Strategies for Cloud Agnostic Architectures

Strategies for Cloud Agnostic Architectures

Introduction

If your business uses cloud computing–as most businesses do these days–it’s very likely that you have at least one public cloud solution. Ninety-one percent of organizations have adopted the public cloud. What’s more, a full half of large enterprises now spend more than $1.2 million every year on their public cloud deployments.

The “public cloud” refers to cloud computing services such as storage, software, and virtual machines that are provided by third parties over the internet. Some of the biggest public cloud providers are Amazon Web Services, Microsoft Azure, and Google Cloud Platform.

Increasingly, however, companies are growing interested in a “cloud agnostic” strategy. So what does “cloud agnostic” mean, and how can your own business be cloud agnostic? 

This article has all the answers.

Cloud Agnostic: Definition and Examples

cloud agnostic infrastructure

One of the greatest benefits of cloud computing is its flexibility. If you’re running out of storage, for example, your public cloud solution can automatically scale it up for you so that your operations will continue seamlessly.

Being “cloud agnostic” takes this idea of the flexible cloud one step further. As the name suggests, cloud agnostic organizations are those capable of easily running their workloads and applications within any public cloud.

The fact that an organization is “cloud agnostic” doesn’t mean that it’s completely indifferent as to which cloud provider it uses for which workloads. Indeed, the organization will likely have established preferences for their cloud setup, based on factors such as price, region, and the offerings from each provider.

Rather, being cloud agnostic means that you’re capable of switching tracks to a different public cloud provider should the need arise, with minimal hiccups and disruption to your business.

Why Do Companies Want to Be Cloud Agnostic?

benefits of being cloud agnostic

It’s hardly surprising that more companies are looking to be cloud agnostic, given that 84 percent of enterprises now use a multi-cloud strategy. This involves using two or more public cloud solutions, allowing you to take advantage of the differentials in features or prices between providers.

Another reason that companies want to be cloud agnostic is to avoid vendor lock-in. Cloud computing has revolutionized the ways that companies do business. It does so by giving them access to more products and services without having to support and maintain their own hardware and infrastructure. However, this increased reliance on cloud computing also comes with the risk of dependency.

Management consulting firm Bain & Company finds that 22 percent of companies see vendor lock-in as one of their top concerns about the cloud. “Vendor lock-in” is a phenomenon when a business becomes overly dependent on products or services from one of its vendors. This is highly dangerous if the vendor hikes its prices, stops providing a certain offering, or even ceases operations.

The world of cloud computing is rife with vendor lock-in horror stories. One example is that of Nirvanix, a cloud storage firm that went out of business and gave clients only two weeks to move their data. While it might seem impossible for an Amazon or Google to go out of business, remembering companies like AOL show that it’s not completely unrealistic for a vendor to cut services. By making your company more flexible and adaptable, being cloud agnostic inoculates against the risk of vendor lock-in.

Cloud Agnostic: Pros and Cons

Cloud agnostic architectures

The Pros of Being Cloud Agnostic

  • No vendor lock-in: As mentioned above, being cloud agnostic makes the risk of vendor lock-in much less likely. Companies that are cloud agnostic can “diversify their portfolio” and become more resilient to failure and changes in the business IT landscape.
  • More customization: Using a strategy that’s cloud agnostic and multi-cloud lets you tweak and adjust your cloud roadmap exactly as you see fit. You don’t have to miss out on a feature that’s exclusive to a single provider just because you’re locked into a different solution.
  • Redundancy. Having systems in place across various clouds means you are covered should any one encounter problems.

The Cons of Being Cloud Agnostic

  • Greater complexity: Being cloud agnostic sounds great on paper, but the realities of implementation can be much more difficult. Creating a cloud strategy with portability built in from the ground up generally incurs additional complexity and cost.
  • “Lowest common denominator”: If you focus too much on being cloud agnostic, you may only be able to use services that are offered by all of the major public cloud providers. Even if AWS has a great new feature for your business, for example, you may be reluctant to use it unless you can guarantee that you can replicate it in Microsoft Azure or Google Cloud Platform. While more of a choice in enterprise strategy than a drawback, it is something to be aware of.

Strategies for Being Cloud Agnostic

A number of articles say that being truly cloud agnostic is a “myth.” These pieces argue that “cloud agnostic” is a state that’s not realistic or even desirable for most organizations.

In fact, being entirely cloud agnostic is an ideal that may or may not be achievable for you. Unless you are sure that the future won’t change, it may not be worth the effort to reach this goal. In large part, the tradeoff comes at the expense of your other IT and cloud objectives.

