Archive for the ‘Azure’ Category

When thinking about cloud computing, we immediately think about technology.

Have we ever stopped to think about how much energy this sort of technology requires to operate an average cloud data center, and what is the environmental effect of running such huge data centers around the world?

Data centers generate around 1% of the energy consumed around the world, daily.

Data centers consume a lot of energy – electricity (for running the servers) and water (for cooling the servers).

The more energy a common data center consumes, the bigger its carbon footprint (the total amount of greenhouse gases that is generated by running a data center).

In the past couple of years, there is a new concept for professionals working with cloud services, with high environmental awareness called cloud sustainability.

The idea behind it (from a cloud provider’s point of view) is to achieve 100% renewable energy – replace fuel-based electricity with wind and solar power, within a few years.

All major cloud providers (AWS, Azure, and GCP) put a lot of effort into building a new data center to be powered by green energy and making changes to the existing data center to lower their emissions as much as possible and use green energy as well.

To remain transparent to their customers, the major cloud providers have created carbon footprint tools:

• AWS customer carbon footprint tool

https://docs.aws.amazon.com/awsaccountbilling/latest/aboutv2/what-is-ccft.html

• Microsoft Sustainability Calculator

https://aka.ms/SustainabilityCalculator

• GCP Carbon Footprint

https://cloud.google.com/carbon-footprint

• Cloud Carbon Footprint (Open source) tool

https://www.cloudcarbonfootprint.org/docs/getting-started

Indeed, most of the responsibility for keeping the cloud data centers green is under the responsibility of the cloud providers, since they build and maintain their data centers, but what is our responsibility as consumers?

As an example, here is AWS’s point of view regarding the shared responsibility model, in the context of sustainability:

Source: https://docs.aws.amazon.com/wellarchitected/latest/sustainability-pillar/the-shared-responsibility-model.html

How to act as responsible cloud consumers?

Region selection

Review business requirements (compliance, latency, cost, service, and features), and pay attention to regions with a low carbon footprint.

Additional information:

• AWS – What to Consider when Selecting a Region for your Workloads

https://aws.amazon.com/blogs/architecture/what-to-consider-when-selecting-a-region-for-your-workloads

• Carbon-free energy for Google Cloud regions

https://cloud.google.com/sustainability/region-carbon

• Measuring greenhouse gas emissions in data centers: the environmental impact of cloud computing

https://www.climatiq.io/blog/measure-greenhouse-gas-emissions-carbon-data-centres-cloud-computing

Architecture design considerations

Use cloud-native design patterns:

• Microservices – use containers (and Kubernetes) to deploy your applications and leverage the scaling capabilities of the cloud
• Serverless – use serverless (or function as a service) whenever you can decouple your applications into small functions
• Use message queues as much as possible, to decouple your applications and lower the number of requests between the various services/components
• Use caching mechanisms to lower the number of queries to backend systems

Infrastructure considerations

Embed the following as part of your infrastructure considerations:

• Right-sizing – when using VMs, always remember to right-size the VM size to your application demands
• Use up-to-date hardware – when using VMs, always use the latest VM family types and the latest block storage type, to suit your application demands
• ARM-based processors – consider using ARM processors (such as AWS Graviton Processor, Azure Ampere Altra Arm-based processors, GCP Ampere Altra Arm processors, and more), whenever your application supports the ARM technology (for better performance and lower cost)
• Idle hardware – monitor and shut down (or even delete) unused or idle hardware (VMs, databases, etc.)
• GPU – use GPUs only for tasks that are considered more efficient than CPUs (such as machine learning, rendering, transcoding, etc.)
• Spot instances – use spot instances, whenever your application supports sudden interruptions
• Schedule automatic start and stop of VMs – use scheduling capabilities (such as AWS Instance scheduler, Azure Start/Stop VMs, GCP start and stop virtual machine (VM) instances, etc.) to control the behavior of your workload VMs
• Managed services – prefer to use PaaS or managed services (from databases, storage, load-balancers, and more)
• Data lifecycle management – use object storage (or file storage) lifecycle policies to archive or remove unused or unnecessary data
• Auto-scaling – use the cloud built-in capabilities to scale horizontally according to your application load
• Content Delivery Network – use CDN (such as Amazon CloudFront, Azure Content Delivery Network, Google Cloud CDN, etc.) to lower the amount of customer traffic to your publicly exposed workloads

Summary

Sustainability and green computing are here to stay.

Although the large demand for cloud services has a huge environmental impact, I strongly believe that the use of cloud services is much more environmentally friendly than any use of legacy data center, for the following reasons:

• Efficient hardware utilization (nearly 100% of hardware utilization)
• Fast hardware replacement (due to high utilization)
• Better energy use (high use of renewable energy sources to support the electricity requirements)

I advise all cloud customers, to put sustainability higher in their design considerations.

Additional reading materials

• AWS Well-Architected Framework – Sustainability Pillar

https://docs.aws.amazon.com/wellarchitected/latest/sustainability-pillar/sustainability-pillar.html

• Microsoft Azure Well-Architected Framework – Sustainability

https://learn.microsoft.com/en-us/azure/architecture/framework/sustainability/sustainability-get-started

• Google Cloud – Design for environmental sustainability

https://cloud.google.com/architecture/framework/system-design/sustainability

Storing data in the cloud, raise questions regarding data protection.

