Aws development essentials pdf download

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Select Yes if prompted. Configure the Linux Instance Modify the web server by adding the following index. If you are an experienced Linux user, you should know the basics of vi, the default text editor. Otherwise you may want to check out some vi basics. Enter the following. Press Escape, followed by: :wq to save and quit after you add the PHP code above.

Connect to the web server Click Yes, Allocate Right-click on it and choose Associate from the pop-up menu: A popup will allow you to associate the EIP with one of your running instances.

Now your Elastic IP address is associated to your instance: Now, verify the new IP address of your web server in a browser: Congratulations! Deploy a simple application on EC2 3. Then distribute load by viewing the application Overview This lab will walk you through the process of creating an Elastic Load Balancer ELB to load balance traffic across several EC2 instances in a single Availability Zone. You will deploy a simple application on EC2 instances over which you will distribute load by viewing the application in your browser.

This lab introduces you to a very basic example of bootstrapping your instances using the meta-data service to get you thinking about more complicated patterns that you might want to implement to configure instances as they are started. Now click on Launch Instance. Next select Launch Classic Wizard and click Continue. It is possible to start your instances using the QuickLaunch wizard, but for the purposes of this lab we want to see all the settings step by step, which is the way the Classic Wizard captures instance information.

In this case this is an Amazon maintained Linux distribution with access to all the software repositories we require to install software for this lab such as Apache and PHP 5. We want to start more than one instance for this lab, so change the number of instances to 2 and click Continue. In the next screen we will use the User Data field to bootstrap our instance, running a custom script to install the necessary packages Apache and PHP and sample code PHP scripts that we will use in this lab.

User data provides a mechanism to pass information to the Amazon metadata service, which instances request information from at launch time. One property of the metadata service is that shell scripts passed in will be executed. In our case we will bootstrap using the script shown in the box below. Next you can click continue on this screen, but you will notice that should you require, you are able to edit the size of the root volume, plus add aditional disks to the instance at boot time.

Give your new web servers a nice name like Essentials Lab Servers and click Continue. For this lab, we will reuse the key pair we created in the earlier lab. You also have the option of creating a new key pair. You need to add a rule for both ports. Your instances will now start. First check the instances we started have finished their creation cycle by monitoring them to make certain they are running. Now we can grab the public DNS entry allocated to each server so that we can use this to hit the server in our web browser.

This is the web page returned by the PHP script that was installed when the instance when it started. It is a simple script that interrogates the metadata service and returns the instance ID and where it is running. This will be information that will help you see which instance you are hitting when we put an Elastic Load Balancer in front of them.

Create an Elastic Load Balancer ELB You now have two web servers, but you need a load balancer in front of these servers to give your users a single location for accessing both servers and to balance user requests across your simple web server farm.

The ping path is the location on our web servers the ELB will check is returning a healthy response to keep instances in service or not. We are lowering this to speed things up for our lab. Click continue to accept the advanced options. Note that these options can be changed in the future, and configure how the ELB Health Check will be performed including the health check protocol, port, and path as well as the health check interval, timeout, and heath thresholds.

It will take a couple of minutes to spin up your load balancers, attach your web servers, and pass the health checks. Click on your load balancer, select the Instances tab, and wait until the instances status changes from Out of Service to In Service. Your ELB is ready when this happens. Note: ELBs work across availability zones and they also scale elastically as demand dictates. You can view these metrics by clicking on the CloudWatch tab in the console. The metrics are reported as they are encountered and can take several minutes to show up in CloudWatch.

The following screenshot shows CloudWatch graphing the HealthyHostCount, which transitioned from zero healthy hosts to two shortly after the ELB was created for this lab.

In normal operation we would advise that these servers be located in separate availability zones to enable your application to be fault tolerant. The interface calling conventions by which an application program accesses operating system and other services.

An API is defined at source code level and provides a level of abstraction between the application and the kernel or other privileged utilities to ensure the portability of the code.

Web Services can convert your application into a Web-application, which can publish its function or message to the rest of the world. Web services are application components. It is designed to enable communications between clients and servers. It is used for popular software programming languages to enable rapid development against AWS services without having to use the granular APIs directly. They provide a layer of abstraction on top of the APIs.

This is a quick and easy way to create scripts. AWS does not stand still and features are being constantly introduced to make the AWS platform more powerful, hence the tools need to be updated to make use of the latest API improvements so CLI tools will need to be updated from time to time. Asynchronous of a computer, means having each operation started only after the preceding operation is completed.

Tags are managed AWS resources. Tags are key, value pair that you define. It contains all the information necessary to boot instances of your software. Instance is a result of running a system. IaaS is an abbreviation for Infrastructure as a service.

It is the "computing" in cloud computing. Compute is a feature that allows one to take advantage of thousand of networked servers. Utility Computing is the packaging of computing resources, such as computation and storage, as a metered service similar to a traditional public utility such as electricity, water, natural gas, or telephone network. Eventual Consistency is one of the consistency models used in the domain of parallel programming, for example in distributed shared memory, distributed transactions, and Optimistic replication.

Auto Scaling Group: An Auto Scaling group is a representation of multiple Amazon EC2 instances that share similar characteristics, and that are treated as a logical grouping for the purposes of instance scaling and management. For example, if a single application operates across multiple instances, you might want to increase or decrease the number of instances in that group to improve the performance of the application.

