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RedCap

Blog : The 3G4G Blog: RedCap

PPT Slide + Links : Beginners: Introduction to 5G Reduced Capability (RedCap) Devices

3GPP CRs : How to find RedCap in 3GPP

In Spec

23.501

Section 5.41 NR RedCap UEs differentiation

Section 5.3.2.3

The AMF may also determine more precise RAT Type information based on further information received from NGRAN:

- The AMF may determine the RAT Type to be LTE-M as defined in clause 5.31.20; or

- The AMF may determine the RAT Type to be NR using unlicensed bands, as defined in clause 5.4.8.

- The AMF may determine the RAT Type to be one of the RAT types for satellite access, as defined in

clause 5.4.10.

- The AMF may determine the RAT Type to be NR RedCap as defined in clause 5.41.

Section 5.41

5.41 NR RedCap UEs differentiation

This functionality is used by the network to identify traffic to/from UEs accessing over NR RedCap, e.g. for charging

differentiation.

An NR RedCap UE using NR shall provide an NR RedCap indication to the NG-RAN during RRC Connection

Establishment procedure as defined in TS 38.300 [27].

When the UE has provided an NR RedCap indication to the NG-RAN during RRC Connection Establishment, the NGRAN

shall provide an NR RedCap Indication to the AMF in the Initial UE Message (see clause 4.2.2.2.1 of

TS 23.502 [3] and TS 38.413 [34]).

When the AMF receives an NR RedCap Indication from NG-RAN in an Initial UE Message, the AMF shall store the

NR RedCap Indication in the UE context, consider that the RAT type is NR RedCap and signal it accordingly to the

SMSF during registration procedure for SMS over NAS, to the SMF during PDU Session Establishment or PDU

Session Modification procedure. The PCF will also receive the NR RedCap RAT type indication when applicable, from

the SMF during SM Policy Association Establishment or SM Policy Association Modification procedure.

During handover from E-UTRA to NR, the target NG-RAN (i.e. gNB) provides the NR RedCap indication to AMF in

NGAP Path Switch Request message during Xn handover, or NGAP Handover Request Acknowledge message during

N2 handover (including intra 5GS N2 handover and EPS to 5GS handover) based on the UE capability information

provided by the source RAN to the target RAN as specified in TS 38.300 [27].

The NFs interacting with CHF shall include the NR RedCap as RAT type.

Upon AMF change, the source AMF shall provide the "NR RedCap Indication" to the target AMF.

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Kubernetes - Namespace

Default namespace created by Kubernetes 

• kube-system
• kube-public
• default

How it is helpful ?

• These different namespace 
• can be used by same cluster for DEV and PROD environment.
• they will have isolated resources between them so that DEV will have their own resource and PROD will have their own resource.
• they will have their own policies that define who can do what
• Each namespace has a max limit of resources and are not allowed to use more than its allowed limit. 
• We can define policy such that for example, while working in DEV environment, we can't modify a resource of PRODUCTION.

Example usage of custom namespace. 

• Dev
• Test
• Prod

How to name resources of a namespace?

• Same namespace 
• Resources in same namespace ca access via db-service
• Different namespace
• Resources in other namespace can access resources in other namespace via ( say DEV resources via - db-service.dev.svc.cluster.local 

Name details

db-service.dev.svc.cluster.local 

• db-service - Name of the service
• dev - namespace
• svc - service
• cluster.local - domain.

How to create Namspace Or Pod Inside Namespace

• Create in default namespace
• kubectl create -f pod-definition.yml -f
• Create a particular namespace
• kubectl create -f pod-definition --namespace dev
• kubectl create -f pod-definition -n dev
• Create a Namespace
• kubectl create -f namespace-dev.yaml
• kubectl create namespace dev
• Sample Yaml file for Namespace
• apiVersion: v1
• kind: Namespace
• metadata:
• name: dev
• Create POD under a namespace through Yaml file
• apiVersion: v1
• kind: Pod
• metadata
• name: myapp-pod
• namespace: dev
• label
• app: myapp
• type: front-end
• spec
• container:
• name: nginx-container
• image: nbinx
• Accessing Namespace
• Scenario -1 : Access default namespace
• kubectl get pods 
• kubectl get pods --namespace dev
• kubectl get pods --namespace prod

• Change the default namespace to namespace = dev
• kubectl config set-context  $(kubectl config current-context) --namespace dev
• kubectl get pods  <<<< This will show Pods from namespace = DEV.
• kubectl get pods --namespace default << To access default namespace
• kubectl get pods --namespace prod << To access PROD namespace
• Resource Quota
• Resources can be limited in a namespace.
• apiVersion: v1
• kind: ResourceQuota
• metadata:
• name: compute-quota
• namespace: dev
• spec:
• hard:
• pods: "10",
• requests-cpu: "4"
• requests-memory: 5Gi
• limts-cpu: "10"
• limits-memory: 10Gi

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Kubernetes- Deployment

- Defines how to deploy application in production environment.