Data can be customers’ data (PII, healthcare data, credit cards, etc.), company data (financial information, trade secrets, security vulnerabilities, etc.), or any information with value to our organization.

As in the traditional data center, we still have concerns regarding who has access to our data and what can he do with the access provided.

In this blog post, I will review the required controls for protecting data stored in cloud services.

Data discovery and classification

The first action we need to take regarding sensitive data is discovery and classification.

Data classification is the action of assigning labels or categories to our data, such as public information, internal, confidential, highly confidential, etc.

Discovery tools allow us to detect where we store sensitive information in storage locations such as object storage, file storage, databases, and more.

Examples of services for the discovery process:

• Amazon Macie – discover sensitive information stored in Amazon S3 buckets.
• Microsoft Purview – map and discover data on-premise and in the cloud.

Entitlement

Entitlement deals with the questions – who has access, to what resources, and what can he do with his access rights?

In any access request, we should always make sure the identity (human, service account, computer account, etc.) is authenticated against our system, preferably using a central identity provider.

Once the identity is authenticated against our system, we need to make sure it has proper access rights to take the exact number of privileges required to accomplish its desired task, according to the principle of least privilege (such as view configuration, read customer data, update records, etc.)

Entitlement combines authentication with authorization.

Examples of services for entitlements:

• AWS IAM Access Analyzer – detects AWS resources with permissions belonging to external identities and generates least privilege policies.
• Azure AD Identity Governance – assists in making sure an identity has the right access to the right resource.

Encryption

To protect data, we need to protect it in any state the data resides:

• Data in transit – all cloud services (from object storage, file storage, and databases) support encryption in transit using TLS protocol. Unlike the traditional data center where encryption in transit was either not supported or required an additional effort from our side, in the cloud, services support encryption in transit by default, and in many cases, we have no option to disable this feature.
• Data at rest – all cloud storage services (from object storage, file storage, and databases) support encryption at rest using the AES256 algorithm.

In the traditional data center, encryption key management and key rotation were challenging.

Today, most cloud providers allow us to choose between encryption at rest using encryption keys generated and managed by the cloud provider, or using encryption keys that we generate and control (to minimize the risk of rough cloud provider admin having access to our data).

Examples of services for storing encryption keys and sensitive data:
AWS KMS – controls the entire lifecycle of cryptographic keys.
AWS Secrets Manager – controls the entire lifecycle of secrets, credentials, API keys, etc.
Azure Key Vault – controls the entire lifecycle of cryptographic keys, secrets, credentials, API keys, etc.

• Data in use – even if we encrypt the data while in transit and while at rest, at some point, we need to have the data accessible for reading or update, while in the memory of a server in the cloud. The common name for this technology is “confidential computing“, which in most cases relies upon hardware capabilities to encrypt data and make sure data in memory is kept confidential.

Examples of solutions that provide confidential computing capabilities:

• AWS Nitro Enclaves – isolates data stored in the memory of EC2 instances.
• Azure Confidential Computing – isolates data stored in the memory of virtual machines and Azure Kubernetes Service nodes.

Auditing and threat detection

The final action we need to take protecting data is to audit who accessed our data and detect anomalous behavior with actions performed on our data.

Although it is considered a detective control, it is still an important phase in data control.

Examples of services that perform audit trails:

• AWS CloudTrail – record all API actions done on AWS services.
• Azure Monitor – record all operations done on Azure resources.

Now that we record all actions, we need a solution to review the logs and notify us about anomalous behavior that requires our attention.

Examples of threat detection services:

• Amazon GuardDuty – detect anomalies from (among other) CloudTrail logs.
• Microsoft Defender for Cloud – detect anomalies in actions conducted against services such as Azure SQL and Azure storage.

Summary

In this blog post, I have reviewed the necessary controls for protecting data stored in the cloud.

It is essential to understand that to get effective protection for data stored in the cloud, we must configure strong controls of both encryption at rest (preferred with customer-managed encryption keys), combined with entitlement process (which enforces the least privilege) – we cannot rely on single security control and pray that no unauthorized person will ever access our data.

After deploying several workloads in the public cloud, making mistakes, failing, fixing, and beginning using the cloud for production workloads, it is now the time to think about the next step in cloud adoption.

To be able to fully embrace the benefits of the public cloud, the scale, the elasticity, and the short time it takes to deploy new resources, it is time to put automation in place.

Automation allows us to do the same tasks over and over again, deploying the same configuration to multiple environments (Dev, Test, Prod) and get the same results – no human errors (assuming you have tested your code…)

Automation can be achieved in various ways – from using the CLI, using the cloud vendor’s SDK (languages such as Python, Go, Java, and more), or using Infrastructure as Code (such as Terraform, AWS CloudFormation, Azure Resource Manager, and more).

In this article, we shall review some of the common alternatives for using automation using code.

Why use code?

The clear benefit of using code for automation is the ability to have change management. Simply choose your favorite source control (such as GitHub, AWS CodeCommit, Azure Repos, and more), upload your scripts and have the version history of your code, and be able to know at each stage who made changes to the code.

