Hybrid Cloud Solutions: AWS Outposts vs. Azure Stack vs. HCI

Powerful tech firms like Google, Amazon and Microsoft have the benefit of resources to push their own tech forward. However, it is still a competitive market out there even for them. They are competing with each other and now hyper-converged infrastructure platforms are also competing for similar clients.

The Perfect Collision of Public and On-Prem

Sometimes businesses must consider the best of both public and on-prem. On-site or on-prem can tackle specific scenarios of storage, regulatory constraints, latency, and architectural compatibility. There are other advantages, but it is obvious that there is a market for hybrid cloud infrastructure. Big players such as Amazon and Microsoft are responding to this demand and delivering their own hybrid cloud solutions – AWS Outposts and Azure Stack to compete with HCI and protect their territory. If a business requires hybrid, they can get that but within the ecosystem of the public cloud platform. It allows them to extend their tools and features into on-prem and provide for efficient skills, management and troubleshooting. This proved to be another effective way for Microsoft and AWS to deliver public cloud services to enterprises, whilst also supplying a stable and predictable on-premise cloud environment but creating a vendor lock-in. HCI, on the other hand, approaches the problem by removing the dependency on a single cloud provider.

What is AWS Outposts?

AWS Outposts is Amazon's way of making their own AWS-based services available for on-premises use and was seen a few years back as a departure from their well-known method: public cloud only. Apart from running AWS on-prem, these services allow customers to extend AWS virtual private clouds into their on-prem environments. What this means is that a single virtual private cloud can rely on resources from the public cloud, as well as the data center. Through it all, you get benefits such as:
  • Best of both worlds: low-latency or geographical requirements stay on-prem, and other workloads can run in Amazon cloud. This is one of the biggest advantages and the reason a lot of companies choose hybrid cloud solutions over full-on public cloud.
  • Simplifying workloads migration: that’s right, AWS Outposts actually makes things a lot easier when it comes to workloads migration between on-premise and the cloud. Some similar efforts were made in healthcare (check out this PDF), where Outposts provided a consistent environment for running applications in the cloud and on-premises, with simplifying deployment of applications.

What is Microsoft Azure Stack?

It took a bit of time, but Microsoft Azure Stack has grown. It has now evolved into a robust package with several distinct products and services within the suite. Initially, Microsoft launched it as a simplified way of hosting Azure services on premises. Today’s edition of Azure Stack denotes a choice of services:
  • Azure Stack Edge (cloud managed appliance) – Enables companies to run managed virtual machine (VM) and container workloads on premises. This is done through Windows Server, although Azure Stack Edge offers the added advantage of managing workloads with a common tool set; no matter where they are running – in the cloud or on prem.
  • Azure Stack Hub (cloud-native integrated system) – this service within Azure Stack is used to run cloud applications and handle workloads on premises. Certain businesses wish to remain on premises due to technological and regulatory barriers. Microsoft’s elegant hybrid cloud solution handles workloads, regardless of location.
  • Azure Stack HCI (hyper-converged infrastructure) – As the very name suggests, this product is just an edition of Azure Stack running on HCI hardware.

What is Hyper-Converged Infrastructure (HCI)?

Hyper-converged infrastructure (HCI) has been evolving for years and is a convergence of compute, networking, and storage infrastructure into a single platform, deployed and managed via a single UI. HCI is an effective way of eliminating some of the disparate manual work during the deployment process. The goal is to be able to deploy a system in a more straightforward process. HCI often removes the burden of requiring specialist team members with unique hardware skills for compute, networking, or storage. Recently, HCI has been evolving towards public cloud. Vendors have embraced integrations with public cloud providers to offer flexibility of hybrid solutions. Companies now seek HCI to facilitate an effective multi-cloud strategy, thereby reaping the benefits of both worlds. According to this research, these benefits enable companies to reduce their data footprint in the cloud, and decrease cloud costs. It can be valuable for storage persistence across private and hybrid clouds. In addition, HCI has a proven function as a practical platform for edge computing. For instance, companies like VMWare and Cisco use HCI to bring edge computing architecture closer to users and devices that require it.

AWS Outposts, Azure Stack and HCI – Who Wins?

