Cloud computing is becoming more popular as a way for companies to optimize all the usual business benchmarks— performance, availability, compliance, scalability, security, you name it. One popular approach gaining serious traction to accomplish just that is the use of distributed cloud services, which enable companies to allocate cloud resources across multiple geographic locations or data centers.
A distributed cloud model can certainly offer these benefits, but it can also introduce unintended complexity. This is what you need to know to decide whether a distributed cloud model is right for your business.
Exploring the Benefits of a Distributed Cloud Model
Under the right circumstances, a distributed cloud model offers a lot for companies seeking to maximize benefits in cloud operations. Gartner believes that by 2024, most cloud service platforms will include provisions for distributed cloud services. By integrating distributed cloud services where it’s most appropriate, companies could take advantage of the following benefits.
Imagine an application or service that has a broad geographic reach or needs to be accessed by users in multiple locations. In that case, a distributed cloud model can help improve performance and reduce latency by distributing cloud resources closer to end-users instead of relying on a central processing server. A study from Forrester on the potential of edge computing sees companies increasingly deploying software wherever it runs best, opening new opportunities for distributed cloud strategies.
In real life, distributed cloud helps companies that need to prioritize speed despite serving users from multiple geographies. One example is a company that provides video streaming services to users around the world. By distributing content across multiple data centers, the company can reduce latency and improve the speed and reliability of content delivery, enhancing the user experience and reducing the risk of buffering and other performance issues.
If the application or service experiences high traffic volume, a distributed cloud model can help improve performance by distributing resources across multiple data centers and reducing the load on any single location. This provides a significant differentiation point for companies seeking to stand out in a noisy eCommerce world.
For example, a company that provides e-commerce services may experience high traffic volume during peak shopping seasons like Christmas. In previous years, customers may experience frustrations closer to the big day due to crashes and slow speeds from a massive uptick in website traffic. By using a distributed cloud model, the company can allocate resources across multiple data centers to handle the increased traffic, ensuring that the application remains available and responsive to users.
Latency, or the delay between when a user sends a request to a cloud-based application or service and when the response is received, is one way a company can ruin a customer’s experience. If low latency and high performance are vital to the application or service, a distributed cloud model can help improve that performance by reducing the distance between users and the cloud resources they’re trying to access.
Companies that provide real-time gaming services are under a lot of pressure to deliver a seamless gaming experience no matter when the player accesses the game. This challenge requires very low latency to ensure a smooth and responsive gaming experience. A distributed cloud model moves those cloud resources closer to the end user, allowing the company to maintain gaming speed and load times whether the user is accessing the game close to company headquarters or not.
Redundancy and Failover
No one would purposefully choose network instability, but some use cases have better tolerance for unplanned downtime than others. If the application or service requires high levels of availability and resilience, a distributed cloud model can offer greater redundancy and failover capabilities.
For example, a company that provides mission-critical financial services may require high levels of availability and resilience to ensure that services remain available even in the event of a localized outage. Using a distributed cloud model, the company can allocate resources across multiple data centers and ensure that services remain available even if one or more data centers experience an outage.
Companies increasingly operate across borders, making governance and regulatory compliance a beast. If the application or service needs to comply with specific regulations or data sovereignty requirements that require data to be stored and processed in specific geographic locations, a distributed cloud model can help ensure compliance.
Healthcare companies everywhere are under strict scrutiny to ensure that patient data is stored and processed safely. A distributed cloud model moves those resources to specific geographic locations that comply with regulations and reduces network traffic of sensitive data. By using a distributed cloud model, the company balances compliance with these regulations while still providing fast and reliable access to the data for healthcare professionals.
Scalability is critical for businesses competing in an increasingly agile business landscape. If the application or service needs to scale up or down quickly and easily to accommodate changing demand, a distributed cloud model can provide greater scalability and flexibility than a centralized cloud model.
Imagine a company that provides online education services. This company might experience fluctuations in demand based on the academic calendar or the popularity of specific courses. By using a distributed cloud model, the company can quickly and easily add or remove resources as needed to accommodate these fluctuations in demand—or even anticipate them—ensuring that students have fast and reliable access to course materials and online resources. The company stands out from competitors, and students stay happy (at least in this area).
Cloud security is on everyone’s mind but requires high levels of expertise within a shared model with providers. While a distributed cloud model can provide many benefits, it can also increase the risk of security breaches and data leaks since data is stored and processed in multiple locations.
For example, a company that provides financial services may require very high levels of security to protect sensitive customer data. While a distributed cloud model can provide greater redundancy and failover capabilities to ensure that services remain available, it can also increase the risk of security breaches if data is not adequately protected and secured across all data centers.
As companies build their cloud infrastructure, considering resource allocation is a critical part of deploying a cloud model that actually delivers measurable business value. A distributed cloud model can optimize resource allocation and reduce costs by distributing resources across multiple data centers if the application or service has high resource utilization.