Another benefit of using code for automation is the fact that the Internet is full of samples you can find to automate (almost) anything in your cloud environment.

The downside of doing everything using code, is the learning curve required by your organization’s IT or DevOps teams, learning new languages, but once they pass this stage, you can have all the benefits of the scripting languages.

Automation – the AWS way

If AWS is your sole cloud provider, you should learn and start using the following built-in services or capabilities offered by AWS:

Infrastructure as Code

• AWS CloudFormation – The built-in IaC for deploying and managing AWS resources.

Reference: https://github.com/aws-cloudformation/aws-cloudformation-samples

• AWS Cloud Development Kit (AWS CDK) – Ability to write CloudFormation templates, based on common programming languages such as Python, Java, DotNet, and more.

Reference: https://github.com/aws-samples/aws-cdk-examples

Policy as Code

• Service control policies (SCPs) – Managing permissions in AWS Organizations.

Reference: https://docs.aws.amazon.com/organizations/latest/userguide/orgs_manage_policies_scps_examples.html

CI/CD pipeline

• AWS CodePipeline – A fully managed continuous delivery service.

Reference: https://docs.aws.amazon.com/codepipeline/latest/userguide/tutorials.html

Containers and Kubernetes

• Amazon ECS – Container management service based on the AWS platform.

Reference: https://docs.aws.amazon.com/AmazonECS/latest/developerguide/example_task_definitions.html

• Amazon Elastic Kubernetes Service (EKS) – Managed Kubernetes service.

Reference: https://github.com/aws-quickstart/quickstart-amazon-eks

Automation – the Azure way

If Azure is your sole cloud provider, you should learn and start using the following built-in services or capabilities offered by Azure:

Infrastructure as Code

• Azure Resource Manager templates (ARM templates) – The built-in IaC for deploying and managing Azure resources.

Reference: https://github.com/Azure/azure-quickstart-templates

• Bicep – Declarative language for deploying Azure resources.

Reference: https://github.com/Azure/azure-docs-bicep-samples

Policy as Code

• Azure Policy – Enforce organizational standards across the Azure organization.

Reference: https://github.com/Azure/azure-policy

CI/CD pipeline

• Azure Pipelines – A fully managed continuous delivery service.

Reference: https://github.com/microsoft/azure-pipelines-yaml

Containers and Kubernetes

• Azure Container Instances – Container management service based on the Azure platform.

Reference: https://docs.microsoft.com/en-us/samples/browse/?products=azure&terms=container%2Binstance

• Azure Kubernetes Service (AKS) – Managed Kubernetes service.

Reference: https://github.com/Azure/AKS

Automation – the Google Cloud way

If GCP is your sole cloud provider, you should learn and start using the following built-in services or capabilities offered by GCP:

Infrastructure as Code

• Google Cloud Deployment Manager – The built-in IaC for deploying and managing GCP resources.

Reference: https://github.com/GoogleCloudPlatform/deploymentmanager-samples

Policy as Code

• Google Organization Policy Service – Programmatic control over the organization’s cloud resources.

Reference: https://cloud.google.com/resource-manager/docs/organization-policy/org-policy-constraints#how-to_guides

CI/CD pipeline

• Google Cloud Build – A fully managed continuous delivery service.

Reference: https://github.com/GoogleCloudPlatform/cloud-build-samples

Containers and Kubernetes

• Google Kubernetes Engine (GKE) – Managed Kubernetes service.

Reference: https://github.com/GoogleCloudPlatform/kubernetes-engine-samples

Automation – the cloud agnostic way

If you plan for the future, plan for multi-cloud. Look for solutions that are capable of connecting to multiple cloud environments, to decrease the learning curve of your DevOps team learning the various scripting languages and being able to deploy workloads on several cloud environments.

Infrastructure as Code

• Hashicorp Terraform – The most widely used IaC for deploying and managing resources on both cloud and on-premise.

Reference: https://registry.terraform.io/browse/providers

Policy as Code

• Hashicorp Sentinel – Policy as code framework that compliments Terraform code.

Reference: https://www.terraform.io/cloud-docs/sentinel/examples

CI/CD pipeline

• Jenkins – The most widely used open-source CI/CD tool.

Reference: https://www.jenkins.io/doc/pipeline/examples/

Containers and Kubernetes

• Docker – The most widely used container run-time for deploying applications.

Reference: https://github.com/dockersamples

• Kubernetes – The most widely used container orchestration open-source platform.

Reference: https://github.com/kubernetes/examples

Summary

In this post, I have reviewed the most common solutions that allow you to automate your workloads’ deployment, management, and maintenance using various scripting languages.

Some of the solutions are bound to a specific cloud provider, while others are considered cloud agnostic.

Use automation to fully embrace the power and benefits of the public cloud.

If you don’t have experience writing code, take the time to learn. The more you practice, the more experience you will gain.

As Werner Vogels, the Amazon CTO always says – “Go Build”.

My name is Eyal, and I am a cloud architect.

I have been in the IT industry since 1998 and began working with public clouds in 2015.

Over the years I have gained hands-on experience working on the infrastructure side of AWS, Azure, and GCP.

The more I worked with the various services from the three major cloud providers, the more I had the urge to compare the cloud providers’ capabilities, and I have shared several blog posts comparing the services.