Azure Stack HCI is now a solid addition to the Azure Stack package. In time, it may turn out to be a competitor against traditional HCI. The primary advantage here is that Azure Stack HCI connects VMs to a variety of Azure cloud services. These services improve the cluster with facets such as cloud-based monitoring, Site Recovery, and VM backups. Additionally, you get a central view of all Azure Stack HCI deployments within the Azure portal. Plus, you can manage the cluster with existing tools (Windows Admin Center, System Center, and PowerShell). Azure Stack HCI also denotes scalability for storage and compute. Briefly, with this product Microsoft may just grab any companies looking to handle the challenges of high-performance workloads. Meanwhile, Amazon is already allowing data centers to run on the AWS Outposts infrastructure. It is a very sturdy bridge between on-prem services and Amazon public cloud, so it’s only a matter of time before we see it evolve into something even more powerful. We have already seen VMware on AWS Cloud – the highly popular hybrid cloud service, which allows businesses to migrate legacy applications and data to the public cloud (keeping them modernized and fresh). HCI has its place and it’s bolstered when there are existing capital investments which will swing TCO calculations in its favour. However, this will depend on how you want to map the future of your business. Having any kind of hybrid cloud solution that involves on-premise can initially seem as just the right ticket. However, there are so many variables to take into consideration. Consequently, businesses can go down the road of fast judgment and wrong decision making (especially when we’re talking about cloud migration, and processes like refactoring). There are so many new and exciting things happening in the cloud market. Before diving into any hefty financial business commitment, you might also want to discover the key differences between the public and private cloud environments.

Date/Time

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

Location

600 5th ave. NY, NY

Grid Computing in the Investment World: Adapting to the New Cloud Landscape

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Traditionally, we’ve seen various companies falter without being able to streamline their business operations or properly handle their workloads. With the growth of cloud technology, some tremendously powerful solutions have surfaced. On top of that, grid computing offers an extra element of elasticity that comes with the underlying technology resources. We have already covered some of the basics in our previous blog post, Infinitely Scalable Clusters, Grid Computing 101. As we explore this topic further, we unveil the benefits of grid computing, cloud tech, and serverless tech.

How Grid Computing Yields Faster Results

Businesses, but more crucially financial services, are constantly challenged with managing ever growing data sets. Additionally, they are facing massive overheads and processes that are generally time-consuming. As a result, most of these companies are now relying on some form of HPC (high performance computing) grids for valuing portfolios, calculating risk, etc.

There are some key underlying common factors to consider when moving grid computing to the cloud:

  • Scale: anything that has flexible workloads; running small workloads or running a huge workload that scales out across a massive resource, or even not running at all, which leads us into the next factor;
  • Intermittency: either from scheduled batch operations that come into play on a once-a-night, or once-a-week basis or workloads which are triggered by certain external irregular events. You have to admire the simplicity of it – resources are spun up and distributed across the grid only when needed. This leads us into the following factor;
  • Parallelism: this means running operations concurrently, hence the overall problem is split up into smaller pieces so that it can be distributed across multiple worker nodes in parallel streams. The result is numerous nodes working together efficiently towards the answer. That may not be feasible in some workloads as it depends on the underlying logic, code, data and cost/benefit.

In practice, what all of this applies to is kind of irrelevant – so, it doesn’t matter if it’s trading, portfolio analysis, finance, or backend accounting. We’ve even seen it in use cases such as sales and marketing. Ultimately, cloud grid computing is a powerful weapon only when it improves the business and adds value. Naturally, it all falls into place when you are solving business problems, better, quicker, and at a lower cost. As it’s known from the project management triangle (link) ticking all three boxes is impossible, however this doesn’t seem to be a limitation with cloud grid computing and we’ve seen scenarios where all three have been achieved – better results, at a cheaper rate reducing the time from days to hours.

Valuable Use Cases

From our experience, even though the cloud significantly lowers the barriers to entry into grid computing, the cloud adoption process can be a challenge. We have seen some recurring themes to the choices firms face: what cloud provider to opt for, what data storage to use, what types of compute, how to transition from legacy languages to more modern, data gravity and governance?

There is no single solution for all cases, but we have to focus on the nature of the business, its data and the output it requires out of that data. Then it is a balancing act between the many variables how to achieve this. The more challenging scenarios are migrating existing legacy workloads from on-premise into a hybrid solution between on-premise and cloud. This often requires compromises around data management and compute resources.

