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Updated by Charles Bystock on 06/14/2022

A distributed cloud-connected architecture allocates IT assets across multiple redundant systems encompassing on-premise, cloud, and everything in-between. There are many benefits to not putting all your IT eggs in one basket, but there also are many challenges in using a distributed cloud infrastructure. What are these challenges and how can they be overcome?

Understanding the edge: the distributed cloud architecture

Distributed cloud models are an attractive alternative to traditional on-premise data centers. Over the past decade, leveraging a public cloud computing provider meant your data would likely be housed in a massive centralized data center, featuring thousands of servers and a full IT team to monitor them. While it offered economies of scale, it also had drawbacks — latency had to be managed along with the potential for zone service dropouts.

Today, billions of connected devices are collecting data on a micro scale. If all these processes happen in a cloud data center, the Internet would quickly become overwhelmed, no matter how scalable the network. Edge computing pushes some of that processing horsepower closer to the Internet of Things (IoT) devices to cut down on network congestion.

Edge computing features smaller, more agile interconnected data centers spread across geographies. It’s an emerging type of distributed infrastructure that seeks to move workloads closer to the end-users request. Some of the benefits of edge distributed models include:

  • Cures the latency issue found in traditional server farms by reducing the physical distance between the end-user and the cloud infrastructure location.
  • Reduces the increasingly heavy loads riding on Internet networks.
  • Lowers data management costs.
  • Provides redundancy; if one asset fails, the remaining distributed attributes continue to function.
  • Improves data security and end-user or customer privacy.

Simply put, edge computing reduces the number of miles the data needs to travel to reach you, the end-user, by locating the server infrastructure closer than traditional cloud infrastructures. Although the benefits of this architecture are easy to picture, there are also some drawbacks.


Challenges of distributed infrastructure models

Edge makes sense for our IoT-driven applications, but there are a few challenges, including:

  • Bandwidth:  Traditionally, bandwidth is a focus for data centers; however, this focus will shift as more computing is added to edge networks.
  • Heterogeneity: Heterogeneous computing improves efficiency by adding dissimilar components to handle tasks.
  • Transparency: Transparency conceals the separated components in a distributed network to allow the disparate pieces to work in sync.
  • Concurrency: This allows several clients to access shared resources.
  • Security: Security is simply easier when resources are consolidated in a data center.
  • Backup: Backup of dispersed data requires new data protection strategies.

Managing to the edge of a network will require CIOs to come up with new models to handle the challenge. How can organizations better manage the complexities of edge computing?


Managing the complexities
InfoWorld says the key to managing the complexity of distributed IT architectures is to remove the complexity. Although this may feel like an oxymoron, in fact, it likely makes sense as the IT operational workload increases and budgets decline. The old command and control structures won’t work in these sprawling and sometimes risk-laden infrastructures. How can CIOs manage the growing complexities of cost, security, and compliance? InfoWorld suggests:

Developing a self-serve architecture to reduce the IT operational burden.

  • Enabling developers with new tools for faster application builds and deployment.
  • Adding transparency around labor and infrastructure costs.
  • Improving governance of dispersed models.

Network World agrees that simplification of edge computing models is critical. They suggest handling data collection, storage, and analysis at the edge instead of sending information to the cloud or to an enterprise computing system. This could help with security, latency, and bandwidth. Network World also suggests edge models that simplify data transmission so that lower level IT teams can easily monitor these processes. They suggest, “The key to cutting complexity is implementing edge technologies that can be remotely managed while offering continuous availability.” New hybrid cloud model software can help teams simplify their distributed models.
Managing the complexity of edge distributed models requires a clearly defined IT strategy and business case for leveraging the strongest aspects of cloud, hybrid, on-premise, and edge architectures. The Windsor Group Sourcing Advisory offers our customers the expertise to help evolve approaches and maximize all available resources to improve IT functions. Talk to our team today.