RESISTing Reliability Degradation through Proactive Reconfiguration

Transcription

1 RESISTing Reliability Degradation through Proactive Reconfiguration D. Cooray, S. Malek, R. Roshandel, and D. Kilgore Summarized by Haoliang Wang September 28, 2015

2 Motivation An emerging class of system - Situated Software System Predominantly pervasive, embedded and mobile Software system is subject to dynamical contextual changes Most applications like emergency response are mission- critical Reliability matters Reliability analysis at design- time is insufficient System reliability (and other QoS) depends on its runtime characteristics Adaptation at runtime is necessary Adaptation using reactive approach Adapts to changes after degradation not good enough Prediction- based proactive adaptation is preferred

3 Challenges Proactively re- configure the system before performance degradation Effectively estimate the reliability of a complex system at runtime Determine the optimal system architecture at runtime

4 RESIST Framework Resilient Situated Software System Component- level Reliability Analyzer Configuration Reliability Analyzer Configuration Selector Context- Aware Middleware Provides support for execution, monitoring and adaptation of a software system

5 RESIST Framework (Cont. ) RESIST is Goal Management layer solution in the three layer architectural model for self- managed system

6 RESIST Framework (Cont. ) System Model The system is divided into several functional components which have their own reliability Each component is allocated to a process The system reliability is determined by the architecture, the individual components, and the context Failure Model Fail- stop detectable by middleware facilities Component failure Effects are contained within the boundary of component Process failure Occurs when one of its components exits prematurely. Other components running on it will also fail

7 Component-level Analysis Discrete Time Markov Chain (DTMC) Estimate the component reliability A stochastic process with a set of states S = {S 1, S 2, S 3,, S N } Transition matrix A = {a ij }, where a ij is the probability of transitioning from S i to S j Reliability of the component is computed by solving the steady state probability of not being in any failure state How to derive the transition matrix A?

8 Component-level Analysis (Cont. ) Hidden Markov Models (HMMs) Learn from the runtime data and estimate the transition probability matrix A stochastic process with a set of states S = {S 1, S 2, S 3,, S N } Transition matrix A = {a ij }, where a ij is the probability of transitioning from S i to S j A set of observations O = {O 1, O 2, O 3,, O M } Observation matrix E = {e ik }, where e ik is the probability of observing event O k in state S i Baum- Welch algorithm is used to train and solve the HMM and obtain the converged transition matrix A

9 Component-level Analysis (Cont. ) An example for estimating component reliability A robot controller behavior model States S = {idle, estimating, planning, moving, failed} Running Baum- Welch algorithm on the observation sequence and we can obtain the transition matrix A Solve for the steady state probability vector [0.1966, , , , ] Controller component reliability is = 99.67%

10 Component-level Analysis (Cont. ) Estimate the near future by incorporating the context Define a set of contextual parameters C = {C 1, C 2,, C x } If a kj is a transition probability from state S k to state S j in matrix A which is affected by changes in a specific contextual parameters C n, then a kj = μ(a kj, ΔC n ), where μ is a context- specific function quantifying the impact of contextual change on the transition probability. The remaining transition probabilities in the row are adjusted proportionately such that: a kj + a kf + Σa km = 1.

11 Configuration-level Analysis Markov- based system- level reliabilityestimation System reliability is estimated compositionally based on the reliability of individual components Map the components and the interactions between them into a DTMC, where a state is one or more components in concurrent execution System reliability is computed as, R = ( 1)./0 E R. I M where M is a k k matrix whose elements are, M i, j = 4 R %P %6, s % reaches s 6 and i k 0, otherwise where R % is the reliability of state s % and E is the determinant of the remaining matrix excluding the last row of the first column of (I M)

12 Configuration-level Analysis (Cont. ) An example for estimating system reliability Suppose we obtain the initial component reliability for the Controller and Navigator to be C = , N = and assume others are 100% reliable Based on the observed data, we can obtain the transition probability for each state and therefore M Solving the model yields a system reliability of 93.85%

13 Configuration-level Analysis (Cont. ) Impact of architectural style E.g., Replicating components to improve system reliability

14 Configuration-level Analysis (Cont. ) Impact of deployment architecture E.g., Reallocating components to different processes to improve system reliability

15 Configuration Selection Configuration selection as an optimization problem The optimal configuration in RESIST is defined as one that satisfies the system s reliability requirement, while improving other quality attributes of concern In other words, given the decision variables, p % Ζ / represents the number of replicas for component i x %6 [0, 1] indicates if component i is placed on process j the objective is to find an architectural configuration C such that, C = argmax (W) X S Z[\]%^_ `a6bc^%dbe U S (C) s. t. i 1,, t, p % w %, w Ζ / i 1,, t, i 6j0 x %6 = 1 R C δ, δ ε R, 0 < δ 1 where U S is a utility function indicating the preference for quality attribute q R(C) is the expected reliability of a given architecture C

16 Configuration Selection (Cont. ) Configuration reliability R(C) Assume the component may either be replicated or share a process with other components Express with a binary variable q % = 1 if i^i component shares a process; 0 if otherwise. q % = 1 X x %6 p (1 x.6 ) 6j0.q% Thus, the effective reliability of component i is, r %rss = q % r %tuvwr + (1 q % )r %wry where, z i r %tuvwr = X r % x %6 p [r. x %6 + (1 x.6 )] 6j0 ^.q% r %wry = 1 1 r % 0/{ Finally, the system reliability can be computed as specified in configuration- level analysis ^

17 Configuration Selection (Cont. ) Time- complexity analysis Suppose we have P = number of processes C = number of components N = maximum number of replicas This implies that there O(P W ) ways of allocating components to processes O(N W ) ways replicating components Therefore, total possible configuration is O((NP) W ) NP Problem However the solution space may be significantly pruned by imposing architectural constrains

18 Evaluation Implementation Mobile emergency response system prototype XTEAM is used to control system s operational profile Prism- XM is used to gather the runtime data Matlab is used to generate and solve HMM model Evaluation Criteria Validity of reliability predictions Effectiveness of proactive re- configuration Performance overhead

19 Evaluation (Cont. ) Validity of Reliability Prediction Use Bump Probability as the contextual parameter which affect the transition probability from moving state to estimating.

20 Evaluation (Cont. ) Proactive Reconfiguration

21 Evaluation (Cont. ) Overhead of Component Reliability Analysis

22 Summary RESIST is framework that maintain the reliability of the situated software system through proactive reconfiguration of the software architecture Three major components Component reliability analysis Configuration reliability analysis Configuration selector Three key contributions Incorporation of multiple sources of information, particularly contextual information Automatically find the optimal architectural configuration Proactively adapt the system before the system s reliability degrades