IDEAGEN Performance Engineering for Cloud Computing LERO

1. Performance Engineering • Cloud overview
Cloud overview
for Cloud Computing • Previous results
Previous results
• E
Example: Logging
l L i
John Murphy
John Murphy
Performance Engineering Lab • Future directions
8th European Performance Engineering Workshop ‐ EPEW 2011 Lero © 2011. Slide 1 8th European Performance Engineering Workshop ‐ EPEW 2011 Lero © 2011. Slide 2
Challenges in the Cloud
The same What is Cloud Computing?
approach(es) can
be applied to
solve queueing
problems in very XXXX as a Service ....
different areas While (Hype=True)
While (Hype=True)
Can the same
{
approach(es) for Replace XXXX with Anything
Replace XXXX with Anything
Performance
Engineering be }
applied to solve
problems in very
different areas Cloud Washing Required
8th European Performance Engineering Workshop ‐ EPEW 2011 Lero © 2011. Slide 3 8th European Performance Engineering Workshop ‐ EPEW 2011 Lero © 2011. Slide 4 22
4

2. Cloud Users Evolving Critical Systems
Cloud Computing
“As‐A‐Service”
market sizing
Report II, October
Report II, October
2010
Michael Armbrust, Armando Fox, Rean Griffith, Anthony D. Joseph, Randy Katz, Andy
Konwinski, Gunho Lee, David Patterson, Ariel Rabkin, Ion Stoica, and Matei Zaharia.
2010. A view of cloud computing. Commun. ACM 53, 4 (April 2010), 50‐58.
2010 A view of cloud computing Commun ACM 53 4 (April 2010) 50 58
8th European Performance Engineering Workshop ‐ EPEW 2011 Lero © 2011. Slide 5 22
5 8th European Performance Engineering Workshop ‐ EPEW 2011 Lero © 2011. Slide 6 22
6
Challenges in the Cloud Challenges in the Cloud
Flavours of Cloud Computing So what’s really new:
Public Cloud (Amazon, Google, Microsoft) • Infinite computing resources on demand
Private Cloud (many) • No cap ex
Hybrid Cloud Computing • Pay for what you use
Surge / Utility Computing
Surge / Utility Computing
8th European Performance Engineering Workshop ‐ EPEW 2011 Lero © 2011. Slide 7 22
7 8th European Performance Engineering Workshop ‐ EPEW 2011 Lero © 2011. Slide 8 22
8

3. Infinite computing resources on demand Infinite computing resources on demand
Ability to follow surges in workload
• No capacity planning required
• Speed of surge important to provide capacity
• Data center utilisation low
• Lower price possible due to statistical multiplexing of
many demands (self‐similarity an issue)
d d ( lf i il i i )
Workload varies over the day
Workload varies over the season
Workload varies over the season
Workload varies with events
8th European Performance Engineering Workshop ‐ EPEW 2011 Lero © 2011. Slide 9 22
9 8th European Performance Engineering Workshop ‐ EPEW 2011 22
Lero © 2011. Slide 10 10
Pay for what you use Challenges in the Cloud
• Amazon: Physical Hardware (EC2 instances), control Ten Challenges in Cloud Computing [1]
kernel upwards, lots of state information 1. Business Continuity & Service Availability
• Google: Applications on AppEngine (web applications), 2. Data Lock In
separation between compute and storage 3. Data Confidentiality/Auditability
• Microsoft: Azure more flexible than the AppEngine
Mi ft A fl ibl th th A E i 4. Performance Unpredictability
4 P f U di t bilit
5. Scalable Storage
Cost: 1 machine for 1000 hours = 1000 machines for 1 hour
Cost: 1 machine for 1000 hours = 1000 machines for 1 hour 6. Bugs in Large Scale Distributed Systems
6 Bugs in Large Scale Distributed Systems
7. Scaling Quickly
Pay as you go, or usage based pricing (not renting)
y y g , g p g( g) 8. Reputation Fate Sharing
Pay per box, or pay per resources used 9. Data Transfer Bottlenecks
10.Software Licensing
[1] ”A View of Cloud Computing”, by Michael Armbrust, Armando Fox, Rean Griffith, Anthony D. Joseph, Randy Katz, Andy
Konwinski, Gunho Lee, David Patterson, Ariel Rabkin, Ion Stoica, and Matei Zaharia
22 22
8th European Performance Engineering Workshop ‐ EPEW 2011 Lero © 2011. Slide 11 11 8th European Performance Engineering Workshop ‐ EPEW 2011 Lero © 2011. Slide 12 12

