1. Handling Uncertainty in Autonomic Systems Shang-Wen Cheng and David Garlan {zensoul,garlan}@cs.cmu.edu CMU Architecture-Based Language & Environment Group Intl Workshop on Living with Uncertainties November 5, 2007 Disney

2. Handling Uncertainty Shang-Wen Cheng © IWLU 2007 2 Big Picture Autonomic Monitor-Analyze-Plan-Execute loop introduces inherent uncertainties Detecting the existence of a problem Deciding on adaptation actions Carrying out the actions Architecture-based instantiation provides techniques Smoothing, model distribution, prediction Capture cost and effect, estimate likelihood Explicit settling time

3. Handling Uncertainty Shang-Wen Cheng © IWLU 2007 3 Autonomic MAPE Loop Reference standard Monitor-Analyze-Plan-Execute Loose coupling uncertainties Analyze – problem detection Plan – adaptation choice Execute – outcome Knowledge? Architecture! Global system perspective Important behaviors and properties Explicit system constraints Proven tradeoff, analysis techniques MonitorExecute PlanAnalyze Knowledge Managed element Architecture-based self-adaptation Architectural Model

4. Handling Uncertainty Shang-Wen Cheng © IWLU 2007 4 Rainbow: Instantiating MAPE Model Manager maintains architectural knowledge of system Architecture Analyzer detects constraint violation in model trigger Adaptation Engine selects a strategy to maximize utility adapt MonitorExecute PlanAnalyze Knowledge Managed element

5. Handling Uncertainty Shang-Wen Cheng © IWLU 2007 5 Does a Problem Exist? Stochastic uncertainty of system properties, e.g., CPU Transient rise above threshold not necessarily worth adaptation Steady trend toward but not beyond threshold still problematic Three solution techniques Moving-average filter to smooth stochastic Explicit probability distribution as envelop to determine problem Resource prediction to anticipate problem y HI t t t y LO ?

6. Handling Uncertainty Shang-Wen Cheng © IWLU 2007 6 Which Adaptation Action? Strategy selection uncertainty Cost and effect of actions Outcome of action steps Three-step solution technique Assign cost and effect attributes to action steps Estimate the likelihood of known effects of an action step Compute expected aggregate attribute vector of a strategy and then score with utility function Given tree X w/ children A, B, & p A, p B … E A (X) = Agg_AV(X) = p A Agg_AV(A) + p B Agg_AV(B) + … Score = 0.58 [latency: low; quality: textual; cost: 1; disruption: 3] u latency (), u quality (), u cost (), u disruption () (w latency, w quality, w cost, w disruption ) = (0.5, 0.3, 0.1, 0.1) [= 1] P0 T1 T2 T3 75% 100% 25% [-1000, -2, 1, 3] [-1000, +2, 3, 2] [+500, +2, 5, 1] [-2000, +0, 4, 5] [-500, +0, 1, 1.25] [375, 1.5, 3.75,.75] [-125, 1.5, 4.75, 2] [900, 5, 4.75, 2] [0.4, 1, 0.2, 0.6]

7. Handling Uncertainty Shang-Wen Cheng © IWLU 2007 7 Did Action Succeed? Wait-time uncertainty for action execution Too short: misjudge outcome of strategy Too long: unacceptable latency for adaptation Solution technique Adopt a concept of settling time from control theory wait time Explicit description of time window to wait for action execution y(k) ksks strategy SimpleReduceResponseTime() { boolean c0 = responseTime() > RespTimeLimit; t0: (c0) -> switchToTextualContent() { t1: (#[prob{t1}] success @[1000/*ms*/]) -> done ; t2: (#[prob{t2}] c0 @[2000/*ms*/]) -> enlistServer(1) { t2a: (success @[1000/*ms*/]) -> done ; }}}

8. Handling Uncertainty Shang-Wen Cheng © IWLU 2007 8 The End Summary Inherent uncertainties in autonomic MAPE loop Architecture-based Rainbow solves 3 uncertainty sources Stochastic props smoothing, model distribution, prediction Strategy selection capture cost & effect, estimate likelihood Wait time of execution explicit settling time Questions? Shang-Wen Cheng and David Garlan {zensoul,garlan}@cs.cmu.edu Carnegie Mellon University