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MD850: e-Service Operations Capacity Management
Overview <ul><li>Motivation </li></ul><ul><li>Background </li></ul><ul><li>Capacity Management </li></ul><ul><ul><li>Manuf...
Motivation
Motivation <ul><li>Many e-Service Problems Exist that Affect Service Quality and Hurt Revenues </li></ul><ul><ul><li>Downt...
Motivation <ul><li>52% of e-Service applications failed to scale according to original design expectations (Newport Group ...
Background
Background Capacity Concepts <ul><li>Capacity </li></ul><ul><ul><li>rate  at which output can be produced by an operating ...
Background Capacity Concepts <ul><li>Capacity Utilization </li></ul><ul><ul><li>= [actual output]/[ design capacity ] </li...
Capacity Management in Manufacturing Operations
Capacity Management Manufacturing Operations <ul><li>Supply Chain Processes </li></ul><ul><li>Supply Chain Modularity </li...
Capacity Management Manufacturing Operations <ul><li>Common Approaches </li></ul><ul><ul><li>Forecasting demand </li></ul>...
Capacity Management Manufacturing Operations <ul><li>Demand-Side Management of Capacity </li></ul><ul><ul><li>Counter-cycl...
Capacity Management Manufacturing Operations <ul><li>Medium-term to Short-term approaches </li></ul><ul><ul><li>Aggregate ...
Capacity Management Manufacturing Operations <ul><li>Capacity Strategies </li></ul><ul><ul><li>Facility </li></ul></ul><ul...
Capacity Management in Person-to-Person Service Operations
Capacity Management Person-to-Person Services <ul><li>Capacities to Manage </li></ul><ul><ul><li>Facilitating Goods </li><...
Capacity Management Person-to-Person Services <ul><li>Demand Side Management of Customer Behaviors </li></ul><ul><ul><li>A...
Capacity Management Person-to-Person Services <ul><li>Service Facility Design </li></ul><ul><ul><li>Queuing model of servi...
Capacity Management in  e-Service Operations
Capacity Management e-Service Operations <ul><li>e-Service Capacities to Manage </li></ul><ul><ul><li>Digital network capa...
Capacity Management e-Service Operations Digital Networks Networks of Physical Objects People Client/  Server Distributed ...
Capacity Management e-Service Capacity <ul><li>Determinants of e-Service Capacity </li></ul><ul><ul><li>Number of customer...
Capacity Management Determiners of e-Service Capacity Desired Capacity  =  Average  Digital Content Demand Per User x  Num...
Capacity Management Determiners of e-Service Capacity
Capacity Management Determiners of e-Service Capacity <ul><li>Common e-Service Problems and Causes </li></ul><ul><ul><li>L...
Capacity Management e-Service Operations <ul><li>Typical e-Service Infrastructure Managed to Achieve Desired Capacity </li...
Capacity Management Strategies for e-Service Capacity <ul><li>Strategies for Managing e-Service Capacity </li></ul><ul><ul...
Managing e-Service Demand Patterns
Capacity Management e-Service Demands 6am 12pm  6pm 12am 6am 12pm 6pm 12am Day 1  Day 2  6am 12pm  6pm 12am 6am 12pm 6pm 1...
Managing the e-Service Product
Capacity Management e-Service Reference Model  (Menasce and Almeida,  Scaling for e-Business , 2000) Characteristics of th...
Capacity Planning Translating the Service-Product into Loads  Characteristics of the Business Navigational Structure &  Fu...
Managing the e-Service Process
Capacity Management e-Service Process Strategies <ul><li>Implications of Classic Aggregate Planning Strategies </li></ul><...
Capacity Management e-Service Process Strategies <ul><li>Implications of Classic Capacity Expansion Strategies </li></ul><...
Capacity Management e-Service Process Strategies <ul><li>Implications of Classic Capacity Expansion Strategies </li></ul><...
e-Service Capacity Planning, Analysis, and Management
e-Service Capacity Planning, Analysis, and Management <ul><li>Best Practices </li></ul><ul><ul><li>Set conservative goals ...
e-Service Capacity Planning, Analysis, and Management <ul><li>Best Practice Objectives </li></ul><ul><ul><li>Minimized Lat...
e-Service Capacity Planning, Analysis, and Management <ul><li>Best Practice Metrics </li></ul><ul><ul><li>End User Respons...
e-Service Capacity Planning, Analysis, and Management <ul><li>Best Practice Guidelines </li></ul><ul><ul><li>Focus on crit...
Capacity Management Techniques During e-Service Design and Development
Capacity Management Design & Development Stage <ul><li>Basic Question:  How to design communication between e-Service appl...
Capacity Management Design & Development Stage <ul><li>Commonly Observed Design Problems </li></ul><ul><ul><li>General </l...
Capacity Management Design & Development Stage <ul><li>Commonly Observed Design Problems </li></ul><ul><ul><li>Database </...
Capacity Management Design & Development Stage <ul><li>Load Balancing </li></ul><ul><ul><li>Objective is to be able to all...
Capacity Management Design & Development Stage <ul><li>Outsource network storage for cache capacity </li></ul><ul><ul><li>...
Capacity Management Design & Development Stage <ul><li>Basic Question:   Theoretically, how many people can my homepage be...
Capacity Management Design & Development Stage <ul><li>Load Analysis and Testing Tools </li></ul><ul><ul><li>Brute Force C...
Capacity Management Design & Development Stage <ul><li>Simulation Based Load Testing </li></ul><ul><ul><li>Employ virtual ...
Capacity Management Design & Development Stage <ul><li>Major Vendors & Applications </li></ul><ul><ul><li>Mercury Interact...
Capacity Management Design & Development Stage Development Deployment Deployment Deployment Software Product Service M.I. ...
Capacity Management Techniques After e-Service Deployment
Capacity Management After e-Service Deployment <ul><li>General Patterns </li></ul><ul><ul><li>As richness of content incre...
Capacity Management After e-Service Deployment <ul><li>General Responses </li></ul><ul><ul><li>Decrease content richness (...
Capacity Management After e-Service Deployment <ul><li>Content Tuning </li></ul><ul><ul><li>Serve out content at a lower l...
Capacity Management After e-Service Deployment <ul><li>Content Tuning </li></ul><ul><ul><li>Possible techniques: </li></ul...
Capacity Management After e-Service Deployment <ul><li>Managing customers’ efficiency </li></ul><ul><ul><li>New customers ...
Capacity Management After e-Service Deployment <ul><li>Basic Question:  Is resource X running? </li></ul><ul><ul><li>Capac...
Capacity Management After e-Service Deployment <ul><li>Basic Question:  Is my site running? </li></ul><ul><ul><li>Capacity...
