Managing Complex Network Operation with Predictive Analytics
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1 PNNL-SA AAAI 009 Spring Symposium Series Technosocial Predictive Analytics Managing Complex Network Operation with Predictive Analytics Z. Henry Huang Pak Chung Wong Patrick Mackey Yousu Chen Jian Ma Kevin Schneider Frank L. Greitzer Pacific Northwest National Laboratory March 009
2 Presentation Outline Background on Complex Networks and Complex Network Operation Predictive Analytics for Decision Support Visual Analytics for Risk Assessment Visual Trending Analysis Clustering Analysis Conclusion and Future Work
3 Background Complex Networks General Features Complex structure Wide geographical coverage Large volume of data Complex data/it support systems Critical role of human operators Source: Time Warner Telecom Source: US Airways
4 Background Complexity of Network Operations Physical Attacks Human Errors Ice & Lightening Storms Dig-Ins Cyber Attacks Map Source: Time Warner Telecom Vehicular Collisions
5 Background Inadequacy in Network Operations Today s Network Operation Tools Mainly experience-based Raw/Tabular presentation dominates Overwhelmed operators No support for real-time situational awareness and decision making Technical gap between data and actionable information Tabular raw presentation of violations without processing
6 Need for Predictive Analytics for Decision Support Decision Support with Predictive Analytics for Command and Control of Complex Networks Is the network becoming compromised? Recognize developing problems What would the problem cause to the network? Predict consequences of failures How effective would our response be to the problem? Evaluate potential remedial actions Enabling predictive complex network operations
7 . Visual Analytics for Risk Assessment () Risk Index Based on Network Stress Node index R% = node stress level Link index R% = link stress level Multi-Layer Risk Index Superimpose risk indices for multiple possible configurations R% = max(r% i ) Superimpose
8 . Visual Analytics for Risk Assessment () Visual Representation of Risk Indices Based on HaveGreen in C#, using Managed DirectX Gaussian color mapping with green/gray/red scale 00 layers of possible futures in the western power grid
9 . Visual Analytics for Risk Assessment () Statistical Analysis for Deriving Network Risk Indices System Risk Index Regional Risk Index Arithmetic Average Risk Index η = γ i R% n Geometric Average Risk Index Risk Index: [ ( )] ni R% = i ni i i i n R % = aη + aγ i i i
10 . Visual Analytics for Risk Assessment (4) Time Series Analysis Scenario: western power grid with increasing stress and lost element 6 Stress Level Lost a large nuclear power plant 7:00 7:0 8:00 8:0 9:00 Time (hour)
11 . Visual Trending Analysis () Statistical Analysis to Identify Security Trend Increase in risk index (i.e. size and color intensity) indicates imminent problems System trend Regional trend R% R% R% Trending Past Now Future Time
12 . Visual Trending Analysis () Automatic Recognition of Merging and Separation of Security Regions Complex trend evolving patterns 4 Merging/ Splitting 4 Merging Stealing Splitting Stealing/ Splitting 4 4 5
13 . Visual Trending Analysis () Combination of Structural Analysis and Statistical Analysis via Relation Matrix Statistical analysis for risk indices of individual regions Structural analysis to identify evolving patterns R R R 0 40 Evolve
14 . Visual Trending Analysis (4) Case Studies western US power grid System trending System trend Lost a large nuclear power plant Trending
15 . Visual Trending Analysis (5) Case Studies western US power grid Regional trending Regional trend Lost a large nuclear power plant Trending
16 . Visual Trending Analysis (6) Case Studies western US power grid Regional evolving patterns
17 . Clustering Analysis Identify relationship between configurations and affected assets Enable operators to focus on important information Affected Assets Pattern Configurations
18 Conclusion and Future Work () Predictive analytics can play a key role in complex network operations Color contoured map representation of large amount of operation data for improved situational awareness Predictive capability by analyzing the trend of the visual representation Converting large amounts of operation data into actionable information Actual models and data of the western North American power grid demonstrate the validity of the predictive analytics.
19 Conclusion and Future Work () Future Work Probability Analysis for Multiple Predictions Enable the management of multiple possible futures Interactive Evaluation of Remedial Actions Provide guidance for preventing and mitigating failures Prediction 0% 0% 50% past now future Time Operator Actions: reconfiguration, re-dispatch, load shedding,
20 Questions?
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