Predictive Cloud Computing with Big Data Professional Golf & Tennis Forecasting

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1 Big Data & Analytics Predictive Cloud Computing with Big Data Professional Golf & Tennis Forecasting Sandy Carter, Aaron K. Baughman, Brandon Frankel, Brian O Connell, Herbie Pearthree, Clay Upton 1

2 Introduction Sandy Carter IBM General Manager: Cloud Ecosystem & Developers 2

Journey To Cloud Computing Centralization

3 Journey To Cloud Computing Centralization Virtualization Cloud Computing Public Private Hybrid Gartner defines cloud computing as a style of computing in which scalable and elastic IT-enabled capabilities are delivered as a service using Internet technologies. 3

4 8 Major Tournaments 107 Competition Days 15 Mobile Applications 8 Web Sites 8 Mobile Sites 2B Page Views 80M Unique Users 10M Live Scoring Updates Tournament Season 2015 Jan Australian Open: 01/13 01/31 Apr Masters: 04/06 04/12 May Jun Jul Roland Garros: 05/16 06/07 Tony Awards: 06/07 USGA M: 06/15 06/21 Wimbledon: 06/22 07/12 Aug USTA: 08/25 09/13 Sep Oct China Open: 09/26 10/04 4

5 Problem & Challenge Herbie Pearthree IBM Senior Technical Staff Member: Continuous Availability Services Brandon Frankel Formally an IBM Extreme Blue Member, now at Optimizely 5

6 Operational Analytics Provision Cloud Reactive Tuning When, Where, How Much? 6

7 We need a 20 minute advantage Brute force will not work Quick reflexes will not work Predicting the pucks arrival will 7 Art contribution by Herbie Pearthree "What made Wayne Gretzky the greatest hockey player of all time his ability to predict where the puck was going to be an instant before it arrived. In other words, it was Gretzky s brain that made him exceptional. The Two-Second Advantage: How we succeed to Anticipating the Future Just Enough by Vivek Ranadive

8 Why 20 Minutes? Initiate Build Request Create Virtual Machine Install Operating System (OS) Create Filesystem Configure OS Deploy Tools and Agents Register Tools and Agents Install and license software Synchronize Files Configure and Verify, Test Open Firewalls for flows Audit & Security Healthchecks Check in Assets Enable Monitoring Add to Server Load Balancing 8

9 Why 20 Minutes? Initiate Build Initiate Request Build Initiate Build Request Request Create Virtual Create Machine Virtual Create Virtual Machine Machine Install Operating Install System Operating Install (OS) Operating System (OS) System (OS) Create Filesystem Create Filesystem Create Filesystem Configure OS Configure OS Configure OS Deploy Tools and Deploy Agents Tools and Deploy Tools and Agents Agents Register Tools and Register Agents Tools Register Tools and Agents and Agents Install and license Install software and license Install and license software software Synchronize Files Synchronize Files Synchronize Files Configure and Verify, Configure Test and Configure and Verify, Test Verify, Test Open Firewalls Open for flows Firewalls Open Firewalls for flows for flows Audit & Security Healthchecks Audit & Security Audit & Security Healthchecks Healthchecks Check in Assets Check in Assets Check in Assets Enable Monitoring Enable Enable Monitoring Add to Monitoring Server Load Add Balancing to Server Add to Server Load Balancing Load Balancing 9

10 5 Hours 53 Minutes Concludes at 1:37 a.m. The more advanced your organization's data analytics, the more equipped you are to make smarter decisions faster 10

11 The business need and advantages Reactive Proactive Predict the unpredictable Simulate without historical data Analyze through social media Adjust in real time 11

12 Predict the unpredictable Analyze through social media? Simulate without historical data Adjust in real time 12

13 A proactive model leads to benefits 100% availability Reduced costs Less human and computing resources needed Server Capacity Server Load Reactive Model Proactive Model Time 13

14 Approach & Methods Aaron Baughman Data Analytics Software Engineer: IBM Master Inventor Clay Upton Formally an IBM Extreme Blue Member, now President and CEO of Radial Inc. 14

15 Streaming Scores, Stats, Schedule Streaming Social Information Streaming Web Content graphite-project / carbon Data Visualization β µ Advanced Analytics Streaming Access Logs Historical Data Generated Data 15

