Remove italics, correct CamelCase typos in titles

docs/admin/daemons.md

@@ -88,8 +88,8 @@ created by the Daemon controller have the machine already selected (`.spec.nodeN
 when the pod is created, so it is ignored by the scheduler).  Therefore:
 
  - the [`unschedulable`](/docs/admin/node/#manual-node-administration) field of a node is not respected
-   by the daemon set controller.
+   by the DaemonSet controller.
- - daemon set controller can make pods even when the scheduler has not been started, which can help cluster
+ - DaemonSet controller can make pods even when the scheduler has not been started, which can help cluster
    bootstrap.
 
 ## Communicating with DaemonSet Pods
@@ -98,8 +98,7 @@ Some possible patterns for communicating with pods in a DaemonSet are:
 
 - **Push**: Pods in the DaemonSet are configured to send updates to another service, such
   as a stats database.  They do not have clients.
-- **NodeIP and Known Port**: Pods in the Daemon Set use a `hostPort`, so that the pods are reachable
+- **NodeIP and Known Port**: Pods in the DaemonSet use a `hostPort`, so that the pods are reachable via the node IPs.  Clients know the list of nodes ips somehow, and know the port by convention.
-  via the node IPs.  Clients knows the list of nodes ips somehow, and know the port by convention.
 - **DNS**: Create a [headless service](/docs/user-guide/services/#headless-services) with the same pod selector,
   and then discover DaemonSets using the `endpoints` resource or retrieve multiple A records from
   DNS.

docs/admin/sysctls.md

@@ -9,7 +9,7 @@ assignees:
 
 This document describes how sysctls are used within a Kubernetes cluster.
 
-## What is a _Sysctl_?
+## What is a Sysctl?
 
 In Linux, the sysctl interface allows an administrator to modify kernel
 parameters at runtime. Parameters are available via the `/proc/sys/` virtual

docs/user-guide/cron-jobs.md

@@ -9,7 +9,7 @@ title: Cron Jobs
 * TOC
 {:toc}
 
-## What is a Cron Job?
+## What is a cron job?
 
 A _Cron Job_ manages time based [Jobs](/docs/user-guide/jobs/), namely:
 

docs/user-guide/jobs.md

@@ -8,7 +8,7 @@ title: Jobs
 * TOC
 {:toc}
 
-## What is a job?
+## What is a Job?
 
 A _job_ creates one or more pods and ensures that a specified number of them successfully terminate.
 As pods successfully complete, the _job_ tracks the successful completions.  When a specified number

docs/user-guide/pods/index.md

@@ -10,7 +10,7 @@ title: Pods
 _pods_ are the smallest deployable units of computing that can be created and
 managed in Kubernetes.
 
-## What is a pod?
+## What is a Pod?
 
 A _pod_ (as in a pod of whales or pea pod) is a group of one or more containers
 (such as Docker containers), the shared storage for those containers, and

docs/user-guide/replication-controller/index.md

@@ -8,18 +8,18 @@ title: Replication Controller
 * TOC
 {:toc}
 
-## What is a replication controller?
+## What is a ReplicationController?
 
-A _replication controller_ ensures that a specified number of pod "replicas" are running at any one
+A _ReplicationController_ ensures that a specified number of pod "replicas" are running at any one
-time. In other words, a replication controller makes sure that a pod or homogeneous set of pods are
+time. In other words, a ReplicationController makes sure that a pod or homogeneous set of pods are
 always up and available.
 If there are too many pods, it will kill some. If there are too few, the
-replication controller will start more. Unlike manually created pods, the pods maintained by a
+ReplicationController will start more. Unlike manually created pods, the pods maintained by a
-replication controller are automatically replaced if they fail, get deleted, or are terminated.
+ReplicationController are automatically replaced if they fail, get deleted, or are terminated.
 For example, your pods get re-created on a node after disruptive maintenance such as a kernel upgrade.
-For this reason, we recommend that you use a replication controller even if your application requires
+For this reason, we recommend that you use a ReplicationController even if your application requires
-only a single pod. You can think of a replication controller as something similar to a process supervisor,
+only a single pod. You can think of a ReplicationController as something similar to a process supervisor,
-but rather than individual processes on a single node, the replication controller supervises multiple pods
+but rather than individual processes on a single node, the ReplicationController supervises multiple pods
 across multiple nodes.
 
