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Serverless Computing: AWS vs. Azure vs GCP Cloud Comparison

For well over a decade now, we’ve seen Microsoft, Google and Amazon competing fiercely in a cloud services war, with serverless computing being one of the biggest battlefields. We have been talking about various serverless technologies on numerous occasions here at Hentsu. For the most part, the term ‘serverless’ actually relates to a variety of topics, from public cloud technology in general, to ephemeral computing and grid computing. Now, let’s take a step back first to understand how things work.

Serverless Computing Explained

With the utilization of serverless computing, any company can essentially devote all efforts towards the core business without worrying about the underlying infrastructure that supports it. The business is charged by the serverless vendor based on computation. The benefit, of course, is not reserving and paying for any fixed bandwidth or servers being used. In short, serverless computing removes infrastructure management tasks like server or cluster provisioning, patching, OS maintenance, and so on.

Misconception About Serverless

The name ‘serverless’ can actually sound confusing to those who are unfamiliar with its purpose. So, serverless does not involve the exclusion of servers when running workloads, distributing applications, etc. Using serverless or rather serverless architecture is a reference to the basic notion of developers utilizing software that’s hosted within the public cloud space. Software developers use serverless tech to compose code and then run that code on a cloud platform, and then directing that code towards a specific task or goal.

AWS – Pros, Cons and Tools

Amazon is a powerful force in the realm of public cloud. The sheer amount of operations, services and tools that are being handled and delivered via AWS is staggering. All these services are pushed through a strong network of data centers. This unlocks an array of capabilities, massive resources and being able to handle a large number of users. AWS continues to grow at a breath-taking pace. It successfully outmatches its chief competitors (MS and Google) on various aspects.

AWS Tools 

AWS features next-generation tooling, and they are very good at pushing it forward. SageMaker, for instance, is a powerful service used to label, build, train, fine-tune and deploy machine learning models. Amazon also uses Lex, to power its Alexa services, its Greengrass IoT messaging service as well as Lambda (used for serverless computing). Lex unlocks a cutting-edge deep learning facets involving automatic speech recognition (ASR). It converts speech to text, and natural language understanding (NLU) to understand the point of the text. Building applications with these tools allows businesses to engage with users better than ever before. On the DevOps side, AWS also has a variety of in-house offerings. For developing and implementing code, you can opt for CodeCommit, CodePipeline, CodeDeploy, and more.

Lambda as a Business Model

Lambda is AWS’s main feature when it comes to serverless. With Lambda customers use a unit of code for a function or a task, to achieve certain results. The customer leases this piece of code for a certain amount of time until the required tasks are carried out. AWS then charges for the memory that’s used to carry out the function, and for the time this function or service is active. Simple and highly effective. To give you a bit more perspective on this, Lambda is tool that powers the biggest internet TV network, Netflix. Remember, this is a network that boasts over 190 million subscribers as of Q2, 2020.

Machine Learning and AI

In addition to SageMaker, and other AI-based tools and services, AWS also delivers something called DeepLens. This is AI powered camera is utilized for developing and deploying machine learning algorithms for optical character recognition, object recognition, image recognition, and so on. AWS also uncovered Gluon, which was created as an open-source deep learning library for both developers and non-developers. It’s used to build and quickly train neural networks, but without requiring any knowledge of complex AI programming.

The Cons  

Amazon does have a disadvantage and a lot of enterprises working with this particular public cloud provider will know it. AWS’s cost structure can be a bit difficult to understand. If a business runs complex and heavy workloads via AWS, managing costs effectively will not be easy. Such a drawback tends to ward off enterprises from using AWS services. To be fair though, Amazon's vast array of tech, cloud-based services and tools, usually counterweighs these drawbacks.

Microsoft Azure – Pros, Cons and Tools

Serverless computing is a very hot topic on today’s tech market. There is a common misconception that Microsoft came late to the party. What Microsoft did is simply give itself a powerful kickstart by repurposing its own valuable software for the cloud. This includes Office, SQL Server, Windows Server, Dynamics Active Directory, .Net, Sharepoint, and more. Microsoft’s solutions and software have remained in use over the years. All these solutions are neatly integrated into the Azure suite, which remains the chief benefit of this provider. So, when users jump into Azure, they are usually quite familiar with the surroundings. Such characteristics build loyalty, especially from existing MS users and enterprises. For instance, Microsoft offers discounts for enterprises who are already using their software and this gives them a huge advantage over the competition.

