1. Predicting Enterprise Application Performance Measures through Time-series Forecasting
Daniel Elsner, 21st August 2017, Scientific advisor: Pouya Aleatrati

2. Agenda Motivation and Approach Research Artifact Research Questions
Data Architecture
Project Plan
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3. “Companies are sitting on a treasure trove
Motivation
Problem Domains in Application Performance Monitoring (APM)
Performance
Availability
Maintainability
“Companies are sitting on a treasure trove
– if only they knew how to use it.”
A. Samuel, Wall Street Journal, 2015
Evolution in Enterprise Architecture (EA)
Growing complexity and high business relevance of Enterprise Architecture (EA)
Detect root causes and reduce complexity of distributed, large-scale systems
Detect root causes, reduce complexity and lead to a higher agility in EA management
Harness potential of monitoring data
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4. Initial Research Questions
What consecutive patterns can be identified by
analyzing performance flaws in APM data? 1
How can APM data be used to forecast
availability insufficiencies in advance of occurrence? 2
What proactive actions can be derived to avoid
the identified patterns and increase performance and availability? 3
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5. Approach Data ETL Data Interpretation Research Artifact ML APM
Techniques
APM
Data
Gain insights and create value from Application Performance Monitoring (APM) data by applying machine learning techniques.
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6. 10+ potential use cases identified
Approach
Literature
Review
APM
Performance Analysis EA
Software Engineering
Machine Learning
Reviewed work from
10+ potential use cases identified
Use Case
Conceptualization 11
Use
Cases
Data
Sources 3
Problem
Domains
Discussing with
Researchers
Use Case
Evaluation
Evaluate use cases by
Interviewing Experts
 Define Use Case
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7. Time-series Forecasting
Research Artifact
Time-series Forecasting
e.g. Response Time [ms]
e.g. Crash Occurence
From historical sequential APM data create forecasts
for performance measures and incident probabilities.
Linear/Non-linear Regression
Gaussian mixture model / Gaussian Processes
(Recurrent) Neuronale Netze
Hidden Markov
Autoregressive integrated moving average
Kalman/Partikelfilter 3
Optional
Automatic ticket severity evaluation driven by ML 1
Forecasting of relevant performance measures 2
Incident prediction
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8. Research Questions 1 2 3 How accurately can a time-series forecasting
model predict APM measures? 1
How well can a time-series forecasting model predict availability lacks
(i.e. application crashes) in enterprise applications/services? 2
To what extend can we evaluate automatic
ticket severity classification by analyzing APM data? 3
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9. Data Architecture – Layers and Data Sources
Application
Client
Web Server
Web Server
Middleware
Ticketing (Incident) Data
Application Server
Application Server
Application Server
Application Server
Database
Layer
Database
Database
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10. Data Architecture – Dimensions and Measures
User
Device
Application
Activity
Location
Time
Application
App. Server
Webserver
Database
Event
Time
Method
Endpoint
Measures
Measures
Crash rate
Response Time
Hang Time
Crash rate
Response Time
Request Load
Resource Util.
Network I/O
Resource Util.
Network I/O
Method Count
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11. Design + Implementation
Project Plan
Initial Meeting
Prof. Matthes
Final
Presentation
Kickoff
Conceptualization
Data Exploration
Use Case Definition
Initial Research
Research Artifact
Pipeline (ETL)
Model
Design + Implementation
Thesis
Evaluation
Writing Thesis
August
September
October
November
December
January
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12. Cand. M. Sc.
Daniel Elsner
17135

13. Backup
<Date> Short Title
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