In 2021 I was approached by PACKT publishing after they came across one of my blog posts on social media, and they offered me the opportunity to write a book about cloud security, comparing AWS, Azure, and GCP services and capabilities.

Over the years I have published many blog posts through social media and public websites, but this was my first experience writing an entire book with the support and assistance of a well-known publisher.

As with any previous article, I began by writing down each chapter title and main headlines for each chapter.

Once the chapters were approved, I moved on to write the actual chapters.

For each chapter, I first wrote down the headlines and then began filling them with content.

Before writing each chapter, I have done research on the subject, collected references from the vendors’ documentation, and looked for security best practices.

Once I have completed a chapter, I submitted it for review by the PACKT team.

PACKT team, together with external reviewers, sent me their input, things to change, additional material to add, request for relevant diagrams, and more.

Since copyright and plagiarism are important topics to take care of while writing a book, I have prepared my diagrams and submitted them to PACKT.

Finally, after a lot of review and corrections, which took almost a year, the book draft was submitted to another external reviewer and once comments were fixed, the work on the book (at least from my side as an author) was completed.

From my perspective, the book is unique by the fact that it does not focus on a single public cloud provider, but it constantly compares between the three major cloud providers.

From a reader’s point of view or someone who only works with a single cloud provider, I recommend focusing on the relevant topics according to the target cloud provider.

For each topic, I made a list of best practices, which can also be referenced as a checklist for securing the cloud providers’ environment, and for each recommendation I have added reference for further reading from the vendors’ documentation.

If you are interested in learning how to secure cloud environments based on AWS, Azure, or GCP, my book is available for purchase in one of the following book stores:

• Amazon:

https://www.amazon.com/Cloud-Security-Handbook-effectively-environments/dp/180056919X

• Barnes & Noble:

https://www.barnesandnoble.com/w/cloud-security-handbook-eyal-estrin/1141215482?ean=9781800569195

• PACKT

https://www.packtpub.com/product/cloud-security-handbook/9781800569195

When organizations debate workload migration to the cloud, they begin to realize the number of public cloud alternatives that exist, both U.S hyper-scale cloud providers and several small to medium European and Asian providers.

The more we study the differences between the cloud providers (both IaaS/PaaS and SaaS providers), we begin to realize that not all cloud providers are built the same.

How can we select a mature cloud provider from all the alternatives?

Transparency

Mature cloud providers will make sure you don’t have to look around their website, to locate their security compliance documents, allow you to download their security controls documentation, such as SOC 2 Type II, CSA Star, CSA Cloud Controls Matrix (CCM), etc.

What happens if we wish to evaluate the cloud provider by ourselves?

Will the cloud provider (no matter what cloud service model), allow me to conduct a security assessment (or even a penetration test), to check the effectiveness of his security controls?

Global presence

When evaluating cloud providers, ask yourself the following questions:

1. Does the cloud provider have a local presence near my customers?
2. Will I be able to deploy my application in multiple countries around the world?
3. In case of an outage, will I be able to continue serving my customers from a different location with minimal effort?

Scale

Deploying an application for the first time, we might not think about it, but what happens in the peak scenario?

Will the cloud provider allow me to deploy hundreds or even thousands of VM’s (or even better, containers), in a short amount of time, for a short period, from the same location?

Will the cloud provider allow me infinite scale to store my data in cloud storage, without having to guess or estimate the storage size?

Multi-tenancy

As customers, we expect our cloud providers to offer us a fully private environment.

We never want to hear about “noisy neighbor” (where one customer is using a lot of resources, which eventually affect other customers), and we never want to hear a provider admits that some or all of the resources (from VMs, database, storage, etc.) are being shared among customers.

Will the cloud provider be able to offer me a commitment to a multi-tenant environment?

Stability

One of the major reasons for migrating to the cloud is the ability to re-architect our services, whether we are still using VMs based on IaaS, databases based on PaaS, or fully managed CRM services based on SaaS.

In all scenarios, we would like to have a stable service with zero downtime.

Will the cloud provider allow me to deploy a service in a redundant architecture, that will survive data center outage or infrastructure availability issues (from authentication services, to compute, storage, or even network infrastructure) and return to business with minimal customer effect?

APIs

In the modern cloud era, everything is based on API (Application programming interface).

Will the cloud provider offer me various APIs?

From deploying an entire production environment in minutes using Infrastructure as Code, to monitoring both performances of our services, cost, and security auditing – everything should be allowed using API, otherwise, it is simply not scale/mature/automated/standard and prone to human mistakes.

Data protection

Encrypting data at transit, using TLS 1.2 is a common standard, but what about encryption at rest?

Will the cloud provider allow me to encrypt a database, object storage, or a simple NFS storage using my encryption keys, inside a secure key management service?

Will the cloud provider allow me to automatically rotate my encryption keys?

What happens if I need to store secrets (credentials, access keys, API keys, etc.)? Will the cloud provider allow me to store my secrets in a secured, managed, and audited location?

In case you are about to store extremely sensitive data (from PII, credit card details, healthcare data, or even military secrets), will the cloud provider offer me a solution for confidential computing, where I can store sensitive data, even in memory (or in use)?

