1. Pankaj Kumar Qinglan Zhang Sagar Davasam Sowjanya Puligadda Wei Liu
Cloud Computing Term Project Cloud Monitoring and Scaling Merged CD Group
Pankaj Kumar
Qinglan Zhang
Sagar Davasam
Sowjanya Puligadda
Wei Liu

2. Overview What we have done Future work Openstack installation
Create and manage VM instances
Monitoring tools: Ganglia & Zenoss
Future work
Compare monitoring tools and present union of data metrics from Ganglia and Zenoss
Performance analysis and VM scaling
Controlling and computing availability

3. Architecture Diagram of the setup
Cloud for Monitoring, Scaling and performance measurement
Ganglia and Zenoss Tools
UBUNTU cloud OS
VM-1
Ganglia and Zenoss Tools
UBUNTU cloud OS
VM-1
UBUNTU cloud OS
VM-1
Ganglia and Zenoss Tools
Similar VM-2 and VM- 3
Similar VM-2 and VM-3
Similar VM-2 and VM-3
OPENSTACK
OPENSTACK
OPENSTACK
UBUNTU 12.04
UBUNTU 12.04
UBUNTU 12.04
Host 1 –
Host 2 –
Host 3 –

4. Openstack installation
Use devstack multi-node installation
Network configuration & NTP
Add User & Set Up SSH
Configure Controller & Slaves

5. Issues and best practices
Devstack script does not work well. The problems we have solved are as follow:
Mysql conflicts
Invalid download link
Permission denial
Su; chown –R stack:stack devstack/;
No reboot post devstack installation
VM installation using openstack dashboard

6. Create and manage VM instance
Verify the Identity Service installation
Verify the Image Service installation
Enable Networking
Generate a keypair and choose a flavor
Launch the instance

7. Scaling Analyze the performance result recorded by Ganglia and Zenoss.
Set threshold to scale out or shrink back
Try dynamic strategies to scale automatically

8. Availability Monitor the availability
Test availability in different situations:
Shut down server
Shut down single vm
Shut down multiple vms
Turn on vms

9. Ganglia Vs Zenoss

10. Zenoss Performance Metrics
Aggregate Reports
CPU usage
Free memory
Free swap
Network Input and Output
Availability - Percentage of time that a device or component is considered available
CPU Utilization – Shows monitored interfaces, devices, load average and % utility

11. Zenoss Performance Metrics
Filesystem Utilization - Shows total bytes, used bytes, free bytes, and percentage utilization for each device.
Interface Utilization - Shows the traffic through all network interfaces monitored by the system.
Interface’s rated bandwidth
Average input traffic
Average output traffic
Total average traffic across interface
% utilization of bandwidth

12. Zenoss Performance Metrics
Memory Utilization - Provides system-wide information about the memory usage for devices in the system.
Total memory
Available memory
Cache memory
Buffered memory
Percentage of memory utilized

13. Zenoss Extended Monitoring
Apache Web Server
DNS
File Transfer Protocol
Internet Relay Chat
Jabber Instant Messaging
LDAP Response Time
MySQL Database
Network News Transport Protocol
Network Time Protocol
ONC – System Remote Procedure Call
Webpage Response Time (HTTP)
VMware esxtop
Xen Virtual Hosts

14. Ganglia Vs Zenoss
Zenoss can automatically discover hosts and start monitoring them automatically. Ganglia requires an agent to run on every host to gather information
Ganglia does not monitor events, Zenoss reports about event count, event queue length
Zenoss can monitor services but Ganglia can not
Status of a running task can be received by Zenoss tool but Ganglia can not be used for

15. Zenoss Monitoring System Architecture

16. Zenoss features
Zenoss offers visibility over the entire IT stack, from network devices to applications.
Automatic discovery,
Inventory via CMDB,
Availability monitoring,
Easy-to-read performance graphs,
Sophisticated alerting,
An easy-to-use web portal

17. Why Both Ganglia and Zenoss
More data and parameters received from both tools will make
Decision making easier
Decision making robust and efficient
Decision and action will be more reliable

18. Union of data read by Ganglia and Zenoss Tool
UBUNTU cloud OS
VM-1
Project Architecture Diagram
Scale out to meet availability – add VMs
All
VMs
Ganglia and Zenoss Tools
VM provisioning Module
Union of data read by Ganglia and Zenoss Tool
read_availability < demanded_availability
Scaling algorithm
Yes
Decision making Module

19. How to Scale Scale up(horizontal) or Scale out (vertically )?
scale up: add resources to a single node
scale out: add more nodes to a system
We decide
Scale out
Add VMs on nodes in system
So when we scale our system?
System availability is under our expectation.
Scaling !!!

