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Understanding AWS Placement Groups

Understanding AWS Placement Groups: Maximizing Performance and Reliability in EC2 Deployments

AWS Placement Groups are a handy capability of Amazon Web Services (AWS) which allows users to enhance network optimization and fault tolerance of their EC2 instances. If you are running performance-intensive applications or controlling large distributed operating system domains, placement groups let you leverage how the EC2 instances are spread through the AWS deployment, allowing a better focus on networking, latency, and faults recovery.

What is a Placement Group?

A placement group is a way to control how your EC2 instances are placed on the underlying hardware in the AWS cloud. By influencing the physical location and network proximity of your instances,

All nodes within the placement group can talk to all other nodes within the placement group at a full line rate of 10 Gbps single traffic flow within the enclosure without any over-subscription-related delay.

Best Use Cases:

High-performance computing (HPC): Applications that require low latency, such as real-time data analytics or financial services.

Distributed machine learning: applications that need fast, low-latency communication between instances.

Benefits:

Low latency: The physical proximity of instances ensures minimal delay in communication.

High throughput: Instances can utilize high bandwidth for inter-instance data transfer.

Types of Placement Groups?

Cluster Placement Group

Packs instances close together inside an Availability Zone. This strategy enables workloads to achieve the low-latency network performance necessary for tightly coupled node-to-node communication that is typical of HPC applications.

A cluster placement group is a logical grouping of instances within a single Availability Zone. A cluster placement group can span peered VPCs in the same region. Instances in the same cluster placement group enjoy a higher per-flow throughput limit of up to 10 Gbps for TCP/IP traffic and are placed in the same high-bisection bandwidth segment of the network.

Cluster Placement Groups are great for applications that require low latency or high throughput. They work best when most of the network traffic occurs between the instances in the group, making them ideal for workloads that depend on quick data exchange. If your application falls into this category, using a Cluster Placement Group can help improve performance significantly!

Partition Placement Group

Spread your instances across logical partitions such that groups of the cases in one partition do not share the underlying hardware with groups of instances in different partitions. This strategy is typically used by large distributed and replicated workloads, such as Hadoop, Cassandra, and Kafka.

Partition placement groups help reduce the likelihood of correlated hardware failures for your application. When partition placement groups are used, Amazon EC2 divides each group into logical segments called partitions.

Amazon EC2 ensures that each partition within a placement group has its own racks. Each rack has its own network and power source. No two partitions within a placement group share the same racks, allowing you to isolate the impact of a hardware failure within your application.

Each partition comprises multiple instances. The instances in a partition do not share racks with the instances in the other partitions, allowing you to contain the impact of a single hardware failure to only the associated partition.

Partition placement groups can be used to deploy large distributed and replicated workloads, such as HDFS, HBase, and Cassandra, across distinct racks.

Spread Placement Group

Strictly places a small group of instances across distinct underlying hardware to reduce correlated failures.

A spread placement group is a group of instances that are each placed on distinct racks, with each rack having its own network and power source.

Spread placement groups are recommended for applications that have a small number of critical instances that should be kept separate from each other. Launching instances in a spread placement group reduces the risk of simultaneous failures that might occur when instances share the same racks.

Placement Group Rules and Limitations

General Rules and Limitations

  • The name that you specify for a placement group must be unique within your AWS account for the Region.
  • An instance can be launched in one placement group at a time; it cannot span multiple placement groups.
  • You can’t merge placement groups.
  • Instances with a tenancy of a host cannot be launched in placement groups.
  • On-demand capacity Reservation and zonal Reserved Instances provide a capacity reservation for EC2 instances in a specific Availability Zone. The capacity reservation can be used by instances in a placement group. However, it is not possible to explicitly reserve capacity for a placement group.

Conclusion

AWS Placement Groups are a useful way to make your EC2 performance better, depending on what your application needs for networking and availability. By picking the right type of placement group, you can find the best mix of performance, cost, and reliability for your tasks.

Thanks for reading and stay tuned for more.

If you have any questions concerning this article or have an AWS project that requires our assistance, please reach out to us by leaving a comment below or email us at sales@accendnetworks.com.


Thank you!

