With all the options involved, multi-cloud can be reached via many different routes. What makes the difference today? Containers.

Containers and application design 

Software containers provide a way to package application elements as small and transferable elements. Rather than virtualisation – where virtual machines include operating systems and applications to run – containers are much smaller. They run on top of the Linux operating system and tap into the kernel, sharing resources across all container images and making the application containers themselves more efficient.

More importantly, the most popular container orchestration system, Kubernetes, has made it possible to move these container images between different providers and carry on running. Rather than being tied to internal data centre deployments, to specific data centre locations or to any single service provider, enterprises can migrate easily as and when suits them. For DevOps teams, container orchestration should make the move to container-based applications easier, even while the underlying infrastructure can become more complex.

According to Gartner, more than 75 percent of global organisations will be running containerised applications in production in 2022, compared with under 30 percent today. Container options like Kubernetes and Docker have grown rapidly in adoption – according to our research, around 30 percent of companies are running Docker on AWS in 2019, up from 18 percent in 2016 and 24 percent in 2017. Similarly, native Kubernetes deployments have grown from 8 percent in 2017 to 20 percent in 2019.

The reason for this is that containers represent a big change in how applications are deployed. Using containers, you can scale up and down in response to demand levels much faster than traditional IT infrastructures or virtualised environments allow. Using microservices design and API connections, you can also replace or extend these application components more easily than traditional application designs allow.

Theory vs practice

In theory, you can lift a set of containers from your internal data centre and run them to deliver a service in a public cloud, on a managed service, and bring that service back again too. Kubernetes should, therefore, help companies to adopt a hybrid cloud or multi-cloud strategy where the organisation has more control over its destiny. For the public cloud providers, the advent of Kubernetes should drive up more adoption of their services too. In practice, multi-cloud is still in its early stages – according to our research, currently, around nine percent of companies are running multiple clouds.

While multi-cloud deployments are still in their initial stages there is a strong correlation between this and use of Kubernetes – while around 20 percent of AWS customers use Kubernetes, this goes up to 59 percent for those running AWS and Google Cloud Platform (GCP) and to 80 percent for those running AWS, GCP, and Azure together.

Theory, Meet Practice

 However, the move to containers can be more challenging in the longer term. The ability to run containers in the same way wherever the right infrastructure is located should provide more freedom to deal with availability and scalability concerns. What needs to be considered is how to wrap the right management approach around containers, so that the right decisions can be made consistently over time.

For DevOps teams, this means looking at observability – how to get the right data on details like each container instance through up to the whole application service. Observability comes from using application and infrastructure logs, metrics and tracing data together to build up the best possible picture of these new services.

Without data, it is difficult for developers building applications in containers to see problems. Similarly, it can be hard for infrastructure and operations teams to know that they are running applications in the most efficient way, or if they need to budget for additional capacity. From a practical perspective, gathering this data from each machine image or container is itself challenging, too.

While it is possible to orchestrate and manage containers at a massive scale, the observability side has not kept up. For companies with applications running across multiple data centre locations, or combinations of internal and external cloud services, this observability should be critical in order to control spend and budget allocation. However, it can easily be missed.

The specifics of containers

The first issue is that containers don’t produce data in the same way as virtual machines or more traditional physical servers. Each container can provide data on operations, but the information will stay within the container unless it is collected and centralised. For teams running applications across multiple locations, this process has to be considered from the start.

Containers also lose their data records when they shut down. For services that rely on flexibility and scaling up and down in response to demand levels, this can make it extremely difficult to track use patterns and application behaviour across different sites. Without this machine data, it becomes hard to run these multi-cloud applications over time.

Secondly, each of these images can be run anywhere. Rather than information automatically being gathered in one place, you either have to correlate data from multiple containers running across different instances or look at data specifically from the application itself. Neither of these approaches will be enough on its own to provide the right level of context for making decisions.

Getting a complete overview of these new applications – whether they are hosted internally, externally or as a mix of both – will be essential if you make the move to implement containers. Without this data, you run the risk of allocating resources in the wrong ways. By understanding the landscape developing around your applications, you can back the right approach for the future, rather than taking the wrong route. Regardless of who is viewed as winning the multi-cloud crown, it’s worth understanding how the race was won too.

Written by Mark Pidgeon, Vice President Technical Services at Sumo Logic. 

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The campaign website has had over 100,000 unique visitors and 53,381 registered players, resulting in over 1042 applications to fill 110 vacancies with the Dutch Police.