 ReplicationController is often abbreviated to "rc" or "rcs" in discussion, and as a shortcut in
@@ -42,7 +42,7 @@ $ kubectl create -f ./replication.yaml
 replicationcontrollers/nginx
 ```
 
-Check on the status of the replication controller using this command:
+Check on the status of the ReplicationController using this command:
 
 ```shell
 $ kubectl describe replicationcontrollers/nginx
@@ -79,7 +79,7 @@ echo $pods
 nginx-3ntk0 nginx-4ok8v nginx-qrm3m
 ```
 
-Here, the selector is the same as the selector for the replication controller (seen in the
+Here, the selector is the same as the selector for the ReplicationController (seen in the
 `kubectl describe` output, and in a different form in `replication.yaml`.  The `--output=jsonpath` option
 specifies an expression that just gets the name from each pod in the returned list.
 
@@ -106,22 +106,22 @@ labels (i.e. don't overlap with other controllers, see [pod selector](#pod-selec
 Only a [`.spec.template.spec.restartPolicy`](/docs/user-guide/pod-states/) equal to `Always` is allowed, which is the default
 if not specified.
 
-For local container restarts, replication controllers delegate to an agent on the node,
+For local container restarts, ReplicationControllers delegate to an agent on the node,
 for example the [Kubelet](/docs/admin/kubelet/) or Docker.
 
 ### Labels on the ReplicationController
 
-The replication controller can itself have labels (`.metadata.labels`).  Typically, you
+The ReplicationController can itself have labels (`.metadata.labels`).  Typically, you
 would set these the same as the `.spec.template.metadata.labels`; if `.metadata.labels` is not specified
 then it is defaulted to  `.spec.template.metadata.labels`.  However, they are allowed to be
-different, and the `.metadata.labels` do not affect the behavior of the replication controller.
+different, and the `.metadata.labels` do not affect the behavior of the ReplicationController.
 
 ### Pod Selector
 
 The `.spec.selector` field is a [label selector](/docs/user-guide/labels/#label-selectors).  A replication
 controller manages all the pods with labels which match the selector.  It does not distinguish
 between pods which it created or deleted versus pods which some other person or process created or
-deleted.  This allows the replication controller to be replaced without affecting the running pods.
+deleted.  This allows the ReplicationController to be replaced without affecting the running pods.
 
 If specified, the `.spec.template.metadata.labels` must be equal to the `.spec.selector`, or it will
 be rejected by the API.  If `.spec.selector` is unspecified, it will be defaulted to
@@ -148,50 +148,50 @@ If you do not specify `.spec.replicas`, then it defaults to 1.
 
 ### Deleting a ReplicationController and its Pods
 
-To delete a replication controller and all its pods, use [`kubectl
+To delete a ReplicationController and all its pods, use [`kubectl
-delete`](/docs/user-guide/kubectl/kubectl_delete/).  Kubectl will scale the replication controller to zero and wait
+delete`](/docs/user-guide/kubectl/kubectl_delete/).  Kubectl will scale the ReplicationController to zero and wait
-for it to delete each pod before deleting the replication controller itself.  If this kubectl
+for it to delete each pod before deleting the ReplicationController itself.  If this kubectl
 command is interrupted, it can be restarted.
 
 When using the REST API or go client library, you need to do the steps explicitly (scale replicas to
-0, wait for pod deletions, then delete the replication controller).
+0, wait for pod deletions, then delete the ReplicationController).
 
 ### Deleting just a ReplicationController
 
-You can delete a replication controller without affecting any of its pods.
+You can delete a ReplicationController without affecting any of its pods.
 
 Using kubectl, specify the `--cascade=false` option to [`kubectl delete`](/docs/user-guide/kubectl/kubectl_delete/).
 
-When using the REST API or go client library, simply delete the replication controller object.
+When using the REST API or go client library, simply delete the ReplicationController object.
 
-Once the original is deleted, you can create a new replication controller to replace it.  As long
+Once the original is deleted, you can create a new ReplicationController to replace it.  As long
 as the old and new `.spec.selector` are the same, then the new one will adopt the old pods.
 However, it will not make any effort to make existing pods match a new, different pod template.
 To update pods to a new spec in a controlled way, use a [rolling update](#rolling-updates).
 
