### SIG Multi-cluster (formerly known as SIG Federation)

[SIG Multi-cluster][] is responsible for infrastructure that supports
the efficient and reliable management of multiple Kubernetes clusters,
and applications that run in and across multiple clusters.

For the 1.8 release, SIG Multicluster focussed on expanding the set of

inherent in its design. This PR exposes a second, independent pruning alpha powered by a new standard named `ApplySets`. An `ApplySet` is a server-side object (by default, a Secret; ConfigMaps are also allowed) that kubectl can use to accurately and efficiently track set membership across `apply` operations. The format used for `ApplySet` is set out in  [KEP 3659](https://github.com/kubernetes/enhancements/issues/3659) as a low-level specification.  Other tools in the ecosystem can also build on this specification...

* A new client `k8s.io/client-go/metadata.Client` has been added for accessing objects generically. This client makes it easier to retrieve only the metadata (the `metadata` sub-section) from resources on the cluster in an efficient manner for use cases that deal with objects generically, like the garbage collector, quota, or the namespace controller. The client asks the server to return a `meta.k8s.io/v1 PartialObjectMetadata` object for list, get, delete, watch, and patch...

in mind. It is strongly typed and garbage-collected and has explicit
support for concurrent programming.  Programs are constructed from
<i>packages</i>, whose properties allow efficient management of
dependencies.
</p>

<p>
The syntax is compact and simple to parse, allowing for easy analysis
by automatic tools such as integrated development environments.
</p>

table[i-1], table[0], m) } out.resetFor(m) out.limbs[0] = 1 out.montgomeryRepresenta(m) tmp := NewNat().ExpandFor(m) for _, b := range e { for _, j := range []int{4, 0} { // Square four times. Optimization note: this can be implemented // more efficiently than with generic Montgomery multiplication. out.montgomeryMul(out, out, m) out.montgomeryMul(out, out, m) out.montgomeryMul(out, out, m) out.montgomeryMul(out, out, m) // Select x^k in constant time from the table. k := uint((b >> j) & 0b1111)...

SIG Windows is also including several addition to this release:
 - Direct Server Return (DSR) mode support, allowing large numbers of services to scale up efficiently
 - Windows containers  now honor CPU limits
 - Performance improvements for collections of metrics and summary

### Increase the Kubernetes support window to one year

table[i-1], table[0], m) } out.resetFor(m) out.limbs[0] = 1 out.montgomeryRepresenta(m) tmp := NewNat().ExpandFor(m) for _, b := range e { for _, j := range []int{4, 0} { // Square four times. Optimization note: this can be implemented // more efficiently than with generic Montgomery multiplication. out.montgomeryMul(out, out, m) out.montgomeryMul(out, out, m) out.montgomeryMul(out, out, m) out.montgomeryMul(out, out, m) // Select x^k in constant time from the table. k := uint((b >> j) & 0b1111)...