Gamification of the recruitment process

Steam.nl, an award-winning employer branding agency based in Amsterdam, is the creative brain behind #Crimediggers, the online gaming experience created for the Dutch National Police force. Including a rich narrative of interactive experiences, the game is part of a recruitment campaign aimed at finding skilled IT professionals wanting to pursue a career in criminal investigative work.

“In a tight labour market, we were challenged with finding a target audience not aware of the opportunities a career in digital forensics could offer” said Hans Kroonen, CEO at Steam. “We had a clear vision of how we wanted to engage with this audience, and knew we needed a complex and multi-faceted gaming application to be at the core of a more traditional marketing drive”.

Supported by Docker-based container architecture

Steam used Cloud 66, a London-based tech startup to build and deploy Docker-based container architecture, consisting of several integrated polyglot applications. “The ease of using Cloud 66 to build and deploy #Crimediggers was instrumental to creating a multi-layered, interactive app experience” said Kroonen. Different stages of the game had potential recruits interact with image-based evidence, decode forensics data, solve cryptic hacker alerts and combine their findings with other players to advance through each level.

Digital skills needed in national police forces

“The evolving threats and forensics requirements of a connected world, mean we have a real need for digital skills to help us combat crime,” said Barend Frans, Head of the Digital Forensics Team in Amsterdam for the Dutch National Police. “This initiative was all about finding the next generation of digital and financial investigation experts. Creating such a sophisticated technical deployment in support of the campaign would have taken us weeks, if not months to operationalise in-house. The team did an exceptional job, and I’m very pleased with the results”.

“#Crimediggers is a terrific example of how agencies like Steam are using Cloud 66 to overcome challenging technical hurdles for their clients” said Khash Sajadi, CEO and cofounder of Cloud 66. “As engineers ourselves, we’re very focused on creating the tools needed by developers to help deploy applications seamlessly”.

Edited from press release by Cecilia Rehn.

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“You can observe a lot by just watching” – Yogi Berra, Hall of Fame Baseball catcher, manager and coach. Well known for his malapropisms.

If you can learn a lot by watching, you can also learn a lot by asking. So when we wanted to find out the current thinking about use of containers with databases and other applications, we asked. And 200 IT professionals told us what they think.

Increase in spend on container-based technology

For one thing, the survey results prove beyond doubt that the move towards containerisation is nearly unstoppable. More than 4 out of 5 respondents expected their companies to increase their spend in container-based technology.

What may be a bit surprising to some is the use of containers for stateful or data-centric applications. Containers have gained an incredible level of mindshare among developers, and there is a near unanimity that technology such as Docker’s will soon become the standard way to pack and deploy stateless applications. Opinions, however, can be divided on how useful containers are for performance-sensitive data applications, such as databases. As someone who has spent his lifetime working with databases, I can vouch for how transformative a container-based platform can be for these applications, but we wanted to hear what data professionals and IT managers thought about it.

And this is where the survey results may hold a surprise or two. Three out of 4 respondents told us they are actively looking to run data applications within containers. The drivers cited for this trend were not surprising. Respondents pointed to the ability to consolidate workload (without losing performance or predictability) and to reduce performance ahead (as compared to hypervisors-based virtualisation) as leading factors prompting greater adoption of containers.

In fact container use is growing for all things data. Containers are emerging as the preferred platform for running databases, with approximately half the respondents doing so. About 40% of respondents indicated they have deployed Big Data applications such as Hadoop and Spark within containers.

Vibrant container technology landscape

The other surprising survey finding is the vibrant container technology landscape. While Docker is certainly getting its share of interest and adoption, system container technologies such as LXC and LXD remain as the preferred containerisation technology for running data-centric applications, with 60% citing their use. That is understandable because, unlike application containers technology such as Docker that are designed to run a single process or service, System Containers are essentially lightweight VMs that can run multiple services and applications, have their own host name and IP addresses that you can SSH into, and pretty much manage like VMs.

The use of System Containers with data applications therefore provides many of the benefits of a hypervisor-based virtualisation but with bare metal performance and much lower management overhead because of shared OS and binaries.

We tried to get a feeling for how far along the respondents’ organisations are in their adoption of containers, and a majority are using them to some degree. Containers are already used in production at 35% of respondents’ companies, and another 26% are experimenting with them.

So what do we make of all this? For one thing the findings are consistent with the approach we have taken with Robin’s containerisation platform. Performance-sensitive applications such as databases should demonstrate superior performance on a containers-based platform thanks to the ability to consolidate without compromising performance or predictability. Robin’s vision is to provide enterprises a high-performance and elastic platform for stateful and mission-critical applications. We think a containerised approach will prove the right one, and it would seem our survey respondents agree.

Edited for web by Cecilia Rehn.

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