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Exploring AWS Data Migration Services

Exploring AWS Data Migration Services: Unlocking Seamless Cloud Transitions

Migrating data to the cloud is a critical part of any modern business transformation. Whether moving small datasets, large-scale workloads, or entire databases. Moving substantial amounts of data ranging from terabytes to petabytes presents unique challenges. AWS provides many data migration tools to help organizations transition smoothly and securely. In this blog, we’ll explore the most common AWS Data migration options, and help you decide which one is best suited for your choice.

OFFLINE DATA TRANSFER

The AWS Snow Family: simplifies moving data into and out of AWS using physical devices, bypassing the need for high-bandwidth networks. This makes them ideal for organizations facing bandwidth limitations or working in remote environments.

AWS Snowcone: The smallest member of the Snow Family, Snowcone, is a lightweight edge computing and data transfer device.

Features

  • Compact, rugged device ideal for harsh environments.
  • Used to collect, process, and move data to AWS by shipping the device back to AWS.
  • Pre-installed DataSync agent: This allows for seamless synchronization between Snowcone and AWS cloud services, making it easier to transfer data both offline and online.
  • 8 TB of usable storage.

Best Use Cases

Edge data collection: Gathering data from remote locations like oil rigs, ships, or military bases.

Lightweight compute tasks: Running simple analytics, machine learning models, or edge processing before sending data to the cloud.

AWS Snowball

Overview: A petabyte-scale data transport and edge computing device.

Available in two versions

Snowball Edge Storage Optimized: Designed for massive data transfers, offers up to 80 TB of storage.

Snowball Edge Compute Optimized: Provides additional computing power for edge workloads and supports EC2 instances, allowing data to be processed before migration.

Best Use Cases

Data center migration: When decommissioning a data center and moving large amounts of data to AWS.

Edge computing tasks: Processing data from IoT devices or running complex algorithms at the edge before cloud migration.

AWS Snowmobile

A massive exabyte-scale migration solution, designed for customers moving extraordinarily large datasets. The Snowmobile is a secure 40-foot shipping container.

Features

  • Can handle up to 100 PB per Snowmobile, making it ideal for hyperscale migrations.
  • Data is transferred in a highly secure container that is protected both physically and digitally.
  • Security features: Encryption with multiple layers of protection, including GPS tracking, 24/7 video surveillance, and an armed escort during transit.

Best Use Cases

Exabyte-scale migrations: Ideal for organizations with huge archives of video content, scientific research data, or financial records.

Disaster recovery: Quickly evacuating vast amounts of critical data during emergencies.

Hybrid and Edge Gateway
AWS Storage gateway

AWS Storage Gateway provides a hybrid cloud storage solution that integrates your on-premises environments with AWS cloud storage. It simplifies the transition to the cloud while enabling seamless data access.

Types of Storage Gateway:

File Gateway: Provides file-based access to objects in Amazon S3 using standard NFS and SMB protocols. Ideal for migrating file systems to the cloud.

Tape Gateway: Emulates physical tape libraries, archiving data to Amazon S3 and Glacier for long-term storage and disaster recovery.

Volume Gateway: Presents cloud-backed storage volumes to your on-premises applications. Supports EBS Snapshots, which store point-in-time backups in AWS.

Best Use Cases:

Seamless cloud integration: Extending on-premises storage workloads to AWS without disrupting existing workflows.

Archiving and backup: Cost-efficient tape replacement for data archiving and disaster recovery in Amazon S3 and Glacier.

Additional AWS Data Migration Services

Beyond physical devices, AWS offers several other tools for seamless and efficient data migration, each designed for different needs:

AWS Data Exchange: Simplifies finding, subscribing to, and using third-party data within the AWS cloud. Ideal for organizations that need external data for analytics or machine learning models.

Best Use Cases

Third-party data access: Easily acquire and use data for market research, financial analytics, or AI model training.

Conclusion

Migrating large volumes of data to the AWS cloud is a complex but manageable task with the right approach. By carefully selecting the appropriate tools and strategies, you can achieve a very smooth transition and successful outcome.

Thanks for reading and stay tuned for more.

If you have any questions concerning this article or have an AWS project that requires our assistance, please reach out to us by leaving a comment below or email us at sales@accendnetworks.com.


Thank you!

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Understanding AMI in AWS

Understanding AMI in AWS: A definitive guide to cloud resilience

Amazon Machine Image (AMI) and Snapshots play crucial roles in the storage, replication, and deployment of EC2 instances. This guide not only demystifies the significance of AMIs but also unveils effective AWS backup strategies, including the vital process of taking AMI backups.