Here are some of the approaches we use at Hentsu to solve these challenges:

  • Multi-cloud load balancing across different clouds for resource capacity/cost
  • Or multi-cloud to use specific compute or data management features for certain workloads
  • Abstracting workloads to portable patterns – Docker/Kubernetes, Terraform
  • Serverless workloads, even down to Function as a Service

Discover more about the serverless approach and refactoring legacy applications in the cloud.

More often the migrations are completely away from on-premise, skipping even the hybrid model, simply because of the added complexity dealing with on-premise equipment. There are some approaches to the hybrid model using solutions such as AWS Outposts, Azure Stack and Google Anthos, as well as solutions such as OpenStack.

Spinning Up Resources, and Empowering Users

Various organizations are utilizing different analytic tools on separate clusters, and this can cause a so-called “cluster sprawl.” Cloud computing is something of a turn-key solution in that regard, thanks to various cloud-based PaaS offerings.

“In some ways, the cluster sprawl has been enabled through the ease with which end users can spin up resources and access different cloud platforms. We’ve seen this across clients, with the democratization of technology and empowerment of teams and users. We work to mitigate this cluster sprawl through templating, reusable patterns, and tight ‘business guardrails’ around what users can do. The other key approach we take is having tight configuration management. This can mean anything from basic having awareness of where things are running, to moving the entire platform into code in Git, and we extensively use Terraform to keep on top of that. That helps keep in check of what the infrastructure is and how it is spun up, deployed and used across different cloud providers and teams. At the end of the day, as tech we need balance empower of the users with also getting the right business value as quickly and efficiently as possible” Hentsu CEO, Marko Djukic.

There is a fine line between too much locking down and too much freedom. The cloud tech is always innovating and evolving, which is one of the key attractions of cloud platforms. What used to be great a year ago, is suddenly replaced with something better and evolved, and tech needs to enable getting that into the end user’s as quickly as possible.

Three Key Recommendations for Cloud Grid Computing

So, let's examine certain essentials when it comes to cloud grid computing:

  1. Understanding workloads and the target cloud architectures, thus avoiding the many pitfalls of misapplied cloud tech
  2. Knowing the nature and value of your business.
  3. Looking for agile solutions that bring security, scalability, and cost-effectiveness.

As cloud technology grows and evolves, our knowledge expands, and Hentsu can help with challenges you may be facing as you form your own business strategy around the public cloud space.

To learn even more about the advantages Grid Computing, check out full interviews with Marko Djukic and our other Hentsu cloud experts.

Tell us about the technology challenges that you face – we would love to hear from you: hello@hentsu.com

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 Download interview with our CEO Marko Djukić

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Date/Time

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

Location

600 5th ave. NY, NY

Infinitely Scalable Clusters: Grid Computing 101

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We are currently seeing a major change in the IT industry, and the cloud environment has turned into a safe haven. With a variety of major businesses switching to the public cloud, we have also seen a huge drive towards scaling, and automation. Generally, both the technology and financial sectors are facing a huge shift from having complex computing tasks done manually towards more streamlined and automated processes. That's where grid computing comes in.

When going for cloud adoption, “businesses are pushing PaaS first and that has a lot of positive effects. To begin with, things are made easier right off the bat, because essentially all the building blocks are there, so any initial business can run workloads and get going within a day or two, rather than wasting too much time to set up the foundation,” stated Hentsu CEO, Marko Djukic.

An increasing amount of businesses are implementing grid computing clusters to handle massive workloads.

What is Grid Computing?

Grid Computing refers to making use of the shared power of a cluster of computers to process computationally intensive tasks that would otherwise bog down a single workstation. To put it simply, jobs can be submitted from one computer to the grid, which then processes the data and returns the output to the user. Importantly, multiple people can simultaneously make use of the same grid by intelligently managing how the computers in the grid allocate resources. This allows for significantly improved workflow for companies whose work is optimized for grid computing.

Opening with a much needed refresher on public and private cloud, as well as defining key terminology, the talk flowed into an investigation of the challenges companies face when working with different data sets and a look at solutions currently available to companies.

