CloudStack’s VM placement policies don’t fit the current need of the industry. We have to create external tools to load balance VMs on hosts. We have an idea (which is under heavy development) to implement an AI algorithm to manage VM placement on a host. The algorithm is triggered based on a criteria (like when there is no place for a VM with 64G Ram and 32 core), which results in changing the placement of the VMs on a system. My name is Sina, I'm a software engineer with a passion for distributed applications, computer networks, and the cloud. To turn my passion into a career, I joined Computer Network Lab at my university as a research assistant and developed various network and cloud tools. After that, I joined the LeaseWeb company to expand my knowledge of complex production cloud environments. Currently, I am the Software Engineer in the CloudStack team of LeaseWeb. I’m responsible for all the infrastructure and management tasks for our public and private cloud, which runs in 8 regions in 7 different countries on 3 different continents. ----------------------------------------- CloudStack Collaboration Conference 2022 took place on 14th-16th November in Sofia, Bulgaria and virtually. The day saw a hybrid get-together of the global CloudStack community hosting 370 attendees. The event hosted 43 sessions from leading CloudStack experts, users and skilful engineers from the open-source world, which included: technical talks, user stories, new features and integrations presentations and more.

1. Leaseweb CloudStack
Using AI to place VMs

2. Sina Kashipazha
• Software Engineer @Leaseweb
• Love Java and Cloud
• Hobbies:
• Cooking
• Coding
• Scale models

3. • Virtual Private Servers
• Private Cloud Resource pools
• Leaseweb Internal
Globally operating Infrastructure as
a Service provider. Active
CloudStack platforms in:
• Germany
• Netherlands
• Singapore
• United Kingdom
• United States

4. 1. Better use of
resources
Targets
3. Save costs
2. Ability to compare
the placements

5. The How
How we approached this initiative and what we have learned along the way

6. Supervised Learning
• When input and output, both labels are known
• model learns from data to predict output for similar input data
Unsupervised Learning
• When output data is unknown
• it is needed to find patterns in data given
Reinforcement Learning
• Algorithms learn to perform an action from experience.
• Here algorithms learn through trial and error, which action yields the greatest rewards.
• The objective is to choose actions that maximize the expected reward over a given amount of time.
Which AI?

7. Simulation
Elements
• Simulation of hosts, VMs, Service Offerings
Functionalities
• Only required functionality for VM placement
Evaluation
• Way of comparing the state of the system
Fuzzy
• Fuzzy functions to describe the Cloudstack Status

8. Let’s see the code J