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1 ΘPAD: Online Performance Anomaly Detection with Tillmann Bielefeld 1 1 empuxa GmbH, Kiel KoSSE-Symposium Application Performance Management (Kieker Days 2012) November 29, Wissenschaftszentrum Kiel Tillmann Bielefeld (empuxa GmbH) ΘPAD w/ Kieker Nov. 29, Kiel 1 / 27

2 Agenda 1 Monitoring at XING 2 OPAD s Architecture 3 Evaluation 4 Results 5 Conclusion Tillmann Bielefeld (empuxa GmbH) ΘPAD w/ Kieker Nov. 29, Kiel 2 / 27

3 Thesis Goals Author Tillmann Carlos Bielefeld Advisory Prof. Dr. Wilhelm Hasselbring Dipl.-Inform. André van Hoorn Dr. Stefan Kaes (XING AG) Case Study at XING AG 1 Design of online performance anomaly detection concept (ΘPAD) 2 ΘPAD implementation as plugin 3 ΘPAD integration with case study system 4 Θnline Performance Anomaly Detection Diploma Thesis Tillmann Carlos Bielefeld Θnline Performance Anomaly Detection for Large-Scale Software Systems Diploma Thesis Christian-Albrechts-Universität zu Kiel PAD Tillmann C. Bielefeld: Online performance anomaly detection for large-scale software systems March Diploma Thesis, Kiel Univ. Tillmann Bielefeld (empuxa GmbH) ΘPAD w/ Kieker Nov. 29, Kiel 3 / 27

4 Existing Logjam-based XING Monitoring at XING Logjam-based monitoring already in Tillmann Bielefeld (empuxa GmbH) ΘPAD w/ Kieker Nov. 29, Kiel 4 / 27

5 Integration of ΘPAD in XING s Architecture Monitoring at XING Servers App Support Importer Log Database Logjam XWS (API) DB Background s logging/monitoring architecture Tillmann Bielefeld (empuxa GmbH) ΘPAD w/ Kieker Nov. 29, Kiel 5 / 27

6 Integration of ΘPAD in XING s Architecture Monitoring at XING Servers App Support Importer Log Database Logjam XWS (API) DB Background ΘPAD Tillmann Bielefeld (empuxa GmbH) ΘPAD w/ Kieker Nov. 29, Kiel 5 / 27

7 Example JSON Logging Message Monitoring at XING {... } "count": , "memcache_time": , "api_time": , "db_time": , "view_time": , "total_time": , "api_calls": Input data received via AMQP and processed by ΘPAD Tillmann Bielefeld (empuxa GmbH) ΘPAD w/ Kieker Nov. 29, Kiel 6 / 27

8 ΘPAD gets instantiated at startup of the Kieker server. Both main architectural components High-Level of Kieker, ΘPAD Monitoring Architecture and Analysis are used to route the measurements to the plugin. The data flow from input to output is illustrated in Figure 4.4. OPAD s Architecture Measurement Queue C «Adapter» AMQPBridge «artifact» Monitoring «artifact» Analysis «component» ΘPAD Plugin P Java Pipe Time Series Storage Alerting Queue X Figure 4.4: The coarse-grained architecture follows the linear data 1 AMQP messages transformed into Kieker monitoring records flow of the approach (see Chapter 3). The AMQPBridge adapter 2 ΘPAD: translates pipes-and-filters the monitored processing system s measurements of records to Kieker records 3 ΘPAD and therefore results passed makesto ΘPAD alerting reusable queueinand other time-series environments storage (NFR4). This graphic uses the AMQP notation of Figure Tillmann Bielefeld (empuxa GmbH) ΘPAD w/ Kieker Nov. 29, Kiel 7 / 27

9 ΘPAD Processing Steps OPAD s Architecture <<Reader>> Time Series Extraction Time Series Forecasting Anomaly Score Calculation Anomaly Detection Alerting (e.g., AMQP) Tillmann Bielefeld (empuxa GmbH) ΘPAD w/ Kieker Nov. 29, Kiel 8 / 27

10 Step 1: Time Series Extraction ΘPAD Processing Steps (cont d) OPAD s Architecture <<Reader>> Time Series Extraction Time Series Forecasting Anomaly Score Calculation Anomaly Detection Alerting (e.g., AMQP) Continuous Time Discrete Time Series X 4 2 } } }} f f f } f Event on ES Discretization Function Time Series X Current Time select sum(value) as aggregation from MeasureEvent.win:time_batch( 1000 msec ) Tillmann Bielefeld (empuxa GmbH) ΘPAD w/ Kieker Nov. 29, Kiel 9 / 27

11 Step 2: Time Series Forecasting ΘPAD Processing Steps (cont d) OPAD s Architecture <<Reader>> Time Series Extraction Time Series Forecasting Anomaly Score Calculation Anomaly Detection Alerting (e.g., AMQP) W Tillmann Bielefeld (empuxa GmbH) ΘPAD w/ Kieker Nov. 29, Kiel 10 / 27