What is an Amazon Machine Image (AMI)?

An Amazon Machine Image (AMI) is an image that provides the software that is required to set up and boot an Amazon EC2 instance. Each AMI also contains a block device mapping that specifies the block devices to attach to the instances that you launch.

An Amazon Machine Image (AMI) is a template that contains the information required to launch an EC2 instance, it is the encapsulation of server configuration. Each AMI also contains a block device mapping that specifies the block devices to attach to the instances that you launch.

You can launch multiple instances from a single AMI when you require multiple instances with the same configuration. You can use different AMIs to launch instances when you require instances with different configurations, as shown in the following diagram.

You can launch multiple instances from a single AMI when you require multiple instances with the same configuration. You can use different AMIs to launch instances when you require instances with different configurations, as shown in the following diagram.

Key Components of an AMI:

Root Volume: Contains the operating system and software configurations that make up the instance.

Launch Permissions: Determine who can use the AMI to launch instances.

Block Device Mapping: Specifies the storage devices attached to the instance when it’s launched.

AMIs can be public or private:

Public AMIs: Provided by AWS or third-party vendors, offering pre-configured OS setups and applications.

Private AMIs: Custom-built by users to suit specific use cases, ensuring that their application and infrastructure requirements are pre-installed on the EC2 instance.

Types of AMIs:

EBS-backed AMI: Uses an Elastic Block Store (EBS) volume as the root device, allowing data to persist even after the instance is terminated.

Instance store-backed AMI: Uses ephemeral storage, meaning data will be lost once the instance is stopped or terminated.

Why Use an AMI?

Faster Instance Launch: AMIs allow you to quickly launch EC2 instances with the exact configuration you need.

Scalability: AMIs enable consistent replication of instances across multiple environments (e.g., dev, test, production).

Backup and Recovery: Custom AMIs can serve as a backup of system configurations, allowing for easy recovery in case of failure.

Creating an AMI

Let’s now move on to the hands-on section, where we’ll configure an existing EC2 instance, install a web server, and set up our HTML files. After that, we’ll create an image from the configured EC2 instance, terminate the current configuration server, and launch a production instance using the image we created. Here’s how we’ll proceed:

Step 1: Configuring the instance as desired

Log in to the AWS Management Console with a user account that has admin privileges, and launch an EC2 instance.

I’ve already launched an Ubuntu machine, so next, I’ll configure Apache webserver and host my web files on the instance.

SSH into your machine using the SSH command shown below.

Update your server repository.

Install and enable Apache.

Check the status of the Apache web server.

Now navigate to the HTML directory using the below command.

Use the vi editor to edit and add your web files to the HTML directory, run sudo vi index.html

Exit and save the vi editor.

We’ve successfully configured our EC2 instance to host the web application. Now, let’s test it by copying the instance’s IP address and pasting it into your web browser.

Our instance is up and running, successfully hosting and serving and serving our web application.

Step 2: Creating an image of the instance

Select the running EC2 Instance, select the actions drop-down button, and navigate to Image and Templates then click Create Image.

Provide details for your Image

Select tag Image and snapshots together then scroll down and click Create Image.

Once the Image is available and ready for use, you can now proceed and delete your setup server.

Step 3: Launch new EC2 instance from the created Image.

We will now use the created Image to launch a new EC2 instance, we will not do any configuration since the application has our app already configured.

Let’s proceed and launch our EC2 instance, click launch Instance from the EC2 dashboard.

Under Applications and OS, select my AMI, then select owned by me.

Select t2. Micro, then scroll down.

Select your key pairs.

Configure your security groups

Review and click Create Instance.

Step 4: Test our application.

Once our application is up and running, grab the public IP and paste it into your browser.

We have created an EC2 instance from an AMI with an already configured application. Application achieved.

Clean up. This brings us to the end of this article.

Conclusion

Amazon Machine Images (AMIs) are fundamental tools in managing EC2 instances. Understanding how to effectively use AMIs can help optimize your AWS environment, improving disaster recovery, scaling capabilities, and data security.

Thanks for reading and stay tuned for more.

If you have any questions concerning this article or have an AWS project that requires our assistance, please reach out to us by leaving a comment below or email us at sales@accendnetworks.com.