Nevertheless, there are a number of “low-hanging fruit” technologies that you can adopt on the path toward being cloud agnostic. These will be advantageous for your business no matter where you stand on the cloud agnostic spectrum.

For example, container technologies such as Docker and Kubernetes are an invaluable part of being cloud agnostic. Essentially, a “container” is a software unit that packages source code together with its libraries and dependencies. This allows the application to be quickly and easily ported from one computing environment to another.

Another tactic for being cloud agnostic is to use managed database services. These are public cloud offerings in which the provider installs, maintains, manages, and provides access to a database. The major public clouds such as AWS, Microsoft Azure, and Google all offer theoretical possibilities for migrating between providers.

That said, using products such as IronWorker that can deploy on any cloud, including fully on-premise deploys, is the easiest and most cost effective way to remain cloud agnostic. This is because with virtually one click, you can save your settings and deploy to whatever environment your enterprise wishes. In short, simplicity equals operational cost efficiency.

Conclusion

Technologies such as containers and managed database services will go far toward making your business more flexible and adaptable. This is true even if not completely cloud agnostic. If you do decide to become a cloud agnostic organization, consider using the services of Iron.io. Set up a consultation with us today to find out how our cloud agnostic IronFunctions platform can help your developers become more productive and efficient.

Google Cloud Run: Review and Alternatives

Introduction

Google Cloud Run is a new cloud computing platform that’s hot off the presses from Google, first announced at the company’s Google Cloud Next conference in April 2019. Google Cloud Run has generated a lot of excitement (and a lot of questions) among tech journalists and users of the public cloud alike, even though it’s still in beta.

We will discuss the ins and outs of Google Cloud Run in this all-in-one guide, including why it appeals to many Google Cloud Platform customers, what are the features of Google Cloud Run, and a comparison of the Google Cloud Run alternatives.

What Is Google Cloud Run (And How Does It Work?)

What is serverless computing?

To answer the question “What is Google Cloud Run?,” we first need to define serverless computing.

Often just called “serverless,” serverless computing is a cloud computing paradigm that frees the user from the responsibility of purchasing or renting servers to run their applications on.

(Actually, the term “serverless” is a bit of a misnomer: The code still runs on a server, just not one that the user has to worry about.)

Cloud computing has soared in popularity over the past decade. This is thanks in large part to the increased convenience and lower maintenance requirements. Traditionally, however, users of cloud services have still needed to set up a server, scale its resources when necessary, and shut it down when you’re done. This has all changed with the arrival of serverless.

The phrase “serverless computing” is applied to two different types of cloud computing models:

  • BaaS (backend as a service) outsources the application backend to the cloud provider. The backend is the “behind the scenes” part of the software for purposes such as database management, user authentication, cloud storage, and push notifications for mobile apps.
  • FaaS (function as a service) still requires developers to write code for the backend. The difference is this code is only executed in response to certain events or requests. This enables you to decompose a monolithic server into a set of independent functionalities, making availability and scalability much easier.

You can think of FaaS serverless computing as like a water faucet in your home. When you want to take a bath or wash the dishes, you simply turn the handle to make it start flowing. The water is virtually infinite, and you stop when you have as much as you need, only paying for the resources that you’ve used.

Cloud computing without FaaS, by contrast, is like having a water well in your backyard. You need to take the time to dig the well and build the structure, and you only have a finite amount of water at your disposal. In the event that you run out, you’ll need to dig a deeper well (just like you need to scale the server that your application runs on).

Regardless of whether you use BaaS or FaaS, serverless offerings allow you to write code without having to worry about how to manage or scale the underlying infrastructure. For this reason, serverless has come into vogue recently. In a 2018 study, 46 percent of IT decision-makers reported that they use and evaluate serverless.

What are containers?

docker containers

Now that we’ve defined serverless computing, we also need to define the concept of a container. (Feel free to skip to the next section if you’re very comfortable with your knowledge of containers.)

In the world of computing, a container is an application “package” that bundles up the software’s source code together with its settings and dependencies (libraries, frameworks, etc.). The “recipe” for building a container is known as the image. An image is a static file that is used to produce a container and execute the code within it.

One of the primary purposes of containers is to provide a familiar IT environment for the application to run in when the software is moved to a different system or virtual machine (VM).

Containers are part of a broader concept known as virtualization, which seeks to create a virtual resource (e.g., a server or desktop computer) that is completely separate from the underlying hardware.

Unlike servers or machine virtualizations, containers do not include the underlying operating system. This makes them more lightweight, portable, and easy to use.

