This paper explores architectural alternatives for distributed edge computing, from traditional ROBO (remote office/branch office) setups to modern hybrid and cloud-native applications. It compares the cost, performance, and operational trade-offs of enterprise-class servers, two-node standby clusters, and autonomous hyperconverged systems such as Scale Computing Platform, optimizing IT for edge computing network architecture.
Designing IT Infrastructure for Distributed Edge Locations
Your enterprise is an ever-evolving mix of application workloads that are required to support operations across dispersed locations that may number in the tens, hundreds, or thousands. If you are like most, you are responsible for maintaining legacy applications (Windows/Linux and VMs), as well as supporting current and future needs for new, modern application architectures (such as containers, cloud-native apps, IoT, and analytics). These applications need to be distributed to the edge of your business, close to where data is generated, analyzed, and used for real-time decisions and actions. Many of these applications generate enormous volumes of data and require state-of-the-art compute processing resources capable of maintaining low-latency, autonomous operation that is not dependent on remote network connectivity.
Traditional Edge Infrastructure: Challenges & Limitations
Deployment Considerations for Edge Infrastructure
Before exploring modern alternatives, it’s important to understand what drives infrastructure decisions at the edge. Edge environments present a unique combination of constraints—technical, operational, and financial—that make infrastructure design especially critical.
Key deployment considerations include:
- Capital Expenditures (CapEx): Traditional mirrored systems and over-provisioned hardware drive up upfront investment, often with underutilized resources.
- Operational Expenses (OpEx): Distributed environments multiply ongoing costs for power, cooling, maintenance, and remote support.
- Redundancy Requirements: High availability is critical, but legacy systems rely on inefficient failover approaches that limit true fault tolerance.
- Management Complexity: Limited on-site IT staff and inconsistent configurations across locations increase the risk and cost of human error.
- Scalability Constraints: Adding capacity or expanding services at remote sites often requires significant re-architecture or full system replacement.
These challenges explain why legacy infrastructure often falls short—and why a more efficient, resilient approach is needed to support modern edge workloads.
A Better Way: Hyperconverged Solutions for Distributed Edge Computing
Yes. A better way is to design the system to make normal failures normal, even expected. Design the system and applications to remain healthy and protected, even in the event of a disk failure or a whole node going down. Assume one thing (disk, node, or other component) may always be down, and when that’s not the case, things are even better.
Flexible Deployment Options: Single-Node and Two-Node Edge Configurations
Much of this paper has focused on the benefits of SC//Platform that begins with three or more nodes and the reasons why this configuration is the most popular and generally the most economical. But, it should be noted that SC//Platform can also be deployed in two-node configurations and even single-node, fully managed systems with various levels of redundancy available.
SC//Platform can offer custom two-node cluster configurations with a very low-cost tiebreaker component that actually runs the same SC//HyperCore software stack as standard nodes (and is therefore fully integrated with and managed with the rest of the cluster) - but optionally does not provide compute and/or storage resources to the system.
One case where this might make sense is to use already deployed server pairs5, but update them with SC//HyperCore centralized management functionality and autonomous orchestration capabilities.
Further, Scale Computing offers single-node edge systems with the full SC//HyperCore software stack that can be used to run applications independently at edge locations but with the ability to replicate to another cluster that could be at a remote/central data center or cloud or even be a second single node system in the same location, each replicating data to the other for redundancy.
These configurations provide centralized management, monitoring, and deployment capability of SC//Platform and may be suitable for locations and applications that are not sensitive to downtime or that are “stateless” in that they do not create/update data that needs to be protected in real time. Recovery in those cases may involve simply redeploying a new appliance and applications from templates. However, they do experience many of the same limitations with the systems described above. For example, not only would a single SC//HyperCore node not allow immediate local failover in the event of a server failure, but options like non-disruptive rolling upgrades with applications running are obviously not an option, unlike clustered SC//HyperCore configurations.
Both of these configurations offer upgrade and expansion paths to a higher level of functionality by adding additional SC//HyperCore nodes in the future, providing full three-node cluster functionality and its associated benefits.
Why Hyperconverged is the Future of Distributed Edge
Modern edge infrastructure must be more than just functional—it must be resilient, cost-effective, and scalable. Hyperconverged systems, like the SC//Platform, are designed specifically to meet these needs across diverse edge environments.
- Hyperconverged systems reduce downtime risks by eliminating single points of failure and enabling automated failover and recovery.
- Three-node clusters offer an ideal balance of performance, cost efficiency, and fault tolerance for edge deployments.
- SC//Platform delivers scalable, autonomous edge infrastructure, minimizing management overhead across thousands of locations.
- Flexible deployment models support everything from micro-branches to larger edge data centers, adapting to a wide range of IT requirements.
Contact Scale Computing for a custom TCO analysis and see how hyperconverged edge solutions can simplify operations, improve uptime, and reduce costs at scale.
1. Beyond the scope of this paper -- it’s also important to consider full recovery time requirements for applications – including the entire OS, application software, and data, as well as historical retention policies to allow rollback in the event of undesirable changes such as ransomware, failed OS, or application updates or data corruption. SC//Platform offers scheduled snapshot retention, remote snapshot replication to a centralized data center or cloud, as well as third-party backup software integrations to comprehensively address these needs across edge locations.
2. If each of two servers contains 4 x 2TB SSDs for a total of 16TB raw storage. If each server first created a local RAID array (either RAID6 or RAID5 with spare) from the disks, that would result in 4TB of usable storage. Which would be mirrored to the RAID array on the second system. So ultimately, that system can store 4TB of data, or 25% of the raw 16TB storage.
3. In any so-called “2-node cluster” or mirrored pair server system, to allow a surviving server to positively prove that a secondary server is actually down and not still running applications or modifying data, some reliable system of preventing what is known as a “split-brain” cluster operation is required. This generally takes the form of some “tiebreaker” device or devices used to verify which servers are actually up and which are actually down. If node 1 loses communication with node 2, it can contact the tiebreaker(s) to determine if the tiebreaker has visibility to node 2. Generally, each node is also required to confirm “I’m alive” at some regular interval with the tiebreaker(s), else it is assumed to be down or to disconnect itself and stop running applications. In any case, that “third vote” tiebreaker essentially becomes a critical 3rd part of the overall system. In some architectures, it actually becomes more critical than the compute nodes or servers themselves.
4. The example configurations in this paper focus on RAM and ignore application CPU core count issues for simplicity. Both can impose significant and costly sizing constraints on the machine types/classes that are suitable for each type of configuration. If these example VMs each demand 2 physical CPU cores of compute, even in failover mode, the three-node cluster above could satisfy that with economical Core i5 CPUs. Running those same 4 VMs on a single server would require a total of 8 physical cores, which is not possible using any Core series CPU or most entry-level Xeon-based systems. That CPU core requirement on a single server likely would require moving up to systems that support the higher-cost Xeon Scalable Processor Series (Silver, Bronze, Gold, Platinum CPUs.)
5. SC//HyperCore is available pre-loaded on a wide range of existing Scale Computing appliance models as well as software-only licensing for a wide range of existing servers. For large-scale deployments, Scale Computing can test and certify many existing or preferred x86-based server/system configurations as part of a large-scale enterprise licensing arrangement.