071510 sun b_1515_feldman_stephen_forpublic


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2010 BbWorld presentation on Going Virtual with a 100% online presence.

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071510 sun b_1515_feldman_stephen_forpublic

  1. 1. Scaling Blackboard for Large Scale Distance Learning Communities Steve Feldman, sfeldman@blackboard.com
  2. 2. online learning * Learning that takes place partially or entirely over the Internet.
  3. 3. The Online Momentum Shift •  66% of degree-granting post-secondary institutions in the US offer online, hybrid/blended online and other distance education courses.1 •  Over 4.6 million students were taking at least one online course during the fall 2008 term; a 17 percent increase over the number reported the previous year.2 •  The 17 percent growth rate for online enrollments far exceeds the 1.2 percent growth of the overall higher education student population. •  By 2020, 50% of high school students will take an online course.1 3  
  4. 4. Communities are Getting Larger •  State and County Initiatives •  Consortium Programs and strategic alliances between institutions. •  Content distribution networks •  New sources or revenue to reach markets and students that were not historically accessible –  Non-traditional students are being marketed to
  5. 5. Stakes are Getting Higher •  Competition for funding by government •  Competition for revenue by students •  Learning modality changing with each technological innovation •  User expectations and online behavior changing constantly •  Hours of availability fighting toward mission critical –  Often VLEs identified as 24x7 mission critical systems, but resources to support are more like 8 x 5
  6. 6. What are we modeling… Hundreds to Thousands Concurrent Sessions Large  Ac3ve   Larger pages, graphics/ Communi3es   video, client-side interactions Scalability   Performance   Richer   Connected   Heavy   Content  and   Adop3on  of   User   Learning   Advanced   Experience   Modality     Tools   Availability   Emphasis on Asynchronous & Synchronous Collaboration Longer ClickStreams Extended/ & Disposable Access Frequent   Time  in   System  
  7. 7. scalability* The ability for a distributed system to expand by accommodating greater levels of load while maintaining similar levels of performance.
  8. 8. Scalable Deployments •  Emphasis on adoption of virtualization technologies –  Virtualization technology transparent to guest OS and application. –  Why: Take advantage of CPU and Memory expansion •  Emphasis on fast provisioning –  Provisioning technology such as Dell AIM, VMWare deployment technology and XenServer deployment technology –  Why: Solved problems to minimize human error and fast deployment. •  Emphasis on diskless systems –  Hardware is just “rented” space for CPU, Memory and Network. –  Why: Speed of network and storage so fast, why be dependent on “wired” solutions.
  9. 9. performance* The amount of useful work accomplished by a computer system compared to the time and resource used. Alternative Definition: Response time plus latency.
  10. 10. Responsive Deployments •  Large 64-bid address space… –  It’s cheaper today than 4 years ago –  Technology is heading this direction –  It’s not a bad thing… •  Plentiful CPU worker threads… –  Use only which you need –  Take advantage of hyperthreading and MT technology –  Partition via virtualization •  Many bigger…distributed environments •  Continuous maintenance –  If you want to make your systems remain fast, you have to “service” the roads. Lots of litter and potholes out there.
  11. 11. What is Performance? •  Performance is quantifiable and measureable •  Performance is also perception •  Mostly recognized from a cognitive perspective –  Instantaneous –  Immediate –  Continuous –  Captive Response   Latency   Performance   Time  
  12. 12. Realistic Approaches to Achieve Performance •  Eliminate interface and resource contention. –  Better to have more capacity than queuing •  Know your user behavior. •  Optimize for the saturated and low-bandwidth network conditions. –  Enable Compression –  Optimize Images –  Cache Static Content •  Large JVM memory allocations are not a bad thing, but rather something to expect with Java-based applications. –  Large JVM (4GB to 16GB) with aggressive options you understand. •  Two keys to the database –  Continuous maintenance –  Understand the key queries and how the CBO handles
  13. 13. availability* The capability to service a functional request without issue under conditions of desired performance and workload scalability.