Capacity Management After e-Service Deployment <ul><li>Basic Question:  What portions of my “service-product” are popular?...
Capacity Management After e-Service Deployment <ul><li>Log File Analysis </li></ul><ul><ul><li>Analyze http requests store...
Capacity Management After e-Service Deployment <ul><li>Customer Profiling (via data mining) </li></ul><ul><ul><li>Translat...
Network of Site Paths Subset of Paths Having Some Relationship  (Business Activities) Within an E-Service WWW Site Log (ht...
WWW Site Content Content Located Together  Within an E-Service WWW Site Log (http GET commands for pages visited) Goods X ...
Capacity Management After e-Service Deployment <ul><li>Basic Question:  How do popular areas of my site affect my capacity...
Capacity Management After e-Service Deployment Enter Home Page Search Page Add to Cart Pay Page 1 Page 2 Page 3 Page 4 pro...
Capacity Management After e-Service Deployment Enter Home Exit Enter Enter Home Browse Exit Browse Add to Cart Pay Exit Th...
Capacity Management After e-Service Deployment <ul><li>Web Application Performance Monitoring </li></ul><ul><ul><li>Web ap...
Capacity Management After e-Service Deployment <ul><li>Web Performance Monitoring </li></ul><ul><ul><li>Active (synthetic)...
Capacity Management After e-Service Deployment <ul><li>Web Performance Monitoring </li></ul><ul><ul><li>Passive (observati...
Capacity Management After e-Service Deployment <ul><li>Based on actual end user data </li></ul><ul><li>High degree of deta...
Capacity Management After e-Service Deployment <ul><li>Site Monitoring Software/Services </li></ul><ul><ul><li>Perform Sys...
Capacity Management After e-Service Deployment <ul><li>Site Monitoring Software/Services </li></ul><ul><ul><li>Create a Us...
Capacity Management After e-Service Deployment
Capacity Management After e-Service Deployment <ul><li>Prominent Vendors & Applications </li></ul><ul><ul><li>Software Pro...
Capacity Management After e-Service Deployment <ul><li>“Test on Demand” Services </li></ul><ul><ul><li>Deliverables </li><...
Capacity Management After e-Service Deployment Development Deployment Deployment Deployment Software Product Service M.I. ...
Software Demonstration
Software Demonstration Mercury Interactive Astra LoadTest <ul><li>Some basic capabilities </li></ul><ul><ul><li>Record cus...
Software Demonstration Mercury Interactive Astra LoadTest <ul><li>Some basic capabilities </li></ul><ul><ul><li>Run scenar...
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  1. 1. MD850: e-Service Operations Capacity Management
  2. 2. Overview <ul><li>Motivation </li></ul><ul><li>Background </li></ul><ul><li>Capacity Management </li></ul><ul><ul><li>Manufacturing Operations </li></ul></ul><ul><ul><li>Person-to-Person Service Operations </li></ul></ul><ul><ul><li>e-Service Operations </li></ul></ul><ul><li>Capacity Management Strategies </li></ul><ul><li>Software Demonstration </li></ul><ul><ul><li>MercuryInteractive’s Astra Load Test </li></ul></ul>
  3. 3. Motivation
  4. 4. Motivation <ul><li>Many e-Service Problems Exist that Affect Service Quality and Hurt Revenues </li></ul><ul><ul><li>Downtime </li></ul></ul><ul><ul><li>Slow times (service slowdown) </li></ul></ul><ul><ul><ul><li>37% of site pages exhibit unacceptable performance as defined by their managers (Mercury Interactive 2001) </li></ul></ul></ul><ul><ul><li>Unpredicted traffic volume spikes </li></ul></ul><ul><ul><li>Transaction failure </li></ul></ul><ul><ul><ul><li>1 in 20 transactions fail on the average corporate website (Mercury Interactive 2001) </li></ul></ul></ul>
  5. 5. Motivation <ul><li>52% of e-Service applications failed to scale according to original design expectations (Newport Group 1999) </li></ul><ul><ul><li>Automated load testing tools were not used </li></ul></ul><ul><ul><ul><li>60% used no load testing tool prior to deployment </li></ul></ul></ul><ul><ul><ul><li>34% used load testing late in development or post deployment only </li></ul></ul></ul><ul><ul><li>Budget and time overruns above average </li></ul></ul><ul><ul><ul><li>23.5 days, $67,083 </li></ul></ul></ul><ul><ul><li>Scalability expectations were not realistic </li></ul></ul><ul><ul><ul><li>Thought they would handle 4300 concurrent users on average </li></ul></ul></ul><ul><ul><ul><li>Actually could only handle 2200 concurrent users on average </li></ul></ul></ul>
  6. 6. Background
  7. 7. Background Capacity Concepts <ul><li>Capacity </li></ul><ul><ul><li>rate at which output can be produced by an operating unit (e.g., a machine, a process, a facility, a company) </li></ul></ul><ul><ul><li>capacity = [number of units of output]/[time period] </li></ul></ul><ul><li>Design Capacity </li></ul><ul><ul><li>maximum rate at which the process can operate on a continual basis under ideal conditions </li></ul></ul><ul><li>Effective Capacity </li></ul><ul><ul><li>rate of production that can be achieved for extended periods under normal conditions , taking into account … [various infrastructure and environmental factors] </li></ul></ul>
  8. 8. Background Capacity Concepts <ul><li>Capacity Utilization </li></ul><ul><ul><li>= [actual output]/[ design capacity ] </li></ul></ul><ul><li>Capacity Efficiency </li></ul><ul><ul><li>= [actual output]/[ effective capacity ] </li></ul></ul>0 Design Capacity Effective Capacity Actual Output Period #5 Actual Output Period #8 Managerial Focus On: Loss in Production Capability
  9. 9. Capacity Management in Manufacturing Operations
  10. 10. Capacity Management Manufacturing Operations <ul><li>Supply Chain Processes </li></ul><ul><li>Supply Chain Modularity </li></ul><ul><li>Facilities </li></ul><ul><li>Facility Location </li></ul><ul><li>Facility Focus </li></ul><ul><li>Process Design </li></ul><ul><li>Product Design </li></ul><ul><li>Product Variety </li></ul><ul><li>Product Quality </li></ul><ul><li>Production Scheduling </li></ul><ul><li>Materials Management </li></ul><ul><li>Maintenance </li></ul><ul><li>Job Design and Personnel Management </li></ul>
