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Presented at Gluecon 2014 by James Urquhart and Rob Dickinson of Dell. "Mining" for application performance "gold" is harder than ever before. Applications are becoming parts of larger complex systems, and developers need to understand how the system behaves as much as how each application behaves. Following transaction details is an excellent way of monitoring application performance in these systems, and triggering automation activities such as autoscaling and recovery.
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Mining for Gold
How Browser, Network, and Application Trace Mash-Ups Can Boost Application Performance
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Introducing Dell Software• Quest Software, Enstratius are the foundation• Part of “World’s Largest Startup” with 110k
people• Sponsors at GlueCon and Velocity• 6 employees in Boulder (shameless hometown plug)
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“Mining” for performance “gold” is more difficult than it
ever was.
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Our “units of deployment” may be getting a bit simpler
But our systems are getting much more complex…
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We used to shoot for stability in a controlled environment
But we now shoot for resiliency in an ever changing
environment
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How do we handle this complexity?
Observe, Measure and Filter
Understand Intrinsic
Latencies
Understand the
Transaction Path
Understand User Behavior
Understand Resource
Contention
Improve Logical Complexity Tune, Balance, Scale
How are your tools and techniques helping you do this today?
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Applications: Defined by Infrastructure?
CDN 3rd party
Challenges of Scale, Co-location, Tenancy, Cost
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Applications Should be Defined by Activity
How do you troubleshoot and assess problems in complex dynamic environments? How are activities identified and understood?
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System Complexity is Emergent/Entropic
What about NoSQL? RoR? Ansible? AWS? (or magic unicorns?)
Complexity is linked with market success, is driven by customers, contributors, competitors, investors, partners, even time itself
Complexity doesn’t really fade,but takes on new forms
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Complete Discrete Transaction Traces
• End user data (navigation timing and full content)
• Transaction path and parameters and timing
• Related backend resources (databases, queues)
• Full detail of user session in context
Web server
App server
DB server
End users InternetFirewall
Trace and merge before aggregation
Related Infrastructure
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Why Discrete Traces?
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Response Time Breakdown
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Response Time Analysis (multi-dimensional)
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So, your next question should be “If I’m analyzing all this
data in a constantly changing environment, how the heck
do I keep up?”
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Answer?:
Automation
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The only way to ensure
resilience at scale is to make each agent (or
cluster of agents) as “self-
resilient” as possible.
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In an automated system, provisioning, scaling, failure tolerance, and disaster recovery are
different names for the same workflows• The trigger for the workflow is the
distinguishing characteristic• All are executions of the same workflows
addressing insufficient or excess computing resources
• All tend to focus on at the workload level
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Anything you can monitor can serve as a trigger for a scaling workflow
• Raw resource utilization• Basic application metrics• Understanding the transaction path
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Aggregated and/or discrete transaction data can help identify the
proper workload to scale/recover/etc.*
*No way of predicting exactly which measure is best for all time
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Example:Uses aggregate order
rate for specific movies by region to make scaling
decisions.What scales determined
by APM data.
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Example:
Several financial services firms combine market trading volume with
transaction volume to determine when to scale.
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How do we handle this complexity?
Observe, Measure and Filter
Understand Intrinsic
Latencies
Understand the
Transaction Path
Understand User Behavior
Understand Resource
Contention
Improve Logical Complexity Tune, Balance, Scale
How are your tools and techniques helping you do this today?
Software Group
Questions and Answers
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