Thank you!

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AWS Recycle Bin

AWS Recycle Bin: Your Key to Enhanced Data Protection and Recovery

Introduction

As more and more companies depend on the cloud infrastructure to run their businesses, at the same time they need a strong strategy to protect and recover their data, as accidental deletions or unexpected failures can lead to critical data loss, resulting in downtime and potential financial losses.  AWS well known for its wide range of services, provides various tools to keep data safe and retrievable. Among these, the AWS Recycle Bin service stands out as a powerful feature to improve data recovery options. This blog explores the AWS Recycle Bin service, what it offers, and how to use it to protect your important resources.

Getting to Know AWS Recycle Bin

The Recycle Bin service, in the context of AWS, is a concept often used to describe a safety mechanism for storing and recovering deleted resources. While AWS doesn’t offer a specific service named “Recycle Bin,” users can implement similar functionality using AWS services like AWS Lambda and Amazon S3.

In this setup, when a resource is deleted, it isn’t immediately gone forever. Instead, it’s moved to a specific storage area, often called a “Recycle Bin” or “Trash,” where it remains for a set period before being permanently deleted. This serves as a safety net, allowing users to easily recover accidentally deleted resources without needing to go through complicated backup and recovery processes.

By using AWS Lambda functions and S3 event notifications, you can automate the transfer of deleted resources to the Recycle Bin and set up policies for how long they should be retained before final deletion. This approach strengthens your data protection and management strategy within your AWS environment.

The AWS Recycle Bin is particularly useful when mistakes happen or automated systems accidentally delete resources. By enabling the Recycle Bin, you ensure that even if a resource is deleted, it can still be restored, preventing data loss and avoiding service interruptions.

Benefits of AWS Recycle Bin

Enhanced Data Protection: It allows you to recover deleted resources within a specified period, reducing the risk of permanent data loss.

Compliance and Governance: It ensures that data is not permanently lost due to accidental deletions, which is essential for maintaining audit trails and adhering to data retention policies.

Cost Management: By setting appropriate retention periods, you can manage storage costs effectively.

Let’s now get to the hands-on.

Implementation Steps

Make sure you have an EC2 instance up and running.

Get EBS Volume Information

AWS Elastic Block Store (EBS) is a scalable block storage service provided by Amazon Web Services (AWS), offering persistent storage volumes for EC2 instances. To view your block storage, in the EC2 dashboard move to the storage tab.

Take a Snapshot of the Volume

An AWS EBS snapshot is a point-in-time backup of an EBS volume stored in Amazon S3. It captures all the data on the volume at the time the snapshot is taken, including the data that is in use and any data that is pending EBS snapshots are commonly used for data backup, disaster recovery, and creating new volumes from existing data.

On the left side of EC2 UI, click Snapshots then click Create Snapshot.

In the Create snapshot UI, under resource types, select volumes. Then under volume ID, select the drop-down button and select your EBS volume.

Scroll down and click Create Snapshot.

Success.

Head to the Recycle Bin console and click the Create retention rule.

Fill in retention rule details.

Under retention settings for resource type select the drop-down button and select EBS Snapshot, then tick the box apply for all resources, then for retention period, select one day.

For the Rule lock settings, select Unlock.

Rule Created

Now go ahead and delete the snapshot

Open the recycle bin

Click on the snapshot present in the recycle bin

Objective achieved

Snapshot recovered successfully

Conclusion

The AWS Recycle Bin service offers a valuable layer of protection against accidental deletions, ensuring that critical resources like EBS snapshots and AMIs can be recovered within a defined period. Whether you’re protecting against human error or looking to strengthen your disaster recovery strategy, AWS Recycle Bin is an essential tool in your AWS toolkit.

This brings us to the end of this article.

Thanks for reading and stay tuned for more.

If you have any questions concerning this article or have an AWS project that requires our assistance, please reach out to us by leaving a comment below or email us at sales@accendnetworks.com.


Thank you!

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AWS Code Commit Obsolete

AWS CodeCommit Obsolete: Transitioning from AWS CodeCommit and Steps for a Seamless Migration

AWS CodeCommit, Amazon Web Services’ fully managed version control service, has been a leading solution for developers and organizations seeking a scalable, secure, and reliable version control system. However, AWS recently announced that it will no longer accept new customers for CodeCommit, effective June 6, 2024.

