Edge computing deployments have unique constraints that are significantly different from the ones with which typical data center deployments contend. After all, by definition, edge deployments are away from normal support services, far from the sanitized data center, and deeply enmeshed in the real work of the organization where they must deliver high value without disrupting other business activities.

So, organizations pursuing edge deployments should step back and carefully consider all the different factors that must be weighed for edge computing and then look for a solution that addresses the most crucial. Let’s take a look at four of the most crucial.

Tip 1: Keep the Footprint Small

It might seem an obvious point, but many products sold as “edge” equipment are not actually designed for that purpose—they’re simply marketed for that role or, at best, minimally adapted for edge requirements.

Because some vendors just sell standard data center equipment for edge use without accounting for the less-than-perfect environment that may be encountered there, edge installations are acquiring a reputation for being unexpectedly troublesome.

That’s why it’s vital that edge equipment should be conceived from the start for that purpose, with sufficient ruggedness built in to handle the wider range of issues that are typical at the edge, whether that edge is the shop floor, a warehouse, or somewhere in a retail establishment.

Edge components and systems need to be thought of as “universal” products that can be deployed when and where needed, with few limitations, and made appropriately secure in any given environment.

Tip 2: Autonomous and Resilient

Edge computing is no place for daintiness: It’s where real work gets done, some of it dirty, messy, hot, and noisy. So, in addition to needing only a basic physical environment and simple power and connectivity, prudent adopters make sure the edge setup (hardware and software) is designed to be failure resistant, able to recover from many problems autonomously, to protect data, and to maintain operations in almost any circumstance.

What does that look like in practice? As much as possible, physical interventions onsite need to be avoided—no buttons that say “press here” to restart something. Instead, everything should be callable and able to respond when power is made available. Furthermore, autonomy should be part of the basics, delivering no-nonsense reboots and allowing most other maintenance tasks to be initiated remotely.

Tip 3: Easily Scalable and Expandable

Edge environments are dynamic, with new applications being deployed regularly and data volumes growing exponentially, creating new demands on edge infrastructure. It’s critical that this infrastructure is designed to accommodate that growth and expand and upgrade the edge micro-data center with new resources and applications as easily as the initial edge deployment.

Failure to plan for expansion of the edge environment can lead to expensive forklift upgrades or multiple independent islands of infrastructure to manage, with all the complexity and cost associated with that kind of choice.

Tip 4: Standardized and Simple To Manage

For all but the smallest of organizations, this is perhaps the most important consideration because edge may involve multiplying sites and types of equipment on the network.

If approached haphazardly, without a plan, edge can quickly spawn hard-to-manage complexity that can strain IT staff and have company-wide implications. To keep from becoming a nightmare, edge systems should take a standardized approach requiring little or no customization and minimal skills in installation.

To that end, edge should embrace infrastructure as code (IaC), which simplifies change control.

Repeatability means that service and support is standardized so staff doesn’t need to research each installation before responding to a problem; instead, use a consistent approach and methodology. It’s a model for efficiency used across every other domain, from manufacturing to medicine, but too often ignored in edge deployments.

Similarly, management must not require specialized IT staff on site; upgrades and infrastructure scaling must be non-disruptive; the foundation must be self-healing; and IT specialists must be able to manage the entire edge fleet seamlessly at scale.

Finally, look for zero-touch provisioning. This is a device-configuration process that can be operated automatically and eliminates most of the burden on IT administrators when setting up, maintaining, or upgrading an edge system.

If you’re interested in learning more about our Edge Computing solutions can transform your IT infrastructure, start your free trial today!

Artificial intelligence (AI) is becoming an everyday piece of technology in modern homes. It is now not uncommon to find smart home devices such as Google Hubs and wearable technology such as Apple Watches in a typical home – each including a smart assistant (i.e. Siri or Alexa) that uses AI. With the introduction of self-driving cars, this type of technology will soon touch almost every aspect of our lives.

Currently, most AI technology relies heavily on the cloud, using a collection of data that is stored there. Unfortunately, this reliance on the cloud can lead to latency issues as the process of data traveling between data centers and the device can be affected by several factors. Today, this problem is merely annoying when it comes to smart home devices and wearable technology, but it could prove to be dangerous when self-driving cars start to become prevalent.

How HCI and Edge Computing is Powering the Future of Artificial Intelligence

To solve this issue, the logical step is for AI to adopt a combination of hyperconverged infrastructure (HCI) and edge computing. HCI allows technology to operate within a smaller hardware design while edge computing is the processing of data outside of the traditional data center, typically on the edge of a network. When combined with HCI and edge computing, AI benefits from drastically reduced latency, as edge computing enables the data to reside on the edge of the device’s network, and allows for new data to be stored, accessed and later uploaded to the cloud. Meanwhile, HCI would allow for all of this to operate in something as small as an Amazon Echo Spot and soon, even a smart watch.

While the cloud has played an important role in making AI a commonly used technology, adopting the combination of HCI and edge computing will give AI the tools needed to grow to the next level, making it faster and smarter without the restrictions of latency.

Scale Computing offers an industry-leading and award winning HCI and edge computing solution. Our HC3 virtualization platform combines servers, storage, and virtualization into a single solution, and we recently launched the HE150 HC3 Edge appliance, which is small enough to be deployed almost anywhere.