Degree of Complexity Prescriptive Predictive Descriptive Analytic Investigation Implementation Stochastic Optimization Optimization How can we achieve the best outcome including the effects of

16 Degree of Complexity Prescriptive Predictive Descriptive Analytic Investigation Implementation Stochastic Optimization Optimization How can we achieve the best outcome including the effects of variability? How can we achieve the best outcome? Powell Optimization Genetic Algorithm Predictive Modeling What type of event occurred? Sporting Events Forecasting What if these trends continue? Historical Traffic Simulation What could happen during play? Game Play Estimation Alerts What actions are needed? Cloud Resources Query/Drill Down Ad hoc Reporting What exactly is the problem in the cloud? How many, how often, where did the sporting data occur? Real-time Dashboards Standard Reporting What happened during the tournament? Predicted Resources Competitive Advantage 16

17 Pre-Processors Post-Processors Streaming Analytics 1 2 Time-Series Ensemble Partial Average Forecaster Adjusted Average Forecaster Cubic Forecaster Quadratic Forecaster Vector Forecaster 3 Distributed Processes Feature Extractors Predictive Modeling Model Training Model Apply 8 Residual Forecaster 9 Merged Cohort Deep Analytics on Sharded Data Distributed Chained Simulation Chained Simulation 1 Chained Simulation 2 17 * *

18 Compute a Merged Cohort Y expected server demand m minutes past start of day h half life t time (seconds) since start of day s slope of parabola t max W max X t B x estimated time for parabola peak maximum weight feature vector for modeling model coefficients Forecasting Ensemble Algorithm Online Training Set fw f ( m) F E ( Y m, t) = fw f ( m) f f ( t) f Offline Training Set Predictive Modeling Algorithm 2 fw ( t) = s( t t Merged max ) + wmax Cohort ( Y m, t, x ) max( fw( t) E( Y x ), E( Y m, t) ) E = 2 m h t = E Y x = β x + β x β ( t ) 1 t1 2 t 2 t x x t x Evaluate by Minimizing the Root Mean Squared Error (RMSE) G n n 18 ground truth for server demand at minute n the minute position min N n ( E( Y m, t, x ) G ) N t n n 2

19 3 Types of Simulation to generate events that are inputs into Unstructured Information Management Architecture (UIMA) 1 st Tier: Simulate 45 minutes; 2 nd Tier: Simulate 30 minutes Start Simulation 1-Tier 2-Tier 3-Tier N-Tier Data Independent Only need Tee Times Hybrid Need Tee Times and holes Data Dependent Need Tee Times, holes, scores, and tournament information Not very accurate Compounding Errors Cannot handle weather delay Cannot handle split or shotgun tees 19 Accuracy improves over time Can handle different golf formats Can handle weather delays Cannot handle player withdrawals Only simulate one day at a time Computationally expensive to run Data needs to be close to perfect Can simulate an entire tournament Can handle any golf format Can handle player status

Feature Extraction as inputs into event based model 11 Golf Features, 6 Tennis Features, 3 Real time Log Features, 9 Social Features 23 Features for a Golf Event 18 Features for a Tennis Event Inputs

20 Feature Extraction as inputs into event based model 11 Golf Features, 6 Tennis Features, 3 Real time Log Features, 9 Social Features 23 Features for a Golf Event 18 Features for a Tennis Event Inputs Holes Playing Featured Groups Featured Holes Playoff Prediction Proximity to Lead Web Exclusive Real time log count Real time log velocity Real time log Acceleration Twitter Streaming Count Twitter Streaming Acceleration Twitter Streaming Velocity Twitter Streaming Average Twitter Streaming Max Twitter Streaming Skew Twitter Streaming Kurtosis Twitter Streaming Sum Player Popularity 20

21 Linear model produces R 2 score 0.92 Produces an average percentage error of 5% [5,000-10,000] Bagging nor boosting significantly improved accuracy 21

Time Shifter Final Forecast Residual Forecaster Historical Forecaster Partial Forecaster Adjusted Average Forecaster

22 Preprocessing Calculate Seasonality Align Curves Impute Values Event Forecaster Simulation Feature Extraction Spike Prediction Post Processing Flat Line Proc. Time Shifter Final Forecast Residual Forecaster Historical Forecaster Partial Forecaster Adjusted Average Forecaster Cubic Forecaster Vector Forecaster Quadratic Forecaster Historical Post Processing Decline Processor Ensure Exists Processor Minimum Processor Moving Average Proc. Noise Processor Weighted Smoothing Proc. 22

