1.
Monitoring
Challenges
Monitorama June 2016
Adrian Cockcroft
@adrianco

2.
What does @adrianco do?
@adrianco
Technology Due
Diligence on
Deals
Presentations at
Companies and
Conferences
Tech and Board
Advisor
Support for
Portfolio
Companies
Consulting and
Training
Networking with
Interesting PeopleTinkering with
Technologies
Vendor
Relationships
Previously: Netflix, eBay, Sun Microsystems, Cambridge Consultants, City University London - BSc Applied Physics

3.
Monitorama 2014…

4.
Monitorama 2016
What problems does monitoring address?
Why isn’t this a solved problem already?
Who gets disrupted by what?
Stuff I’ve been tinkering with

5.
Measuring business value
Problem detection and diagnosis

6.
“Ultimately business value is what
the business values, and that is that.”
Mark Schwartz CIO DHS/DCIS

7.
Business Value of Monitoring
Customer happiness
Cost efficiency
Safety and security
Compliance

8.
Business Value of Monitoring
Customer happiness
Cost efficiency
Safety and security
Compliance

9.
Customer Happiness
Time to value
Availability
Response time

10.
Cost Efficiency
Utilization
Optimization
Automation

11.
Why isn’t this a solved
problem already?

12.
Why isn’t there one
standard for monitoring?

13.
Why isn’t there one
standard for monitoring?
We tried that once, immediately obsoleted by rise of Windows NT
X/Open Universal Measurement Architecture - 1997
http://pubs.opengroup.org/onlinepubs/009657299/c427-1/front.htm

14.
Monitoring Evolution
Challenges
Platform - Entities - Hierarchy
Interfaces - Metrics - Schema
Scale - Ephemerality
Different vendors and tools in
each generation…

15.
1970’s Mainframes
Monitoring Evolution
Challenges
Platform - Entities - Hierarchy
Interfaces - Metrics - Schema
Scale - Ephemerality
Different vendors and tools in
each generation…

16.
1970’s Mainframes
1980’s Minicomputers
Monitoring Evolution
Challenges
Platform - Entities - Hierarchy
Interfaces - Metrics - Schema
Scale - Ephemerality
Different vendors and tools in
each generation…

17.
1990’s Unix Servers
1970’s Mainframes
1980’s Minicomputers
Monitoring Evolution
Challenges
Platform - Entities - Hierarchy
Interfaces - Metrics - Schema
Scale - Ephemerality
Different vendors and tools in
each generation…

18.
1990’s Unix Servers
1970’s Mainframes
2000’s Windows on x86
1980’s Minicomputers
Monitoring Evolution
Challenges
Platform - Entities - Hierarchy
Interfaces - Metrics - Schema
Scale - Ephemerality
Different vendors and tools in
each generation…

19.
1990’s Unix Servers
1970’s Mainframes
2000’s Windows on x86
1980’s Minicomputers
2000’s Linux on x86
Monitoring Evolution
Challenges
Platform - Entities - Hierarchy
Interfaces - Metrics - Schema
Scale - Ephemerality
Different vendors and tools in
each generation…

20.
1990’s Unix Servers
1970’s Mainframes
2000’s Windows on x86
1980’s Minicomputers
2000’s Linux on x86
2000’s VMware on blades
Monitoring Evolution
Challenges
Platform - Entities - Hierarchy
Interfaces - Metrics - Schema
Scale - Ephemerality
Different vendors and tools in
each generation…

21.
1990’s Unix Servers
1970’s Mainframes
2000’s Windows on x86
1980’s Minicomputers
2000’s Linux on x86
2000’s VMware on blades
2010’s Public cloud
Monitoring Evolution
Challenges
Platform - Entities - Hierarchy
Interfaces - Metrics - Schema
Scale - Ephemerality
Different vendors and tools in
each generation…

22.
1990’s Unix Servers
1970’s Mainframes
2000’s Windows on x86
1980’s Minicomputers
2000’s Linux on x86
2000’s VMware on blades
2010’s Public cloud
2010’s Containers
Monitoring Evolution
Challenges
Platform - Entities - Hierarchy
Interfaces - Metrics - Schema
Scale - Ephemerality
Different vendors and tools in
each generation…