4. Typical Enterprise Systems Data Data Everywhere
• 150 billion GB (exabytes) of data created in 2005; Eight
times that amount (1,200 exabytes) in 2010
( , y )
• The amount of enterprise data will grow about 650% over
the next five years, the vast majority of it unstructured, or
not included in any database.
• Log data is the fasted‐growing data source at large
organizations
• Many organizations are currently producing terabytes of
log data per month
log data per month
8th European Performance Engineering Workshop ‐ EPEW 2011 Lero © 2011. Slide 13 8th European Performance Engineering Workshop ‐ EPEW 2011 Lero © 2011. Slide 14
Cloud Services Log Management
• Not deployed in house
Not deployed in house • Automatic Collection, Analysis & Visualization of Log
Automatic Collection, Analysis & Visualization of Log
Data
• Services need to handle 1000’s of customers
Services need to handle 1000 s of customers
• Use Cases:
• Services need to handle 1000’s of enterprise systems
Services need to handle 1000 s of enterprise systems Problem Determination
Problem Determination
Operations
• Processing higher volumes of data required
Processing higher volumes of data required Security
Compliance & Auditing
8th European Performance Engineering Workshop ‐ EPEW 2011 Lero © 2011. Slide 15 8th European Performance Engineering Workshop ‐ EPEW 2011 Lero © 2011. Slide 16

5. Log Maths Typical Log Volumes
Customer Type Log Volumes Events per Second Events per Day
Large Cloud Provider 50 Terabyes per Day 2,000,000 172,000,000,000
100,000 log messages / second
100,000 log messages / second Large Social Media 25 Terabytes per Day 1,000,000
1 000 000
Organisation
x 300 bytes / log message = 28.6 MB Telecom Middleware/ 1 Terabyte per Day 50,000
Applications
x 3600 seconds 100 6 GB / hour
x 3600 seconds ~ 100.6 GB / hour Large Organisation 300 GB Per Day 15,000
x 24 hours ~ 2.35 TB / day (>1000 employees)
Online Marketing Org 100 GB per day 5,000 432,000,000
x 365 days 860 5 TB / year
x 365 days ~ 860.5 TB / year Small 10 GBs per Day 500
Data Centre
x 3 years ~ 2.52 PB SAAS Educational Tools 5Gbs Per Day 250
Single IBM Test Team 2 GBs per Day 100
Online Multimedia 700Mbs Per Day 35
From Anton Chuvakin’s Blog Aug 2010
http://chuvakin.blogspot.com/ Early Stage Start up
E l St St t 50Mbs Per Day
50Mb P D 25 2,000,000
2 000 000
8th European Performance Engineering Workshop ‐ EPEW 2011 Lero © 2011. Slide 17 8th European Performance Engineering Workshop ‐ EPEW 2011 Lero © 2011. Slide 18
Partial results in log management Real Time Correlation Engine RTCE
• High volume data processing
• IBM & UCD Research (since 2007)
IBM & UCD Research (since 2007)
• Correlation
• In house deployment
In house deployment
• Searching / Indexing
• In use across 10’s of IBM teams (Dublin US China)
In use across 10 s of IBM teams (Dublin, US, China)
• Pattern detection (symptom database)
• Ability to process 80 000 events per second
Ability to process 80,000 events per second
• Real time requirements
q
8th European Performance Engineering Workshop ‐ EPEW 2011 Lero © 2011. Slide 19 8th European Performance Engineering Workshop ‐ EPEW 2011 Lero © 2011. Slide 20

6. RTCE details logentries.com
Network of Componenta
Nodea
agents
Log
Nodeb Nodec Agent • Log Management as a Service
Log Management as a Service
Log
Built on Amazon Web services
Noded Componentb
Testing environment
Scales Horizontally
Node detail Inter-agent
communication o CPU
Presentation o Storage
St
Web server Agent
Distributed File System/ NoSQL DBs (Hadoop)
Distributed File System/ NoSQL DBs (Hadoop)
Needs to handle TB per day (2TB per customer)
p y( p )
Usera Userb Userc Userd
8th European Performance Engineering Workshop ‐ EPEW 2011 Lero © 2011. Slide 21 8th European Performance Engineering Workshop ‐ EPEW 2011 Lero © 2011. Slide 22
21
Key log research challenges Conclusions
• Cloud computing is a tag for the next while...
• Scalable hardware resources
Cloud
Auto scaling • Major issues still to be fully addressed
• Indexing large volumes of data in real time • Previous performance engineering in enterprise,
No SQL / Columnar Storage
grid, data centre or mainframe research can
grid data centre or mainframe research can
probably feed into the solutions
• Processing millions of events per second
Bloom Filters Thank you!
22
8th European Performance Engineering Workshop ‐ EPEW 2011 Lero © 2011. Slide 23 8th European Performance Engineering Workshop ‐ EPEW 2011 Lero © 2011. Slide 24 24