  11. 11. Capacity Management Manufacturing Operations <ul><li>Common Approaches </li></ul><ul><ul><li>Forecasting demand </li></ul></ul><ul><ul><li>Translate demands (d 1 , d 2 , …, d n ) for products (1, …, n) into capacity requirements </li></ul></ul><ul><ul><li>Break-Even Analysis </li></ul></ul><ul><ul><li>Decision Analysis </li></ul></ul>
  12. 12. Capacity Management Manufacturing Operations <ul><li>Demand-Side Management of Capacity </li></ul><ul><ul><li>Counter-cyclic products </li></ul></ul><ul><ul><ul><li>Toro - lawnmowers, snowblowers </li></ul></ul></ul><ul><ul><li>Promotion </li></ul></ul><ul><ul><li>Pricing </li></ul></ul>
  13. 13. Capacity Management Manufacturing Operations <ul><li>Medium-term to Short-term approaches </li></ul><ul><ul><li>Aggregate planning </li></ul></ul><ul><ul><ul><li>chase demand strategy </li></ul></ul></ul><ul><ul><ul><li>level demand/workforce strategy </li></ul></ul></ul><ul><ul><li>Inventory management </li></ul></ul><ul><ul><li>Materials management </li></ul></ul><ul><ul><li>Operations scheduling </li></ul></ul><ul><ul><li>Personnel scheduling </li></ul></ul><ul><li>Typical capacity analysis & management tools </li></ul><ul><ul><li>Optimization </li></ul></ul><ul><ul><li>MRP … CRP </li></ul></ul><ul><ul><li>Inventory review </li></ul></ul><ul><ul><li>Queueing </li></ul></ul><ul><ul><li>Simulation </li></ul></ul><ul><ul><li>Optimization </li></ul></ul>
  14. 14. Capacity Management Manufacturing Operations <ul><li>Capacity Strategies </li></ul><ul><ul><li>Facility </li></ul></ul><ul><ul><ul><li>Product-Organized : facility makes one product type </li></ul></ul></ul><ul><ul><ul><li>Process-Organized : multiple products or parts made through one process </li></ul></ul></ul><ul><ul><ul><li>Market-Organized : close physical proximity to the customer </li></ul></ul></ul><ul><ul><ul><li>Focused Factory/Plant-Within-A-Plant </li></ul></ul></ul><ul><ul><li>Capacity Expansion </li></ul></ul><ul><ul><ul><li>Demand-Leading : maintain excess capacity </li></ul></ul></ul><ul><ul><ul><li>Demand-Trailing : capacity lags behind demand </li></ul></ul></ul><ul><ul><ul><li>Demand-Matching : match capacity to demand </li></ul></ul></ul><ul><ul><ul><li>Steady Expansion : add capacity on regular basis, based on long-term needs, but not on demand fluctuations </li></ul></ul></ul>
  15. 15. Capacity Management in Person-to-Person Service Operations
  16. 16. Capacity Management Person-to-Person Services <ul><li>Capacities to Manage </li></ul><ul><ul><li>Facilitating Goods </li></ul></ul><ul><ul><ul><li>When produced internally, same issues as manufacturing </li></ul></ul></ul><ul><ul><ul><li>Often procured from supplier </li></ul></ul></ul><ul><ul><li>Services </li></ul></ul><ul><ul><ul><li>Persons who can deliver service at a certain rate </li></ul></ul></ul><ul><ul><li>Information </li></ul></ul><ul><ul><ul><li>Printed information … either inventoried, or printed on demand at some rate </li></ul></ul></ul><ul><ul><ul><li>Communicated in person at some rate </li></ul></ul></ul>
  17. 17. Capacity Management Person-to-Person Services <ul><li>Demand Side Management of Customer Behaviors </li></ul><ul><ul><li>Appointment reminders </li></ul></ul><ul><ul><li>Pricing </li></ul></ul><ul><ul><ul><li>penalty for being late </li></ul></ul></ul><ul><ul><ul><li>yield-management </li></ul></ul></ul><ul><ul><li>Wave scheduling </li></ul></ul><ul><ul><li>Capacity sharing </li></ul></ul><ul><ul><ul><li>One printer, 20 professors </li></ul></ul></ul><ul><ul><li>Ask customer to stop consuming/conserve </li></ul></ul><ul><ul><ul><li>Energy efficient light bulbs </li></ul></ul></ul><ul><ul><li>Stop filling demand/shut off service </li></ul></ul><ul><ul><ul><li>California electric service </li></ul></ul></ul>
  18. 18. Capacity Management Person-to-Person Services <ul><li>Service Facility Design </li></ul><ul><ul><li>Queuing model of service system queues </li></ul></ul><ul><ul><li>Simulation models to study activities within system and to estimate effective capacity, service times, and so forth </li></ul></ul><ul><li>Daily/Weekly Capacity Management </li></ul><ul><ul><li>Service Personnel/Staff Scheduling </li></ul></ul><ul><ul><ul><li>Nurses, police, paramedics </li></ul></ul></ul><ul><ul><ul><ul><li>optimization - integer linear programming </li></ul></ul></ul></ul>
  19. 19. Capacity Management in e-Service Operations
  20. 20. Capacity Management e-Service Operations <ul><li>e-Service Capacities to Manage </li></ul><ul><ul><li>Digital network capacities </li></ul></ul><ul><ul><li>Goods network capacities </li></ul></ul><ul><ul><li>Person-to-Person service network capacities </li></ul></ul><ul><ul><li>Inter-layer digital service capacities </li></ul></ul>Service Personnel Service Networks, Intelligent Goods Networks Digital Networks & Digital Content Service-Product Service-Process
  21. 21. Capacity Management e-Service Operations Digital Networks Networks of Physical Objects People Client/ Server Distributed Component Applications Service-Product Service-Process Info: Digital Content
  22. 22. Capacity Management e-Service Capacity <ul><li>Determinants of e-Service Capacity </li></ul><ul><ul><li>Number of customers and internal users </li></ul></ul><ul><ul><ul><li>demand </li></ul></ul></ul><ul><ul><li>“Service-Product” </li></ul></ul><ul><ul><ul><li>site content </li></ul></ul></ul><ul><ul><li>“Service-Process” </li></ul></ul><ul><ul><ul><li>server capacity and configuration of hardware and software </li></ul></ul></ul>
  23. 23. Capacity Management Determiners of e-Service Capacity Desired Capacity = Average Digital Content Demand Per User x Number of Users Per Time Period = Load on Hardware Per User / Number of Supported Concurrent Users Hardware Capacity (design) Goods Info Services Info Digital Services Content Consumed Service-Product Web browsers Wireless apps Other processes (e.g., ERP system) User Mix Server Network Infrastructure Software Modules Service-Process
  24. 24. Capacity Management Determiners of e-Service Capacity