  14. 14. What is Availability? •  High-availability offerings mask the effects of a system failure in order to minimize the impact of access and functional use of a system to a community of users. •  Simple Definition: –  Percentage of time the system is in its operational state. •  You will often hear the concept of 3x9’s, 4x9’s or even 5x9’s –  Planned versus Unplanned •  Availability = (Total Units of Time – Downtime) / Total Units of Time –  8760 hours in a year –  Downtime = 10 hours –  Availability = (8760 – 10)/8760 = 99.88%
  15. 15. Quick View into Availability Statistics Availability  Percentage  Model   Unexpected  Down8me  per  Year   90%   36.5  days   95%   18.25  days   98%   7.30  days   99%   3.65  days   99.5%   1.83  days   99.8%   17.52  hours   99.9%   8.76  hours   99.95%   4.38  hours   99.99%   52.6  minutes   99.999%   5.26  minutes   99.9999%   31.5s  
  16. 16. Realistic Views of Availability •  If the application is not functioning as expected, but you can login, is it available? –  Perception versus Reality –  If it’s slow, do my users feel just as bad as if they received an error? •  How do you plan for unexpected? –  Practice really does make perfect •  Do I treat the calendar from a date and time perspective differently from an availability perspective? –  Will my users cause problems if I take the site down during low usage periods/dates? –  Will the users even know that something happened? –  Can I recover fast enough?
  17. 17. Realistic Approaches to Achieve Availability •  Strategically picking redundancy in the architecture. –  Servers and storage make sense to a degree –  Monitoring makes sense –  Do advanced clustering architectures really make a difference? –  Do the costs of a dedicated DR facility and site make sense? •  Choosing the right initiatives based on the resources available to manage –  Don’t set your administrators up to fail. –  If you don’t have the capabilities on-site, don’t be skeptical of outsourcing the problem. •  Balance costs over goals –  Choose the right places to put your pennies. –  Make the business drive the decision…it’s their money!
  18. 18. Deployment: Availability •  VLEs are different beasts today then in the past. –  Communities are bigger –  Sessions last longer –  Content is richer –  Key point: Adoption is greater and users expect their sites up 24 x 7 x 365 •  Architecture is designed for many parallel instances of the product scaled in a horizontal fashion. –  Distributed physical deployments –  Virtualization is a key element •  Database failover more important than horizontal database scalability. –  Emphasis on vertical database scalability
  19. 19. Deployment: Advanced Monitoring •  Measurement is the secret sauce for successful deployments. –  Most reliable and scalable deployments measure beyond the server infrastructure •  Different types of measurements –  System/Environmental measurements –  Business measurements –  Synthetic measurements •  Collecting is only part of the prize –  Need to analyze the data to drive business decisions from the data.
