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Deployment models for cloud computing
Deployment models for cloud computing
When selecting a cloud strategy, a company must consider factors such as required cloud application components, preferred resource management tools, and any legacy IT infrastructure requirements.
The three cloud computing deployment models are cloud-based, on-premises, and hybrid.
- Cloud-based deployment
Run all parts of the application in the cloud.
Migrate existing applications to the cloud.
Design and build new applications in the cloud.
In a cloud-based deployment model, you can migrate existing applications to the cloud, or you can design and build new applications in the cloud. You can build those applications on low-level infrastructure that requires your IT staff to manage them. Alternatively, you can build them using higher-level services that reduce the management, architecting, and scaling requirements of the core infrastructure.
For example, a company might create an application consisting of virtual servers, databases, and networking components that are fully based in the cloud.
- On-premises deployment
Deploy resources by using virtualization and resource management tools.
Increase resource utilization by using application management and virtualization technologies.
On-premises deployment is also known as a private cloud deployment. In this model, resources are deployed on premises by using virtualization and resource management tools.
For example, you might have applications that run on technology that is fully kept in your on-premises data center. Though this model is much like legacy IT infrastructure, its incorporation of application management and virtualization technologies helps to increase resource utilization.
- Hybrid deployment
Connect cloud-based resources to on-premises infrastructure.
Integrate cloud-based resources with legacy IT applications.
In a hybrid deployment, cloud-based resources are connected to on-premises infrastructure. You might want to use this approach in a number of situations. For example, you have legacy applications that are better maintained on premises, or government regulations require your business to keep certain records on premises.
For example, suppose that a company wants to use cloud services that can automate batch data processing and analytics. However, the company has several legacy applications that are more suitable on premises and will not be migrated to the cloud. With a hybrid deployment, the company would be able to keep the legacy applications on premises while benefiting from the data and analytics services that run in the cloud.
Benefits of cloud computing
Benefits of cloud computing
Consider why a company might choose to take a particular cloud computing approach when addressing business needs.
Trade upfront expense for variable expense
Stop spending money to run and maintain data centers
Stop guessing capacity
Benefit from massive economies of scale
Increase speed and agility
Go global in minutes
Amazon Elastic Compute Cloud (Amazon EC2)
Amazon Elastic Compute Cloud (Amazon EC2)
Amazon Elastic Compute Cloud (Amazon EC2) provides secure, resizable compute capacity in the cloud as Amazon EC2 instances.
Imagine you are responsible for the architecture of your company's resources and need to support new websites. With traditional on-premises resources, you have to do the following:
Spend money upfront to purchase hardware.
Wait for the servers to be delivered to you.
Install the servers in your physical data center.
Make all the necessary configurations.
By comparison, with an Amazon EC2 instance you can use a virtual server to run applications in the AWS Cloud.
You can provision and launch an Amazon EC2 instance within minutes.
You can stop using it when you have finished running a workload.
You pay only for the compute time you use when an instance is running, not when it is stopped or terminated.
You can save costs by paying only for server capacity that you need or want.
Amazon EC2 instance types
Amazon EC2 instance types
Amazon EC2 instance types are optimized for different tasks. When selecting an instance type, consider the specific needs of your workloads and applications. This might include requirements for compute, memory, or storage capabilities.
To learn more, select the + symbol next to each category.
General purpose instances: General purpose instances provide a balance of compute, memory, and networking resources. This instance family would not be the best choice for the application in this scenario. Compute optimized instances are more well suited for batch processing workloads than general purpose instances.
Compute optimized instances: to execute a bactch of workload.
Memory optimized instances Memory optimized instances are more ideal for workloads that process large datasets in memory, such as high-performance databases.
Accelerated computing instances
Storage optimized instances Storage optimized instances are designed for workloads that require high, sequential read and write access to large datasets on local storage. The question does not specify the size of data that will be processed. Batch processing involves processing data in groups. A compute optimized instance is ideal for this type of workload, which would benefit from a high-performance processor.