  25. 25. Capacity Management Determiners of e-Service Capacity <ul><li>Common e-Service Problems and Causes </li></ul><ul><ul><li>Long response time from end users’ point of view </li></ul></ul><ul><ul><li>Long response time as measured by the servers </li></ul></ul><ul><ul><li>Memory leaks </li></ul></ul><ul><ul><li>High CPU usage </li></ul></ul><ul><ul><li>Too many open connections between the application and end users </li></ul></ul><ul><ul><li>Lengthy queues for end user requests </li></ul></ul><ul><ul><li>Too many table scans of the database </li></ul></ul><ul><ul><li>Erroneous data returned </li></ul></ul><ul><ul><li>HTTP errors </li></ul></ul><ul><ul><li>Pages not available </li></ul></ul>
  26. 26. Capacity Management e-Service Operations <ul><li>Typical e-Service Infrastructure Managed to Achieve Desired Capacity </li></ul><ul><ul><li>Application Server Software </li></ul></ul><ul><ul><li>Web Server Software </li></ul></ul><ul><ul><li>Database Software </li></ul></ul><ul><ul><li>Networking Software </li></ul></ul><ul><ul><li>Load Balancing Software </li></ul></ul><ul><ul><li>Application Server Hardware </li></ul></ul><ul><ul><li>Web Server Hardware </li></ul></ul><ul><ul><li>Database Hardware </li></ul></ul><ul><ul><li>Networking Hardware </li></ul></ul>
  27. 27. Capacity Management Strategies for e-Service Capacity <ul><li>Strategies for Managing e-Service Capacity </li></ul><ul><ul><li>Manage demand </li></ul></ul><ul><ul><li>Manage “Service-Product” </li></ul></ul><ul><ul><li>Manage “Service-Process” </li></ul></ul>
  28. 28. Managing e-Service Demand Patterns
  29. 29. Capacity Management e-Service Demands 6am 12pm 6pm 12am 6am 12pm 6pm 12am Day 1 Day 2 6am 12pm 6pm 12am 6am 12pm 6pm 12am Day 1 Day 2 Demand Surge Cyclical Random & Infrequent
  30. 30. Managing the e-Service Product
  31. 31. Capacity Management e-Service Reference Model (Menasce and Almeida, Scaling for e-Business , 2000) Characteristics of the Business Navigational Structure & Function Patterns of Customer Behavior Site Architecture and Service Demands Business Model Functional Model Customer Model Resource Model Technological View Business View Internal Metrics External Metrics
  32. 32. Capacity Planning Translating the Service-Product into Loads Characteristics of the Business Navigational Structure & Function Business Model Functional Model Service- Product Goods Services Information WWW Site Content A Network of Paths Between Pages/Objects Heim and Sinha (2000), Spiller and Lohse (1998) Menasce and Almeida (2000)
  33. 33. Managing the e-Service Process
  34. 34. Capacity Management e-Service Process Strategies <ul><li>Implications of Classic Aggregate Planning Strategies </li></ul><ul><ul><li>chase demand strategy </li></ul></ul><ul><ul><ul><li>add a server when you sense it is needed </li></ul></ul></ul><ul><ul><ul><li>take away a server when it is not needed </li></ul></ul></ul><ul><ul><li>level demand strategy </li></ul></ul><ul><ul><ul><li>build an inventory of e-Services (IMPOSSIBLE) </li></ul></ul></ul>
  35. 35. Capacity Management e-Service Process Strategies <ul><li>Implications of Classic Capacity Expansion Strategies </li></ul><ul><ul><li>Demand-Leading : maintain excess capacity </li></ul></ul><ul><ul><ul><li>expensive solution </li></ul></ul></ul><ul><ul><ul><li>service level stays reasonable </li></ul></ul></ul><ul><ul><ul><li>customers satisfied </li></ul></ul></ul><ul><ul><li>Demand-Trailing : capacity lags behind demand </li></ul></ul><ul><ul><ul><li>queue of requests builds </li></ul></ul></ul><ul><ul><ul><li>service slows as servers average capacity across all requests </li></ul></ul></ul><ul><ul><ul><li>server computers grind to a halt when demand severely surpasses capacity … time to purchase new servers </li></ul></ul></ul><ul><ul><ul><li>dissatisfied customers </li></ul></ul></ul>
  36. 36. Capacity Management e-Service Process Strategies <ul><li>Implications of Classic Capacity Expansion Strategies </li></ul><ul><ul><li>Demand-Matching : match capacity to demand </li></ul></ul><ul><ul><ul><li>as demand grows (shrinks), in-house (and outsourced) servers are dynamically added (removed) </li></ul></ul></ul><ul><ul><ul><ul><li>capacity for static files (GIF, JPEG, .EXEs, etc.) outsourced to a third-party </li></ul></ul></ul></ul><ul><ul><ul><ul><li>as demand grows (shrinks), third-party senses growth and re-allocates files across a larger (smaller) subset of their network </li></ul></ul></ul></ul><ul><ul><ul><ul><li>you manage basic content (e.g., HOME.HTML file) </li></ul></ul></ul></ul><ul><ul><ul><li>caching strategy (Akamai, Inktomi, others vendors) </li></ul></ul></ul><ul><ul><li>Steady Expansion : add capacity on regular basis, based on long-term needs, but not on demand fluctuations </li></ul></ul><ul><ul><ul><li>intermittent periods of poor service responsiveness </li></ul></ul></ul><ul><ul><ul><li>if demand grows too fast, system may fail, customers dissatisfied </li></ul></ul></ul>
  37. 37. e-Service Capacity Planning, Analysis, and Management
  38. 38. e-Service Capacity Planning, Analysis, and Management <ul><li>Best Practices </li></ul><ul><ul><li>Set conservative goals with the intent to expand them out in a systematic fashion </li></ul></ul><ul><ul><ul><li>Have a plan for short-term incremental achievements of long-term goals </li></ul></ul></ul><ul><ul><li>Integrate load testing into the development process of web applications early and often </li></ul></ul><ul><ul><ul><li>Always test the limits of any e-Service application prior to going live </li></ul></ul></ul><ul><ul><li>Leverage pre-deployment test assets in the production environment to monitor live web application performance </li></ul></ul><ul><ul><li>Set a load capacity threshold for growth which is continuously monitored </li></ul></ul><ul><ul><ul><li>At 70-80% of the existing system’s handling capacity, start executing plans to add more capacity </li></ul></ul></ul><ul><li>(Source: Newport Group 1999, 2000) </li></ul>
  39. 39. e-Service Capacity Planning, Analysis, and Management <ul><li>Best Practice Objectives </li></ul><ul><ul><li>Minimized Latency </li></ul></ul><ul><ul><ul><li>Keep waiting time between making a request and beginning to see a result as low as possible </li></ul></ul></ul><ul><ul><li>Maximized Throughput </li></ul></ul><ul><ul><ul><li>Number of items potentially processed per unit time should be as high as possible </li></ul></ul></ul><ul><ul><li>Mid-to-high Utilization </li></ul></ul><ul><ul><ul><li>Actual capacity utilization of components should be kept around 75% … latency suffers as you go over this level </li></ul></ul></ul><ul><ul><li>Maximized Efficiency </li></ul></ul><ul><ul><ul><li>Keep overall performance high and cost low </li></ul></ul></ul>
