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Ict based smart monitoring system

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  Introduction: Intelligent monitoring is defined using different computational methods that provide the customers with relevant tools and information in monitoring, control, manage, and optimize the network [5]. The integration of modern information technology with the cyber-physical world produces new applications, such as ubiquitous computing, which renders interactive computers usable though essentially invisible to people [6]. It seeks to break away from desktop computing to include consumers anywhere and at all times with computer resources [7]. The goal is to break away from desktop computers to provide consumers with computing resources anywhere and anytime [8]. Since its inception, the Internet of Things (IoT) has taken the globe by storm. The theory of wireless technology linking everything sums up the IoT [9]. IoT is the connection of devices, software, sensors, actuators, and physical objects are embedded in the network, cars, home appliances, and other products that help t
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My Experience in LGM-SOC

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 Hello everyone we all know that now a days open source has become a new craze between all of us we are just attracted towards. Few months before I applied in the LGM SOC as  I started contributing over there it help me a lot in merging my first pr in the open source project. letsgrowmore.in To say in short: It’s a project of which the source code is open to everyone, and everyone is able and allowed to participate, be it by writing bugfixes, major changes, … Most commercial products clearly don’t fall under this category, seeing as they’re in house and the code is only accessible to developers of that company. Github, however, is filled with projects that are. All you need to do, is pull a project, create a branch, and start coding. Of course, if you first get on the same page as the maintainers, the chances of your changes being merged into the master branch are bigger. A very well known example of an Open Source project: Linux operating systems. You find a bug? go ahead and fix it.
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Set Up and Configure a Cloud Environment in Google Cloud: Challenge Lab

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  Task - 1 : Create development VPC manually :- gcloud compute networks create griffin-dev-vpc --subnet-mode custom gcloud compute networks subnets create griffin-dev-wp --network=griffin-dev-vpc --region us-east1 --range=192.168.16.0/20 gcloud compute networks subnets create griffin-dev-mgmt --network=griffin-dev-vpc --region us-east1 --range=192.168.32.0/20 ---------------------------------------------------------------------------------------------------------------------------------------------- Task - 2 : Create production VPC manually :- gsutil cp -r gs://cloud-training/gsp321/dm . cd dm sed -i s/SET_REGION/us-east1/g prod-network.yaml gcloud deployment-manager deployments create prod-network \     --config=prod-network.yaml cd .. ---------------------------------------------------------------------------------------------------------------------------------------------- Task - 3 : Create bastion host :- gcloud compute instances create bastion --network-interface=network=griffin-
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Location: Mumbai, Maharashtra, India
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Deploy to Kubernetes in Google Cloud: Challenge Lab

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  [GSP318] : Deploy to Kubernetes in Google Cloud: Challenge Lab :- ---------------------------------------------------------------------------------------------------------------------------------------------- Task - 1 : Create a Docker image and store the Dockerfile :- gcloud auth list gsutil cat gs://cloud-training/gsp318/marking/setup_marking.sh | bash gcloud source repos clone valkyrie-app cd valkyrie-app cat > Dockerfile <<EOF FROM golang:1.10 WORKDIR /go/src/app COPY source . RUN go install -v ENTRYPOINT ["app","-single=true","-port=8080"] EOF docker build -t valkyrie-app:v0.0.1 . cd .. cd marking ./step1.sh ---------------------------------------------------------------------------------------------------------------------------------------------- Task - 2 : Test the created Docker image :- cd .. cd valkyrie-app docker run -p 8080:8080 valkyrie-app:v0.0.1 & cd .. cd marking ./step2.sh ---------------------------------------------------
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Location: Mumbai, Maharashtra, India
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Build a Website on Google Cloud: Challenge Lab

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GSP319 : Build a Website on Google Cloud: Challenge Lab :- ---------------------------------------------------------------------------------------------------------------------------------------------- Task 1: Download the monolith code and build your container :- git clone https://github.com/googlecodelabs/monolith-to-microservices.git cd ~/monolith-to-microservices ./setup.sh cd ~/monolith-to-microservices/monolith npm start gcloud services enable cloudbuild.googleapis.com gcloud builds submit --tag gcr.io/${GOOGLE_CLOUD_PROJECT}/fancytest:1.0.0 . ---------------------------------------------------------------------------------------------------------------------------------------------- Task 2: Create a kubernetes cluster and deploy the application :- gcloud config set compute/zone us-central1-a gcloud services enable container.googleapis.com gcloud container clusters create fancy-cluster --num-nodes 3 kubectl create deployment fancytest --image=gcr.io/${GOOGLE_CLOUD_PROJECT}/fancyt
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Location: Mumbai, Maharashtra, India
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Perform Foundational Data, ML, and AI Tasks in Google Cloud

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  Task 1: Run a simple Dataflow job In this task, you have to transfer the data in a CSV file to BigQuery using Dataflow via Pub/Sub. First of all, you need to create a BigQuery dataset called  lab  and a Cloud Storage bucket called with your project ID. 1.1 Created a BigQuery dataset called  lab In the Cloud Console, click on  Navigation Menu  >  BigQuery . Select your project in the left pane. Click  CREATE DATASET . Enter  lab  in the Dataset ID, then click  Create dataset . Run  gsutil cp gs://cloud-training/gsp323/lab.schema .  in the Cloud Shell to download the schema file. View the schema by running  cat lab.schema . Go back to the Cloud Console, select the new dataset  lab  and click  Create Table . In the Create table dialog, select  Google Cloud Storage  from the dropdown in the Source section. Copy  gs://cloud-training/gsp323/lab.csv  to  Select file from GCS bucket . Enter  customers  to “Table name” in the Destination section. Enable  Edit as text  and copy the JSON dat
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Location: Mumbai, Maharashtra, India
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