ARM Servers in AWS: Cost-Effective Cloud Computing

In the vast landscape of cloud computing, a new contender has emerged, promising efficiency and cost-effectiveness: ARM servers. As AWS embraces this technology with its Graviton processors, it's essential for businesses and developers to understand its potential advantages and limitations.

The Rise of ARM Servers

Traditionally, servers have predominantly used x86 processors. However, ARM, known for its dominance in the mobile device sector, is making inroads into the server world. The primary allure? Power efficiency. ARM processors, being based on the RISC (Reduced Instruction Set Computing) architecture, inherently consume less power, leading to notable operational savings.

Cost Benefits of ARM in AWS

Several factors contribute to the cost-effectiveness of ARM servers:

Simplified Architecture: ARM's RISC foundation means it can execute a reduced set of instructions more rapidly, often leading to cost and performance benefits.

Power Consumption: The reduced power needs of ARM processors translate to lower cooling and operational costs in server environments.

Advanced Manufacturing: ARM chips can benefit from state-of-the-art manufacturing processes. Although it's worth noting that both ARM and x86 benefit from modern manufacturing techniques, ARM's design can sometimes lead to additional savings.

Optimized Server Density: The compact nature of ARM-based servers allows for more dense configurations in data centers.

AWS's Embrace of ARM

Amazon Web Services (AWS) has been at the forefront of the ARM server movement with its bespoke Graviton processors:

Graviton2 Instances: Offering up to 40% better price-performance metrics compared to their x86 counterparts, these instances are based on the Arm Neoverse N1 core.

A1 Instances: Ideal for general-purpose tasks, these instances use the AWS Graviton processor, which is anchored on the Arm Cortex-A72 core.

Ideal Applications for ARM Servers

Certain workloads are particularly well-suited for ARM servers:

• Machine Learning & Artificial Intelligence
• Content Delivery Networks (CDNs)
• Web Servers
• Databases
• High-Performance Computing (HPC)
• Containerized Applications

Potential Limitations

While ARM servers have numerous advantages, they aren't a one-size-fits-all solution:

1. Instruction Set Dependencies: Workloads dependent on specific instruction sets, like Intel's AVX-512, may not be optimized for ARM.
2. Memory Demands: For memory-intensive tasks, selecting the right ARM instance type is crucial.
3. Operating System Compatibility: Major operating systems like Ubuntu and Amazon Linux 2 are ARM-friendly, but not every OS version might be.

Selecting the Right ARM-based AWS Instance

For those considering a switch or trial of ARM-based servers in AWS, understanding the available instance types is crucial. Here are two prominent ARM-based EC2 instance types:

1. T4g Instances:

Use Case: These are part of the AWS burstable general-purpose instance family. They are well-suited for workloads with moderate CPU usage that occasionally need to burst.

Features: T4g instances provide a baseline level of CPU performance with the ability to burst CPU usage to a higher level using CPU credits. They offer a balanced mix of compute, memory, and network resources.

Processor: Powered by the AWS Graviton2 processor, T4g instances can deliver up to 40% better price-performance over comparable x86-based T3 instances.

    2. M6g Instances:

Use Case: These are designed for general-purpose workloads, such as application servers, mid-size data stores, microservices, and cluster computing.

Features: M6g instances offer a balance of compute, memory, and networking resources. They are ideal for workloads that need consistent performance and can take advantage of improved price-performance.

Processor: Like the T4g, M6g instances are also powered by the AWS Graviton2 processor, delivering significant performance improvements over the previous generation M5 instances.

For users looking to optimize their cloud expenditure and enhance performance, both T4g and M6g instances provide compelling options. However, it's essential to benchmark these instances with your specific workloads to determine the best fit.

Review more details here.

Compute – Amazon EC2 Instance Types – AWS

 

Two-Tier Authentication in Microservices Architecture

 

Microservices are decoupled, self-contained units, which makes security pivotal. Two-tier authentication can offer an extra layer of protection. By integrating both AWS Cognito (for cloud-based authentication) and FAMS (an on-premises solution), we can create a robust authentication mechanism for such architectures.