Scenarios

• Old instance running - Multiple Pod having applications are running in production environment.
• New version of application is available - need to upgrade seamlessly- without any downtime.
• Rolling update
• For this we don't want to upgrade all of them at once as this may impact accessibility. 
• One by one upgrade - And we might want to upgrade one after other.
• In case of failure - we should be able to rollback the upgrade.
• Multiple change to environment - 
• Ex - Scaling the environment, Modifying resource allocation etc.
• And this change should be done for all PODS together and then activated for all Pods as the same time.

Kubernetes Deployment

• Comes at the top of the hierarchy.
• Comes at top of Pod and Replica Set.

• Available with
• Rolling update
• Undo changes
• Pause changes
• Resume changes

How to create deployment

Yaml file 

• same as ReplicaSet except for change in KIND.

apiVersion: apps/v1   

kind: Deployment      << Only change done for Deployment

metadata:

  name: myapp-replicaset

  labels:

    app: myapp

    type: front-end

spec:

  template:   <<< Here need to provide template for POD. We just to insert metadata + spec section of POD.

    metadata:

      name: myapp-pod

      labels:

        app: myapp

        type: front-end

    

    spec:   

      containers: 

        name: nginx-container

        image: nginx

  replicas: 3   <<< This is sibling of template

  selector:        

    matchLabels:

      type: front-end

Commands

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 Kubernetes Architecture, POD, ReplicaSet & Replica Controller

1. Introduction

What is Node ? 

•  Node is a machine - Physical or Virtual on which Kubernetes is installed,
• Node is where containers will be launched by Kubernetes.

What will happen to Nodes in which application is running fails.

• Application goes down -- Means Kubernetes goes down.

Why do we need to have more than one node OR Why do we need CLUSTER?

• Because of one node on which Kuberneres is installed goes down then application will also go down. So for redundancy, we need to have more than one node.
• So we have more than one node grouped together.
• So even if one node goes down, application is still accessible from other nodes.

Now we have CLUSTER ? 

- So who is managing cluster ? 

- Where is the information about members of clusters stored ?

- How are nodes monitored ?

- When a node failed, how do u move workload of one node to other worked node ?

• This is the work of Master node. ( M, S, M, F )
• Manage, Store, Monitor, Failure Handling

When u install Kubernetes, what components are installed?

•  Api server
• Acts as Front end for kubernetes
• User, Management Devices, Command Line interfaces
• talk to API server to interact with Kubernetes cluster.
• etcd
• distributed, reliable key-value storage to store
• all data used to manage cluster.
• Also implements locks in the cluster, so that there is no conflict
• 
  
• kubelet
• agent that runs on each node of the cluster.
• makes sure that conatiners are running on nodes as expected.
• container runtime
• Underlying software that run containers.
• For example - docker.
• controller
• Brain behind orchestration.
• Responds when nodes, containers or end points goes down.
• Take decision to bringup new containers in case of failure.
• schedular
• For distributing work across containers on various nodes.

Master & Worker Nodes

How some node became master and some node became worker nodes ?

- Master node that has 

•     Kube-API server, becomes master node.
• All information is stored in KEY-VALUE ( etcd ) store in master.
• Master also has
• controller and 
• schedular

- Worked node has 

•     kubelet agent, so it becomes worker node.
• Through kubelet-agent worker node can interact with MASTER.
• TO provide health information of worker node.
• And CARRY out instruction from MASTER.

What is kubectl

- This is KUBE command line control

• kubectl run
• to install cluster
• kubectl cluster-info
• View information about cluster
• kubectl get nodes 
• To list all nodes of the cluster

Docker Vs Container

Docker 

- container solution.

Kubernetes 

• kubernetes came to orchestrate docker. and they were tightly coupled.
• But later Kubernetes provided a layer Container Runtime Interface ( CRI ) that can take into any container solution ex - docker, rkt, as long as they adhere to OCI standard (Open Container standard)
• CRTICTL 
• This is command line interface (CLI command) for CRI
• This will work for any container intarface.

POD

Steps - 

- Application is built

- Has been put in docker image

- And Kubernetes can pull the image.