23.
1990’s Unix Servers
1970’s Mainframes
2000’s Windows on x86
1980’s Minicomputers
2000’s Linux on x86
2000’s VMware on blades
2010’s Public cloud
2010’s Containers
2010’s Serverless
Monitoring Evolution
Challenges
Platform - Entities - Hierarchy
Interfaces - Metrics - Schema
Scale - Ephemerality
Different vendors and tools in
each generation…

24.
Why don’t monitoring
vendors adapt and survive?

25.
Cost per node drops
Revenue opportunity decreases
Waves of disruption
New vendors have new
schema’s, an order of
magnitude lower cost per node,
and many more shorter lived
nodes to monitor

26.
$Millions (illustrative order of magnitude costs)
Cost per node drops
Revenue opportunity decreases
Waves of disruption
New vendors have new
schema’s, an order of
magnitude lower cost per node,
and many more shorter lived
nodes to monitor

27.
$Millions (illustrative order of magnitude costs)
$1M
Cost per node drops
Revenue opportunity decreases
Waves of disruption
New vendors have new
schema’s, an order of
magnitude lower cost per node,
and many more shorter lived
nodes to monitor

28.
$100K
$Millions (illustrative order of magnitude costs)
$1M
Cost per node drops
Revenue opportunity decreases
Waves of disruption
New vendors have new
schema’s, an order of
magnitude lower cost per node,
and many more shorter lived
nodes to monitor

29.
$100K
$Millions (illustrative order of magnitude costs)
$10K
$1M
Cost per node drops
Revenue opportunity decreases
Waves of disruption
New vendors have new
schema’s, an order of
magnitude lower cost per node,
and many more shorter lived
nodes to monitor

30.
$100K
$Millions (illustrative order of magnitude costs)
$10K
$1M
$5K
Cost per node drops
Revenue opportunity decreases
Waves of disruption
New vendors have new
schema’s, an order of
magnitude lower cost per node,
and many more shorter lived
nodes to monitor

31.
$100K
$Millions (illustrative order of magnitude costs)
$10K
$1M
$5K
$1K per core
Cost per node drops
Revenue opportunity decreases
Waves of disruption
New vendors have new
schema’s, an order of
magnitude lower cost per node,
and many more shorter lived
nodes to monitor

32.
$100K
$Millions (illustrative order of magnitude costs)
$10K
$1M
$5K
$1K per core
$100’s per month
Cost per node drops
Revenue opportunity decreases
Waves of disruption
New vendors have new
schema’s, an order of
magnitude lower cost per node,
and many more shorter lived
nodes to monitor

33.
$100K
$Millions (illustrative order of magnitude costs)
$10K
$1M
$5K
$1K per core
$100’s per month
$10’s per month
Cost per node drops
Revenue opportunity decreases
Waves of disruption
New vendors have new
schema’s, an order of
magnitude lower cost per node,
and many more shorter lived
nodes to monitor

34.
$100K
$Millions (illustrative order of magnitude costs)
$10K
$1M
$5K
$1K per core
$100’s per month
$10’s per month
$1’s per month
Cost per node drops
Revenue opportunity decreases
Waves of disruption
New vendors have new
schema’s, an order of
magnitude lower cost per node,
and many more shorter lived
nodes to monitor

35.
Vendor Landscape

36.
A Tragic Quadrant
Ability to scale
Ability to
handle
rapidly
changing
microservices
In-house tools
at web scale
companies
Most current
monitoring & APM
tools
Next generation
APM
Next generation
Monitoring
Datacenter
Cloud
Containers
100s 1,000s 10,000s 100,000s
Lambda

37.
A Tragic Quadrant
Ability to scale
Ability to
handle
rapidly
changing
microservices
In-house tools
at web scale
companies
Most current
monitoring & APM
tools
Next generation
APM
Next generation
Monitoring
Datacenter
Cloud
Containers
100s 1,000s 10,000s 100,000s
Lambda
Vendors - tell me where you belong on this plot…

38.
Tinkering

39.
Simulated Microservices
Model and visualize microservices
Simulate interesting architectures
Generate large scale configurations
Stress test real monitoring tools
Code: github.com/adrianco/spigo
Simulate Protocol Interactions in Go
Simian Army Visualizations
ELB Load Balancer
Zuul
API Proxy
Karyon
Business Logic
Staash
Data Access Layer
Priam
Cassandra Datastore
Three
Availability
Zones
Denominator
DNS Endpoint