For example, a company that provides data-intensive analytics services may require large amounts of computing resources to process and analyze data. Using a distributed cloud model, the company can distribute computing resources across multiple data centers to optimize resource allocation and reduce costs while still providing fast and reliable access to customer data.
Discerning the most appropriate use cases for a distributed cloud model
So what does all this look like in real life? Let’s take a look at some applications where a distributed cloud model is a clear choice.
- Companies prioritizing content delivery: A distributed cloud model is ideal for delivering content, such as video, images, and large files, to end-users around the world because distributing content across multiple data centers allows companies to reduce latency and improve the speed and reliability of content delivery.
- Edge computing applications: Edge computing involves processing data closer to the source of the data, which reduces latency and improves the user experience. A distributed cloud model is well-suited for edge computing since it allows companies to deploy computing resources at the edge, closer to end-users.
- Disaster recovery preparation: A distributed cloud model can improve disaster recovery capabilities by providing greater redundancy and failover capabilities. By distributing cloud resources across multiple data centers, companies can ensure that services and data remain available even in a localized outage.
- Cloud mature organizations: Companies with a lot of experience executing operations in the cloud might begin looking at distributed cloud models as a way to further refine their cloud strategy.
While a distributed cloud model can be suitable for many use cases, there are some situations where it may not be the best fit. To help companies visualize what an ill-suited distributed cloud model might look like, are some examples of use cases where a distributed cloud model might be unsuitable:
- Small-scale applications: A distributed cloud model may be unnecessary and could add unnecessary complexity and cost, or small-scale applications with low traffic volume and limited geographic reach.
- Applications that require strong data consistency: Financial applications, for example, may not be suitable for a distributed cloud model due to the increased complexity of ensuring data consistency across multiple data centers.
- Applications with low-latency requirements: Applications such as high-frequency trading, where even a few seconds delay could have serious consequences, may not be well-suited to a distributed cloud model due to the increased latency caused by spreading resources across multiple data centers without strategic architecture plans.
- Applications with high-security requirements: A distributed cloud model can cause an increased risk of security breaches and data leaks if companies don’t have the expertise to execute a consistent security plan and close loopholes.
- Applications with low resource utilization: In some cases, applications with low resource utilization may not be well-suited to a distributed cloud model since the additional infrastructure and management resources required by a distributed model could outweigh the benefits.
Additionally, companies with a lack of in-house expertise or low cloud maturity may also miss out on potential benefits. A distributed model requires a deft hand in building and executing a secure and efficient architecture. Companies at the beginning of their cloud journey may want to utilize simpler methods for their immediate use cases.
Is Distributed Cloud Right for Your Use Case? A Quick Checklist to Help You Decide
So now we understand the benefits of distributed cloud, as well as which applications are best and worst suited for this type of infrastructure. To determine once and for all if your use case is a good option.
- Does the application or service have a broad geographic reach, or is it limited to a specific region or location?
- Is the application or service experiencing high traffic volume, or is it relatively low volume?
- Are low latency and high performance critical for the application or service (be very judicial with this question)?
- Does the application or service require high levels of redundancy and failover capabilities to ensure availability and minimize downtime?
- Does the application or service need to comply with specific regulations or data sovereignty requirements that may require data to be stored and processed in specific geographic locations?
- Will the application or service need to scale up or down quickly and easily to accommodate changing demand?
- Does the application or service require high levels of security and data protection, and can these requirements be met in a distributed cloud model?
- Does the application or service have high resource utilization that could benefit from resource optimization and allocation in a distributed cloud model?
By considering these factors, companies can determine whether a distributed cloud model is likely to improve their target use case or simply create more chaos. While there are potential drawbacks to using a distributed cloud model, careful evaluation of the specific requirements and constraints of the use case can help companies make informed decisions that optimize benefits while mitigating the risks.
Deploying a distributed cloud model enables greater flexibility and performance—if the application is the right one.
Companies need to put the time into understanding what specific requirements and constraints are in play before deciding whether a distributed cloud model is the best fit. By considering all the factors, companies can determine whether a distributed cloud model is likely to improve the performance, availability, compliance, scalability, and security of their application or service.
The future for the distributed cloud looks promising as it continues to gain traction among businesses of all sizes thanks to emerging technologies such as edge computing and 5G networks. Additionally, greater control over data sovereignty and compliance, thanks to geographic independence, can help companies comply with local regulations and data privacy laws. As cloud technology continues to improve, we can expect to see even greater adoption of distributed cloud services in the years to come.
Elizabeth Wallace is a Nashville-based freelance writer with a soft spot for data science and AI and a background in linguistics. She spent 13 years teaching language in higher ed and now helps startups and other organizations explain – clearly – what it is they do.