Scaling Blackboard for Large
Scale Distance Learning
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
Communities are Getting Larger
• State and County Initiatives
• Consortium Programs and
strategic alliances between
• Content distribution networks
• New sources or revenue to
reach markets and students
that were not historically
– 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
• User expectations and online behavior
• Hours of availability fighting toward
– Often VLEs identified as 24x7 mission
critical systems, but resources to support
are more like 8 x 5
What are we modeling…
Hundreds to Thousands
Larger pages, graphics/ Communi3es
Emphasis on Asynchronous
& Synchronous Collaboration
Longer ClickStreams Extended/
& Disposable Access
scalability* The ability for a distributed
system to expand by accommodating greater levels
of load while maintaining similar levels of
• Emphasis on adoption of virtualization technologies
– Virtualization technology transparent to guest OS and
– Why: Take advantage of CPU and Memory expansion
• Emphasis on fast provisioning
– Provisioning technology such as Dell AIM, VMWare
deployment technology and XenServer deployment
– Why: Solved problems to minimize human error and fast
• Emphasis on diskless systems
– Hardware is just “rented” space for CPU, Memory and
– 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.
• 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
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
– 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
– 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
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
• 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
– 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!
• 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
– Emphasis on vertical database scalability
Deployment: Advanced Monitoring
• Measurement is the secret sauce for successful
– 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
Lifecycle of Measurement
Different Types of Monitoring
What is Synthetic Monitoring?
• Automated monitoring technique to measure the
functional behavior of a system, sub-system or
• 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
• 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
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
• 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
– Quest FxM
– Coradiant TrueSight
– Oracle Real User Experience Insight
• 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
– 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
• 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
• 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
– Foglight FxM seemless integration with Foglight Application
Cartridges and Database Performance Analysis
– Coradiant TrueSight integration with Dynatrace APM (Coradiant
– 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
• Most vendors have rule agents to begin sampling with a
greater degree of instrumentation when certain rules are
• Retain statistics for extended periods of time (greater than
1 year) for annual, month, weekly, daily and hourly
• 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.
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Scaling Blackboard for Large Scale Distance Learning