Interest in distributed network architectures is increasing day by day for several reasons. On the one hand, distributed networks allow enterprise architects to build a highly reliable foundation from which mission-critical applications can operate, even in the event of a major network or server outage.
Other reasons why distributed architectures are gaining traction include their ability to easily scale and adapt to rapidly changing application and service flows. These benefits translate into network infrastructures that can adapt to technological changes in business practices without having to constantly “drop and replace” expensive hardware.
In this comprehensive introductory guide, we cover the following:
- define a distributed network;
- provide deployment examples of common use cases;
- compare the distributed network architectural model to centralized and decentralized networks; and
- highlight the benefits and challenges of a typical distributed network deployment.
What is a Distributed Network?
A distributed network is a collection of several independently managed and collectively managed networks. In most cases, these networks are geographically separated to provide better reliability and offer multiple entry points called points of presence to provide better performance for users spread across multiple physical locations. Each network within a distributed network architecture can interact with others for service resiliency, performance gains, and automated resource sharing.
Although each network within a distributed network architecture can operate independently, management and monitoring is centralized. Thus, various network and network security policies can be created once and disseminated throughout the network. This ensures a uniform policy across the entire network infrastructure. Likewise, all monitoring and alerting functions are managed from a single NetOps management panel for true end-to-end visibility.
Distributed Networking Examples and Use Cases
The number of distributed networking use cases seems to be increasing day by day. Here are two popular examples:
- Secure Access Service Edge (SASE). Globally distributed SaaS architectures are an example of a distributed network. SASE is a SaaS use case that is gaining popularity thanks to the explosion of the remote workforce. With SASE, end users can connect to remote applications and services through one of several independent SASE gateways that provide network security services for all enterprise traffic flows. Each SASE node operates independently of all others and redirects users to other headend locations when a more preferred node is identified.
- Edge computing for the IoT. The explosion of IoT to monitor various city, campus, building or factory operations forces the need for advanced IT services. For IoT deployments that require low-latency network access for IoT data collection and analysis, a distributed network model consisting of multiple edge compute nodes is often required.
Centralized vs Decentralized vs Distributed Networks
When comparing distributed network architectures with centralized and decentralized alternatives, some differences are obvious, while others remain a bit vague. First, let’s compare the similarities and differences between distributed and centralized networks, and then move on to analyzing decentralized versus distributed networks.
Centralized vs Distributed
A centralized network architecture resembles a traditional network. With this model, endpoints connect to a single application or resource in a client-server fashion. If the central server or the network on which the server operates fails, an outage will occur. Thus, from the point of view of redundancy, centralized architectures may fail to maintain network and application services in the event of a major failure.
In teleworkers, legacy remote access VPN architectures often use a centralized network model. This is because the design requires all remote users to connect to a single VPN headend at the edge of the corporate network, creating a single point of failure.
Unlike centralized networks, a distributed architecture uses a clustered model to serve business applications. These network and server clusters are in constant communication with all the others and can share resources and redirect users to different cluster groups for more reliability and to improve application performance.
Decentralized vs Distributed
Although it is easy to conceptualize the difference between centralized and distributed networks, things become more difficult when comparing decentralized and distributed networks. In fact, many people confuse decentralized with distributed, assuming they mean the same thing. In fact, the two models have distinct differences.
A decentralized network distributes workloads and data across multiple networks and systems, much like distributed systems. However, unlike distributed networks, where each node in a cluster contains all the applications and data needed to operate, a decentralized network architecture disperses various services, functions, and data to specific locations. Thus, decentralized networks are highly dependent on each other and cannot operate independently like a distributed network can.
Additionally, decentralized networks typically do not have a centralized control plane from which all systems can be managed. Instead, these separate workloads are independently controlled.
Visualization of centralized, decentralized and distributed networks
To better conceptualize centralized, decentralized, and distributed architectures, take a look at the network diagrams below.
Note that with a centralized model, all servers connect to a single centralized network from which to operate. Servers within decentralized networks, on the other hand, are linked as needed based on the specific application/service function required and the physical location of that resource. Finally, a distributed network operates as completely independent nodes in a fully meshed design for maximum reliability and performance.
Advantages and Challenges of Distributed Networks
The use of technology to achieve business goals is at an all time high. Compared to just ten years ago, we have witnessed monumental changes in the way companies are digitally transforming their processes. This often requires radical changes to the architecture of their underlying network infrastructure.
Here are common examples of these business and technical changes:
- network and server virtualization
- cloud computing
- containers and serverless architectures
- advanced computing
- work from home policies
As such, distributed networks offer the following advantages in an era where rapid business and technological change go hand in hand:
- Application and service reliability. Since each node in a distributed network cluster can operate independently of all others, failures over large sections of the network do not cause service outages.
- Scalability. Nodes can be added or removed as needed depending on the level of redundancy and performance required.
- Rearrange traffic flows based on business changes. New applications or changes in application usage can be quickly processed because traffic rates and north-south and east-west loads are adjustable, reducing bottlenecks.
- Centralized control. Network performance configurations and network security are centrally managed. This ensures that the policy is uniform from end to end.
As with any new technology, however, the downsides must also be considered. Currently, these disadvantages include the following:
- Architectural complexity. Compared to centralized networks, distributed network architectures have more layers of software abstraction. As such, these layers add to the overall complexity from a deployment and management perspective.
- Skills gap. Internal NetOps personnel need to learn new skills and knowledge to keep a distributed network running optimally. These skills are in high demand – so it can be difficult to find and retain technical talent within an organization for the foreseeable future.
- Migration and management cost. Migrating from traditional, centralized networks – the most common architecture today – to a distributed architecture requires significant investment when designing and implementing the new infrastructure. Once the network is operational, however, this investment can be recouped if the use of network automation and machine learning is properly implemented.
Applications, services and network architectures evolve
While we can determine what an optimal network architecture looks and feels like today, understand that future progress is inevitable. It is an ongoing process driven by business goals and the applications and services required to achieve them. So, just like the changes of the past that happened to get us to where we are today from an architecture perspective, note that this is an endless cycle where performance, Network reliability and scalability continue to improve and evolve.