23 USTA 2013 Finals Day Blue = actual ground truth Red = Loess Interpolation 23

Convert a Forecast to Cloud Provisions P( t) = max(1, f ( t) + α β f(t)=the forecast number of hits at a particular time α b =hit buffer zone to ensure the PCC does not under provision β r =capacity

24 Convert a Forecast to Cloud Provisions P( t) = max(1, f ( t) + α β f(t)=the forecast number of hits at a particular time α b =hit buffer zone to ensure the PCC does not under provision β r =capacity for a server r b ) Current Time Takes 20 minutes to provision a server Save up to 51% of computing hours or hours per day Blue = Cloud Capacity Threshold Red = Event Based Prediction Yellow = Time-Series Prediction Purple = Actual Traffic 24

25 Results, Impact & Conclusion Brian O Connell IBM Senior Technical Staff Member & Master Inventor: Continuous Availability Services Sandy Carter IBM General Manager: Cloud Ecosystem & Developers 25

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35 Press Interviews Conferences Print Invention Over 16 Patent Files Several High Value Client Innovative Leadership Expands Conversations 35

36 Compute Hours Compute Impact Traditional vs. PCC Allocations Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Traditional PCC Results based on 2014 US Open Tennis tournament Modeled over an entire event season for full year impact Assumes $50, per compute hour Compute Full year savings of 44,500 compute hours Yearly savings of $2.2M Grew 2014 cloud revenue by 60% Intellectual Property License value for 16 patents over 20 year period projected at $80M Our people can shift focus to customer driven tournament demands and not the demands of the cloud platform 36

37 Sports & Entertainment Public <sports organization> announced 2.3M tickets were available fans rushed the site only to find sorry, we can not process your request. <major metropolitan city> website crashed amid overwhelming visitor volume encouraged by the mayor who strongly urged public use during Hurricane Irene. Retail launch of the Missoni Collection at <retailer> was a huge failure when <retailers website> crashed due to high traffic Finance <major US bank> website slowed considerable for 5 days after announcing $5 fees when using debit cards helped spawn the Occupy movement. Entertainment <website> crashed repeatedly due to high visitor volumes when it launched its limited edition princess dolls collection the popularity was unexpected Technology <new smart phone > on-line availability caused huge spikes in website traffic downed the site or slowed it considerably 37 * Quotes and statements from :

38 Operations Research Making the Cloud Smart Predictable Adaptable Always-On 38

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40 IBM Corporation All Rights Reserved. The information contained in this publication is provided for informational purposes only. While efforts were made to verify the completeness and accuracy of the information contained in this publication, it is provided AS IS without warranty of any kind, express or implied. In addition, this information is based on IBM s current product plans and strategy, which are subject to change by IBM without notice. IBM shall not be responsible for any damages arising out of the use of, or otherwise related to, this publication or any other materials. Nothing contained in this publication is intended to, nor shall have the effect of, creating any warranties or representations from IBM or its suppliers or licensors, or altering the terms and conditions of the applicable license agreement governing the use of IBM software. References in this presentation to IBM products, programs, or services do not imply that they will be available in all countries in which IBM operates. Product release dates and/or capabilities referenced in this presentation may change at any time at IBM s sole discretion based on market opportunities or other factors, and are not intended to be a commitment to future product or feature availability in any way. Nothing contained in these materials is intended to, nor shall have the effect of, stating or implying that any activities undertaken by you will result in any specific sales, revenue growth or other results. Performance is based on measurements and projections using standard IBM benchmarks in a controlled environment. The actual throughput or performance that any user will experience will vary depending upon many factors, including considerations such as the amount of multiprogramming in the user's job stream, the I/O configuration, the storage configuration, and the workload processed. Therefore, no assurance can be given that an individual user will achieve results similar to those stated here. All customer examples described are presented as illustrations of how those customers have used IBM products and the results they may have achieved. Actual environmental costs and performance characteristics may vary by customer. 40

Introduction to the IBM MessageSight appliance for Mobile Messaging and M2M

Introduction to the IBM MessageSight appliance for Mobile Messaging and M2M Arnaud Mathieu and Andrew Schofield IBM Software Group Session TSM-1986 2013 IBM Corporation Please Note IBM s statements regarding