You can use the Auto Scaling group to automatically scale the number of instances or maintain a fixed number of instances. Health Check: A health check is a call to check on the state of each instance in an Auto Scaling group. If the instance returns any other state other than running, Auto Scaling considers the instance to be Unhealthy. Auto Scaling then terminates the instance and launches another one to take its place.

This ensures that your Auto Scaling group is consistent and operating normally. For more information, see Maintaining Current Scaling Level. Launch Configuration: A launch configuration captures the parameters necessary to create new EC2 instances.

You can attach only one launch configuration to an Auto Scaling group at a time. When you attach a new or updated launch configuration to your Auto Scaling group, any new instances will be launched using the new configuration parameters.

Existing instances are not affected. When Auto Scaling needs to scale down, it first terminates instances that have an older launch configuration. Tagging: Tagging is an Auto Scaling group tag is a tool for organizing your Auto Scaling resources and providing additional information for your Auto Scaling group such as software version, role, or location.

Auto Scaling group tags work like Amazon EC2 tags; Auto Scaling group tags provide search, group, and filter functionality. These tags have a key and value that you can modify. You can also remove Auto Scaling group tags any time. In most cases, you will need two triggers—one trigger for scaling up and another for scaling down. For example, if you want to scale up when your CPU usage increases to 80 percent, you need to configure a CloudWatch alarm and create an Auto Scaling policy.

Auto Scaling determines what to do by using the instructions in the scaling policy. If you also want to scale down when your CPU usage decreases to 40 percent, you need a second trigger.

In other words, you need to configure a separate CloudWatch alarm to detect the 40 percent threshold and create a separate Auto Scaling policy that scales down.

Policy: A policy is a set of instructions for Auto Scaling that tells the service how to respond to CloudWatch alarm messages. You can configure a CloudWatch alarm to send a message to Auto Scaling whenever a specific metric has reached a triggering value. When the alarm sends the message, Auto Scaling executes the associated policy on an Auto Scaling group to scale the group up or down.

Schedule Update: A scheduled update is a call to Auto Scaling that is scheduled for a future time. Currently, updates are supported only to min-, max-, and desired capacity. Scaling Activity: A scaling activity is a long-running process that implements a change to your Auto Scaling group, such as changing the size of the group.

It can also be a process to replace an instance, or to perform any other long-running operations supported by the service. Cooldown is the period of time after Auto Scaling initiates a scaling activity during which no other scaling activity can take place.

A cooldown period allows the effect of a scaling activity to become visible in the metrics that originally triggered the activity. This period is configurable, and gives the system time to perform and adjust to any new scaling activities such as scale-in and scale-out that affect capacity.

Alarm: Alarm is an Amazon CloudWatch alarm, an object that watches over a single metric. An alarm can change state depending on the value of the metric. When an alarm changes state it executes one or more actions. To create an alarm, use the Amazon CloudWatch PutMetricAlarm action to specify the metric to watch, the threshold values for the metric, the number of evaluation periods, and, optionally, one or more Amazon Simple Notification Service actions to perform when the alarm changes state.

Metric: A metric is the fundamental concept for Amazon CloudWatch and represents a time-ordered set of data points. Either you or AWS products publish metric data points into Amazon CloudWatch and you retrieve statistics about those data points as an ordered set of time- series data. The data points represent the values of that variable over time. For example, the CPU usage of a particular Amazon EC2 instance is one metric, and the latency of an elastic load balancer is another.

Amazon CloudWatch stores your metric data for two weeks. You can publish metric data from multiple sources, such as incoming network traffic from dozens of different Amazon EC2 instances, or requested page views from several different web applications. You can request statistics on metric data points that occur within a specified time window.

Namespaces: Amazon CloudWatch namespaces are conceptual containers for metrics. Metrics in different namespaces are isolated from each other, so that metrics from different applications are not mistakenly aggregated into the same statistics.

Every metric has specific characteristics that describe it, and you can think of dimensions as categories for those characteristics. Dimensions help you design a conceptual structure for your statistics plan. Time Stamp: with Amazon CloudWatch, each metric data point must be marked with a time stamp. The time stamp can be up to two weeks in the past and up to one day in the future. If you do not provide a time stamp, Amazon CloudWatch creates a time stamp for you based on the time the data element was received.

Units: represent your statistic's unit of measure. For example, the units for the Amazon EC2 Networking metric is Bytes because Networking tracks the number of bytes that an instance receives on all network interfaces. Statistics: are metric data aggregations over specified periods of time. Aggregations are made using the namespace, metric name, dimensions, and the data point unit of measure, within the time period you specify.

The following table describes the available statistics. Period: is the length of time associated with a specific Amazon CloudWatch statistic. Each statistic represents an aggregation of the metrics data collected for a specified period of time.

You can adjust how the data is aggregated by varying the length of the period. A period can be as short as one minute 60 seconds or as long as two weeks 1,, seconds Region: Each Amazon Region is designed to be completely isolated from the other Amazon Regions. This achieves the greatest possible failure independence and stability, and it makes the locality of each Amazon resource unambiguous. Appendix A. Create an AWS Account 2.

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