Azure Serverless

On the serverless computing front, Microsoft has quite a few things on offer. Their services include powerful DevOps and helpful tools. These allow developers to build serverless applications from their own developer environment. Therefore, you can easily build, test and deploy functions, containers, and Kubernetes-based apps. Another benefit is getting cloud-hosted private git repos utilizing Azure DevOps. Setting up continuous integration/continuous delivery (CI/CD) is straightforward as well. Other advantages denote automatic package management, automatically triggering builds, and deploying to Kubernetes, Azure Web Apps, and so on. All of this is massively valuable for DevOps purposes. In response to AWS’s Lambda, Microsoft launched Azure Functions, their own powerful serverless computing tool. It is now used across the globe for simplifying complex orchestration challenges, faster solutions development, swift code deployment across multiple targets, and more.

ML for Serverless

That’s right, MS has its own way of employing AI and ML (Machine Learning) to improve your productivity. It’s possible to build, train, and deploy models on Azure Machine Learning. What’s more, Microsoft’s DevOps for machine learning lets you imbue your serverless apps with highly effective AI and machine learning algorithms. They’ve also introduced cognitive computing, which enables your serverless application to “see, hear, speak, understand and interpret” the needs of users via natural methods of communication. The complex process is accomplished with Azure Cognitive Services and using an API. MS also utilizes chat bots. The Azure Bot Service interacts intelligently with users via Skype, Microsoft Teams, Slack, Office 365, and Twitter.

The Cons  

On the negative side, huge traffic during the COVID-19 crisis has taken a toll on Microsoft’s communication apps such as MS Teams. However, the company strives to improve this with each passing month by adding a variety of features and functionalities. Of course, a major disadvantage that comes to mind with Azure is vendor lock-in (to be fair, that's a problem for any PaaS solution). Over the years, Microsoft's Azure Functions was said to have downsides. Deploying, authoring, testing, and executing a function was often too hard in any environment outside of Azure and the Azure portal. Mind you, it's already being stated that MS has made efforts to improve this.

Google Cloud Platform – Pros, Cons and Tools

Experienced users instantly know the biggest benefits of Google Cloud Platform (GCP). Google is known for creating the Kubernetes standard. Their specialty involves high compute offerings. As a provider, they are known for featuring significant scale and load balancing. While Google Cloud competes on the market with powerful resources, they have hit the cloud market much later than AWS or Azure. They are not focused on enterprise, albeit they did launch cutting-edge tooling for DevOps. When it comes to machine learning, the company has presented some ground-breaking solutions as well.

Cloud Functions, App Engine, Cloud Run

Using the provider’s Cloud Functions gives you a chance to spin up code on demand, responding to events that originate from anywhere. It’s simple to create applications that scale from zero to global-scale. This is accomplished without provisioning or managing a single server. With App Engine, you can utilize well-known dev languages and tools. With zero server management or configuration deployments, developers can focus on building highly scalable applications without large management overhead. Furthermore, Google’s Cloud Run allows you to Run stateless HTTP containers on a fully managed platform or on Anthos. Also, another option is to utilize an open API and runtime environment built on Kubernetes – Knative. This enables you to run workloads anywhere. Google’s huge advantage is its proprietary tools. For example, BigQuery and BigTable are based on Google’s ‘Colossus’ structure. The company utilizes Colossus for its own search architecture. Other providers can’t quite match that scale. Furthermore, another cool addition to G suite is Spanner. This product features atomic clocks to keep SQL databases synchronised between their data centres.

Google Compute

In all fairness, Google Compute is more the equivalent of AWS EC2 or Azure VMs, but it’s still a solid benefit of this provider. Google highlights this as their services and their basic compute platform. Supporting Windows and Linux, users can custom configure their platform or a get pre-defined machine type. As you’d expect, GCP concentrates on Kubernetes deployment, since this is the provider’s area of expertise.

The Cons

As we mentioned earlier, Google has arrived a bit late to the cloud market. Consequently, the offerings aren’t as rich and varied as that of its Amazon and MS counterparts. Google doesn’t boast the number of global data centers like Amazon or MC – although it’s been noted that they are expanding in that area. Historically, in terms of moving to the cloud spaces, large businesses do not opt for Google too often. Google proved to be more of an DevOps focused and open-source focused provider. In terms of storage solutions, Google is a bit lacking, with poor backup options. Mind you, the provider does offer SQL and NoSQL support.

