Make list endings consistent; remove ML reference
Red Sky Ops is a tool for experimenting with application configurations in Kubernetes. With Red Sky Ops installed on your cluster, you can run experiments that patch cluster state and measure the outcome via one or more metrics.
-When used with a Red Sky Ops API server, the Red Sky Ops Manager tunes your Kubernetes applications automatically using machine learning. Without the enterprise server, `redskyctl` and the Red Sky Ops Manager can be used to manually experiment on your cluster, or integrated into existing automation workflows.
+When used with a Red Sky Ops API server, the Red Sky Ops Manager tunes your Kubernetes applications automatically. Without the enterprise server, `redskyctl` and the Red Sky Ops Manager can be used to manually experiment on your cluster, or integrated into existing automation workflows.
Although Red Sky Ops was developed to tune performance, it can run non-performance-related experiments on your application as well. This documentation describes the concepts and capabilities of Red Sky Ops generically, and typically includes performance-related examples for relevant concepts.
@@ -18,14 +18,14 @@ Kubernetes has several built-in mechanisms for managing container compute resour
Once containers have these limits set, Kubernetes can leverage them in several ways:
-* The Kubernetes scheduler uses the resource requests to efficiently schedule pods onto nodes based on resource needs and availability ("bin packing")
-* If pods exceed their CPU or memory limits, Kubernetes may evict them from their host node
-* When used in combination with Quality of Service (QoS) classes or pod priority, Kubernetes can go a step further, choosing which pods to schedule and evict based on the quality of service they require
+* The Kubernetes scheduler uses the resource requests to efficiently schedule pods onto nodes based on resource needs and availability ("bin packing").
+* If pods exceed their CPU or memory limits, Kubernetes may evict them from their host node.
+* When used in combination with Quality of Service (QoS) classes or pod priority, Kubernetes can go a step further, choosing which pods to schedule and evict based on the quality of service they require.
* To accommodate demand on pods, Kubernetes can "horizontally" scale them out by adding more replicas via Horizontal Pod Autoscaling (HPA). The HPA controller examines the CPU resource request and calculates utilization as a fraction of the requested amount to determine when to scale. If utilization is higher or lower than the target, it adds or removes pods.
While powerful, these tools are missing some aspects of resources untuned:
-* Developers must devise CPU and memory requests and limits themselves, perhaps based on performance analysis locally or in a developer environment. (Scaling resources rather than replicas is sometimes referred to as Vertical Pod Scaling)
+* Developers must devise CPU and memory requests and limits themselves, perhaps based on performance analysis locally or in a developer environment. (Scaling resources rather than replicas is sometimes referred to as Vertical Pod Scaling.)
* Kubernetes cannot adjust application-specific settings that may have significant impact on performance from workload to workload. For instance: JVM heap size, shard size in Elasticsearch, or working memory on a self-hosted database would not be exposed through a consistent interface in Kubernetes, even if they may be set by environment variables or through a `ConfigMap`.
Red Sky Ops was developed to address these shortcomings. It supports flexibly parameterizing application manifests; running trials with specific values filled into those manifests; taking measurements to determine the efficacy of trials; and grouping those trials into overarching experiments.