  20. 20. Lifecycle of Measurement Define  Metrics:   Goal  SeVng   Iden3fy  Method  of   Reset  Expecta3ons:   Gathering:  Isolate   New  Ini3a3ves   Tools  and  Processes   Recommend   Implement   Changes:  Show   Instrumenta3on:   Business  Value   Begin  Measuring   Align  to  KPI/ROI:   Share  with   Stakeholders  
  21. 21. Different Types of Monitoring Synthe3c  Monitoring   Real  User  Monitoring   Performance  Forensic  Monitoring  
  22. 22. What is Synthetic Monitoring? •  Automated monitoring technique to measure the functional behavior of a system, sub-system or component. •  Typically a scheduled activity used to measure the availability, responsiveness and functional attributes of a common application scenario. •  Can be executed from any access point to the system in question, both internal or external. •  Also considered “Active” Monitoring of a system •  Not intended to supply load, but rather perform sampling of performance and availability •  Two methods: –  HTTP Simulation or Real Browser Emulation
  23. 23. Tools for Synthetic Transactions •  You can really use any form of HTTP emulation tool like JMeter, Grinder, MSTS, LoadRunner, SilkPerformer, SOASTA, etc… •  Some monitoring software systems like Foglight, SiteScope, Nagios, CA IntroScope, Argent Defender •  External services: Keynote, Gomez (Compuware), WebMetrics, AlertSite, Pingdom, SiteUpTime •  Browser based solution: Selenium
  24. 24. Strategies for Synthetic Transactions •  Site and Host Ping Tests should run on a multi- second basis (15s to 30s) •  Common, yet critical paths targeting functional systems for availability should run on a continuous interval (x < 5 minutes). •  Complicated paths focusing on performance and availability should run every 30 to 60 minutes. •  Repeated tests when desired SLA or outcome not achieved
  25. 25. What is Real User Experience Monitoring? •  Passive web monitoring that observes web traffic to measure the user experience. •  Provides both quality of service and responsiveness metrics in order to gauge service levels of performance and availability. •  Typically a continuous activity watching silently in a parallel channel or as a pass through channel. •  Able to capture characteristics about the entire HTTP stream to be used for forensics and user incidents. •  Most vendors package as an appliance, but beginning to see the rise of “virtual” appliances. •  Synthetic monitoring is just not enough…
  26. 26. Tools for RUM Monitoring •  Dominated by commercial vendors who have a niche in web performance and/or application performance management. –  Quest FxM –  Coradiant TrueSight –  Oracle Real User Experience Insight –  Tealeaf –  CA/NetQoS •  Rise in new tools coming from network equipment vendors like Cisco, Opnet and Citrix/NetScaler
  27. 27. Strategies for RUM Monitoring •  Identify areas of dense usage in order to highlight performance, availability and functional experience in most common components of system. •  Start with a wide lens of traffic watching and slowly narrow the area of focus to minimize the “purge” of data. •  The “purge” of data is going to happen, so be prepared to move the data out of the system into an alternative repository. –  Some of the vendors have already solved this problem via an Enterprise Data Warehouse (eg: Coradiant BI) •  Most of these tools can show –  Time 2 First Byte, Host Latency, Network Latency and E2E •  Avoid the trap of focusing on Time 2 First Byte –  You are serving an entire application from client to server
  28. 28. What is Performance Forensic Monitoring? •  Deliberate instrumentation approach to capture performance characteristics about an application deployment. •  Measures resource and interface statistics not typically visible from the application directly. •  Provides data points about application code execution that can be tied down to both the user and/or the application component. •  Can’t measure everything, but can sample consistently. –  Certain data points can be captured on a continuous basis such as Java/J2EE container statistics
  29. 29. Tools for Forensic Monitoring •  Recommended tool sets tie the PFM tool with the RUM tool. –  Foglight FxM seemless integration with Foglight Application Cartridges and Database Performance Analysis –  Coradiant TrueSight integration with Dynatrace APM (Coradiant AV) –  CA NetQoS integration with CA Wily IntroScope –  Oracle RUE Insight with Oracle Enterprise Manager for Applications and Databases. •  Limited supply of open source tools that can perform a fraction of the functionality. –  No known integrations with RUM tools –  Point based tools per container (not aggregators) –  Example tools: JConsole, Java VisualVM
  30. 30. Strategies for Forensic Monitoring •  Measure the essentials such as container interfaces and resources. •  Most vendors have rule agents to begin sampling with a greater degree of instrumentation when certain rules are broken. •  Retain statistics for extended periods of time (greater than 1 year) for annual, month, weekly, daily and hourly comparison purposes. •  Construct trending thresholds for alert purposes to invoke a planning exercise in advance of an incident. –  Yes application forensics can be used for trending purposes for events in the future as they are based on events in the past as points of reference.
  31. 31. Please provide feedback for this session by emailing BbWorldFeedback@blackboard.com. The subject of the email should be title of this session: Scaling Blackboard for Large Scale Distance Learning Communities