When you say the word “container,” most enterprise IT staff will immediately think of one, or both, of Docker and Kubernetes. These are the two most popular container solutions.

  • Docker is a runtime environment that seeks to automate the deployment of containers.
  • Kubernetes is a “container orchestration system” for Docker and other container tools, which means that it manages concerns such as deployment, scaling, and networking for applications running in containers.

Like serverless, containers have dramatically risen in popularity among users of cloud computing in just the past few years. A 2018 survey found that 47 percent of IT leaders were planning to deploy containers in a production environment, while 12 percent already had. Containers enjoy numerous benefits: platform independence, speed of deployment, resource efficiency, and more.

Containers vs. serverless: A false dilemma

Given the massive success stories of containers and serverless computing, it’s hardly a surprise that Google would look to combine them. The two technologies were often seen as competing alternatives before the arrival of Google Cloud Run.

Both serverless and containers are intended to make the development process less complex. They do this by automating much of the busy work and overhead. But they go about it in different ways. Serverless computing makes it easier to iterate and release new application versions, while containers ensure that the application will run in a single standardized IT environment.

Yet nothing prevents cloud computing users from combining both of these concepts within a single application. For example, an application could use a hybrid architecture, where containers can pick up the slack if a certain function requires more memory than the serverless vendors has provisioned for it.

As another example, you could build a large, complex application that mainly has a container-based architecture, but that hands over responsibility for some backend tasks (like data transfers and backups) to serverless functions.

Rather than continuing to enforce this false dichotomy, Google realized that serverless and containers could complement one another, each compensating for the other one’s deficiencies. There’s no need for users to choose between the portability of containers and the scalability of serverless computing.

Enter Google Cloud Run…

What is Google Cloud Run?

In its own words, Google Cloud Run “brings serverless to containers.” Google Cloud Run is a fully managed platform that is capable of running Docker container images as a stateless HTTP service.

Each container can be invoked with an HTTP request. All the tasks of infrastructure management–provisioning, scaling up and down, configuration, and management–are cleared away from the user (as typically occurs with serverless computing).

Google Cloud Run is built on the Knative platform, which is an open API and runtime environment for building, deploying, and managing serverless workloads. Knative is based on Kubernetes, extending the platform in order to facilitate its use with serverless computing.

In the next section, we’ll have more technical details about the features and requirements of Google Cloud Run.

Google Cloud Run Features and Requirements

Features

Google cites the selling points below as the most appealing features of Google Cloud Run:

  • Easy autoscaling: Depending on light or heavy traffic, Google Cloud Run can automatically scale your application up or down.
  • Fully managed: As a serverless offering, Google Cloud Run handles all the annoying and frustrating parts of managing your IT infrastructure.
  • Completely flexible: Whether you prefer to code in Python, PHP, Pascal, or Perl, Google Cloud Run is capable of working with any programming language and libraries (thanks to its use of containers).
  • Simple pricing: You pay only when your functions are running. The clock starts when the function is spun up, and ends immediately once it’s finished executing.

There are actually two options when using Google Cloud Run: a fully managed environment or a Google Kubernetes Engine (GKE) cluster. You can switch between the two choices easily, without having to reimplement your service.

In most cases, it’s best to stick with Google Cloud Run itself, and then move to Cloud Run on GKE if you need certain GKE-specific features, such as custom networking or GPUs. However, note that when you’re using Cloud Run on GKE, the autoscaling is limited by the capacity of your GKE cluster.

Google Cloud Run requirements

Google Cloud Run is still in beta (at the time of this writing). This means that things may change between now and the final version of the product. However, Google has already released a container runtime contract describing the behavior that your application must adhere to in order to use Google Cloud Run.

Some of the most noteworthy application requirements for Google Cloud Run are:

  • The container must be compiled for Linux 64-bit, but it can use any programming language or base image of your choice.
  • The container must listen for HTTP requests on the IP address 0.0.0.0, on the port defined by the PORT environment variable (almost always 8080).
  • The container instance must start an HTTP server within 4 minutes of receiving the HTTP request.
  • The container’s file system is an in-memory, writable file system. Any data written to the file system will not persist after the container has stopped.

With Google Cloud Run, the container only has access to CPU resources if it is processing a request. Outside of the scope of a request, the container will not have any CPU available.

In addition, the container must be stateless. This means that the container cannot rely on the state of a service between different HTTP requests, because it may be started and stopped at any time.

The resources allocated for each container instance in Google Cloud Run are as follows:

  • CPU: 1 vCPU (virtual CPU) for each container instance. However, the instance may run on multiple cores at the same time.
  • Memory: By default, each container instance has 256 MB of memory. Google says this can be increased up to a maximum of 2 GB.

