to Determine Where Bugs are Likely to be Seen

• Code region behavior (and bug behavior) is determined by region’s context

• Monitor the 1st time a region executes under a context• If buggy, the bug is monitored• if not buggy, only monitor this region with this context once• Over time, almost all executions of a region have a previously seen

context – yields low asymptotic monitoring overhead

• Hard part – efficiently representing, storing and comparing contexts for a region• The context could be the whole program state!

RegionContext Outcome

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Decision to Monitor

first entrance ?context seen

before ?

initialize contextupdate context

record, add current context

use monitored version

use unmonitoredversion

N Y

Y N

code segmententrance

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Target Programs• Our prototype targets sequential code

regions• Determined by how contexts are defined

• Can be used with race-free programs without loss of precision• Target the sequential regions of these

programs

• Use with programs with races is ongoing research

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Implementation Issues

• Define code regions

• Represent and compare contexts

• Interface with existing runtime debugging schemes

• Adhere to overhead constraints

• Adapt to system load

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Defining Code Regions

• Spatial granularity

• Temporal granularity: • Context check frequency

• Context check efficiency• Ideal case: a small context dominates the

behavior of a large piece of code

• Our choice:• Procedure: natural logic boundary

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Approximating Context for Efficiency• Exact Context

• Too large to store and check (might be entire program state)• Represent approximately – tradeoff between precision and

efficiency• Approximated Context

• In-scope global variables, method parameters, in-scope pointers• Values of non-pointer variables are mapped into a compact form

(value invariant – as in DIDUCE ICSE ’02)• Requires 2 integer fields; 2 bitwise operations for each check; 3 bitwise

operations for each update• Pointers tracked by declared (not actual) types•argv approximated by vector length

• Correlations between context elements are lost• If {a=4,b=3} and {a=5,b=8} are two contexts of a region, we track

{a=(4,5), b=(3,8)}

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Simulating Monitoring Schemes• We need to measure performance on a wide

range of runtime monitoring schemes• A generic monitoring scheme

• Inserts instrumentation into application at probability p• Calls a dummy monitoring function, which simulates the

overhead of some real monitoring scheme• Can adjust overhead from zero to arbitrarily large

• Disable dummy monitoring to reveal the asymptotic overhead of Artemis• Only performs the context checks associated with the

cost of monitoring, but not the monitoring• Allows measuring context checking overhead only

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Experiment – Asymptotic Overhead Measured by Simulation

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Two Findings• Performance floor

• As monitoring scheme overhead approaches zero, Artemis overhead is 5.57% of unmonitored program execution time

• When can we use Artemis to improve overhead ? • Break even baseline monitoring overhead• Monitoring overhead > 5.6%, Artemis helps• By solving x=0.0045x+0.0557, x = 5.60%• This covers most of the monitoring techniques

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An Optimization: Reuse Context Sets Across Runs

• Eliminates the initial building of sets of observed contexts• Converges faster to the asymptotic overhead

• Invariant profile:• Dump the context invariants into a file at program exit• Load dumped invariants at the next run

• Invariant profile size is 0.4 ~ 4.7% of program binary size (average 1.7%, std 0.95%)

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Using Artemis (with invariant profile)

source instrumentation build

training Artemis

production run bug report

baseline Artemisinvariant

profile

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Convergence to Asymptotic Overhead – e.g. bzip2 from SPECint

• Asymptotic overhead reduced to < 7.5% (from ~280%)

~7.3%

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Experiments with Real Monitoring Schemes• Measuring how well does (monitoring scheme

guided by Artemis) approximates the capabilities of original monitoring scheme

• Artemis with hardware-based monitoring (AccMon) – detected 3/3 bugs, 2.67 times improvement, in very short-running programs

• Artemis with value invariant detection and checking (C-DIDUCE) – Source-level instrumentation – covered 75% of violations, 4.6 times improvement, in short-running programs

• Full results and details are in the paper

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Conclusions

• General framework

• Eliminate redundant runtime monitoring with context checking

• Improve overhead, low asymptotic overhead in long-running programs

• Small precision loss

• Have enabled practical runtime monitoring of long-running programs