Aim 

- Deploy APplication

- In the form of containers

- On a set of machines

- That are configured as worker nodes.

- In a cluster

But

- Kubernetes does not deploy container directly, it has to be put in POD.

Scale up/down

- We can't add new container inside POD

• We need to create new POD in same node.
• We can create new POD and new Node.

Concept of multi-container pod

• POD can have multiple container.
• But generally they dont have multiple container of same kind
• We can have hlper container
• Ex - Processing file, 

YAML with Kubernetes

Creating POD using YAML based configuration file

Command to create a POD

kubectl create -f pod.yml

Mandatory Values in K8 yaml file

- apiVersion

    - Not a KEY/VALUE, we just pass a string

    - API version that we are using to create Kubernetes

        - Example value - v1 , app/v1

- kind 

    - Not a KEY/VALUE, we just pass a string

    - type of objext we are trying to create 

        - Example - POD, Service, ReplicaSet, Deployment

- Metadata

   - This is dictionary.

   - This is data of the object.

       -- Example - Name, Label etc

       -- Here 'name' and labels are dictionary. (have KEY/Value)

    - NAME and IMAGE are sibling

    - Under "MetaData" we can have only "name" and "label"

        - But under "label" we can define any "key". "value" pair.

- Spec

   -  'Spec' is a dictionary, So Add property under it.

   - Below 'spec', there is 'container'. 'container' is also a list/array 

         - because there can be multiple containers in a pod.

         - So first element in a list indicate that it is first item in the list ( - name )

   - Here name & spec is element inside 'container'

Replica Sets / Replica Controllers.

Replica Controllers

- Are the brain behind Kubernetes

- Have processes that

    - Monitor Kubernetes objects

    - And manage accordingly.

Question  - What is a replica and why do we need replication-controller ?

•     Suppose we have a node + POD + Container running our application
•     What will happen if our application crashes ?
•     So users will no longer to access our application.

Solution

 - If we don't want any disruption, we will need to have multiple POD so that there will not disruption. Even if one fails, other POD will still be accessible.

    Replication Controller 

•   helps running multiple instance of same POD in a Kubernetes cluster.

Two reasons for Replication Controller

• High Availability
•   And so it provides high availability.
•   So if one pod goes down, replication controller quickly instantiates another pod.

• 
  
  

• Load Balancing
• If demand increases, replication controller creates Pods
• On same Node
• Or No multiple Nodes.

Two terms - Replication Controller vs Replica Set

There is minor difference between both but more or less they are same.

• Replication Controller - older way
• Replica Set - newer way.

How to create Replication Controller - using Yaml file.

apiVersion: v1

kind: Replication Controller

metadata:

  name: myapp-rc

  labels:

    app: myapp

    type: fromt-end

spec:   spec defines what in inside the KIND. As POD is inside Replication Controller. So provide template of the Pod.

  template:   <<< Here need to provide template for POD. We just to insert metadata + spec section of POD.

    metadata:

      name: myapp-pod

      labels:

        app: myapp

        type: front-end

    

    spec:   

      containers: 

        name: nginx-container

        image: nginx

But we have something still missing. 

• We have not specified how many PODs to be created? 
• Or how many replicas of POD to be available all the time?
• Solution : Add 'replicas: 3

apiVersion: v1

kind: Replication Controller

metadata:

  name: myapp-rc

  labels:

    app: myapp

    type: fromt-end

spec:   spec defines what in inside the KIND. As POD is inside Replication Controller. So provide template of the Pod.

  template:   <<< Here need to provide template for POD. We just to insert metadata + spec section of POD.

    metadata:

      name: myapp-pod

      labels:

        app: myapp

        type: front-end

    

    spec:   

      containers: 

        name: nginx-container

        image: nginx

replicas: 3   <<< This is sibling of template

How to create ReplicaSet - using Yaml file.

apiVersion: apps/v1    << Note for ReplicationController this is - v1

kind: ReplicaSet

metadata:

  name: myapp-replicaset

  labels:

    app: myapp

    type: front-end

spec:

  template:   <<< Here need to provide template for POD. We just to insert metadata + spec section of POD.

    metadata:

      name: myapp-pod

      labels:

        app: myapp

        type: front-end

    

    spec:   

      containers: 

        name: nginx-container

        image: nginx

replicas: 3   <<< This is sibling of template

selector:           << Only got replicaSet

  matchLabels:

    type: front-end

selector

Defines which POD fall in it.We can define multiple labels.

• Can have labels of PODS define in the same definition
• Can have labels defined somewhere else.

