Scalable Server Load Balancing Inside Data Centers

Dana Butnariu

Princeton University

Computer Science Department

July – September 2010

Joint work with Richard Wang and Professor Jennifer Rexford

Getting started

Client2

Client5

Client4

Switch

Server1

Server2

Server3

Server4

The InternetClient3

Client1

Client6

Getting started

• Client – any device requesting a web service• Server – device which handles a client request and

provides the web service• Data center – location containing a group of servers• Server load – number of client requests a server must

handle• Load balancing – directing a client request to a particular

server, managing server loads according to a certain algorithm

• Switch – device which enables client and server communication

Energy and Load balancing

• Servers: Are located in data centers in different areas of the world. Energy cost and availability varies from one location to the other. Energy cost and consumption depends on client – server distance.

• Load balancing: Tries to lower the energy cost and usage without affecting user

perceived performance. Can achieve this goal by selecting close by data centers. Can achieve this goal by using only certain servers and powering

down the rest.

Load balancing today

• Old approach – have a separate device, the load balancer.

• New approach – implement the load balancing in devices already existent in the network.

• Old approach: Costly device Consumes energy Hard to program Crashes easily

• New approach: Already existing device No additional costs Easy to program and

customize Stable and reliable

What, why and how?

• What: Scalable Server Load Balancing without sacrificing user perceived performance.

• Why: To save energy and lower the cost of energy used to process client requests.

• How: Using a new emerging technology called OpenFlow which enables switch programming.

Project Steps

• Establish the network design.

• Design the load balancing application.

• Implement the load balancing application.

• Test the load balancing application.

Network Design

• Establish the network design. How many clients, servers, switches? How are they connected? What knowledge do they have of one another?

• There is a “Brain”. It is just another computer. It controls switch behavior. It installs rules in the switch. Rules tell switch which server handles a client request.

Network Design

Data Center

Switch

Server 1 Server 2 Server 3 Server 4

Clients

Client request

Send request to servers

Install Load Balancing rules

Brain implementing design algorithm

How it works

• Client sends request for web service.• Request arrives at switch.• Switch decides server to handle request.

• Decision is based on: Closest server so that less energy is used for transport Cheapest path in terms of energy cost Server usage so that less used servers can be powered down

• Server sends a reply back to the client. • Client is provided with the web service.

Load Balancing Components

Partitioning Class

Transitioning Class

Decide servers to be powered down

Old server still handles old client requests

Monitor server usage

Load Balancing Switch

Install rules Statistics Class

New servers handle new client requests

Application Components

• Partitioning: Responsible for implementing the Load Balancing algorithm Decides which server handles which client request

• Transitioning: Ensures that when a server is powered down all new client

requests are handled by another server Ensures that all old client requests are answered by the old server

• Statistics: Provides statistics regarding server usage

Prototype Implementation and Future Steps

• The first prototype for this project was created using: OpenFlow programmable switches. OpenFlow, NOX and Mininet to program the switches. Applications written in Python. Applications tested using VMWare (Virtual Machine) and Debian

version of Linux running on VMWare.

• Future steps: Running the application on a web server and on a real network. Designing a more accurate partitioning component. Adapting the partitioning component according to the statistics

component.

Conclusions

• New solution which saves energy by: Being implemented in already existing hardware, the switches. Finding the path which uses the lowest amount of energy at the

lowest cost. Turning down severs which are handling a small amount of client

requests.

• Solution offers: flexibility due to the software component – the Load Balancing

algorithm can be easily modified. speed due to the underlining hardware component – switch

which applies rules.

Acknowledgements

Thank you: Professor Jennifer Rexford Richard Wang – team member Rob Harrison and David Shue – graduate students Nate Foster – postdoctoral research fellow

Questions?