  40. 40. e-Service Capacity Planning, Analysis, and Management <ul><li>Best Practice Metrics </li></ul><ul><ul><li>End User Response Time </li></ul></ul><ul><ul><ul><li>Measures the performance of an application from the end-user perspective </li></ul></ul></ul><ul><ul><ul><li>The amount of time required for an end user to receive a response or to execute a business transaction </li></ul></ul></ul><ul><ul><li>Application Availability </li></ul></ul><ul><ul><ul><li>Page availability </li></ul></ul></ul><ul><ul><ul><li>Transaction availability </li></ul></ul></ul><ul><ul><ul><li>User-perceived availability </li></ul></ul></ul><ul><ul><li>Reliability </li></ul></ul><ul><ul><li>Correct Content Delivery in Multi-Step Transactions </li></ul></ul><ul><li>(Sources: Newport Group 1999, Mercury Interactive 2001) </li></ul>
  41. 41. e-Service Capacity Planning, Analysis, and Management <ul><li>Best Practice Guidelines </li></ul><ul><ul><li>Focus on critical business processes </li></ul></ul><ul><ul><li>Use the right monitoring solution to meet your business needs </li></ul></ul><ul><ul><li>Get a consistent performance baseline and watch for trends </li></ul></ul><ul><ul><li>Avoid an alerting flood …send alerts conservatively </li></ul></ul><ul><ul><li>Think “recurring” when acting on alerts </li></ul></ul><ul><ul><li>Correlate end-user performance with back-end issues </li></ul></ul><ul><ul><li>When a problem is identified, prioritize IT resources </li></ul></ul><ul><ul><li>Optimize your existing infrastructure </li></ul></ul><ul><ul><li>Define escalation procedures to follow to address performance issues </li></ul></ul><ul><ul><li>Use application performance monitoring to avoid having every department scramble when performance goes bad </li></ul></ul><ul><li>(Source: Mercury Interactive, 2001) </li></ul>
  42. 42. Capacity Management Techniques During e-Service Design and Development
  43. 43. Capacity Management Design & Development Stage <ul><li>Basic Question: How to design communication between e-Service application layers? </li></ul><ul><ul><li>Capacity Issue </li></ul></ul><ul><ul><ul><li>How to set up appropriate connections between tiers? </li></ul></ul></ul><ul><ul><ul><li>How to avoid too few connections between tiers? </li></ul></ul></ul><ul><ul><ul><li>How to avoid bad effects from failure of a server in one of the tiers? </li></ul></ul></ul><ul><ul><li>Methods </li></ul></ul><ul><ul><ul><li>Separation of presentation logic from business logic from database management </li></ul></ul></ul><ul><ul><ul><li>Load balancing </li></ul></ul></ul><ul><ul><ul><li>Outsource network storage for caching of content </li></ul></ul></ul>
  44. 44. Capacity Management Design & Development Stage <ul><li>Commonly Observed Design Problems </li></ul><ul><ul><li>General </li></ul></ul><ul><ul><ul><li>Insufficient memory </li></ul></ul></ul><ul><ul><ul><li>Incompatible service packs and application extensions (e.g., .DLLs) </li></ul></ul></ul><ul><ul><ul><li>Excessive queueing requests </li></ul></ul></ul><ul><ul><ul><li>Too many secure HTTPS connections in use </li></ul></ul></ul><ul><ul><li>Web Server </li></ul></ul><ul><ul><ul><li>Insufficient memory </li></ul></ul></ul><ul><ul><ul><li>Poor web server design </li></ul></ul></ul><ul><ul><ul><li>High CPU usage </li></ul></ul></ul><ul><ul><li>Application Server </li></ul></ul><ul><ul><ul><li>Poor cache management and high CPU usage </li></ul></ul></ul><ul><ul><ul><li>Lack of memory </li></ul></ul></ul><ul><ul><ul><li>Poor session management </li></ul></ul></ul><ul><ul><ul><li>Poor database tuning </li></ul></ul></ul>
  45. 45. Capacity Management Design & Development Stage <ul><li>Commonly Observed Design Problems </li></ul><ul><ul><li>Database </li></ul></ul><ul><ul><ul><li>Inefficient indexing </li></ul></ul></ul><ul><ul><ul><li>Fragmented databases </li></ul></ul></ul><ul><ul><ul><li>Out-of-date statistics </li></ul></ul></ul><ul><ul><ul><li>Faulty application design </li></ul></ul></ul><ul><ul><li>Network </li></ul></ul><ul><ul><ul><li>Inadequate Internet pipe </li></ul></ul></ul><ul><ul><ul><li>Hidden bottlenecks between the customer’s Web site and the ISP </li></ul></ul></ul><ul><ul><ul><li>Faulty hops (misdirected traffic and lost packets) </li></ul></ul></ul><ul><ul><ul><li>Misconfigured software and incompatible hardware </li></ul></ul></ul><ul><li>(Source: Mercury Interactive 2001) </li></ul>
  46. 46. Capacity Management Design & Development Stage <ul><li>Load Balancing </li></ul><ul><ul><li>Objective is to be able to allocate demand – as it is requested – to resources that will provide best service response </li></ul></ul><ul><ul><li>Many sites use N-tier systems with many servers in each tier that perform same functions </li></ul></ul><ul><ul><ul><li>Reliability </li></ul></ul></ul><ul><ul><ul><li>Backup in case of failure and when server maintenance must be performed </li></ul></ul></ul><ul><ul><li>Possible techniques: </li></ul></ul><ul><ul><ul><li>RANDOM DISTRIBUTION – Round-robin (random) allocation of a customer request to an IP address for a web server. If a server fails, customer requests will still be allocated to it until its IP address is removed from service. </li></ul></ul></ul><ul><ul><ul><li>INTELLIGENT DISTRIBUTION – Customer requests are allocated to servers based upon current utilization at each server. The lowest utilization server will receive the next job. </li></ul></ul></ul>
  47. 47. Capacity Management Design & Development Stage <ul><li>Outsource network storage for cache capacity </li></ul><ul><ul><li>Akamai, Inktomi, etc. provide services for storing and serving static content </li></ul></ul><ul><ul><li>Many of these companies are also setting up caching procedures for dynamic content </li></ul></ul>
