NyiNyi

Computing - Technologies
There are certain technologies that are working behind the cloud computing platforms making cloud computing flexible, reliable,usable.
technologies are listed below.
1) Virtualization
2) Service-Oriented Artchitecture (SOA)
3) Grid Computing
4) Utility Computing

Virtualization is a technique, which allows to share single physical instance of an application or resource among multiple organization or tenants (customers). It does so by assigning a logical name to a physical resource and providing a pointer to that physical resource when demanded.

The Multitenant architecture offers virtual Isolation among the multiple tenants and therefore the organizations can use and customize the application as though they each have its own instance running.
-Oriented Architecture
Service-Oriented Architecture helps to use applications as a service for other applications regardless the type of vendor, product or technology. Therefore, it is possible to exchange of data between applications of different vendors without additional programming or making changes to services.
Computing Service Oriented architecture
Computing
Grid Computing refers to distributed computing in which a group of computers from multiple locations are connected with each other to achieve common objective. These computer resources are heterogeneous and geographically dispersed.
Grid Computing breaks complex task into smaller pleces. These smaller pleces are distributed to CPU that reside within the grid.

Models
Deployment models define the type of access to the cloud, i.e how the cloud is located? Cloud can have any of the four types of access: Public, Private, Hybrid and Community.

Cloud
The Public Cloud allows systems and services to be easily accessible to the general public. Public cloud may be less secure because of its openness, e,g e-mail.
Cloud
The private Cloud allows systems and services to be accessible within an organization. It offers increased security because of its private nature.
Cloud
The Hybrid Cloud is mixture of public and private cloud. However, the critical activities are performed using private cloud while the non-critical activities are performed using public cloud.
Models
Service Models are the reference models on which the Cloud Computing is based. These can be categorized into three basic service models as listed below.
1) Infrastructure as a Service (IaaS)
2) Platform as a Service (PaaS)
3) Software as a Service (SaaS)
There are many other service models all of which can take the form like XaaS, i.e Anyting as a Service. This can be Netwrok as a Service, Business as a Service, Identity as a Service, Database as a Service or Strategy as a Service.
The Infrastructure as a Service (IaaS) is the most basic level of service. Each of the service models make use of the underlying service model.
# Infrastructure as a Service (IAAS)
IaaS provides access to fundamental resources such as physical machines, virtual machines, virtual storage, etc...
as a Service (PaaS)
PaaS provides the runtime environment for applications, development & deployment tools, etc..
# Software as a Service (SaaS)
SaaS model allows to use software applications as a service to end users.

Cloud Model☁️☁️
The Private Cloud allows systems and services to be accessible with in an organization.
The Private Cloud is operated only within a single organization.
It may be managed internally or by third-party.


There are many benefits of deploying cloud as private cloud model. The following diagram shows some of those benefits:
SECURITY AND PRIVACY
Private cloud operations are not available to general public and resources are shared from distinct pool of resources, therefore, ensures high security and privacy.
CONTROL
Private clouds have more control on its resources and hardware than public cloud because it is accessed only within an organization.
AND ENERGY EFFICIENCY
Private cloud resources are not as cost effective as public clouds but they offer more efficiency than public cloud.

Here are the disadvantages of using private cloud model:
AREA
Private cloud is only accessible locally and is very difficult to deploy globally.
PRICING
In order to fulfill demand, purchasing new hardware is very costly.
SCALABILITY
Private cloud can be scaled only within capacity of internal hosted resources.
SKILLS
In order to maintain cloud deployment, organization requires more skilled and expertise.

#အားလုံးဘဲမဂၤလာပါ.
Cloud Online Free Course
ကြၽန္ေတာ္ Google Cloud Associate Online ကေန Share လုပ္ေပးသြားမွာ ျဖစ္ပါတယ္.
Cloud ကုိစိတ္ဝင္စားတဲ့ ညီအကုိတုိ႔ Comment မွာ မွတ္ခ်က္ေပးသြားရပါတယ္
တူတူတိမ္ေပၚခုန္တက္ လုိက္ရေအာင္

Google Cloud Associate PDF ျဖစ္ပါတယ္
Google Cloud ကုိစတင္ေလ့လာသူေတြ ဖတ္လုိ႔အဆင္ေျပတဲ့စာအုပ္ေလုဖစ္ပါတယ္.
://mega.nz/fm/38FGxAAT

The Google Cloud Platform certification portfolio
• Associate Cloud Engineer.
• Professional Cloud Architect.
• Professional Data Engineer.
• Professional Cloud Developer.
• Professional Cloud Network Engineer.
• Professional Cloud Security Engineer.

# Associate Cloud Engineer

# Course Outline
The course includes presentations, demonstrations, and hands-on-labs.
1 : Introduction to Google Cloud Platform
• Google Cloud Platform (GCP) Infrastructure
• Using GCP
• Lab : Console and Cloud Shell
• Demo : Projects
• Lab : Infrastructure Preview
2 : Virtual Networks
• Virtual Private Cloud (VPC) , Projects , Networks, Subnetworks, IP addresses, Routes, Firewall rules
• Subnetworks for resource management instead of physical network topology
• Lab : Virtual Networking
• Lab : Bastion Host
3 : Virtual Machine
• Compute Engine
• Lab : Creating Virtual Machines
• Compute options (vCPU and Memory)
• Images
• Common Compute Engine actions
• Lab : Working with Virtual Machines
4 : Cloud IAM
• Organizations, Roles, Members, Service accounts, Cloud IAM best pratices
• Lab : Cloud IAM
5 : Data Storage Services
• Cloud Storage
• Lab : Cloud Storage
• Cloud SQL
• Lab : Cloud SQL
• Cloud Spanner, Cloud Datastore
• Lab : Cloud Datastore
• Cloud Bigtable
7 : Resource Monitoring
• Stackdriver, Monitoring
• Lab : Resource Monitoring (Stackdriver)
• Logging, Error Reporting, Tracing, Debugging
• Lab : Error Reporting and Debugging (Stackdriver)
8 : Interconnecting Networks
• Cloud Virtual Private Network (VPN)
• Lab : Virtual Private Network (VPN)
• Cloud Router, Cloud Interconnect, External Peering Cloud DNS
9 : Load Balancing
• Managed Instance Groups, HTTPS load balancing, Cross-region and content-based load balancing, SSL proxy/TCP proxy load balancing, Network load balancing
• Lab : VM Automation and Load Balancing
10 : Autoscaling
• Autoscaling, Policies, Configuration
• Lab : Autoscaling
11 : Infrastructure Automation with Google Cloud Platform APls
• Infrastructure automation, Images, Metadata, Scripts, Google Cloud API
• Lab : Google Cloud Platform API Infrastructure Automation
12 : Infrastructure Automation with Deployment Manager
• Deployment Manager, Configuration, Cloud Launcher
• Lab : Deployment Manager
13 : Managed Services
• Cloud Dataproc, Cloud Dataflow, BigQuery, Cloud Datalab