Labels & Selectors

• Monitor POD of specified LABEL specified as part of matchLabels
• Always keep the 3 replicas of the POD instance.
• ReplicaSet is 'process' that monitors the PODS and if any POD fails, it creates new POD.

How ReplicaSet knows which POD to monitor ?

• This is done by labels provided as part of matchLabels.

How to change replicas ?

Two commands

• replace
• scale

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8. Data Analysis - Visualization with Matplotlib

 Basic line plot

%matplotlib notebook -- to create visualization in jupyter notebook

import matplotlib.pyplot as plt

import numpy as np

X = np.arange(10)

plt.plot(X)

Create figure and Plot in two lines

1. Create Figure

fig = plt.figure()

2. Create Plot and add plot to figure

ax1 = fig.add_subplot(2,2,1)  --- 2 rows, 2 column and we are selecting 1st plot.

Without creating subplot ( plt.plot)

fig = plt.figure()

plt.plot(np.random.rand(50), 'k-') -- It will go to rightmost bottom.

Creating figure and axis in the same line.

fig, axes = plt.subplots(2,3)

axes[0,1].hist()

Creating same X and Y axis for all subplots.

fig, axes = plt.subplots(2,3, sharex=True, sharey = True)

Remove space between subplots.

fig, axes = plt.subplots(2,3, sharex=True, sharey = True, wspace =0, hspace=0)

Adding color and linestyle.

fig, axes = plt.subplots(2,3, sharex=True, sharey = True, wspace =0, hspace=0, linestyle ="--" , color ='r')

fig, axes = plt.subplots(2,3, sharex=True, sharey = True, wspace =0, hspace=0, "r--") -- short form.

Add Marker

fig, axes = plt.subplots(2,3, sharex=True, sharey = True, wspace =0, hspace=0, linestyle ="--" , color ='r', marker = "o")

Connecting 2 dots

fig, axes = plt.subplots(2,3, sharex=True, sharey = True, wspace =0, hspace=0, linestyle ="--" , color ='r', marker = "o", drawstyle="steps-post") -- steps-pre/steps-mid/steps

Add label

fig, axes = plt.subplots(2,3, sharex=True, sharey = True, wspace =0, hspace=0, linestyle ="--" , color ='r', marker = "o", drawstyle="steps-post", label="line")

X and Y axis label

fig = plt.figure()

ax1= fig.add_subplot(1,1,1)

ticks = ax1.set_xticks([0,10,20,30])

labels = ax1.set_xticklabels(["zero,", "ten","twnety","thirty"])

ax1.plot()

Change orientation and size of the X labels

fig = plt.figure()

ax1= fig.add_subplot(1,1,1)

ticks = ax1.set_xticks([0,10,20,30])

labels = ax1.set_xticklabels(["zero,", "ten","twnety","thirty"], rotation = 90, fontsize="large")

ax1.plot()

X-axis label, Title

ax1.set_xlabel("Xlabels")

ax1.set_title("Title)

Plots

veritical barplot

ax1.bar(["Car", "Truck", "Bus", "Auto"], [10,20,30,40]) -- Categorical + numeric data

horizontal bar plot

ax1.barh(["Car", "Truck", "Bus", "Auto"], [10,20,30,40]) -- Categorical + numeric data

histogram

ax1.hist(X, bin = 50)

pie chart

ax1.pie([10,20,30], labels=["car", "bus", "truck"])

scatter plot

ax1.scatter(x,y, marker="^", color="g")

Box/Violon plot

ax1.boxplot(X)

ax1.violinplot(X)

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1. Python Machine learning - Regularized Linear Model

Linear Regression

from sklearn.linear_model import LinearRegression     

lin_reg = LinearRegression()

lin_reg.fit(X, y)

print(lin_reg.intercept_, lin_reg.coef_)

lin_reg.predict(X_new)

Polynomial Regression

Transform to polynomial feature

from sklearn.preprocessing import PolynomialFeatures

poly_features = PolynomialFeatures(degree =2, include_bias=False)

X_poly = poly_features.fit_transform(X) --- X_poly will have two terms - degree =1 and degree 2

Now use Linear Regression

lin_reg = LinearRegression()

lin_reg.fit(X_poly, y)

print(lin_reg.intercept_, lin_reg.coef_)

Training and test error

from sklearn.metrics import mean_squared_error

from sklearn.model_selection import train_test_split

def plot_learning_curves(model, x, y):