  48. 48. Capacity Management Design & Development Stage <ul><li>Basic Question: Theoretically, how many people can my homepage be served to simultaneously? </li></ul><ul><ul><li>Capacity Issue: </li></ul></ul><ul><ul><ul><li>What is my website’s design capacity? </li></ul></ul></ul><ul><ul><li>Back of the Envelope Method: </li></ul></ul><ul><ul><ul><li>Home page (average) size: 50,000 bits = 6250 bytes </li></ul></ul></ul><ul><ul><ul><li>Rented T3 line = 45,000,000 bits/sec </li></ul></ul></ul><ul><ul><ul><li>[45,000,000 bits/sec] / [50,000 bits/customer] = </li></ul></ul></ul><ul><ul><ul><ul><li>900 happy customers/sec … the “design capacity” </li></ul></ul></ul></ul><ul><ul><ul><li>900 customers/sec * 60 * 60 * 24 = </li></ul></ul></ul><ul><ul><ul><ul><li>77,760,000 happy customers/day </li></ul></ul></ul></ul><ul><ul><ul><ul><li>assuming 900 customers request the page simultaneously at the beginning of each second, and each has a fast enough modem to receive the file by the end of the second </li></ul></ul></ul></ul>
  49. 49. Capacity Management Design & Development Stage <ul><li>Load Analysis and Testing Tools </li></ul><ul><ul><li>Brute Force Calculations </li></ul></ul><ul><ul><ul><li>Queueing theory </li></ul></ul></ul><ul><ul><li>Brute Force Load Testing </li></ul></ul><ul><ul><ul><li>Thousands of employees at their computers </li></ul></ul></ul><ul><ul><li>Simulation-Based Load Testing & Monitoring Software </li></ul></ul><ul><ul><ul><li>Load-Test : “Can our site handle 25 users?” </li></ul></ul></ul><ul><ul><ul><li>Stress-Test : “Stable? Reliable? Over long period?” </li></ul></ul></ul><ul><ul><ul><li>Capacity-Test : “Maximum number of concurrent customers?” </li></ul></ul></ul>
  50. 50. Capacity Management Design & Development Stage <ul><li>Simulation Based Load Testing </li></ul><ul><ul><li>Employ virtual (i.e. fake) users on client computers </li></ul></ul><ul><ul><li>Have virtual users use the e-Service system based on scripted behaviors recorded for them by the load testing program </li></ul></ul><ul><ul><li>Collect data about the performance that the virtual users experience </li></ul></ul><ul><ul><li>Modify service process depending upon the typical performance observed </li></ul></ul>
  51. 51. Capacity Management Design & Development Stage <ul><li>Major Vendors & Applications </li></ul><ul><ul><li>Mercury Interactive </li></ul></ul><ul><ul><ul><li>LoadRunner + WinRunner + TestDirector </li></ul></ul></ul><ul><ul><ul><li>Astra LoadTest + Astra Quicktest + Astra SiteManager </li></ul></ul></ul><ul><ul><li>Segue </li></ul></ul><ul><ul><ul><li>SilkPerformer </li></ul></ul></ul><ul><ul><li>Empirix </li></ul></ul><ul><ul><li>Compuware </li></ul></ul><ul><ul><li>Radware </li></ul></ul>
  52. 52. Capacity Management Design & Development Stage Development Deployment Deployment Deployment Software Product Service M.I. LoadRunner, etc. M.I. Astra LoadTest, etc. Segue SilkPerformer Capacity/Load Test Monitoring
  53. 53. Capacity Management Techniques After e-Service Deployment
  54. 54. Capacity Management After e-Service Deployment <ul><li>General Patterns </li></ul><ul><ul><li>As richness of content increases, capacity decreases (for a fixed service process) </li></ul></ul><ul><ul><li>As customers converge to more actively involved behavior types (are retained, and thus consume more content), capacity decreases (for a fixed service process) </li></ul></ul><ul><ul><li>As network of site paths increases in complexity, the complexity of managing capacity will increase </li></ul></ul>
  55. 55. Capacity Management After e-Service Deployment <ul><li>General Responses </li></ul><ul><ul><li>Decrease content richness (when appropriate) </li></ul></ul><ul><ul><li>Make customers more efficient in their behavior </li></ul></ul><ul><ul><li>Manage site complexity </li></ul></ul>
  56. 56. Capacity Management After e-Service Deployment <ul><li>Content Tuning </li></ul><ul><ul><li>Serve out content at a lower level of richness, or remove some content </li></ul></ul><ul><ul><li>By lowering the number of bytes for each file, less capacity will be used by each individual user </li></ul></ul><ul><ul><li>Saved capacity can then be used to serve additional customers </li></ul></ul>
  57. 57. Capacity Management After e-Service Deployment <ul><li>Content Tuning </li></ul><ul><ul><li>Possible techniques: </li></ul></ul><ul><ul><ul><li>DESIGN TIME – Create separate sets of high resolution and low resolution images for your e-Service, and program your site so that you can change which images are dynamically inserted into your pages </li></ul></ul></ul><ul><ul><ul><li>DESIGN TIME – Create a program that loads an image directory on the web server with high or low resolution images, depending upon present demands </li></ul></ul></ul><ul><ul><ul><li>RUN TIME – Use a program procedure or filter to pre-process each image to a lower resolution, as the image is requested. Once the image has first been processed, the filter could just serve out the lower resolution image. (Note: this approach lowers image bandwidth but increases page processing time.) </li></ul></ul></ul><ul><ul><ul><li>RUN TIME – Change heavily downloaded page from dynamic content (3.5 seconds on average to generate and then download) to static page (1.5 seconds on average to download) </li></ul></ul></ul>
  58. 58. Capacity Management After e-Service Deployment <ul><li>Managing customers’ efficiency </li></ul><ul><ul><li>New customers – difficult to do </li></ul></ul><ul><ul><ul><li>Better site design as you figure out how new customers behave </li></ul></ul></ul><ul><ul><li>Longer-term customers/users – should experience a learning effect that will cause them to be more directed in their activities </li></ul></ul><ul><ul><ul><li>They may consume more content per visit as site stickiness keeps them around </li></ul></ul></ul><ul><ul><ul><li>They may consume less content if they become more efficient at shopping and other tasks on the site </li></ul></ul></ul>