Well architected

A mature cloud provider has a vast amount of expertise to share knowledge with you, about how to build an architecture that will be secure, reliable, performance efficient, cost-optimized, and continually improve the processes you have built.

Will the cloud provider offer me rich documentation on how to achieve all the above-mentioned goals, to provide your customers the best experience?

Will the cloud provider offer me an automated solution for deploying an entire application stack within minutes from a large marketplace?

Cost management

The more we broaden our use of the IaaS / PaaS service, the more we realize that almost every service has its price tag.

We might not prepare for this in advance, but once we begin to receive the monthly bill, we begin to see that we pay a lot of money, sometimes for services we don’t need, or for an expensive tier of a specific service.

Unlike on-premise, most cloud providers offer us a way to lower the monthly bill or pay for what we consume.

Regarding cost management, ask yourself the following questions:

Will the cloud provider charge me for services when I am not consuming them?

Will the cloud provider offer me detailed reports that will allow me to find out what am I paying for?

Will the cloud provider offer me documents and best practices for saving costs?

Summary

Answering the above questions with your preferred cloud provider, will allow you to differentiate a mature cloud provider, from the rest of the alternatives, and to assure you that you have made the right choice selecting a cloud provider.

The answers will provide you with confidence, both when working with a single cloud provider, and when taking a step forward and working in a multi-cloud environment.

References

Security, Trust, Assurance, and Risk (STAR)

https://cloudsecurityalliance.org/star/

SOC 2 – SOC for Service Organizations: Trust Services Criteria

https://www.aicpa.org/interestareas/frc/assuranceadvisoryservices/aicpasoc2report.html

Confidential Computing and the Public Cloud

https://eyal-estrin.medium.com/confidential-computing-and-the-public-cloud-fa4de863df3

Confidential computing: an AWS perspective

https://aws.amazon.com/blogs/security/confidential-computing-an-aws-perspective/

AWS Well-Architected

https://aws.amazon.com/architecture/well-architected

Azure Well-Architected Framework

https://docs.microsoft.com/en-us/azure/architecture/framework/

Google Cloud’s Architecture Framework

https://cloud.google.com/architecture/framework

Oracle Architecture Center

https://docs.oracle.com/solutions/

Alibaba Cloud’s Well-Architectured Framework

https://www.alibabacloud.com/architecture/index

We have recently read a lot of posts about the SolarWinds hack, a vulnerability in a popular monitoring software used by many organizations around the world.

This is a good example of supply chain attack, which can happen to any organization.

We have seen similar scenarios over the past decade, from the Heartbleed bug, Meltdown and Spectre, Apache Struts, and more.

Organizations all around the world were affected by the SolarWinds hack, including the cybersecurity company FireEye, and Microsoft.

Events like these make organizations rethink their cybersecurity and data protection strategies and ask important questions.

Recent changes in the European data protection laws and regulations (such as Schrems II)  are trying to limit data transfer between Europe and the US.

Should such security breaches occur? Absolutely not.

Should we live with the fact that such large organization been breached? Absolutely not!

Should organizations, who already invested a lot of resources in cloud migration move back workloads to on-premises? I don’t think so.

But no organization, not even major financial organizations like banks or insurance companies, or even the largest multinational enterprises, have enough manpower, knowledge, and budget to invest in proper protection of their own data or their customers’ data, as hyperscale cloud providers.

There are several of reasons for this:

1. Hyperscale cloud providers invest billions of dollars improving security controls, including dedicated and highly trained personnel.
2. Breach of customers’ data that resides at hyperscale cloud providers can drive a cloud provider out of business, due to breach of customer’s trust.
3. Security is important to most organizations; however, it is not their main line of expertise.
   Organization need to focus on their core business that brings them value, like manufacturing, banking, healthcare, education, etc., and rethink how to obtain services that support their business goals, such as IT services, but do not add direct value.

Recommendations for managing security

Security Monitoring

Security best practices often state: “document everything”.
There are two downsides to this recommendation: One, storage capacity is limited and two, most organizations do not have enough trained manpower to review the logs and find the top incidents to handle.

Switching security monitoring to cloud-based managed systems such as Azure Sentinel or Amazon Guard​Duty, will assist in detecting important incidents and internally handle huge logs.

Encryption

Another security best practice state: “encrypt everything”.
A few years ago, encryption was quite a challenge. Will the service/application support the encryption? Where do we store the encryption key? How do we manage key rotation?

In the past, only banks could afford HSM (Hardware Security Module) for storing encryption keys, due to the high cost.

Today, encryption is standard for most cloud services, such as AWS KMS, Azure Key Vault, Google Cloud KMS and Oracle Key Management.

Most cloud providers, not only support encryption at rest, but also support customer managed key, which allows the customer to generate his own encryption key for each service, instead of using the cloud provider’s generated encryption key.

Security Compliance

Most organizations struggle to handle security compliance over large environments on premise, not to mention large IaaS environments.

This issue can be solved by using managed compliance services such as AWS Security Hub, Azure Security Center, Google Security Command Center or Oracle Cloud Access Security Broker (CASB).

DDoS Protection

Any organization exposing services to the Internet (from publicly facing website, through email or DNS service, till VPN service), will eventually suffer from volumetric denial of service.