40.
Zipkin Trace for one Spigo Flow

41.
Response Times

42.
See http://www.getguesstimate.com/models/1307
Guesstimate

43.
memcached hit %
memcached response mysql response
service cpu time
memcached hit mode
mysql cache hit mode
mysql disk access mode
Hit rates: memcached 40% mysql 70%
Guesstimate

44.
Spigo Histogram Results
name:
storage.*.*..load00...load.denominator_serv
quantiles: [{50 47103} {99 139263}]
From To Count Prob Bar
20480 21503 2 0.0007 :
21504 22527 2 0.0007 |
23552 24575 1 0.0003 :
24576 25599 5 0.0017 |
25600 26623 5 0.0017 |
26624 27647 1 0.0003 |
27648 28671 3 0.0010 |
28672 29695 5 0.0017 |
29696 30719 127 0.0421 |####
30720 31743 126 0.0418 |####
31744 32767 74 0.0246 |##
32768 34815 281 0.0932 |#########
34816 36863 201 0.0667 |######
36864 38911 156 0.0518 |#####
38912 40959 185 0.0614 |######
40960 43007 147 0.0488 |####
43008 45055 161 0.0534 |#####
45056 47103 125 0.0415 |####
47104 49151 135 0.0448 |####
49152 51199 99 0.0328 |###
51200 53247 82 0.0272 |##
53248 55295 77 0.0255 |##
55296 57343 66 0.0219 |##
57344 59391 54 0.0179 |#
59392 61439 37 0.0123 |#
61440 63487 45 0.0149 |#
63488 65535 33 0.0109 |#
65536 69631 63 0.0209 |##
69632 73727 98 0.0325 |###
73728 77823 92 0.0305 |###
77824 81919 112 0.0372 |###
81920 86015 88 0.0292 |##
86016 90111 55 0.0182 |#
90112 94207 38 0.0126 |#
94208 98303 51 0.0169 |#
98304 102399 32 0.0106 |#
102400 106495 35 0.0116 |#
106496 110591 17 0.0056 |
110592 114687 19 0.0063 |
114688 118783 18 0.0060 |
118784 122879 6 0.0020 |
122880 126975 8 0.0027 |
Normalized probability
Response time distribution
measured in nanoseconds
using High Dynamic
Range Histogram
:# Zero counts skipped
|# Contiguous buckets
Median and 99th
percentile values
service time for
load generator
Cache hit Cache miss

45.
Serverless

46.
Serverless
AWS Lambda - lots of production examples
Google Cloud Functions Azure Functions alpha launched
IBM OpenWhisk - open source
Startup activity: iron.io , serverless.com, apex.run toolkit

47.
Monitorless Architecture
API Gateway
Kinesis S3DynamoDB

48.
Monitorless Architecture
API Gateway
Kinesis S3DynamoDB

49.
Monitorless Architecture
API Gateway
Kinesis S3DynamoDB
Monitorable entities only exist during an execution trace

50.
AWS Lambda Reference Archhttp://www.allthingsdistributed.com/2016/05/aws-lambda-serverless-reference-architectures.html

51.
Serverless Programming Model
Event driven functions
Role based permissions
Whitelisted API based security
Good for simple single threaded code

52.
Serverless Cost Efficiencies
100% useful work, no agents, overheads
100% utilization, no charge between requests
No need for extra capacity for peak traffic
Anecdotal costs ~1% of conventional system
Ideal for low traffic, Corp IT, spiky workloads

53.
Serverless Work in Progress
Tooling for ease of use
Multi-region HA/DR patterns
Debugging and testing frameworks
Monitoring, end to end tracing
Using AWS Lambda to monitor AWS

54.
DIY On-Premise
Serverless Operating Challenges
Scheduling and startup latency
Execution and monitoring overhead
Charging model
Capacity planning

55.
Monitoring Challenges
Too much new stuff
Too ephemeral
Price disruption

56.
Thanks!

57.
Thanks!
Also speaking at: Docker Portland Meetup Wednesday Evening @Puppetlabs - Microservices: Whats Missing

58.
Security
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