 ### Isolating pods from a ReplicationController
 
-Pods may be removed from a replication controller's target set by changing their labels. This technique may be used to remove pods from service for debugging, data recovery, etc. Pods that are removed in this way will be replaced automatically (assuming that the number of replicas is not also changed).
+Pods may be removed from a ReplicationController's target set by changing their labels. This technique may be used to remove pods from service for debugging, data recovery, etc. Pods that are removed in this way will be replaced automatically (assuming that the number of replicas is not also changed).
 
 ## Common usage patterns
 
 ### Rescheduling
 
-As mentioned above, whether you have 1 pod you want to keep running, or 1000, a replication controller will ensure that the specified number of pods exists, even in the event of node failure or pod termination (e.g., due to an action by another control agent).
+As mentioned above, whether you have 1 pod you want to keep running, or 1000, a ReplicationController will ensure that the specified number of pods exists, even in the event of node failure or pod termination (e.g., due to an action by another control agent).
 
 ### Scaling
 
-The replication controller makes it easy to scale the number of replicas up or down, either manually or by an auto-scaling control agent, by simply updating the `replicas` field.
+The ReplicationController makes it easy to scale the number of replicas up or down, either manually or by an auto-scaling control agent, by simply updating the `replicas` field.
 
 ### Rolling updates
 
-The replication controller is designed to facilitate rolling updates to a service by replacing pods one-by-one.
+The ReplicationController is designed to facilitate rolling updates to a service by replacing pods one-by-one.
 
-As explained in [#1353](http://issue.k8s.io/1353), the recommended approach is to create a new replication controller with 1 replica, scale the new (+1) and old (-1) controllers one by one, and then delete the old controller after it reaches 0 replicas. This predictably updates the set of pods regardless of unexpected failures.
+As explained in [#1353](http://issue.k8s.io/1353), the recommended approach is to create a new ReplicationController with 1 replica, scale the new (+1) and old (-1) controllers one by one, and then delete the old controller after it reaches 0 replicas. This predictably updates the set of pods regardless of unexpected failures.
 
 Ideally, the rolling update controller would take application readiness into account, and would ensure that a sufficient number of pods were productively serving at any given time.
 
-The two replication controllers would need to create pods with at least one differentiating label, such as the image tag of the primary container of the pod, since it is typically image updates that motivate rolling updates.
+The two ReplicationControllers would need to create pods with at least one differentiating label, such as the image tag of the primary container of the pod, since it is typically image updates that motivate rolling updates.
 
 Rolling update is implemented in the client tool
 [`kubectl rolling-update`](/docs/user-guide/kubectl/kubectl_rolling-update). Visit [`kubectl rolling-update` tutorial](/docs/user-guide/rolling-updates/) for more concrete examples. 
@@ -200,26 +200,26 @@ Rolling update is implemented in the client tool
 
 In addition to running multiple releases of an application while a rolling update is in progress, it's common to run multiple releases for an extended period of time, or even continuously, using multiple release tracks. The tracks would be differentiated by labels.
 
-For instance, a service might target all pods with `tier in (frontend), environment in (prod)`.  Now say you have 10 replicated pods that make up this tier.  But you want to be able to 'canary' a new version of this component.  You could set up a replication controller with `replicas` set to 9 for the bulk of the replicas, with labels `tier=frontend, environment=prod, track=stable`, and another replication controller with `replicas` set to 1 for the canary, with labels `tier=frontend, environment=prod, track=canary`.  Now the service is covering both the canary and non-canary pods.  But you can mess with the replication controllers separately to test things out, monitor the results, etc.
+For instance, a service might target all pods with `tier in (frontend), environment in (prod)`.  Now say you have 10 replicated pods that make up this tier.  But you want to be able to 'canary' a new version of this component.  You could set up a ReplicationController with `replicas` set to 9 for the bulk of the replicas, with labels `tier=frontend, environment=prod, track=stable`, and another ReplicationController with `replicas` set to 1 for the canary, with labels `tier=frontend, environment=prod, track=canary`.  Now the service is covering both the canary and non-canary pods.  But you can mess with the ReplicationControllers separately to test things out, monitor the results, etc.
 