20. Our goal Add minimum VMs to get “three nines” availability
(Assuming adding VMs will enhance the availability)
Availability %
Downtime per year
Downtime per month*
Downtime per week
90% ("one nine")
36.5 days
72 hours
16.8 hours
95%
18.25 days
36 hours
8.4 hours
97%
10.96 days
21.6 hours
5.04 hours
98%
7.30 days
14.4 hours
3.36 hours
99% ("two nines")
3.65 days
7.20 hours
1.68 hours
99.5%
1.83 days
3.60 hours
50.4 minutes
99.8%
17.52 hours
86.23 minutes
20.16 minutes
99.9% ("three nines")
8.76 hours
43.8 minutes
10.1 minutes
99.95%
4.38 hours
21.56 minutes
5.04 minutes
99.99% ("four nines")
52.56 minutes
4.32 minutes
1.01 minutes
99.999% ("five nines")
5.26 minutes
25.9 seconds
6.05 seconds
% ("six nines")
31.5 seconds
2.59 seconds
0.605 seconds
% ("seven nines")
3.15 seconds
0.259 seconds
seconds

21. Our Algorithm
Initialize several VMs, get the monitoring data and availability.
CPU, Network, I/O, Memory usage percentage
Availability
Run our algorithm to make decision
1. Calculate the initialized weight of each factor
2. Calculate the number of VM we needed
(Wc, Wn,Wi, Wm) = f (Uc, Un,Ui, Um, Ni ,Ni+1, Ai ,Ai+1)
Wc, Wn,Wi, Wm are weights of CPU, Network, I/O and Memory
Uc, Un,Ui, Um are usage of CPU, Network, I/O and Memory
Ni ,Ni+1, Ai ,Ai+1 are number of VMs and Availability in training i and i+1.
3. Lance algorithm every a period of time to make decision and revise the algorithm itself based on data collected by Ganglia and Zenoss
Machine learning: semi-supervised approach

22. Problem Now How can we get the function f? Two possible ways:
1. Generate the function theoretically, and use the collected data to verify and revised the function.
Hard to accomplish for current stage.
2. Collected the data first, and guess the function and use some tool (MATLAB) to generate the function. After training the data, improve the accuracy and revise the algorithm.
This is what we will use.
Example next

23. Guess and Example
From (Wc, Wn,Wi, Wm) = f (Uc, Un,Ui, Um, Ni ,Ni+1, Ai ,Ai+1)
Guess general function to calculate the weights
log − 𝐴 𝑖 1− 𝐴 𝑖+1 = 𝑁 𝑖+1 − 𝑁 𝑖 𝑁 𝑖+1 ∗ 𝑊 𝑐 ∗ 𝑈 𝑐𝑖 + 𝑊 𝑛 ∗ 𝑈 𝑛𝑖 + 𝑊 𝑖 ∗ 𝑈 𝑖𝑖 + 𝑊 𝑚 ∗ 𝑈 𝑚𝑖
Wc, Wn,Wi, Wm are weights of CPU, Network, I/O and Memory
Uc, Un,Ui, Um are usage of CPU, Network, I/O and Memory
Ni ,Ni+1, Ai ,Ai+1 are number of VMs and Availability in training i and i+1.
For example:
When i = 1, 2, 3, 4, we can get 4 equation and calculate the weights.
Data from Ganglia
Data from Zenoss
Number of VMs
CPU usage
Network usage
I/O usage
Memory usage
Availability
Training 1 1
80%
50%
90%
60%
Training 2 2
75%
43%
88%
52%
92%
Training 3 3
69%
38%
70%
48%
96%
Training 4 4
35%
65%
97.8%
 Expected  ?  / / 
 99.9%
𝐴 𝑒𝑥𝑝𝑒𝑐𝑡𝑒𝑑
Goal

24. Example and Guess
After we get the weights, make decision how many VMs should be added.
log − 𝐴 𝑖 1− 𝐴 𝑖+1 = 𝑁 𝑖+1 − 𝑁 𝑖 𝑁 𝑖+1 ∗( 𝑊 𝑐 ∗ 𝑈 𝑐𝑖 + 𝑊 𝑛 ∗ 𝑈 𝑛𝑖 + 𝑊 𝑖 ∗ 𝑈 𝑖𝑖 + 𝑊 𝑚 ∗ 𝑈 𝑚𝑖 )
Get it !
𝑁 𝑖+1 = 𝑁 𝑖 1− log − 𝐴 𝑖 1− 𝐴 𝑒𝑥𝑝𝑒𝑐𝑡𝑒𝑑 ( 𝑊 𝑐 ∗ 𝑈 𝑐𝑖 + 𝑊 𝑛 ∗ 𝑈 𝑛𝑖 + 𝑊 𝑖 ∗ 𝑈 𝑖𝑖 + 𝑊 𝑚 ∗ 𝑈 𝑚𝑖 )
𝐴 𝑒𝑥𝑝𝑒𝑐𝑡𝑒𝑑 = 99.9%
𝑁 𝑖+1 : the total number of VMs after scaling.

25. OpenStack installation Controller Node

26. OpenStack installation on Compute Nodes

27. OpenStack installation on Compute Nodes

28. Instance on Compute node (CirrOS)

29. Instance on Compute node (Ubuntu)

30. Zenoss Installation on Ubuntu VM

31. Zenoss Login

32. Ganglia Installation