Anchoring Your Business to the Right Serverless Strategy

An enterprise may choose to rest its entire business process on the shoulders of any of the three public cloud providers. But in terms of serverless development and serverless technologies, it’s fundamentally about setting up the right strategy for your own business. To point is to anchor your business with a strong value to push it forward. In some instances, serverless tech is an improvement, especially given the powerful cloud resources that become available at the push of a button. When we are talking about applications that are custom-tailored for serverless, that's usually the best way to go considering some of the massive benefits of serverless tech. Many businesses are still worried about how and when they should change cloud providers, or if they should do that at all. The key factor to remember is that with serverless technology, you are not necessarily making radical changes to your business. What you are doing is modernizing your business and letting it evolve and grow.

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Refactoring Legacy Applications: Benefits of the Public Cloud and Serverless Technology

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The demand of the modern-day market is pushing developers and business owners towards better and faster solutions. Companies and are looking to streamline their workflow, and the launch of their respective products and software. In the current technological landscape, application development is getting a powerful boost thanks to the cloud. For most of these companies, refactoring legacy applications is already a familiar occurrence and a necessary step forward.

What is Refactoring?

Refactoring legacy applications is quite simply a natural part of any application development. It is a process that will happen sooner or later. The key thing is being ready to make the move and knowing what’s best for your business when it comes to cloud adoption and cloud migration.

With refactoring you ensure technical debt is kept to an acceptable level. An application will be refactored many times during its lifecycle, thus adding new features and functionality. The goal is refactoring an application to enable components, or all of its features to run on public cloud services.

The challenge is having legacy applications that are mandatory for your business in order to operate, and usually this is not designed for a cloud-based approach – that’s when the process of refactoring jumps in. Rearchitecting or rebuilding can kick off as soon as existing architecture is determined and mapped out.

Making Things More Efficient and Future-Proof

When talking about the mechanics of it, and the basics of refactoring and restructuring code, there are other things to consider. For example, if you got large elements of the application that can have their components separated into external services – in this instance, sort of a hybrid approach that occurs with the cloud migration process. This entails, as we’ve mentioned earlier, preserving essential elements of the existing app as is and moving out parts of the app to utilize within cloud native services.

Improving Agility to Suit Enterprise-level

This is where things get interesting. With this move you’re essentially leveraging the development investment from the public cloud providers, who spend billions to make your business operations cheaper and easier. As a result, you are reducing your overheads, which is one massive benefit. If you have a legacy app or elements of your business on an enterprise scale that is causing you headaches and you constantly have to micromanage it and monitor it, the obvious solution is to use cloud native services instead, therefore giving yourself some time to focus on your core business. That’s another huge benefit. If DevOps isn’t the core of your business, then cloud services will handle that for you.

Utilizing Ephemeral Computing

As of late, we have seen a powerful push towards serverless and a huge part of this is relying on ephemeral computing processes. An increasing amount of businesses are steering their operations towards the pay-as-you-go model. More importantly, we’ve also seen the implementation and use of grid computing and ephemeral computing, both of which are already a vital component in modern-day handling of business workloads.

Serverless Application Refactoring

In this particular instance, we are referring to removing a virtual machine or server where something is hosted and deployed to. In Hentsu we are constantly using the strength of the public cloud to carry this out internally, while relying on powerful tooling such as Azure App Service, AWS Lambda, and AWS/Azure Batch. We also offer huge amounts of vertical and horizontal scaling these services are pay-as-you-go, which allows you to turn the services off and reduce your costs to zero; something that is impossible with private cloud. This is a benefit to both our operations and to what clients require for their own workloads.

There are other numerous advantages that come with the serverless solution. You can check these out below:

  • Cloud native serverless solution unlocks massive processing power when needed.
  • You pay only for what you’re using.
  • Integrated multi-region resiliency across the public cloud.
  • In that environment, you can utilize auto-scalability based on workloads and user demand.
  • Integrated alerts, health checks, automated recovery and restarts if any service fails.

When we’re talking serverless computing, we need to understand what this involves. The main facet of serverless is the power to abstract the servers, infrastructure, and operating systems, and that allows developers to devote their attention to application development.

Beyond that, you also have the opportunity to power everything with code and with the help of Azure cloud you are laying a solid foundation for a smooth DevOps environment. In relation to that, companies can make use of Continuous Integration/Continuous Delivery (CI/CD) of applications to launch fully tested and integrated software versions minutes after development.

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 Download interview with our co-CTO Des Holmes

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Date/Time

Date(s) - 01/01/1970
12:00 AM - 12:00 AM

Location

600 5th ave. NY, NY

Upcoming Events

  • Webinar Series: Pushing the Limits of Microsoft Modern Workplace7 Apr, 2020
  • The Trading Show Europe 201917 Oct, 2019
  • Charity Night At The Gallery25 Sep, 2019
More

 

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