 ### Using ReplicationControllers with Services
 
-Multiple replication controllers can sit behind a single service, so that, for example, some traffic
+Multiple ReplicationControllers can sit behind a single service, so that, for example, some traffic
 goes to the old version, and some goes to the new version.
 
-A replication controller will never terminate on its own, but it isn't expected to be as long-lived as services. Services may be composed of pods controlled by multiple replication controllers, and it is expected that many replication controllers may be created and destroyed over the lifetime of a service (for instance, to perform an update of pods that run the service). Both services themselves and their clients should remain oblivious to the replication controllers that maintain the pods of the services.
+A ReplicationController will never terminate on its own, but it isn't expected to be as long-lived as services. Services may be composed of pods controlled by multiple ReplicationControllers, and it is expected that many ReplicationControllers may be created and destroyed over the lifetime of a service (for instance, to perform an update of pods that run the service). Both services themselves and their clients should remain oblivious to the ReplicationControllers that maintain the pods of the services.
 
 ## Writing programs for Replication
 
-Pods created by a replication controller are intended to be fungible and semantically identical, though their configurations may become heterogeneous over time. This is an obvious fit for replicated stateless servers, but replication controllers can also be used to maintain availability of master-elected, sharded, and worker-pool applications. Such applications should use dynamic work assignment mechanisms, such as the [etcd lock module](https://coreos.com/docs/distributed-configuration/etcd-modules/) or [RabbitMQ work queues](https://www.rabbitmq.com/tutorials/tutorial-two-python.html), as opposed to static/one-time customization of the configuration of each pod, which is considered an anti-pattern. Any pod customization performed, such as vertical auto-sizing of resources (e.g., cpu or memory), should be performed by another online controller process, not unlike the replication controller itself.
+Pods created by a ReplicationController are intended to be fungible and semantically identical, though their configurations may become heterogeneous over time. This is an obvious fit for replicated stateless servers, but ReplicationControllers can also be used to maintain availability of master-elected, sharded, and worker-pool applications. Such applications should use dynamic work assignment mechanisms, such as the [etcd lock module](https://coreos.com/docs/distributed-configuration/etcd-modules/) or [RabbitMQ work queues](https://www.rabbitmq.com/tutorials/tutorial-two-python.html), as opposed to static/one-time customization of the configuration of each pod, which is considered an anti-pattern. Any pod customization performed, such as vertical auto-sizing of resources (e.g., cpu or memory), should be performed by another online controller process, not unlike the ReplicationController itself.
 
-## Responsibilities of the replication controller
+## Responsibilities of the ReplicationController
 
-The replication controller simply ensures that the desired number of pods matches its label selector and are operational. Currently, only terminated pods are excluded from its count. In the future, [readiness](http://issue.k8s.io/620) and other information available from the system may be taken into account, we may add more controls over the replacement policy, and we plan to emit events that could be used by external clients to implement arbitrarily sophisticated replacement and/or scale-down policies.
+The ReplicationController simply ensures that the desired number of pods matches its label selector and are operational. Currently, only terminated pods are excluded from its count. In the future, [readiness](http://issue.k8s.io/620) and other information available from the system may be taken into account, we may add more controls over the replacement policy, and we plan to emit events that could be used by external clients to implement arbitrarily sophisticated replacement and/or scale-down policies.
 
