Scaling Blackboard for Large Scale Distance Learning

Communities

Steve Feldman, sfeldman@blackboard.com

online learning * Learning that takes place partially or entirely over the Internet.

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  

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

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

Connected  Learning  Modality    

Large  Ac3ve  Communi3es  

Heavy  Adop3on  of  Advanced  Tools  

Extended/Frequent  Time  in  System  

Richer  Content  and  

User  Experience  

What are we modeling… Hundreds to Thousands Concurrent Sessions

Emphasis on Asynchronous & Synchronous Collaboration

Longer ClickStreams & Disposable Access

Larger pages, graphics/video, client-side interactions

Performance  

Availability  

Scalability  

scalability* The ability for a distributed system to expand by accommodating greater levels of load while maintaining similar levels of performance.

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.

performance* The amount of useful work accomplished by a computer system compared to the time and resource used.

Alternative Definition: Response time plus latency.

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.

What is Performance? •  Performance is quantifiable and measureable

•  Performance is also perception

•  Mostly recognized from a cognitive perspective –  Instantaneous –  Immediate –  Continuous –  Captive

Response  Time   Latency   Performance  

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

availability* The capability to service a functional request without issue under conditions of desired performance and workload scalability.

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%

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  

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?

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!

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

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.

Lifecycle of Measurement

Define  Metrics:  Goal  SeVng  

Iden3fy  Method  of  Gathering:  Isolate  Tools  and  Processes  

Implement  Instrumenta3on:  Begin  Measuring  

Align  to  KPI/ROI:  Share  with  Stakeholders  

Recommend  Changes:  Show  Business  Value  

Reset  Expecta3ons:  New  Ini3a3ves  

Different Types of Monitoring

Synthe3c  Monitoring  

Real  User  Monitoring  

Performance  Forensic  Monitoring  

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

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

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

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…

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

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

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

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

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.

Please provide feedback for this session by emailing BbWorldFeedback@blackboard.com.

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Scaling Blackboard for Large Scale Distance Learning Communities