It's a clear separation of concerns, with FAMS focusing on user identity and Cognito securing your API. This is a valid and robust approach, particularly if you want to leverage Cognito's capabilities for managing API access without intertwining it with FAMS.

Two-Tier Authentication in Microservices

Microservices often communicate through APIs. The two tiers in this setup are:

On-Premises Authentication (e.g., FAMS): Before accessing cloud-based microservices, authentication through on-prem systems like FAMS ensures that the initial user or service is validated.

Cloud-Based Authentication (e.g., AWS Cognito): After the initial validation, Cognito facilitates the subsequent authentication steps, providing tokens that are required to access microservices' endpoints.

Benefits of Using AWS Cognito with FAMS

·        Seamless Integration: AWS Cognito integrates well with AWS services and can work in tandem with FAMS for initial authentication.

·        Token-based security: After initial authentication with FAMS, Cognito handles token-based authentication for cloud resources.

·        Flexibility: Offers the ability to switch between different authentication providers.

Sequence Flow in a Microservices Environment

 

Conclusion

Two-tier authentication using both FAMS and AWS Cognito offers a comprehensive authentication strategy for microservices, bridging on-premises systems and cloud architectures. It ensures that microservices are only accessed by authenticated clients and services, upholding the principles of security and integrity.

SQL Server Editions: Balancing Availability, Downtime, and Cost

Choosing between SQL Server editions requires a careful balance of high availability benefits against cost implications. For applications that rely on multiple databases, this decision is paramount.

Availability and Downtime:

• SQL Server Enterprise Edition: This edition supports Always On Availability Groups, allowing multiple databases to be included in a single group. In the event of a database failure, all databases in the group failover together, ensuring application consistency. This feature is pivotal for applications that depend on multiple databases.

• SQL Server Standard Edition: The Standard edition only supports Basic Availability Groups, limiting it to a single database per group. This means if one database fails, it doesn't guarantee the failover of other related databases, potentially causing inconsistencies in applications relying on multiple databases.

For applications requiring multiple databases to failover together for consistent performance, the Enterprise edition emerges as the preferred choice.

Cost Comparison:

• SQL Server Enterprise Edition: At 4vCPU and 16GB RAM, the cost is higher but offers advanced features.

• SQL Server Standard Edition: The same configuration is more budget-friendly but might lack some advanced features.

While the Enterprise edition offers advanced features, it comes at a higher cost. If budget constraints are significant and the application can tolerate some downtime, the Standard edition, combined with workarounds like Log Shipping, becomes a viable option.

Log Shipping:

• This method involves periodic backups of the transaction log from the primary database, which are then restored on a secondary database.

• If the primary database fails, the secondary database must be manually activated, which can be time-consuming, especially for large databases.

Variables to Consider in Decision-Making:

1. Number of Databases: For applications that rely on multiple databases, the Enterprise edition is more suitable.

2. Availability Requirements: If high availability is paramount, the Enterprise edition is the recommended choice.

3. Future Scaling: Anticipating growth in user base or data volume? The Enterprise edition, with its advanced features, is a better long-term investment.

4. Cost: While the Standard edition is more budget-friendly, it might require workarounds that could impact RTO.

Conclusion:

For applications that rely on multiple databases and require high availability, the SQL Server Enterprise edition is a clear frontrunner. However, it's essential to balance this need against cost constraints and evaluate the potential implications of workarounds in the Standard edition. Making an informed decision now can save time, and money, and ensure consistent application performance in the long run.

AWS Secret Manager for application running on Amazon EC2 instances

Using AWS Secret Manager

 

 

Introduction

This technical document describes the approach of using AWS Secrets Manager to securely manage secrets and credentials for application running on Amazon EC2 instances. The document outlines the steps to configure the necessary infrastructure components, including AWS Secrets Manager, AWS Identity and Access Management (IAM), and Azure DevOps.