Cloud Run Pricing

Google cloud run pricing

Google Cloud Run uses a “freemium” pricing model: free monthly quotas are available, but you’ll need to pay once you go over the limit. These types of plans frequently catch users off guard. They end up paying much more than expected. According to Forrester, a staggering 58% of companies surveyed said their costs exceeded their estimates.

The good news for Google Cloud Run users is that you’re charged only for the resources you use (rounded up to the nearest 0.1 second). This is typical of many public cloud offerings.

The free monthly quotas for Google Cloud Run are as follows:

  • CPU: The first 180,000 vCPU-seconds
  • Memory: The first 360,000 GB-seconds
  • Requests: The first 2 million requests
  • Networking: The first 1 GB egress traffic (platform-wide)

Once you bypass these limits, however, you’ll need to pay for your usage. The costs for the paid tier of Google Cloud Run are:

  • CPU: $0.000024 per vCPU-second
  • Memory: $0.0000025 per GB-second
  • Requests: $0.40 per 1 million requests
  • Networking: Free during the Google Cloud Run beta, with Google Compute Engine networking prices taking effect once the beta is over.

It’s worthwhile to note you are billed separately for each resource; for example, the fact that you’ve exceeded your memory quota does not mean that you need to pay for your CPU and networking usage as well.

In addition, these prices may not be definitive. Like the features of Google Cloud Run, prices for Google Cloud are subject to change once the platform leaves beta status.

Finally, Cloud Run on GKE uses a separate pricing model that will be announced before the service reaches general availability.

Google Cloud Run Review: Pros and Cons

Because it’s a brand new product product that’s still in beta, reputable Google Cloud Run reviews are still hard to find.

Reaction to Google’s announcement has been fairly positive, acknowledging the benefits of combining serverless computing with a container-based architecture. Some users believe that the reasonable prices will be enough for them to consider switching from similar services such as AWS Fargate.

Other users are more critical, however, especially given that Google Cloud Run is currently only in beta. Some are worried about making the switch, given Google’s track record of terminating services such as Google Reader, as well as their decision to alter prices for the Google Maps API, which effectively shut down many websites that could not afford the higher fees.

Given that Google Cloud Run is in beta, the jury is still out on how well it will perform in practice. Google does not provide any uptime guarantees for cloud offerings before they reach general availability.

The disadvantages of Google Cloud Run will likely overlap with the disadvantages of Google Cloud Platform as a whole. These include the lack of regions when compared with competitors such as Amazon and Microsoft. In addition, as a later entrant to the public cloud market, Google can sometimes feel “rough around the edges,” and new features and improvements can take their time to be released.

Google Cloud Run Alternatives

Since this is a comprehensive review of Google Cloud Run, we would be remiss if we didn’t mention some of the available alternatives to the Google Cloud Run service.

In fact, Google Cloud Run shares some of its core infrastructure with two of Google’s other serverless offerings: Google Cloud Functions and Google App Engine.

  • Google Cloud Functions is an “event-driven, serverless compute platform” that uses the FaaS model. Functions are triggered to execute by a specified external event from your cloud infrastructure and services. As with other serverless computing solutions, Google Cloud Functions removes the need to provision servers or scale resources up and down.
  • Google App Engine enables developers to “build highly scalable applications on a fully managed serverless platform.” The service provides access to Google’s hosting and tier 1 internet service. However, one limitation of Google App Engine is that the code must be written in Java or Python, as well as use Google’s NoSQL database BigTable.

Looking beyond the Google ecosystem, there are other strong options for developers who want to leverage both serverless and containers in their applications.

The most tested Cloud Run alternative: Iron.io

Iron.io is a serverless platform that offers a multi-cloud, Docker-based job processing service. As one of the early adopters of containers, we have been a major proponent of the benefits of both technologies.

The centerpiece of Iron.io’s products, IronWorker is a scalable task queue platform for running containers at scale. IronWorker has a variety of deployment options. Anything from using shared infrastructure to running the platform on your in-house IT environment is possible. Jobs can be scheduled to run at a certain date or time, or processed on-demand in response to certain events.

In addition to IronWorker, we also provide IronFunctions, an open-source serverless microservices platform that uses the FaaS model. IronFunctions is a cloud agnostic offering that can work with any public, private, or hybrid cloud environment, unlike services such as AWS Lambda. Indeed, Iron.io allows AWS Lambda users to easily export their functions into IronFunctions. This helps to avoid the issue of vendor lock-in. IronFunctions uses Docker containers as the basic unit of work. That means that you can work with any programming language or library that fits your needs.

