Best practices for optimizing blackboard learn
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Best Practices for Optimizing Blackboard Learn. Steve Feldman, [email protected] What We’ll Cover. A deployment approach for the ages. How to make use of the new sizing guide. Optimizing the platform components. Flexible and Scalable Application Deployment.

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Best Practices for Optimizing Blackboard Learn

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Best practices for optimizing blackboard learn

Best Practices for Optimizing Blackboard Learn

Steve Feldman,

[email protected]

What we ll cover

What We’ll Cover

  • A deployment approach for the ages.

  • How to make use of the new sizing guide.

  • Optimizing the platform components.

Flexible and scalable application deployment

Flexible and Scalable Application Deployment

Flexible and scalable application deployment1

Flexible and Scalable Application Deployment

  • An ideal deployment will contain…

    • Availability at every edge of the application environment

      • Strategy: Physical distribution of load-balanced systems

      • Strategy: Minimum DB recovery, not necessarily 0 downtime

    • Consumption of every possible machine resource

      • Strategy: Virtualization provisioning

    • Techniques for improving user experience

      • Strategy: Techniques and tools for achieving page-level SLAs

    • Large addressable memory spaces

      • Strategy: 64-bit and large OS process space allocations

Flexible and scalable application deployment2

Flexible and Scalable Application Deployment

  • An ideal deployment will contain…

    • Minimum Storage Recovery Time

      • Strategy: Enterprise storage with Snapshot capabilities

    • Advanced monitoring for operations and planning

      • Strategy: Measurement tools and analytics

    • Automation…Automation…Automation

      • Strategy: Investment in repeatable, reliable automated processes.

Deployment availability

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 resource utilization

Deployment: Resource Utilization

  • Moore’s law is in full effect

    • CPUs are getting faster with more cores

    • Memory is in abundance and cheap

    • Storage is grossly abundant

  • Massive systems can be obtained at low cost, but cannot be saturated in stand-alone configurations.

  • Virtualization offers the opportunity…

    • Deploy with availability in mind

    • Saturate system resources

Deployment improving page responsiveness

Deployment: Improving Page Responsiveness

  • Gzip…Gzip…Gzip…

    • All of our supported browsers handle gzip?

    • Reduces payload

      • Improves lower latency connections like Cable, DSL and Dial-up

    • Minor overhead on the application layer (~2% to ~5%)

      • Have the option to perform at the load-balancer layer

    • Most Bb deployments do not enable Gzip at all

      • Even when enabled, some proxies and software packages mess-up the Accept Encoding Header

  • Optimize your images

    • Page size really does matter

    • Reduce the size without reducing the quality

Deployment large address space

Deployment: Large Address Space

  • As of Blackboard Learn™ Release 9.1 all supported/certified configurations include a 64-bit option.

  • Pushing more processing to client and DB over the last few releases, but major memory management technique is to use more application caches.

    • Memory stays persistent longer

    • Less wasteful from a creation/destruction perspective, but puts greater demands on larger spaces.

  • Most of our application testing focused on 4GB and 8GB JVM deployments on 6GB and 10GB OS spaces.

    • Limited testing at 16GB and 32GB

Deployment storage mttr

Deployment: Storage MTTR

  • Reference architecture pushes for “diskless” boots in which ISCSI or NFS partition resides on an enterprise storage system.

  • Both OS/VM partition and data partition served up from remote storage deployment designed for performance and scalability.

    • Make your hardware work from a CPU, Memory and Network perspective…save the Disk for the experts.

  • Consider scenarios for reducing “Mean Time to Recovery or Repair”

    • Snapshot technology offering minutes for recovery

Deployment advanced monitoring

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.

Deployment automation

Deployment: Automation

  • Goal of moving to 100% unattended and fully automated deployment.

  • Reduce MTTR and prevent disasters

  • Automation requires intimacy…intimacy requires knowledge

  • Use automation for

    • Configuration Management and Deployment

    • Maintenance

    • Repeatable tasks

    • Adaptive tuning

    • Minimize possibility of human error


Sizing the application to hyper thread or not

Sizing the Application: To Hyper-Thread or Not

  • Applies to Intel deployments only

    • “..delivers thread-level parallelism on each processor resulting in more efficient use of processor resources—higher processing throughput—and improved performance on multi-threaded software.” –Intel Corporation

  • Greatly improved in series 5500+ processor

  • Provides double worker thread capacity

  • If it’s not turned on, stop what you are doing and enable it ASAP!