12 Step 3: Anomaly Score Calculation ΘPAD Processing Steps (cont d) OPAD s Architecture <<Reader>> Time Series Extraction Time Series Forecasting Anomaly Score Calculation Anomaly Detection Alerting (e.g., AMQP) Tillmann Bielefeld (empuxa GmbH) ΘPAD w/ Kieker Nov. 29, Kiel 11 / 27

13 Step 4: Anomaly Detection ΘPAD Processing Steps (cont d) OPAD s Architecture <<Reader>> Time Series Extraction Time Series Forecasting Anomaly Score Calculation Anomaly Detection Alerting (e.g., AMQP) Abnormal Score Normal Score Anomaly Threshold Anomaly Detected Tillmann Bielefeld (empuxa GmbH) ΘPAD w/ Kieker Nov. 29, Kiel 12 / 27

14 ΘPAD Web Interface OPAD s Architecture Tillmann Bielefeld (empuxa GmbH) ΘPAD w/ Kieker Nov. 29, Kiel 13 / 27

15 Evaluation Methodology: GQM Evaluation Goal Assess Practicality of Approach Questions How precise is the detection? How accurate is the detection? Metric Number of true positives Metric Number of false negatives Goal/Question/Metric (GQM) plan (excerpt) Tillmann Bielefeld (empuxa GmbH) ΘPAD w/ Kieker Nov. 29, Kiel 14 / 27

16 Manual Identification of Anomalies Evaluation Methodology (cont d) Evaluation 0:00 12:00 23:00 API time Memcache time Other time DB Time View time Manual detection using the visualization tool 8 anomalies were detected Tillmann Bielefeld (empuxa GmbH) ΘPAD w/ Kieker Nov. 29, Kiel 15 / 27

17 ΘPAD Results Evaluation Results aptt.fses.d1min.l15min aptt.fets.d1min.l1h aptt.fmean.d5min.l1h Tillmann Bielefeld (empuxa GmbH) ΘPAD w/ Kieker Nov. 29, Kiel 16 / 27

18 ROC Curves (Introduction) Evaluation (cont d) Results 1 Random Guess True Positive Rate (TPR) 0.5 Better Worse 3 2 Evaluation Run False Positive Rate (FPR) TPR = TP TP + FN = TP F FPR = FP FP + TN = FP NF (1) Tillmann Bielefeld (empuxa GmbH) ΘPAD w/ Kieker Nov. 29, Kiel 17 / 27

19 ROC Curves (ΘPAD Results) Evaluation (cont d) Results 1 1 mean.d1min.l1h ses.d1min.l15min 0,8 0,8 0,6 0,6 2 0,4 1 0,4 0, ,5 1 1 mean.d20min.l2h 0,8 0,6 0,4 0, ,5 1 0, , ,8 0,6 0,4 0,2 aptt.fws.d1h.l24 h 0 0 0,5 1 Tillmann Bielefeld (empuxa GmbH) ΘPAD w/ Kieker Nov. 29, Kiel 18 / 27

20 Accuracy and Precision Evaluation (cont d) Results ACC 1,00 PREC 0,80 0,60 0,40 0,20 0,00 0,00 Accuracy 0,98% 0,07 0,13 0,20 0,27 Detection Threshold ACC = PREC = 1 2 0,14% TP + TN N Precision 0,33 0,40 0,47 0,53 0,60 0,67 0,73 0,80 0,87 0,93 1,00 TP POS = TP TP + FP. (2) = TP + TN TP +FP +FN +TN. (3) Tillmann Bielefeld (empuxa GmbH) ΘPAD w/ Kieker Nov. 29, Kiel 19 / 27

21 Summary and Outlook for Large-Scale Software Systems Diploma Thesis Christian-Albrechts-Universität zu Kiel Author Tillmann Carlos Bielefeld Advisory Prof. Dr. Wilhelm Hasselbring Dipl.-Inform. André van Hoorn Dr. Stefan Kaes (XING AG) Case Study at XING AG Conclusion Θnline Performance Anomaly Detection Diploma Thesis Tillmann Carlos Bielefeld Θnline Performance Anomaly Detection PAD Tillmann C. Bielefeld: Online performance anomaly detection for large-scale software systems March Diploma Thesis, Kiel Univ. Outlook ΘPAD to be released as part of Kieker Follow-up theses on ΘPAD Contact Us Tillmann Bielefeld (empuxa GmbH) ΘPAD w/ Kieker Nov. 29, Kiel 20 / 27

22 Demo Conclusion Win a free empuxa Hoodie! Tillmann Bielefeld (empuxa GmbH) ΘPAD w/ Kieker Nov. 29, Kiel 21 / 27

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