Conclusion

Google Cloud Run represents a major development for many customers of Google Cloud Platform who want to use both serverless and container technologies in their applications. However, Google Cloud Run is only the latest entrant into this space, and may not necessarily be the best choice for your company’s needs and objectives.

If you want to determine which serverless + container solution is right for you, speak with a skilled, knowledgeable technology partner like Iron.io who can understand your individual situation. Whether it’s our own IronWorker solution, Google Cloud Run, or something else entirely, we’ll help you get started on the right path for your business.

Get a Job, Container: A Serverless Workflow with Iron.io

This post originally appeared on DZone

My previous post, Distinguished Microservices: It’s in the Behavior, made a comparison between two types of microservices – real-time requests (“app-centric”) and background processes (“job-centric”). As a follow up, I wanted to take a deeper look at job-centric microservices as they set the stage for a new development paradigm — serverless computing.

Of course, this doesn’t mean we’re getting rid of the data center in any form or fashion — it simply means that we’re entering a world where developers never have to think about provisioning or managing infrastructure resources to power workloads at any scale. This is done by decoupling backend jobs as independent microservices that run through an automated workflow when a predetermined event occurs. For the developer, it’s a serverless experience.

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Distinguished Microservices: It’s in the Behavior

This post originally appeared on DZone

Microservices is more than just an academic topic. It was born out of the challenges from running distributed applications at scale; enabled by recent advancements in cloud native technologies. What started as a hot topic between developers, operators, and architects alike, is now catching on within the enterprise because of what the shift in culture promises — the ability to deliver software quickly, effectively, and continuously. In today’s fast-paced and ever-changing landscape, that is more than just desirable; it’s required to stay competitive.

Culture shifts alone are not enough to make a real impact, so organizations embarking down this path must also examine what it actually means for the inner workings of their processes and systems. Dealing with immutable infrastructure and composable services at scale means investing in operational changes. While containers and their surrounding tools provide the building blocks through an independent, portable, and consistent workflow and runtime, there’s more to it than simply “build, ship, run.”

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Learning from Facebook’s Outage

Making the most of Facebook's outage Thanks to Kārlis Dambrāns for providing the base image. CC BY 2.0

Facebook’s suffered three outages this month; two of which occurred within the span of a week. Ouch. If you know any folks on the FB ops team, now’s a good time to buy ‘em a beer.

Whenever a blip like this appears, it’s a good time for all of us to look at our own infrastructure. Are you prepared?

First things first, what caused the FB outage? Facebook links the most recent to an issue with the Graph API. The September 22nd issue was due to a hiccup with the Realtime Update service. It’s the sort of thing that could happen at any company.

Despite the impact, it’s good to see Facebook has a sense of humor about the downtime. Their response to the update service issue reads, “will post an update here as soon as we know more.” I love the sly wink.

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How Omaze Delivers Once in a Lifetime Experiences Using Iron.io and Rackspace DevOps

Blow Sh*t Up with Arnold Schwarzenegger … Be Drawn Into an Episode of the Simpsons … Celebrate the Patriots Victory with Rob Gronkowski.

These aren’t even bucket list items, these are unattainable items. That is, until Omaze gets involved. Omaze is an organization that was founded to drive significantly more money and awareness for deserving causes through the chance to live out dream experiences.

Charities offer up personalized events with their celebrity partners where everyone has the chance to win by donating to the cause. Each experience offers a range of reward levels from signed t-shirts to personalized Skype sessions to Twitter mentions, and once the experience is placed up on the Omaze site, the countdown begins to the winner of the grand prize. The growing number of high profile celebrities participating to provide such unique opportunities begs the question – what’s your dream experience?

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DockerWorker Unplugged

Today, the world revolves around developers. Digital businesses are becoming a significant part of the landscape. Traditional business thrives on its responsiveness to customers and how it handles business data. People used to talk about the Era of Information Technology, however now we’re in the Era of the Developer.

Fast-moving businesses recognize the need to give developers the tools, platforms, and application services developers require to get things done. Equally important is getting obstructions out of the way of developers and allowing them to move fast. What do developers need to be successful in this modern world? They need self-service, on-demand capabilities, immediate scale, and little to no operations. Simply put, developers want to write code – and do so in a manner that lets them focus on writing code without having to manage tools and infrastructure. The overhead of managing infrastructure or dealing with a mismatch between development and production systems, steals precious cycles from a developer’s main driver – writing code.

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