Only large ISPs have enough bandwidth to handle such an attack before the border gateway (firewall, external router, etc.) will crash or stop handling incoming traffic.

The hyperscale cloud providers have infrastructure that can handle DDoS attacks against their customers, services such as AWS Shield, Azure DDoS Protection, Google Cloud Armor or Oracle Layer 7 DDoS Mitigation.

Using SaaS Applications

In the past, organizations had to maintain their entire infrastructure, from messaging systems, CRM, ERP, etc.

They had to think about scale, resilience, security, and more.

Most breaches of cloud environments originate from misconfigurations at the customers’ side on IaaS / PaaS services.

Today, the preferred way is to consume managed services in SaaS form.

These are a few examples: Microsoft Office 365, Google Workspace (Formerly Google G Suite), Salesforce Sales Cloud, Oracle ERP Cloud, SAP HANA, etc.

Limit the Blast Radius

To limit the “blast radius” where an outage or security breach on one service affects other services, we need to re-architect infrastructure.

Switching from applications deployed inside virtual servers to modern development such as microservices based on containers, or building new applications based on serverless (or function as a service) will assist organizations limit the attack surface and possible future breaches.

Example of these services: Amazon ECS, Amazon EKS, Azure Kubernetes Service, Google Kubernetes Engine, Google Anthos, Oracle Container Engine for Kubernetes, AWS Lambda, Azure Functions, Google Cloud Functions, Google Cloud Run, Oracle Cloud Functions, etc.

Summary

The bottom line: organizations can increase their security posture, by using the public cloud to better protect their data, use the expertise of cloud providers, and invest their time in their core business to maximize value.

Security breaches are inevitable. Shifting to cloud services does not shift an organization’s responsibility to secure their data. It simply does it better.

What exactly is “confidential computing” and what are the reasons and benefits for using it in the public cloud environment?

Introduction to data encryption

To protect data stored in the cloud, we usually use one of the following methods:

· Encryption at transit — Data transferred over the public Internet can be encrypted using the TLS protocol. This method prohibits unwanted participants from entering the conversation.

· Encryption at rest — Data stored at rest, such as databases, object storage, etc., can be encrypted using symmetric encryption which means using the same encryption key to encrypt and decrypt the data. This commonly uses the AES256 algorithm.

When we wish to access encrypted data, we need to decrypt the data in the computer’s memory to access, read and update the data.

This is where confidential computing comes in — trying to protect the gap between data at rest and data at transit.

Confidential Computing uses hardware to isolate data. Data is encrypted in use by running it in a trusted execution environment (TEE).

As of November 2020, confidential computing is supported by Intel Software Guard Extensions (SGX) and AMD Secure Encrypted Virtualization (SEV), based on AMD EPYC processors.

Comparison of the available options

Reference Architecture

AMD SEV Architecture:

Azure Kubernetes Service (AKS) Confidential Computing:

References

· Confidential Computing: Hardware-Based Trusted Execution for Applications and Data

https://confidentialcomputing.io/wp-content/uploads/sites/85/2020/10/ConfidentialComputing_Outreach_Whitepaper-8-5×11-1.pdf

· Google Cloud Confidential VMs vs Azure Confidential Computing

https://msandbu.org/google-cloud-confidential-vms-vs-azure-confidential-computing/

· A Comparison Study of Intel SGX and AMD Memory Encryption Technology

https://caslab.csl.yale.edu/workshops/hasp2018/HASP18_a9-mofrad_slides.pdf

· SGX-hardware listhttps://github.com/ayeks/SGX-hardware

· Performance Analysis of Scientific Computing Workloads on Trusted Execution Environments

https://arxiv.org/pdf/2010.13216.pdf

· Helping Secure the Cloud with AMD EPYC Secure Encrypted Virtualization

https://developer.amd.com/wp-content/resources/HelpingSecuretheCloudwithAMDEPYCSEV.pdf

· Azure confidential computing

https://azure.microsoft.com/en-us/solutions/confidential-compute/

· Azure and Intel commit to delivering next generation confidential computing

https://azure.microsoft.com/en-us/blog/azure-and-intel-commit-to-delivering-next-generation-confidential-computing/

· DCsv2-series VM now generally available from Azure confidential computing

https://azure.microsoft.com/en-us/blog/dcsv2series-vm-now-generally-available-from-azure-confidential-computing/

· Confidential computing nodes on Azure Kubernetes Service (public preview)

https://docs.microsoft.com/en-us/azure/confidential-computing/confidential-nodes-aks-overview

· Expanding Google Cloud’s Confidential Computing portfolio

https://cloud.google.com/blog/products/identity-security/expanding-google-clouds-confidential-computing-portfolio

· A deeper dive into Confidential GKE Nodes — now available in preview

https://cloud.google.com/blog/products/identity-security/confidential-gke-nodes-now-available

· Using HashiCorp Vault with Google Confidential Computing

https://www.hashicorp.com/blog/using-hashicorp-vault-with-google-confidential-computing

· Confidential Computing is cool!

https://medium.com/google-cloud/confidential-computing-is-cool-1d715cf47683

· Data-in-use protection on IBM Cloud using Intel SGX

https://www.ibm.com/cloud/blog/data-use-protection-ibm-cloud-using-intel-sgx

· Why IBM believes Confidential Computing is the future of cloud security

https://venturebeat.com/2020/10/16/why-ibm-believes-confidential-computing-is-the-future-of-cloud-security/

· Alibaba Cloud Released Industry’s First Trusted and Virtualized Instance with Support for SGX 2.0 and TPM

https://www.alibabacloud.com/blog/alibaba-cloud-released-industrys-first-trusted-and-virtualized-instance-with-support-for-sgx-2-0-and-tpm_596821

When an organization needs to select a public cloud service provider, there are several variables and factors to take into consideration that will help you choose the most appropriate cloud provider suitable for the organization’s needs.

