NAVER Open Source Seminar - Performance does matter 2019.07.11 --- A talk discussing the various approaches to scaling the measurement, data collection, and recording of distributed traces along the pipeline from the point of instrumentation to the storage of a trace record. It will challenge attendees to (re)consider the cost-benefit of analysis of what a trace is today, and what it might look like in the future. <Speaker> A founder of Autoletics and experienced systems engineer with particular expertise in self-adaptive software runtimes, application performance monitoring and management as well as cost execution optimization and scalability engineering and more...

1. WILLIAM LOUTH
PRODUCT R&D
DISTRIBUTED TRACING WORKSHOP
SEOUL, SOUTH KOREA
JULY 2019
Scaling
Distributed
Tracing

2. Company

3. Product

4. Product

5. @Autoletics

6. Technologies

7. Research
Controllability
Resilience
QoS for Apps
Resource Management
Self-Adaptive
Observability
Service Signals
Episodic Memories
Operability
Visualizations
Learning
Intelligence
Microservices
Containers
Event Architectures
Reactive Systems
Serverless
Actors

9. Observability
Distributed
Tracing
Metrics
Logging
Reality
Activity
Metering Mirrored
Simulation Branch
Signalling
Behavioral
Signalling
Model
Reconstruction • Explorative • Developer • Machine
Operational • Effectiveness • DevOps • Human

10. Measurement Model Memory
Observability

11. Scaling
M
easurem
ent
Overhead
M
odel
Transport
M
em
ory
Storage
Accuracy
Attention
Significance

12. Scales
Short
Long Deep
Shallow Small
Big
Local
Remote
Machine
Human
Signal
Data

13. Cybernetics
Observability Controllability
attention
+ action
perception

14. Coordination
Observability
Controllability
Monitoring
Management
Strategic
Tactical

15. Cognition
Information
Significance
Observability
Monitoring
Sensory
Sem
antics

16. Communication
Objects Observer
Signals State
Inference

17. Collection
Memory
Model
Measurement

18. Collective
M
em
ory
M
odel
Measurement
M3 M3 M3
M3 M3
M3

19. Conduction
Instrument Measure Collect Transmit Store
Data•Cost•Time

20. A
B C
D E
Trace

21. Transmit
A
B C
D E
M3

22. Federate
A
B
C
D E
M3
M3
M3
M3M3

23. Backtrace
A
B C
D E
M3

26. Sample
Random Conditional
Windowing

27. Limit
1 2 3 4 12 34 1 2 3 4

29. Discriminate

30. Buffering

31. Drop

32. Discard

33. Delay

34. Degrade
A
B C
D E
A
B C
A

36. Costs
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