Amazon EC2 pricing
Amazon EC2 pricing
With Amazon EC2, you pay only for the compute time that you use. Amazon EC2 offers a variety of pricing options for different use cases. For example, if your use case can withstand interruptions, you can save with Spot Instances. You can also save by committing early and locking in a minimum level of use with Reserved Instances.
On-Demand Instances are ideal for short-term, irregular workloads that cannot be interrupted. No upfront costs or minimum contracts apply. The instances run continuously until you stop them, and you pay for only the compute time you use.
Amazon EC2 Savings Plans AWS offers Savings Plans for several compute services, including Amazon EC2. Amazon EC2 Savings Plans enable you to reduce your compute costs by committing to a consistent amount of compute usage for a 1-year or 3-year term. This term commitment results in savings of up to 66% over On-Demand costs.
Any usage up to the commitment is charged at the discounted plan rate (for example, $10 an hour). Any usage beyond the commitment is charged at regular On-Demand rates.
Later in this course, you will review AWS Cost Explorer, a tool that enables you to visualize, understand, and manage your AWS costs and usage over time. If you are considering your options for Savings Plans, AWS Cost Explorer can analyze your Amazon EC2 usage over the past 7, 30, or 60 days. AWS Cost Explorer also provides customized recommendations for Savings Plans. These recommendations estimate how much you could save on your monthly Amazon EC2 costs, based on previous Amazon EC2 usage and the hourly commitment amount in a 1-year or 3-year plan.
Reserved Instances are a billing discount applied to the use of On-Demand Instances in your account. You can purchase Standard Reserved and Convertible Reserved Instances for a 1-year or 3-year term, and Scheduled Reserved Instances for a 1-year term. You realize greater cost savings with the 3-year option.
At the end of a Reserved Instance term, you can continue using the Amazon EC2 instance without interruption. However, you are charged On-Demand rates until you do one of the following:
Terminate the instance.
Purchase a new Reserved Instance that matches the instance attributes (instance type, Region, tenancy, and platform).
Spot Instances are ideal for workloads with flexible start and end times, or that can withstand interruptions. Spot Instances use unused Amazon EC2 computing capacity and offer you cost savings at up to 90% off of On-Demand prices.
Suppose that you have a background processing job that can start and stop as needed (such as the data processing job for a customer survey). You want to start and stop the processing job without affecting the overall operations of your business. If you make a Spot request and Amazon EC2 capacity is available, your Spot Instance launches. However, if you make a Spot request and Amazon EC2 capacity is unavailable, the request is not successful until capacity becomes available. The unavailable capacity might delay the launch of your background processing job.
After you have launched a Spot Instance, if capacity is no longer available or demand for Spot Instances increases, your instance may be interrupted. This might not pose any issues for your background processing job. However, in the earlier example of developing and testing applications, you would most likely want to avoid unexpected interruptions. Therefore, choose a different EC2 instance type that is ideal for those tasks.
Dedicated Hosts are physical servers with Amazon EC2 instance capacity that is fully dedicated to your use.
You can use your existing per-socket, per-core, or per-VM software licenses to help maintain license compliance. You can purchase On-Demand Dedicated Hosts and Dedicated Hosts Reservations. Of all the Amazon EC2 options that were covered, Dedicated Hosts are the most expensive.
Amazon EC2 Auto Scaling
Amazon EC2 Auto Scaling
If you’ve tried to access a website that wouldn’t load and frequently timed out, the website might have received more requests than it was able to handle. This situation is similar to waiting in a long line at a coffee shop, when there is only one barista present to take orders from customers.
Amazon EC2 Auto Scaling enables you to automatically add or remove Amazon EC2 instances in response to changing application demand. By automatically scaling your instances in and out as needed, you are able to maintain a greater sense of application availability.
Within Amazon EC2 Auto Scaling, you can use two approaches: dynamic scaling and predictive scaling.
Dynamic scaling responds to changing demand.
