Toward Autonomic Grids: Analyzing the Job Flow with Affinity Streaming
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1 Toward Autonomic Grids: Analyzing the Job Flow with Affinity Streaming Xiangliang Zhang, Cyril Furtlehner Julien Perez, Cécile Germain-Renaud, Michèle Sebag TAO team INRIA CNRS Université de Paris-Sud, F-9145 Orsay Cedex, France 29 June 29
2 Contents Motivation: Autonomic Computing G-StrAP: Data Streaming for Jobs Results: Multi-scale Monitoring Conclusion and Perspective 1 Motivation: Autonomic Computing 2 G-StrAP: Data Streaming for Jobs 3 Results: Multi-scale Monitoring 4 Conclusion and Perspective X. Zhang, C. Furtlehner, J. Perez, C. Germain, M. Sebag Toward Autonomic Grids: Analyzing the Job Flow by StrAP
3 Motivations of Autonomic Computing
4 Goals of Autonomic Computing AUTONOMIC VISION & MANIFESTO Self-managing system with the ability of Self-healing: detect, diagnose and repair problems Self-configuring: automatically incorporate and configure components Self-optimizing: ensure the optimal functioning wrt defined requirements Self-protecting: anticipate and defend against security breaches Data Mining for Autonomic Computing
5 Autonomic Grid Computing System
6 Contents Motivation: Autonomic Computing G-StrAP: Data Streaming for Jobs Results: Multi-scale Monitoring Conclusion and Perspective 1 Motivation: Autonomic Computing 2 G-StrAP: Data Streaming for Jobs 3 Results: Multi-scale Monitoring 4 Conclusion and Perspective X. Zhang, C. Furtlehner, J. Perez, C. Germain, M. Sebag Toward Autonomic Grids: Analyzing the Job Flow by StrAP
7 G-StrAP: relies on Affinity Propagation (AP) Affinity Propagation (AP) statistic physics algorithm for clustering (based on messaging passing) a cluster = an exemplar (akin k-centers) the model = set of {exemplar, frequency} [Frey27] Why AP? Traceability: real jobs as exemplars because of categorical variables, e.g., userid, queue name etc No prior knowledge of K, number of clusters quasi optimality wrt. information loss > stability [Meila26]
8 From AP to Large-scale Data Streaming 1 SCALABILITY : from O(N 2 log N) to O(N h+2 h+1 ) Hierarchical Affinity Propagation negligible infromation loss (proof in the paper)
9 From AP to Large-scale Data Streaming 2 Non stationary distribution various Virtual Organization number and expertise of users Streaming AP (StrAP)
10 Non stationary distribution, continue Page-Hinkley statistic (Cumulative-Sum-like test) [Page54,Hinkley7] p t p t m t M t p t changing distribution p t = 1 t P t l=1 p l m t = P t l=1 (p l p l + δ) M t = max{m l } 1 5 PH t = M t m t if PH t > λ, changed detected time t How to set λ???
11 Self-adaptive change detection test Self-adapt λ An optimization problem BIC: F λ = 1 C ( 1 C i=1 n i e j C i d(e j,ei )) + ϕ ρ 2 log N + ηo t loss + size of model + percentage of outlier OPTIMIZATION: ǫ-greedy search from a finite set of λ values λ = argmin{e(f λ }), λ 1 λ 2 λ 3 λ 4... E(F λ1 ) E(F λ2 ) E(F λ3 ) E(F λ4 )... Gaussian Process Regression based on {λ i,f λi } continuous value of λ is generated
12 Contents Motivation: Autonomic Computing G-StrAP: Data Streaming for Jobs Results: Multi-scale Monitoring Conclusion and Perspective 1 Motivation: Autonomic Computing 2 G-StrAP: Data Streaming for Jobs 3 Results: Multi-scale Monitoring 4 Conclusion and Perspective X. Zhang, C. Furtlehner, J. Perez, C. Germain, M. Sebag Toward Autonomic Grids: Analyzing the Job Flow by StrAP
13 G-StrAP : Multi-scale Realtime Monitor Dashboard for Monitoring
14 G-StrAP Dashboard for Grid Monitoring Online Monitoring Percentage of jobs assigned (%) Reservoir exemplar shown as a job vector Clusters LogMonitor is getting clogged Reservoir
15 G-StrAP Dashboard for Grid Monitoring Percentage of jobs assigned (%) Online Monitoring Reservoir exemplar shown as a job vector Clusters Off-line Analysis LogMonitor is getting clogged Reservoir no prior knowledge about failure patterns summarizing Terabyte data
16 Contents Motivation: Autonomic Computing G-StrAP: Data Streaming for Jobs Results: Multi-scale Monitoring Conclusion and Perspective 1 Motivation: Autonomic Computing 2 G-StrAP: Data Streaming for Jobs 3 Results: Multi-scale Monitoring 4 Conclusion and Perspective X. Zhang, C. Furtlehner, J. Perez, C. Germain, M. Sebag Toward Autonomic Grids: Analyzing the Job Flow by StrAP
17 Discussion and Conclusion G-StrAP: linear computational complexity O(N 1+ǫ ) with bounded information loss dealing with non stationary distribution data with self-adaptive change detection test G-StrAP for Grid Monitoring Traceability of failures providing multi-scale models to describing the status of Grid online description of different type of job patterns offline globally analysis good quality clustering wrt. supervised learning
18 Perspectives Theoretical Perspectives: limitation 1: AP single parameter s > self-adapt s limitation 2: better coping with flowing patterns > Hierarchical G-StrAP, absorb new patterns from reservoir more efficiently Applicative Perspectives: profiling users > a user = a histogram of job exemplars customized interface characterize evolution of users or virtual organizations build crisis scenari to test Middleware
19 Xiangliang ZHANG xlzhang
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