  59. 59. Capacity Management After e-Service Deployment <ul><li>Basic Question: Is resource X running? </li></ul><ul><ul><li>Capacity Issue: </li></ul></ul><ul><ul><ul><li>Has server failed? </li></ul></ul></ul><ul><ul><ul><li>Has network router failed? </li></ul></ul></ul><ul><ul><li>Method: </li></ul></ul><ul><ul><ul><li>Systems performance monitoring tools for each infrastructure component </li></ul></ul></ul><ul><ul><ul><li>Tend to monitor and provide information in a stovepipe fashion, but useful if you need to know about a specific resource </li></ul></ul></ul><ul><ul><ul><li>Note – system monitors can report that each individual component is running fine, yet the system overall can exhibit poor performance </li></ul></ul></ul>
  60. 60. Capacity Management After e-Service Deployment <ul><li>Basic Question: Is my site running? </li></ul><ul><ul><li>Capacity Issue: </li></ul></ul><ul><ul><ul><li>Is capacity > 0 right now? </li></ul></ul></ul><ul><ul><ul><li>How long has it been 0? </li></ul></ul></ul><ul><ul><li>Method: </li></ul></ul><ul><ul><ul><li>Simple site monitor, similar to “ping” service on any computer </li></ul></ul></ul><ul><ul><li>Example: </li></ul></ul><ul><ul><ul><li>ArsDigita.com’s free “Uptime” web site monitoring service ... Every 15 minutes, it tries to download a text file called “http://www.mysite.com/textfile.txt” from your web site </li></ul></ul></ul><ul><ul><ul><li>If it fails, it emails you that your site is down </li></ul></ul></ul>
  61. 61. Capacity Management After e-Service Deployment <ul><li>Basic Question: What portions of my “service-product” are popular? </li></ul><ul><ul><li>Capacity Issue: </li></ul></ul><ul><ul><ul><li>Which files are being requested frequently? </li></ul></ul></ul><ul><ul><ul><li>Which content configurations are requested frequently? </li></ul></ul></ul><ul><ul><ul><li>Which processes deliver that content? </li></ul></ul></ul><ul><ul><ul><li>Am I paying too much for my ISP service contract? Can I get by on a lower-bandwidth contract? </li></ul></ul></ul><ul><ul><li>Method: </li></ul></ul><ul><ul><ul><li>Site log file analysis </li></ul></ul></ul><ul><ul><ul><li>Add up all http: transactions made to your web site during some time period </li></ul></ul></ul>
  62. 62. Capacity Management After e-Service Deployment <ul><li>Log File Analysis </li></ul><ul><ul><li>Analyze http requests stored on the server </li></ul></ul><ul><ul><li>Translate requests into aggregate historical demands for certain time period buckets </li></ul></ul><ul><ul><li>Vendors </li></ul></ul><ul><ul><ul><li>Webalizer (www.mrunix.com/webalizer) </li></ul></ul></ul><ul><ul><ul><li>NetTracker (www.sane.com/products/NetTracker) </li></ul></ul></ul><ul><ul><ul><li>many more </li></ul></ul></ul>
  63. 63. Capacity Management After e-Service Deployment <ul><li>Customer Profiling (via data mining) </li></ul><ul><ul><li>Translate customer behaviors within system into common customer profiles </li></ul></ul><ul><ul><li>Link profiles to the resources that are required to service the respective activities </li></ul></ul>
  64. 64. Network of Site Paths Subset of Paths Having Some Relationship (Business Activities) Within an E-Service WWW Site Log (http GET, page referrals between pages ) Subset #1 of paths commonly visited together Subset #2 of paths commonly visited together Subset #3 of paths commonly visted together “ Data Mining” is made up of ... has some business meaning, and is stored in ... Three Consumer Types … 1, 2, and 3 additional analysis Subset #1 … “browse only” Subset #2 … “directly buy” Subset #3 … “browse, then buy” Capacity Management After e-Service Deployment Translating the service product into demand loads
  65. 65. WWW Site Content Content Located Together Within an E-Service WWW Site Log (http GET commands for pages visited) Goods X Services X Content X Goods Y Services Y Content Y Goods Z Services Z Content Z “ Data Mining” is made up of ... has some business meaning, and is stored in ... Three Consumer Types … X, Y, and Z additional analysis Capacity Management After e-Service Deployment Translating the service product into demand loads
  66. 66. Capacity Management After e-Service Deployment <ul><li>Basic Question: How do popular areas of my site affect my capacity? </li></ul><ul><ul><li>Capacity Issue: </li></ul></ul><ul><ul><ul><li>How to link common behaviors to specific capacity providing resources? </li></ul></ul></ul><ul><ul><li>Methods </li></ul></ul><ul><ul><ul><li>Queueing models </li></ul></ul></ul><ul><ul><ul><li>Load testing tools </li></ul></ul></ul><ul><ul><ul><li>Site monitoring tools </li></ul></ul></ul>
  67. 67. Capacity Management After e-Service Deployment Enter Home Page Search Page Add to Cart Pay Page 1 Page 2 Page 3 Page 4 prob. = 0.2 prob. = 0.5 prob. = 0.3 1.0 0.3 0.3 0.6 0.1 0.4 0.8 0.4 0.1 Exit 0.0 1.0 Business Activity = “Browse” Identify user navigation paths within e-Service … then use queueing theory equations to determine long-run impact of various paths
  68. 68. Capacity Management After e-Service Deployment Enter Home Exit Enter Enter Home Browse Exit Browse Add to Cart Pay Exit Three Simple Consumer Behavior “Types” Observable in Navigation Model Identify user navigation paths within e-Service … then use queueing theory equations to determine long-run impact of various paths
  69. 69. Capacity Management After e-Service Deployment <ul><li>Web Application Performance Monitoring </li></ul><ul><ul><li>Web application monitoring tools work to send up red flags when the application under surveillance fails to meet its performance objectives </li></ul></ul>
  70. 70. Capacity Management After e-Service Deployment <ul><li>Web Performance Monitoring </li></ul><ul><ul><li>Active (synthetic) monitoring </li></ul></ul><ul><ul><ul><li>Identify several key transactions in the e-Service </li></ul></ul></ul><ul><ul><ul><li>Create an emulated (or simulated) client for each key transaction </li></ul></ul></ul><ul><ul><ul><li>Execute transactions against the simulated client on a regular basis </li></ul></ul></ul><ul><ul><ul><li>Measure transactions in detail and collect service availability data </li></ul></ul></ul><ul><ul><ul><li>If performance violates some predetermined threshold, send an alert to e-Service manager </li></ul></ul></ul>