Predictive scaling automatically schedules the right number of Amazon EC2 instances based on predicted demand.
Example: Amazon EC2 Auto Scaling
Example: Amazon EC2 Auto Scaling
In the cloud, computing power is a programmatic resource, so you can take a more flexible approach to the issue of scaling. By adding Amazon EC2 Auto Scaling to an application, you can add new instances to the application when necessary and terminate them when no longer needed.
Suppose that you are preparing to launch an application on Amazon EC2 instances. When configuring the size of your Auto Scaling group, you might set the minimum number of Amazon EC2 instances at one. This means that at all times, there must be at least one Amazon EC2 instance running.
When you create an Auto Scaling group, you can set the minimum number of Amazon EC2 instances. The minimum capacity is the number of Amazon EC2 instances that launch immediately after you have created the Auto Scaling group. In this example, the Auto Scaling group has a minimum capacity of one Amazon EC2 instance.
Next, you can set the desired capacity at two Amazon EC2 instances even though your application needs a minimum of a single Amazon EC2 instance to run.
The third configuration that you can set in an Auto Scaling group is the maximum capacity. For example, you might configure the Auto Scaling group to scale out in response to increased demand, but only to a maximum of four Amazon EC2 instances.
Because Amazon EC2 Auto Scaling uses Amazon EC2 instances, you pay for only the instances you use, when you use them. You now have a cost-effective architecture that provides the best customer experience while reducing expenses.
Elastic Load Balancing
Elastic Load Balancing
Elastic Load Balancing is the AWS service that automatically distributes incoming application traffic across multiple resources, such as Amazon EC2 instances.
A load balancer acts as a single point of contact for all incoming web traffic to your Auto Scaling group. This means that as you add or remove Amazon EC2 instances in response to the amount of incoming traffic, these requests route to the load balancer first. Then, the requests spread across multiple resources that will handle them. For example, if you have multiple Amazon EC2 instances, Elastic Load Balancing distributes the workload across the multiple instances so that no single instance has to carry the bulk of it.
Although Elastic Load Balancing and Amazon EC2 Auto Scaling are separate services, they work together to help ensure that applications running in Amazon EC2 can provide high performance and availability.
Example: Elastic Load Balancing
Example: Elastic Load Balancing
Low-demand period
Here’s an example of how Elastic Load Balancing works. Suppose that a few customers have come to the coffee shop and are ready to place their orders.
If only a few registers are open, this matches the demand of customers who need service. The coffee shop is less likely to have open registers with no customers. In this example, you can think of the registers as Amazon EC2 instances.
High-demand period
Throughout the day, as the number of customers increases, the coffee shop opens more registers to accommodate them. In the diagram, the Auto Scaling group represents this.
Additionally, a coffee shop employee directs customers to the most appropriate register so that the number of requests can evenly distribute across the open registers. You can think of this coffee shop employee as a load balancer.
Monolithic applications and microservices
Monolithic applications and microservices
Applications are made of multiple components. The components communicate with each other to transmit data, fulfill requests, and keep the application running.
Suppose that you have an application with tightly coupled components. These components might include databases, servers, the user interface, business logic, and so on. This type of architecture can be considered a monolithic application.
In this approach to application architecture, if a single component fails, other components fail, and possibly the entire application fails.
In a microservices approach, application components are loosely coupled. In this case, if a single component fails, the other components continue to work because they are communicating with each other. The loose coupling prevents the entire application from failing.
When designing applications on AWS, you can take a microservices approach with services and components that fulfill different functions. Two services facilitate application integration: Amazon Simple Notification Service (Amazon SNS) and Amazon Simple Queue Service (Amazon SQS).
Amazon Simple Notification Service (Amazon SNS)
Amazon Simple Notification Service (Amazon SNS)
Amazon Simple Notification Service (Amazon SNS) is a publish/subscribe service. Using Amazon SNS topics, a publisher publishes messages to subscribers. This is similar to the coffee shop; the cashier provides coffee orders to the barista who makes the drinks.
