KubeCon + CloudNativeCon Europe 2018

At Weaveworks, we sometimes need more than the L3/L4 load balancing offered today with the Service abstraction. The Kubernetes Service & Endpoint objects have some extraordinary untapped powers: they can be used to build artisanal, high-level load balancing and session affinity schemes. This talk will go through a few examples: sharding across endpoints based on a layer 7 key, master endpoint election and demonstrate a tiny reverse proxy implementing service affinity using consistent hashing with bounded load.

In this talk, we present performance and scalability numbers from our open source implementation of the Maglev data-plane (part of Google’s load balancing architecture as defined in [1]) in fd.io/VPP, as well as extensions that leverage IPv6 and Segment Routing (SRv6LB [2]) in ways that improve the fairness and reliability for workload balancing in a Data Center. In systems like Kubernetes that serve a large number of connections to micro-service instances in containers, our analytical and experimental results show that processing with SRv6LB is more fairly balanced than with Maglev alone. This results in significantly faster overall response times for end users and more efficient utilization of compute resources, especially under very high load.