In this post, we will review various considerations that will help organizations in the decision-making process.

Business goals

Before deciding to use a public cloud solution, or migrating existing environments to the cloud, it is important that organizations review their business goals. Explore what brings the organization value by maintaining existing systems on premise and what value does the migration to the cloud promise. In accordance with what you discover, decide which systems will be deployed in the cloud first, or which systems your organization will choose to use as managed services.

Review the lists of services offered in the cloud

Public cloud providers publish a list of services in various areas.

Review the list of current services and see how they stand up to your organization’s needs. This will help you narrow down the most suitable options.

Here are some examples of public cloud service catalogs:

· AWS — https://aws.amazon.com/products/

· Azure — https://azure.microsoft.com/en-us/services/

· GCP — https://cloud.google.com/products

· Oracle Cloud — https://www.oracle.com/cloud/products.html

· IBM — https://www.ibm.com/cloud/products

· Salesforce — https://www.salesforce.com/eu/products/

· SAP — https://www.sap.com/products.html

Centrally authenticating users against Active Directory in IaaS / PaaS environments

Many organizations manage access rights to various systems based on an organizational Active Directory.

Although it is possible to deploy Domain Controllers based on virtual servers in an IaaS environment, or create a federation between the on-premise and the cloud environments, at least some cloud providers offer managed Active Directory service based on Kerberos protocol (the most common authentication protocol in the on-premise environments) might ease the migration to the public cloud.

Examples of managed Active Directory services:

· AWS Directory Service

· Azure Active Directory Domain Services

· Google Managed Service for Microsoft Active Directory

Understanding IaaS / PaaS pricing models

Public cloud providers publish pricing calculators and documentation on their service pricing models.

Understanding pricing models might be complex for some services. For this reason, it is highly recommended to contact an account manager, a partners or reseller for assistance.

Comparing similar services among different cloud providers will enable an organization to identify and choose the most suitable cloud provider based on the organization’s needs and budget.

Examples of pricing calculators:

· AWS Simple Monthly Calculator

· AWS Pricing Calculator

· Azure Pricing calculator

· Google Cloud Platform Pricing Calculator

· Oracle Cloud Cost Estimator

Check if your country has a local region of one of the public cloud providers

The decision may be easier, or it may be easier to select one provider over a competitor, if in your specific country the provider has a local region. This can help for example in cases where there are limitations on data transfer outside a specific country’s borders (or between continents), or issues of network latency when transferring large amount of data sets between the local data centers and cloud environments,

This is relevant for all cloud service models (IaaS / PaaS / SaaS).

Examples of regional mapping:

· AWS:

AWS Regions and Availability Zones

· Azure and Office 365:

Azure geographies

o Where your Microsoft 365 customer data is stored

· Google Cloud Platform:

Google Cloud Locations

· Oracle Cloud:

Oracle Data Regions for Platform and Infrastructure Services

· Salesforce:

Where is my Salesforce instance located?

· SAP:

SAP Cloud Platform Regions and Service Portfolio

Service status reporting and outage history

Mature cloud providers transparently publish their service availability status in various regions around the world, including outage history of their services.

Mature cloud providers transparently share service status and outages with customers, and know how to build stable and available infrastructure over the long term, and over multiple geographic locations, as well as how to minimize the “blast radius”, which might affect many customers.

A thorough review of an outage history report allows organizations to get a good picture over an extended period and help in the decision-making process.

Example of cloud providers’ service status and outage history documentation:

· AWS:

AWS Service Health Dashboard

AWS Post-Event Summaries

· Azure:

Azure status

Azure status history

· Google Cloud Platform:

Google Cloud Status Dashboard

Google Cloud Status Dashboard — Incidents Summary

· Oracle Cloud:

Oracle Cloud Infrastructure — Current Status

Oracle Cloud Infrastructure — Incident History

· Salesforce:

Salesforce products status

Salesforce ongoing incidents

· SAP:

SAP Cloud Service Status

SAP Cloud Platform Status Page

Summary

As you can see, there are several important factors to take into consideration when selecting a specific cloud provider. We have covered some of the more common ones in this post.

For an organization to make an educated decision, it is recommended to check what brings value for the organization, in both the short and long-term. It is important to review cloud providers’ service catalogs, alongside a thorough review of global service availability, transparency, understanding pricing models and hybrid architecture that connects local data centers to the cloud.

Today, more and more organizations are moving to the public cloud and choosing open source databases. They are choosing this for a variety of reasons, but license cost is one of the main ones.