In Amazon SNS, subscribers can be web servers, email addresses, AWS Lambda functions, or several other options.
Amazon Simple Notification Service (Amazon SNS)
Amazon Simple Notification Service (Amazon SNS)
Amazon Simple Notification Service (Amazon SNS) is a publish/subscribe service. Using Amazon SNS topics, a publisher publishes messages to subscribers. This is similar to the coffee shop; the cashier provides coffee orders to the barista who makes the drinks.
Amazon Simple Queue Service (Amazon SQS)
Amazon Simple Queue Service (Amazon SQS)
Amazon Simple Queue Service (Amazon SQS) is a message queuing service.
Using Amazon SQS, you can send, store, and receive messages between software components, without losing messages or requiring other services to be available. In Amazon SQS, an application sends messages into a queue. A user or service retrieves a message from the queue, processes it, and then deletes it from the queue.
Serverless computing
Serverless computing
Earlier in this module, you learned about Amazon EC2, a service that lets you run virtual servers in the cloud. If you have applications that you want to run in Amazon EC2, you must do the following:
1
Provision instances (virtual servers).
Upload your code.
Continue to manage the instances while your application is running.
The term “serverless” means that your code runs on servers, but you do not need to provision or manage these servers. With serverless computing, you can focus more on innovating new products and features instead of maintaining servers.
Another benefit of serverless computing is the flexibility to scale serverless applications automatically. Serverless computing can adjust the applications' capacity by modifying the units of consumptions, such as throughput and memory.
An AWS service for serverless computing is AWS Lambda.
AWS Lambda
AWS Lambda
AWS Lambda is a service that lets you run code without needing to provision or manage servers. While using AWS Lambda, you pay only for the compute time that you consume. Charges apply only when your code is running. You can also run code for virtually any type of application or backend service, all with zero administration. For example, a simple Lambda function might involve automatically resizing uploaded images to the AWS Cloud. In this case, the function triggers when uploading a new image.
How AWS Lambda works
How AWS Lambda works
You upload your code to Lambda.
You set your code to trigger from an event source, such as AWS services, mobile applications, or HTTP endpoints.
Lambda runs your code only when triggered.
You pay only for the compute time that you use. In the previous example of resizing images, you would pay only for the compute time that you use when uploading new images. Uploading the images triggers Lambda to run code for the image resizing function.
Containers
Containers
Containers provide you with a standard way to package your application's code and dependencies into a single object. You can also use containers for processes and workflows in which there are essential requirements for security, reliability, and scalability.
Amazon Elastic Container Service (Amazon ECS)
Amazon Elastic Container Service (Amazon ECS)
Amazon Elastic Container Service (Amazon ECS) is a highly scalable, high-performance container management system that enables you to run and scale containerized applications on AWS.
Amazon ECS supports Docker containers. Docker is a software platform that enables you to build, test, and deploy applications quickly. AWS supports the use of open-source Docker Community Edition and subscription-based Docker Enterprise Edition. With Amazon ECS, you can use API calls to launch and stop Docker-enabled applications.
Amazon Elastic Kubernetes Service (Amazon EKS)
Amazon Elastic Kubernetes Service (Amazon EKS)
Amazon Elastic Kubernetes Service (Amazon EKS) is a fully managed service that you can use to run Kubernetes on AWS.
Kubernetes is open-source software that enables you to deploy and manage containerized applications at scale. A large community of volunteers maintains Kubernetes, and AWS actively works together with the Kubernetes community. As new features and functionalities release for Kubernetes applications, you can easily apply these updates to your applications managed by Amazon EKS.
AWS Fargate
AWS Fargate
AWS Fargate is a serverless compute engine for containers. It works with both Amazon ECS and Amazon EKS.
When using AWS Fargate, you do not need to provision or manage servers. AWS Fargate manages your server infrastructure for you. You can focus more on innovating and developing your applications, and you pay only for the resources that are required to run your containers.
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