In this article, we’ll examine the impact of this phase-out, examine alternative version control systems, and offer tips on seamlessly transitioning your repositories.

Adapting to AWS CodeCommit’s Shutdown: Key Impacts and Your Next Step

AWS’s choice to end CodeCommit is part of a bigger plan to simplify its offerings and cut down on duplicate services. The rise in popularity of more powerful platforms like GitHub and GitLab, which provide advanced features and strong community backing, has had a big impact on this change. If you’re still using CodeCommit, the takeaway is clear: you can still access your repositories, but it’s time to start thinking about moving. AWS has given helpful documentation to help you through the switch to a new platform.

Exploring Alternative Version Control Systems

With CodeCommit being phased out, organizations need to explore alternative version control systems, and here are some of the top options.

GitHub: It’s the world’s largest Git repository hosting service and offers extensive features, including GitHub Actions for CI/CD, a vibrant community, and seamless integration with many third-party tools.

GitLab: It stands out for its built-in DevOps capabilities, offering robust CI/CD pipelines, security features, and extensive integration options.

Bitbucket: It is well-suited for teams already using Atlassian products like Jira and Confluence.

Self-Hosted Git Solutions: This is for organizations with specific security or customization requirements.

Migrating your AWS CodeCommit Repository to a GitHub Repository

Before you start the migration, make sure you have set up a new repository and the remote repository should be empty.

The remote repository may have protected branches that do not allow force push. In this case, navigate to your new repository provider and disable branch protections to allow force push.

Log into the AWS management console and navigate to the code commit console, in the AWS CodeCommit console, select the clone URL for the repository you will migrate. The correct clone URL (HTTPS, SSH, or HTTPS (CRC)) depends on which credential type and network protocol you have chosen to use.

In my case, I am using HTTPS.

Step 1: Clone the AWS CodeCommit Repository
Clone the repository from AWS CodeCommit to your local machine using Git. If you’re using HTTPS, you can do this by running the following command:

git clone https://your-aws-repository-url your-aws-repository

Replace your-aws-repository-url with the URL of your AWS CodeCommit repository.

Change the directory to the repository you’ve just cloned.

Step 2: add the New Remote Repository.

Navigate to the directory of your cloned AWS CodeCommit repository. Then, add the repository URL from the new repository provider.

git remote add <provider name> <provider-repository-url>

Step 3. Push Your Repository to the New Provider

Push your local repository to the new remote repository

This will push all branches and tags to your new repository provider’s repository. The provider’s name must match the provider’s name from step 2.

git push <provider name> –mirror

I use SSH keys for authentication, so I will run the git set URL command to authenticate with my SSH keys. Then lastly will run the git push command.

Step 4. Verify the Migration

Once the push is complete, verify that all files, branches, and tags have been successfully migrated to the new repository provider. You can do this by browsing your repository online or cloning it to another location and checking it locally.

Step 5: Update Remote URLs in Your Local Repository

If you plan to continue working with the migrated repository locally, you may want to update the remote URL to point to the new provider’s repository instead of AWS CodeCommit. You can do this using the following command:

git remote set-url origin <provider-repository-url>

Replace <provider-repository-url> with the URL of your new repository provider’s repository.

Step 6: Update CI/CD Pipelines

If you have CI/CD pipelines set up that interact with your repositories, such as GitLab, GitHub, or AWS CodePipeline, update their configuration to reflect the new repository URL. If you removed protected branch permissions in Step 3 you may want to add these back to your main branch.

Step 7: Inform Your Team

If you’re migrating a repository that others are working on, be sure to inform your team about the migration and provide them with the new repository URL.

Step 8: Delete the Old AWS CodeCommit Repository

This action cannot be undone. Navigate back to the AWS CodeCommit console and delete the repository that you have migrated.

Conclusion

By carefully evaluating your options and planning your migration, you can turn this transition into an upgrade for your development processes. Embracing a new tool not only enhances your team’s efficiency but also ensures you stay aligned with current industry standards.

This brings us to the end of this blog.

Thanks for reading and stay tuned for more.

If you have any questions concerning this article or have an AWS project that requires our assistance, please reach out to us by leaving a comment below or email us at sales@accendnetworks.com.


Thank you!