In this post, we will review some of the common alternatives for running MySQL database inside a managed environment.

Legacy applications may be a reason for manually deploying and managing MySQL database.

Although it is possible to deploy a virtual machine, and above it manually install MySQL database (or even a MySQL cluster), unless your organization have a dedicated and capable DBA, I recommend looking at what brings value to your organization. Unless databases directly influence your organization’s revenue, I recommend paying the extra money and choosing a managed solution based on a Platform as a Service model.

It is important to note that several cloud providers offer data migration services to assist migrating existing MySQL (or even MS-SQL and Oracle) databases from on-premise to a managed service in the cloud.

Benefits of using managed database solutions

• Easy deployment – With a few clicks from within the web console, or using CLI tools, you can deploy fully managed MySQL databases (or a MySQL cluster)
• High availability and Read replica – Configurable during the deployment phase and after the product has already been deployed, according to customer requirements
• Maintenance – The entire service maintenance (including database fine-tuning, operating system, and security patches, etc.) is done by the cloud provider
• Backup and recovery – Embedded inside the managed solution and as part of the pricing model
• Encryption at transit and at rest – Embedded inside the managed solution
• Monitoring – As with any managed solution, cloud providers monitor service stability and allow customers access to metrics for further investigation (if needed)

Alternatives for running managed MySQL database in the cloud

Summary

As you can read in this article, running MySQL database in a managed environment in the cloud is a viable option, and there are various reasons for taking this step (from license cost, decrease man power maintaining the database and operating system, backups, security, availability, etc.)

References

• https://aws.amazon.com/rds/mysql/
  https://aws.amazon.com/rds/mysql/pricing/

• https://aws.amazon.com/rds/aurora/
  https://aws.amazon.com/rds/aurora/pricing/

• https://azure.microsoft.com/en-us/services/mysql/
  https://azure.microsoft.com/en-us/pricing/details/mysql/

• https://cloud.google.com/sql
  https://cloud.google.com/sql/pricing#2nd-gen-pricing

• https://www.oracle.com/middleeast/mysql/
  https://www.oracle.com/middleeast/mysql/pricing.html

Is it feasible to run HPC in the cloud? How different is it from running a local HPC cluster? What are some of the common alternatives for running HPC in the cloud?

Introduction

Before beginning our discussion about HPC (High Performance Computing) in the cloud, let us talk about what exactly HPC really means?

“High Performance Computing most generally refers to the practice of aggregating computing power in a way that delivers much higher performance than one could get out of a typical desktop computer or workstation in order to solve large problems in science, engineering, or business.” (https://www.usgs.gov/core-science-systems/sas/arc/about/what-high-performance-computing)

In more technical terms – it refers to a cluster of machines composed of multiple cores (either physical or virtual cores), a lot of memory, fast parallel storage (for read/write) and fast network connectivity between cluster nodes.

HPC is useful when you need a lot of compute resources, from image or video rendering (in batch mode) to weather forecasting (which requires fast connectivity between the cluster nodes).

The world of HPC is divided into two categories:

• Loosely coupled – In this scenario you might need a lot of compute resources, however, each task can run in parallel and is not dependent on other tasks being completed.

Common examples of loosely coupled scenarios: Image processing, genomic analysis, etc.

• Tightly coupled – In this scenario you need fast connectivity between cluster resources (such as memory and CPU), and each cluster node depends on other nodes for the completion of the task. Common examples of tightly coupled scenarios: Computational fluid dynamics, weather prediction, etc.

Pricing considerations

Deploying an HPC cluster on premise requires significant resources. This includes a large investment in hardware (multiple machines connected in the cluster, with many CPUs or GPUs, with parallel storage and sometimes even RDMA connectivity between the cluster nodes), manpower with the knowledge to support the platform, a lot of electric power, and more.

Deploying an HPC cluster in the cloud is also costly. The price of a virtual machine with multiple CPUs, GPUs or large amount of RAM can be very high, as compared to purchasing the same hardware on premise and using it 24×7 for 3-5 years.

The cost of parallel storage, as compared to other types of storage, is another consideration.

The magic formula is to run HPC clusters in the cloud and still have the benefits of (virtually) unlimited compute/memory/storage resources is to build dynamic clusters.

We do this by building the cluster for a specific job, according to the customer’s requirements (in terms of number of CPUs, amount of RAM, storage capacity size, network connectivity between the cluster nodes, required software, etc.). Once the job is completed, we copy the job output data and take down the entire HPC cluster in-order to save unnecessary hardware cost.

Alternatives for running HPC in the cloud

Summary

As you can see, running HPC in the public cloud is a viable option. But you need to carefully plan the specific solution, after gathering the customer’s exact requirements in terms of required compute resources, required software and of course budget estimation.

Product documentation

• Azure Batch

https://azure.microsoft.com/en-us/services/batch/

• Azure CycleCloud

https://azure.microsoft.com/en-us/features/azure-cyclecloud/

• AWS ParallelCluster

https://aws.amazon.com/hpc/parallelcluster/

• Slurm on Google Cloud Platform

https://github.com/SchedMD/slurm-gcp

• HPC on Oracle Cloud Infrastructure

https://www.oracle.com/cloud/solutions/hpc.html