Solution defined in the document is generic and can be applied to programs planning to implement AWS secret manager in their respective projects leveraging Azure Devops

Approach

The approach described in this document consists of the following steps:

·        Configure AWS Secrets Manager to store secrets

·        Create an IAM policy for the EC2 instance to access Secrets Manager

·        Configure Azure DevOps to fetch secrets from Secrets Manager

·        Pass secrets to the .NET Core application using environment variables or application config

 

Detailed Solution

1.      Configure AWS Secrets Manager to store secrets

·        Create a new secret in AWS Secrets Manager for each environment (dev, qa, prod) that the application uses

o   Non Prod

§  Dev_Secrets

§  QA_secrets

§  Qualif_Secrets

o   Prod Secrets

§  Prod_Secrets

·        Alternately we can also store all environment secrets per service.

Note - If you have a small number of services and environments, storing all environment secrets per service may simplify secret management and reduce the risk of misconfigurations. However, if you have a large number of services and environments or require more flexibility, storing secrets per environment may be a better approach.

If you are expecting to develop multiple micro services hence it’s better to go for storing secrets per environment

 

·        Store all the secrets for each environment as a key-value pairs in JSON format

 

2.      Create an IAM policy for the EC2 instance to access Secrets Manager

·        Create IAM Users for prod and non-prod environments

o   Generate AWS access key and secret access key

o   Access Type – Programmatic Access

·        Attach Policy SecretsManagerRead to Users created in step 2

·        If we push the secret to App.config then this is not required

o   Attach IAM roles for EC2 instances for applications to access Secret manager at runtime

o   Attach the appropriate AWS managed policy that grants permissions to access Secrets Manager

 

3.      Configure Azure DevOps to fetch secrets from Secrets Manager

·        Create a new service connection in Azure DevOps for AWS, using the IAM credentials for accessing Secrets Manager

o   Navigate to the "Project Settings" in Azure DevOps and click on "Service connections".

o   Click on "New service connection" and select "AWS".

o   Follow the prompts to enter your AWS access key and secret access key, as well as the region where your AWS resources are located.

4.      Create a new pipeline variable in Azure DevOps for the secret name:

o   In the Azure DevOps pipeline, define variable (e.g., "EnvironmentSecret") and set the value to the name of the secret that you want to fetch from AWS Secrets Manager.

§  E.g: EnvironmentSecret = Dev_Secrets’ – to Identify Build environment

§  Refer secret name created in Step 1

 

5.      Add an Azure DevOps task to fetch the secret:

 

o   In Azure DevOps pipeline, add a task that fetches the secret from AWS Secrets Manager.

o   aws secretsmanager get-secret-value --secret-id $(EnvironmentSecret) --query SecretString --output text

o   aws secretsmanager get-secret-value --secret-id EnvironmentSecret --query SecretString

 

6.      Pass the secret value to your .NET Core application

There are multiple options to store the secret . This can be evaluated and agreed upon

 

o   Use Environment Variable in EC2

§  Via Azure DevOps task and update environment variable in EC2

§  MySecretName ={ SecretString }

 

(OR)

o   Use configuration files

§  Via Azure App Service Deploy task to deploy the configuration file to the Azure App Service that hosts your .NET Core application

 

o   Store the Output secret value from the previous step into a Key value pair

o   Create new Azure DevOps task called "Azure App Service Settings"

(OR)

o   Store the secrets in Secure Files in Azure Pipelines and push it into appconfig file

Go to Pipelines > Library > Secure files

 

7.      Aplication code

.NET Core application to retrieve the value of "MySecretName":

 

string secretValue = Environment.GetEnvironmentVariable("MySecretName");

 

 

 

References

https://learn.microsoft.com/en-us/azure/devops/pipelines/process/variables?view=azure-devops&tabs=yaml%2Cbatch

https://learn.microsoft.com/en-us/azure/devops/pipelines/tasks/reference/azure-app-service-settings-v1?view=azure-pipelines&viewFallbackFrom=azure-devops

https://aws.amazon.com/blogs/modernizing-with-aws/how-to-load-net-configuration-from-aws-secrets-manager/

 

 

 

Azure Cost Optimization - Tips

 It is very important to have tight control of the cost and manage it efficiently.  Cloud Computing has two major tasks. i.e. Store Data and execute code. Networking is an essential element used to tie systems together and implement security.