Moving away from clusters

Moving Away from Clusters

  • Tomcat clusters were introduced back in Blackboard Learn 7.X prior to the transcendence of server virtualization.

    • Only supported 32-bit configurations at the time, but systems were being shipped with 8GB, 16GB and 32+GB of RAM.

    • Needed a way to take advantage of memory, but were limited to a 1.7GB address space.

    • Recommending “distributed” deployment approaches as well.

      • Still applies, but can be achieved differently.

  • Clustering has its advantages, but also has its penalties.

    • Failover not as ideal as one would desire.

  • Best approach is to scale up with 64-bit spaces and distributed JVMs across both virtual and physical configs

Sizing using p a rs

Sizing Using P.A.Rs

  • PAR = Performance Archetype Ratios

    • Methodology for sizing based on units of work that can be applied to “unit of configuration”

  • PARs assume a world of linear units

    • Add units of configuration to meet growing demands of unit of work.

  • PARs based on (4) key resources: CPU, Memory, Disk and I/O and application interfaces (threads and connections).

  • Used for making capacity decisions for sizing both virtual and physical components.

Optimizing the web server

Optimizing the Web Server

  • The web server in the Blackboard Learn configuration is nothing more than a gateway to the application container.

    • When clusters were more relevent, the web server acted as a pseudo load-balancer.

  • Not many opportunities for optimization other than

    • KeepAlives

    • Interfaces

    • Compression

  • It can become a bottleneck if not properly optimized

    • Better to have high ceilings from an interface perspective

Optimizing the jvm

Optimizing the JVM

  • Java hotspot offers standard –X and non-standard –XX options for performance and behavior.

    • -X options are always guaranteed across releases and patches of Java.

    • -XX options must be used with caution as they are subject to change with any release of Java.

  • -XX options should be tested and measured using the production safe arguments.

  • Read the release notes of Java for “performance” updates


Optimizing the jvm1

Optimizing the JVM

  • Cross-platform recommendation for using Concurrent Mark Sweep Collector

    • Best optimized for 64-bit address

    • Combine –XX:+UseConcMarkSweepGCwith –XX:+UseParNewGC

  • Manually size New Space using –XX:NewSize and –XX:MaxNewSize options (1/4 to 1/3 total heap).

    • Consider Survivor Space ratios 4 or lower.

  • Be careful about sharing –XX non-standard options across customers.

    • If you don’t understand what the option does and it’s not recommended by Blackboard, best choice is to not use it.

Optimizing the database sql server

Optimizing the Database: SQL Server

  • # of data files makes no difference on SQL Server for Data and Transaction

  • Allow the data/transaction files to grow as big as they want within reason.

    • What’s reason: 64GB


  • TempDB is completely different story

    • # of files = # of DB Threads

    • Set first X files to a uniform size, set last file to same size with auto-extension ON

    • Determine size need over time

  • Separate volume for paging file

Optimizing the database sql server1

Optimizing the Database: SQL Server

  • Be aware of MDOP: Max Degree of Parallelism

    • Setting to unlimited can have a negative affect on query performance unintentionally.

  • AWE can and does work on 64-bit systems


  • Two nuggets of information:

    • Learn How to Use SQL DMVs

    • Study SQL Server Wait Events and Tuning

Optimizing the database oracle

Optimizing the Database: Oracle

  • Balance I/Os across multiple data files (~2 to 8GB per file).

  • REDO is critical to performance a session/query level.

    • Be aware of how much REDO is being used over time.

    • NOLOGGING will disable, be we rarely use NOLOGGING

  • TEMP is very complex and used for managing transient data.

    • One TEMP file is adequate

    • If latency exists on TEMP, consider introducing TEMP file groups

  • SGA is important, but PGA can be your best friend or your worst enemy with high concurrency.

Optimizing the database oracle1

Optimizing the Database: Oracle

  • Oracle DBO can be your friend

    • Must understand optimizer behavior

    • Willingness to read Cost Execution Plans

  • Using Wait Events and Cost Execution Plans for tuning initiatives

    • Wait events are at a system, session and query level

  • Importance of Statistics and Histograms

    • CBO is just guessing without properly set statistics and histograms.

    • CBO is dependent on your data.

Best practices for optimizing blackboard learn

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