OpenFlow-based server load balancing gone wild

TLDR

Data centers use front‑end load balancers to route client requests to server replicas, but dedicated balancers are expensive, prone to failure, and can become bottlenecks; OpenFlow enables a cheaper alternative by having commodity switches route traffic via controller‑installed rules, yet a per‑connection rule strategy creates too many rules and overloads the controller. We argue that the controller should exploit switch support for wildcard rules for a more scalable solution that directs large aggregates of client traffic to server replicas. We present algorithms that compute concise wildcard rules that achieve a target distribution of the traffic, and automatically adjust to changes in load‑balancing policies without disrupting existing connections. We implement these algorithms on top of the NOX OpenFlow controller, evaluate their effectiveness, and propose several avenues for further research.

Abstract

Today's data centers host online services on multiple servers, with a front-end load balancer directing each client request to a particular replica. Dedicated load balancers are expensive and quickly become a single point of failure and congestion. The OpenFlow standard enables an alternative approach where the commodity network switches divide traffic over the server replicas, based on packet-handling rules installed by a separate controller. However, the simple approach of installing a separate rule for each client connection (or microflow) leads to a huge number of rules in the switches and a heavy load on the controller. We argue that the controller should exploit switch support for wildcard rules for a more scalable solution that directs large aggregates of client traffic to server replicas. We present algorithms that compute concise wildcard rules that achieve a target distribution of the traffic, and automatically adjust to changes in load-balancing policies without disrupting existing connections. We implement these algorithms on top of the NOX OpenFlow controller, evaluate their effectiveness, and propose several avenues for further research.