 Below are some of the tips that can help teams who are looking to optimize costs.

Compute Charges

Compute services are those that take your code and execute it in the cloud. There are several compute resources (around 20) in azure. All these services have virtual machines as their core. Examples are AKS, VMSS, etc

 

 Storage Charges

 

Type	Description	Key points	Factors impact storage cost
Unmanaged Storage	It relies on the user to monitor and react to the accounts Blobs, Files, Table Data, Queue messages	The storage account has a maximum capacity.  5 PiB	GB's Used
		It has Operations per second limit	IO Operations
		Violations will get errors but the advantage is paid for what you use	Region
		Nothing will be changed if no data is in a storage account	Redundancy
		Unmanaged storage is extremely cheap	Access tier(Premium, hot, cool, Archive)
Managed Storage
	Managed disks. Used by VM's	premium disks are very expensive
	e.g.: HDD, (Sold  Premium, Ultra) SSD

 

Guidelines and recommendations for optimizing storage cost.

Total Storage cost covers the cost to store data and the cost to perform file operations

Costs to store files	Cost to perform File Operations(listing, writing, reading on storage accounts)
Are you storing log files for many months that you may not need?	If you expecting millions of reads and writes per month, it might be cheaper to use a premium storage tier that hot one
Are you storing memory dumps?	Premium storage is used more for storage and less for access. Recommended ·        Production load Premium disks, ·        Standard disks - for QA/Dev
Are you storing System backups from years/months ago with no regulatory reasons to keep them	if a file is placed in the cool tier, it will be charged for a min of 30 days even if you delete before then
if you delete VM, ensure deleting associated DISK	Sometimes, People enable read access geo-redundant storage which is 3 or 4 times of local redundant storage. Do you use a secondary endpoint for read purposes? If they just wanted in a different region, then why not use GRS instead of RAGRS?
Premium charges most for storage but charges less for reading, if storage performance is a primary concern then select premium, if this file is not used frequently then it should move to the cool tier. If you don't need that file, you can reduce costs further by moving to the archive	Redundancy impacts costs - LRS is cheap and RA-GRS is expensive. Reserve storage if you use more than 100 TB of data and save 30% of the cost

 

Other Services

 

App Service	Are you creating different appservice plan for multiple webapps	Creating different app service plans may not be needed. Create it only when there is capacity requirements
AKS		Dev, Test, and non-business hrs. reduce to zero instance cluster
Networking	single app gateway can host multiple applications	same with respect to WAF
Media Services	streaming can be standard and premium	98% of workload standard is fine, encoding can be done  2 ways
App Insights	data ingestion	90 days is free, enables data sampling(50%/75%), Keep  a daily cap on data
Log Analytics		Leverage Reserve capacity for data ingestion

 

 

Application DAST - Tool Evaluation

Dynamic Application Security Testing (DAST) is a procedure that actively investigates running applications with penetration tests to detect possible security vulnerabilities. 

To address this growing security threat, businesses are increasingly deploying dynamic application security testing (DAST) tools as part of a more security-forward approach to web application development. DAST tools provide insight into how your web applications behave while they are in production, enabling your business to address potential vulnerabilities before a hacker uses them to stage an attack. As your web applications evolve, DAST solutions continue to scan them so that your business can promptly identify and remediate emerging issues before they develop into serious risks.

we have evaluated a few tools in 2021 against open source vulnerable applications to determine a tool that can be integrated with release pipelines and can be seamlessly integrated into SDLC.

These commercial tools could identify a very tiny subset of known vulnerabilities. It cannot replace manual pen testing but can complement pen testers.    The below tables provide a high-level evaluation summary of the tools shortlisted

Vulnerabilities detected by these tools