How Grid Computing Works - Grid Architecture Explained

To recap, grid computing denotes one main computer that distributes information and tasks across multiple networked computers – all of that usually towards one objective. Let us simplify a bit and illustrate how things operate. Grid computing network frequently has three types of machines:

  • Central Control Node - Server/computer or a group of servers working to controls the entire network and preserving the account of the resources within the network pool. Again this part is used for controlling and not for processing.
  • Provider - A computer working to give resources within the network.
  • User/Client – In basics terms, clients or users can actually utilize the computer on the network regardless of geographical location.

Key Advantages of Grid Computing

One of the key aspects of grid computing is flexibility, and more importantly, computing power. In other words, it boils down to having a single computer grid for large amounts of data rather than placing the demand on a single supercomputer. You can also find out how Grid Computing yields faster results for your business. 

But for now, let's focus on the most frequently asked questions related to grid computing.

Why grid is computing important?
What are the real benefits and biggest advantages of grid computing?

Well, the reasons why so many businesses rely on this particular method of completing joint tasks because it denotes following key advantages:

  • Improved use of existing hardware.
  • General performance increase and quicker handling of complex problems.
  • Easier to collaborate with other organizations.
  • Massive servers for applications can be divided into smaller commodity type servers.
  • With grid environments lead to reduced chances of failure - if one desktop/server fails, other resources pick up the workload.
  • Jobs can be executed in parallel speeding performance.

The Agility of Ephemeral Computing

It has to be highlighted that ephemeral computing has also been a huge part of the innovation process in modern-day tech. It carries tremendous advantages, albeit we have to ask the simple and most obvious question here: what does that mean for businesses and the SaaS industry in particular?

When you describe a process as “ephemeral” it denotes something temporary and brief. Essentially, the notion of dealing with a surplus of servers or indeed a shortage of servers is something that’s not an issue with ephemeral computing. To put it into perspective, ephemeral computing services are agile and will adjust according to the problems and needs at hand.

The Power of Code and Automation

Utilizing ephemeral clusters that scale up and down as needed is quite a boost to handling workloads in general. In short, it means you limit or eliminate convoluted pre-planning and relying on heavy server power.

“It’s basically all about serverless. Code that is distributed across ephemeral compute that handles the analysis and then churns out the answers without having to deal with what’s actually the underlying compute,” says Marko Djukic.

He added: “Compute is just a utility you consume as needed, nothing exists permanently."

To summarize, in PaaS and SaaS scenarios, giving your business operations and workloads the power to shrink automatically, can completely remove any scalability issues you may be experiencing with traditional server-heavy computing methodology.

Traditional tools and PaaS Offerings

There is a breadth of choices for grid computing and how to migrate workloads into cloud environments. We covered some of those, looking at traditional MATLAB setups to more extreme Platform as a Service (PaaS) environments from Google using their Bigquery and Datalab, and ran some live demos ripping though 2TB of full depth market data.

Key points to take away:

  • Horses for courses - What spec’d machine does your task work best in? Many cores in fewer machines, or many smaller cores on multiple machines? Consider how best to configure your cluster.
  • Reduced upfront costs - Unlike traditional Grid Computing clusters that require upfront purchasing of all the machines needed before they can actually be used, cloud solutions let you skip out on paying for hardware.
  • Flexibility - Publicly available solutions can allow you to quickly scale the size and power of your cluster to ensure that you can crunch the data in the time you need.
  • Reducing worker downtime - Having employees sitting around waiting for their code to execute is wasting time and money. By pushing tasks off individual computers and onto the cloud, the bottleneck is alleviated and workers can continue with work.
  • Full Platform as a Service (PaaS) - Allows some very dynamic and fast access to compute across vast data sets, but will usually require significant re-tooling for major hedge fund production environments. When implementing PaaS type grid computing this also involves a more holistic approach across people, processes and technology.
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How Can Hentsu Help?

We've built up a wealth of in-house expertise running grid computing workloads across all three major public clouds - Amazon AWS, Microsoft Azure and Google Compute Engine. We can get you up and running quickly with pre-tested designs and architectures, greatly eroding the overall traditional pains and TCO for running grid computing.

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Get in touch: hello@hentsu.com, we'd love to hear about your grid computing challenges.

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 Download interview with our CEO Marko Djukić

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Date/Time

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

Location

600 5th ave. NY, NY