      X_train, X_val, Y_train, y_val = train_test_split(x, y, test_size=0.2)

      train_error, val_errors = [], []

      for m in range(1, len(X_train)):

             model.fit(X_train[:m], y_train[:m])

             y_train_predict = model.predict(X_train[:m])  -- no of sampling of training is changing from 1 to len(X_train)

             y_val_predict = model.predict(X_val) -- always taken for all validation samples.

              train_errors.append(mean_squared_error(y_train[:m], y_val_predict[:m]))

              val_errors.append(mean_squared_error(y_val, y_val_predict))

plt.plot(np.sqrt(train_errors, "r-+", linewidth=2, label="train"))

plt.plot(np.sqrt(val_errors, "b-+", linewidth=2, label="validation"))           

Using Pipeline

from   sklearn.pipeline import Pipeline

polynomial_regression = Pipeline ([

                                           ("poly features", PolynomialFeatures(degree=10, include_bias=False)),

                                           ("lin reg", LinearRegression())

                                            ])

plot_learning_curve(polynomial_regression, X, y)

Gradient Regression

Batch Gradient Descent

Stochastic Gradient Descent

Mini Batch Gradient Descent

Ridge Regression

from sklearn.linear_model import Ridge

ridge_reg = Ridge(alpha=1, solver="cholesky")

ridge_reg.fit(X,y)

ridge_reg.predict([1.5])

Lasso Regression ( Lest Absolute Shrinkage and Selection Operator Regression )

from sklearn.linear_model import Lasso

lasso_reg = Lasso(alpha = 0.1)

lasso_reg.fit(X, y)

lasso_reg.predict([1.5])

Elastic net

from sklearn.linear_model import ElasticNet

elastic_net = ElasticNet(alpha=0.1, l1_ratio=0.5)

elastic_net.fit(X,y)

elastic_net.predict([1.5])

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7. Data Analysis - Data Aggregation & Grouping

 Split, Apply, Combine

Mean

• X["Values1"].groupby([X["Keys1"]]).mean() -- Mean of "Values1" based on Keys = "Key1"
• X["Values1"].groupby([X["Keys1"],X["Keys2"]]).mean()
• X.groupBy["Keys1"].mean()
• X.groupBy(X["Keys1"], X["Keys2"]).mean()

Count

• X.groupBy(X["Keys1"], X["Keys2"]).size()

GroupBy Clause with FOR Loop

• for name, group in X.groupby([["Key1"]])
• print (name)
• print(group)

Accessing each group in dictionary key

• dict(list(X.groupby([["Key1"]])))

Extracting based on column

• X.groupby(X.dtypes, axis =1) -- column will be seperated based on data types.

Column Grouping

• Create a mapping from column to grouping
• mapColumns={'a':'Group1', 'b':'Group1', 'c':'Group2', 'd':'Group2', 'e':'Group3', 'f':'Group3'}
• X.groupby(mapColumns, axis=1).mean()

Passing Lambda function in groupby

•  X.groupby(lambda x:x<'c', axis =1).mean() -- Two group will be created with TRUE/FALSE column.

Aggregate function

• def max_min(x)
• return x.max() - x.min()
• X.agg([max, min, max_min]) -- For each column min, max, max_min function will be called.
• 
  

Aggregate function with groupby

• X1 = X.groupby(["key1"]) -- Will create dataframe for each groups in "key1"
• X1.agg(min, max, max_min)) -- Will apply aggregate function for each of that column.

Aggregate function with custom defined column name

• X1.agg(("Maximum", max),("Minimum", min),("Maximum_Minimum",max_min))

GroupBy with Apply

• X.groupby().apply(fName)
• X.groupby(["key1"]).apply(lambda x: x.min())  -- Passing lambda function, User defined function can also be applied.

GroupBy with Apply with passing arguments to function

• def minimumFb(X, x):
• return(X.max()-X.min() > x)
• X.groupby(["key1"]).apply(minimumFb(10))

Apply function with bucket analysis

• quartiles = X.cut(X.Values1, 2)
• X.groupby(quartiles).apply(lambda x:x.min())

Pivot Table

• X.pivot_table(values="AvgSizeOfTrip" , index = "Gender" , columns="Group" )

Pivot table with aggregation

• X.pivot_table (values="AvgSizeOfTrip" , index = "Gender" , columns="Group", aggfunc=np.sum)
• X.pivot_table (values="AvgSizeOfTrip" , index = "Gender" , columns="Group", aggfunc={"Col1":np.sum, 'Col2':np.mean}) - different column with different operation.

Pivot table with count

• pd.crosstab(X.gender, X.group)  -- This just counts the frequency.

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