Read more about Scale Computing’s HC3 virtualization platform here.

Over the past ten years, hyperconvergence transformed IT infrastructure by uniting server, storage, and hypervisors. This led to significant reduction in costs and complexity for many enterprise companies, but due to limitations in the ability to reduce the size of nodes, many small and medium-sized businesses were unable to achieve the same efficiencies. In this blog, we explain how HCI has evolved over the past ten years, and why Scale Computing built the first system that helps SMBs scale their IT infrastructure down, in order to scale their businesses up. Two Types of Hyperconverged Solutions There are two types of ways in which hyperconverged solutions handle storage management. The first one is to create a virtual machine dedicated to managing all of the storage resources on a local node. This is called a virtual storage appliance or VSA. This type of solution is generally more inefficient and requires more overhead because the virtual machine is in the data path for all I/O operations that take place on the host and betweens hosts in the cluster. The second option is to use a hypervisor operating system to handle storage operations. Rather than a controller virtual machine, this module is the key player in the data path to handle storage operations. This is how we architect our solutions at Scale Computing due to improved performance and integration with the rest of the I/O stack. Evolution of Storage Locations Most organizations start out very small, often in a single location. Managing IT infrastructure in one location, with IT on-site, is fairly easy. However, as companies grow and require new locations to manage customer service, distribution, additional franchises, etc. it traditionally required IT to build complex systems where on-premises hardware was housed at each building, but managed in the central office. Predictably, this created issues with lack of support since it would be costly to have dedicated support staff at satellite offices; IT organizations struggled to find space and cooling systems for servers; and increased costs because this approach required multiple, expensive multi-tier IT deployments. Supplying Affordable, Dependable IT Infrastructure Solutions at the Edge The collection of use cases and computing needs in satellite locations (like security and point of sale systems) are collectively called the edge, and edge computing is what powers these use functions. Often, edge computing requirements are lesser than what is needed at the primary data center, so nodes can be smaller yet they still need to be highly available, all while being cost-effective and simple to use. Historically, finding this type of a solution for small and medium-sized businesses was difficult because hyperconverged solutions were generally built for enterprise companies where cost is less of a concern. In addition, most hyperconverged solutions are incapable of making nodes smaller, so storage becomes a resource sinkhole. Scaling Down So Businesses Can Scale Up To solve these problems, we launched our HE150 and HE500 series of appliances that consumes a fraction of the processing overhead of other solutions. The Scale Computing Reliable Independent Block Engine (SCRIBE) storage engine is purpose-built to provide highly available, scalable compute and storage services at the core and at the edge with as little overhead as possible. And, the reduced complexity of our solution means that even a store manager or IT generalist can perform maintenance on the cluster, including replacing failed drives, rebooting virtual machines, and rebooting nodes. Avoiding Hardware Vendor Lock-In With Scale Computing Another advantage of our hyperconverged solution is that you are not locked into a specific vendor’s hardware as you scale. The hardware you start with is not what you have to evolve with, and that the mixing and matching avoids large “forklift” upgrades in the future. For edge computing or distributed enterprise, you may start small with Intel NUC and progress to fully racked server clusters (or vice versa). In fact, our customers often mix hardware to match use cases (for example, IntelNUC at edge sites with other brands at core datacenter), or are mixing Scale-on-new appliances with Scale Computing software on older gear, in which case the hardware doesn’t even match within a single cluster. All of this means that you can build the most reliable, flexible, and affordable infrastructure for your business. To learn more about our hyperconverged infrastructure solutions, check out this new whitepaper: Going Small to Get Big: Why SMB and the Edge Have Emerged as Pivotal Use Cases for Hyperconverged Infrastructure.

Today, we’re honored to announce that Scale Computing has been recognized with a Tech Cares 2020 Award from TrustRadius. Scale Computing was selected as one of 116 technology companies that have gone “above and beyond to support their clients and communities in 2020 and during the COVID-19 pandemic.”

To be accepted for the Tech Cares Award, each nominated organization had to be a B2B technology company that demonstrated above-and-beyond caring during the COVID-19 pandemic. Everyone was welcome to nominate an organization for this award, including those who work for or with the company. All nominations were thoroughly vetted by the TrustRadius research team.

At Scale Computing, we took immediate action when companies were forced into work-from-home / work-from-anywhere scenarios to provide a VDI and remote-work solution for their customers. Leveraging virtual desktop infrastructure (VDI) technology, Scale Computing packaged two virtual machine images customers can download for free, and put together a how-to video and trained technical support staff to assist them in deploying the remote work technology solution. The VDI Connection Broker, Gateway, and Agent provide easy, intuitive configuration and management for the Scale Computing HC3 environment, whether using remote access, only on-premise access, or a combination of both. No additional devices or services are needed to get organizations up and running quickly.

“We are proud we could create a secure, innovative and easy solution to support our customers when they needed us,” said Jeff Ready, CEO and co-founder, Scale Computing. “This short-term remote solution prepares customers for future IT transformation with a true VDI deployment. Scale Computing HC3 virtualization and VDI solutions provide a simple solution with rapid deployment, ease of management and high availability.”

To learn more about how we’re serving our customers during this difficult time, check out our customer reviews page.