  71. 71. Capacity Management After e-Service Deployment <ul><li>Web Performance Monitoring </li></ul><ul><ul><li>Passive (observational) monitoring </li></ul></ul><ul><ul><ul><li>Collect data from actual end user activity and store it in a database </li></ul></ul></ul><ul><ul><ul><li>Analyze data for patterns </li></ul></ul></ul><ul><ul><ul><li>Identify several key transactions in the e-Service </li></ul></ul></ul><ul><ul><ul><li>Measure transactions in detail </li></ul></ul></ul><ul><ul><ul><li>Generate performance metrics </li></ul></ul></ul><ul><ul><ul><li>Link performance to activities experienced within the e-Service system </li></ul></ul></ul>
  72. 72. Capacity Management After e-Service Deployment <ul><li>Based on actual end user data </li></ul><ul><li>High degree of detail </li></ul><ul><li>Capture client processing time </li></ul><ul><li>Aids in root-cause analysis </li></ul>Active Passive <ul><li>Data collected opportunistically </li></ul><ul><li>Reactive by nature … to real customer problems </li></ul><ul><li>Potential for collecting excessive amount of data </li></ul><ul><li>24 x 7 monitoring </li></ul><ul><li>Constant controlled data collection </li></ul><ul><li>Data collection provides baseline </li></ul><ul><li>Proactive in nature </li></ul><ul><li>Not based on true end-user data </li></ul><ul><li>Limited insight into the back-end infrastructure performance </li></ul><ul><li>Difficult to execute “real” transactions </li></ul><ul><li>Creation/maintenance of scripts </li></ul>Advantage Drawback
  73. 73. Capacity Management After e-Service Deployment <ul><li>Site Monitoring Software/Services </li></ul><ul><ul><li>Perform Systems Analysis </li></ul></ul><ul><ul><ul><li>Manager must understand the current system architecture </li></ul></ul></ul><ul><ul><ul><li>Translate architecture into load testing objectives </li></ul></ul></ul><ul><ul><ul><li>Define input data for testing </li></ul></ul></ul><ul><ul><ul><li>Choose a testing/monitoring strategy </li></ul></ul></ul><ul><ul><li>Create Virtual User Scripts </li></ul></ul><ul><ul><ul><li>Record scripts that virtual users will use to interact with the e-Service system </li></ul></ul></ul><ul><ul><li>Define User Behaviors </li></ul></ul><ul><ul><ul><li>Specify how the virtual users will behave (random think times, browser types to use, dial-up speeds, etc.) </li></ul></ul></ul>
  74. 74. Capacity Management After e-Service Deployment <ul><li>Site Monitoring Software/Services </li></ul><ul><ul><li>Create a User Scenario </li></ul></ul><ul><ul><ul><li>A scenario is a set of user behaviors combined together to test a web site </li></ul></ul></ul><ul><ul><li>Monitor Performance </li></ul></ul><ul><ul><ul><li>Run the user scenario </li></ul></ul></ul><ul><ul><ul><li>Collect data (client, network components, hardware components, server/software components) on performance for user requests </li></ul></ul></ul><ul><ul><li>Analyze Performance </li></ul></ul><ul><ul><ul><li>Statistics </li></ul></ul></ul><ul><ul><ul><li>Graphs </li></ul></ul></ul>
  75. 75. Capacity Management After e-Service Deployment
  76. 76. Capacity Management After e-Service Deployment <ul><li>Prominent Vendors & Applications </li></ul><ul><ul><li>Software Products </li></ul></ul><ul><ul><ul><li>Mercury Interactive Topaz </li></ul></ul></ul><ul><ul><ul><li>Segue SilkPerformer </li></ul></ul></ul><ul><ul><li>Services </li></ul></ul><ul><ul><ul><li>Mercury Interactive ActiveWatch (Topaz-based service) </li></ul></ul></ul><ul><ul><ul><li>Mercury Interactive ActiveTest (LoadRunner-based service) </li></ul></ul></ul>
  77. 77. Capacity Management After e-Service Deployment <ul><li>“Test on Demand” Services </li></ul><ul><ul><li>Deliverables </li></ul></ul><ul><ul><ul><li>Consulting time </li></ul></ul></ul><ul><ul><ul><li>Set of tailored test scripts for an e-Service </li></ul></ul></ul><ul><ul><ul><li>Application test run </li></ul></ul></ul><ul><ul><ul><li>Final report providing details about the tests </li></ul></ul></ul><ul><ul><ul><li>Suggestions for areas of e-Service process/ infrastructure in need of improvement </li></ul></ul></ul>
  78. 78. Capacity Management After e-Service Deployment Development Deployment Deployment Deployment Software Product Service M.I. LoadRunner, etc. M.I. Astra LoadTest, etc. Segue SilkPerformer M.I. Topaz M.I. ActiveWatch GomezNetworks M.I. ActiveTest Capacity/Load Test Monitoring
  79. 79. Software Demonstration
  80. 80. Software Demonstration Mercury Interactive Astra LoadTest <ul><li>Some basic capabilities </li></ul><ul><ul><li>Record customer activities; save as “script” </li></ul></ul><ul><ul><ul><li>Activities that have meaning </li></ul></ul></ul><ul><ul><ul><ul><li>Content consumption … “service-product” subset </li></ul></ul></ul></ul><ul><ul><ul><ul><li>Economic activity … “checkout system” </li></ul></ul></ul></ul><ul><ul><ul><ul><li>Network paths … typical customer paths through system </li></ul></ul></ul></ul><ul><ul><ul><li>Site and/or service parameters … automatically iterate through subset of or all possible values </li></ul></ul></ul><ul><ul><ul><ul><li>Multiple-option click boxes </li></ul></ul></ul></ul><ul><ul><ul><ul><li>Drop-down options </li></ul></ul></ul></ul><ul><ul><li>Combine multiple customer activities into a demand “load scenario” </li></ul></ul>
  81. 81. Software Demonstration Mercury Interactive Astra LoadTest <ul><li>Some basic capabilities </li></ul><ul><ul><li>Run scenario using actual web site or n-tier service process </li></ul></ul><ul><ul><ul><li>Prior to going “live” </li></ul></ul></ul><ul><ul><ul><li>After going “live” </li></ul></ul></ul><ul><ul><li>Data collection </li></ul></ul><ul><ul><ul><li>Content transaction </li></ul></ul></ul><ul><ul><ul><li>Process technology “monitors”for typical </li></ul></ul></ul><ul><ul><ul><ul><li>WWW servers & hardware </li></ul></ul></ul></ul><ul><ul><ul><ul><li>off-the-shelf WWW component software </li></ul></ul></ul></ul><ul><ul><li>Data analysis </li></ul></ul><ul><ul><ul><li>Averages, stdev, graphs </li></ul></ul></ul><ul><ul><ul><li>Drill-down </li></ul></ul></ul><ul><ul><ul><li>Data export </li></ul></ul></ul>
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