-The replication controller is forever constrained to this narrow responsibility. It itself will not perform readiness nor liveness probes. Rather than performing auto-scaling, it is intended to be controlled by an external auto-scaler (as discussed in [#492](http://issue.k8s.io/492)), which would change its `replicas` field. We will not add scheduling policies (e.g., [spreading](http://issue.k8s.io/367#issuecomment-48428019)) to the replication controller. Nor should it verify that the pods controlled match the currently specified template, as that would obstruct auto-sizing and other automated processes. Similarly, completion deadlines, ordering dependencies, configuration expansion, and other features belong elsewhere. We even plan to factor out the mechanism for bulk pod creation ([#170](http://issue.k8s.io/170)).
+The ReplicationController is forever constrained to this narrow responsibility. It itself will not perform readiness nor liveness probes. Rather than performing auto-scaling, it is intended to be controlled by an external auto-scaler (as discussed in [#492](http://issue.k8s.io/492)), which would change its `replicas` field. We will not add scheduling policies (e.g., [spreading](http://issue.k8s.io/367#issuecomment-48428019)) to the ReplicationController. Nor should it verify that the pods controlled match the currently specified template, as that would obstruct auto-sizing and other automated processes. Similarly, completion deadlines, ordering dependencies, configuration expansion, and other features belong elsewhere. We even plan to factor out the mechanism for bulk pod creation ([#170](http://issue.k8s.io/170)).
 
-The replication controller is intended to be a composable building-block primitive. We expect higher-level APIs and/or tools to be built on top of it and other complementary primitives for user convenience in the future. The "macro" operations currently supported by kubectl (run, stop, scale, rolling-update) are proof-of-concept examples of this. For instance, we could imagine something like [Asgard](http://techblog.netflix.com/2012/06/asgard-web-based-cloud-management-and.html) managing replication controllers, auto-scalers, services, scheduling policies, canaries, etc.
+The ReplicationController is intended to be a composable building-block primitive. We expect higher-level APIs and/or tools to be built on top of it and other complementary primitives for user convenience in the future. The "macro" operations currently supported by kubectl (run, stop, scale, rolling-update) are proof-of-concept examples of this. For instance, we could imagine something like [Asgard](http://techblog.netflix.com/2012/06/asgard-web-based-cloud-management-and.html) managing ReplicationControllers, auto-scalers, services, scheduling policies, canaries, etc.
 
 
 ## API Object
@@ -244,16 +244,16 @@ because unlike `kubectl rolling-update`, they are declarative, server-side, and
 
 ### Bare Pods
 
-Unlike in the case where a user directly created pods, a replication controller replaces pods that are deleted or terminated for any reason, such as in the case of node failure or disruptive node maintenance, such as a kernel upgrade. For this reason, we recommend that you use a replication controller even if your application requires only a single pod. Think of it similarly to a process supervisor, only it supervises multiple pods across multiple nodes instead of individual processes on a single node.  A replication controller delegates local container restarts to some agent on the node (e.g., Kubelet or Docker).
+Unlike in the case where a user directly created pods, a ReplicationController replaces pods that are deleted or terminated for any reason, such as in the case of node failure or disruptive node maintenance, such as a kernel upgrade. For this reason, we recommend that you use a ReplicationController even if your application requires only a single pod. Think of it similarly to a process supervisor, only it supervises multiple pods across multiple nodes instead of individual processes on a single node.  A ReplicationController delegates local container restarts to some agent on the node (e.g., Kubelet or Docker).
 
 ### Job
 
-Use a [`Job`](/docs/user-guide/jobs/) instead of a replication controller for pods that are expected to terminate on their own
+Use a [`Job`](/docs/user-guide/jobs/) instead of a ReplicationController for pods that are expected to terminate on their own
 (i.e. batch jobs).
 
 ### DaemonSet
 
-Use a [`DaemonSet`](/docs/admin/daemons/) instead of a replication controller for pods that provide a
+Use a [`DaemonSet`](/docs/admin/daemons/) instead of a ReplicationController for pods that provide a
 machine-level function, such as machine monitoring or machine logging.  These pods have a lifetime that is tied
 to a machine lifetime: the pod needs to be running on the machine before other pods start, and are
 safe to terminate when the machine is otherwise ready to be rebooted/shutdown.