Borealis release v0.2.0

NSM is released through a set of helm charts, which are easily deployable in your Kubernetes cluster:

helm repo add nsm
helm install nsm/nsm

NOTE: NSM v0.2.0 was released against Helm 2, other version have not been tested.

Please follow the Quickstart Guide for more details.

There are also Examples that illustrate the usage of NSM in more complex scenarios.

Kubernetes Compatibility

NSM is extremely insensitive to changes in Kubernetes, and as such is generally expected to work with post 1.11 versions of Kubernetes without issue.

NSM v0.2.0 has been successfully integration tested with Kubernetes versions:

  • 1.12.10
  • 1.13.12
  • 1.14.10
  • 1.15.11
  • 1.16.2, 1.16.15
  • 1.17.11
  • 1.18.8
  • 1.19.0

Note: Some Kubernetes versions listed here are more recent than NSM v0.2.0 because NSM v0.2.0 has been tested with new Kubernetes versions as they have been released to verify continued compatibility.

Changes since version 0.1.0

v0.2.0 major themes:


In version 0.1.0 (Andromeda) Network Service Mesh supported providing per-Pod Network Services within a single cluster: IntraDomain

In version 0.2.0 (Borealis) Network Service Mesh introduces interdomain support, allowing a Network Service to span multiple clusters: InterDomain

By default, each cluster in Network Service Mesh has a “Network Service Registry Domain” associated with it. Unless other wise specified, Network Service Endpoint Pods (NSEs) in that cluster register in that domain, and Pods seeking to connect to a Network Service (NS) are connected to Network Services in that domain.

With the introduction of interdomain, an NSE in one cluster may register with any “Network Service Registry Domain” including one in a different cluster or even a ‘floating’ domain not associated with any cluster.

Likewise, a Pod can connect to a Network Service that is registered in a different domain. The introduction of interdomain fulfills the Network Service Mesh promise of decoupling ‘where’ a Pod is running from what Network Services it may consume.

Consumption is straightforward and by name, with an interdomain Network Service specifiable as:

${ns name}@${network service domain name}

For example:

Notably, while the default implementation of interdomain utilizes DNS SRV records to allow location of the Network Service Registries for a given domain, that is an implementation detail. Network Service Registry domains do not necessarily have to correspond to DNS domains.


When a Pod connects to a Network Service, a vWire is plugged into that Pod,typically in the form of an additional interface, with non-conflicting-with-the-cluster routes indicating what traffic should go to the Network Service.

Developers don’t generally think in IP addresses, they think in DNS domain names.

In version 0.2.0 each Network Service may optionally provide information on DNS servers for resolution that are accessible via the vWire for that Network Service. Network Service Mesh provides ‘parallel’ DNS resolution across the K8s clusters DNS and the DNS of each Network Service (if provided) using a sidecar and the CoreDNS Fanout Plugin.

DNS Fanout

In this way DNS that is private (not available on the public internet)can be provided for a Network Service without disturbing the normal DNS resolution within the cluster.


Version 0.2.0 supports Spiffe/Spire support for workload identities, paving the way for sophisticated policies governing admission of a Pod or workload to a Network Service.

Network Service Endpoint Developer Improvements

Network Service Mesh has multiple constituencies:

  1. Application Developers - who simply want the simplest path from what they want to having it
  2. Network Service Endpoint Developers - who write the NSEs that provide the Network Services
  3. Operators - who operate the NSM infrastructure (and often the K8s clusters). Note: App Devs are often also the operators, so operations must be very very simple.

Most of our focus has been on Application Developers and Operators, but NSE Developers are also important to the growth of the NSM ecosystem. Several improvements have been made to make it easier to write NSEs.

API simplification

The number and complexity of APIs needed for development is a key factor to the cognitive load involved in successful development. Network Service Mesh strives to keep it’s APIs simple and small in number.

Network Service Mesh previously had two APIs for requesting, closing, and monitoring a connection from a Client to and Endpoint, one for local mechanisms like kernel interface, memif, etc and one for remote mechanisms like vxlan, srv6, gre, etc. These two APIs were almost semantically identical, but for the local/remote mechanism split.
These were simplified down to a single API.

Shift to a chain oriented sdk

Keeping APIs simple and few is key to rapid development. A Network Service Endpoint (NSE) has to supply a very small number of simple GRPC APIs to the outside world to function, but may have to carry out a number of internal activities to meet those requests.

The Network Service Mesh SDK for writing NSEs was adapted to allow the ‘chaining’ together of a list of implementations of those APIs which receive an incoming call, do their piece of the work, and then make an outgoing call to exactly the same API. This allows small bits of functionality to be written as ‘chain elements’ and then reused to construct a wide array of NSEs. An NSE author may only need to write a few ‘chain elements’ to do work that has not been done before and reuse existing chain elements to build out their NSE.

Internal jaeger tracing for chains

As part of the shift to chain a chain oriented SDK, the option to allow jaeger tracing through those internal chain elements was added, allowing for much more detailed tracing not only between separate components, but within those components.

Examples factored out into examples/ repo

Network Service Mesh’s ‘example’ NSEs where factored out of the main repo and into an examples repo to allow for easier consumption and contribution.


Increased testing

Network Service Mesh runs PR by PR testing on:

  1. Vanilla Kubernetes on bare metal on Packet
  2. GKE
  3. EKS
  4. AKS

Testing increased from 269 tests in v0.1.0 to 458 tests in v0.2.0.

Introduction of cloudtest

cloudtest is a multi-cloud testing tool that allows scheduling tests across multiple cluster instances in public clouds.

It is extremely persistent, working around a number of small instabilities in various clouds abilities to reliably provide new clusters on demand. This has allowed a vast expansion of testing.