Cloud Talk
Cloud perspectives by cloud people at Ridge
Sabo Taylor Diab

Cloud (Didn’t) Kill The Data Center Star

Table of Contents

The evolution of computing has always been marked by swings between centralized to distributed models. First there was the mainframe, a centralized computing platform that manages and operates all computing resources to which users can access with “dumb” terminals. Then came the client/server architecture where a powerful server hosts, manages, and delivers compute resources and services to multiple clients (i.e. PCs) on a network with enough compute power to run applications and store data locally.

Distributed computing quickly moved into the mainstream, resulting in massive deployments of servers in data centers across the globe. Over time, data centers have grown into huge complexes with multiple servers consuming ever-growing amounts of compute resources, resulting in increased complexity and mounting operational costs. Cloud computing emerged as an alternative model aimed at reducing the burden of managing and maintaining on-premises data centers. By outsourcing IT infrastructure management to public cloud providers, organizations could gain access to innovative technologies and services, easily scale on-demand and reduce capital expenses using the pay-per-use model.

The cloud has forever revolutionized the way computing resources are provisioned and consumed, offering benefits that were previously unavailable. Still, it is not a panacea for all IT issues. This realization has been driving the continuous innovation and development of different cloud technologies and approaches.
Many organizations started their cloud journey with the implementation of internal, private clouds. This option is appealing for several reasons. In the early days of the cloud, when security concerns were a major barrier to adoption, private clouds offered the ability to provide services to users safely behind the corporate firewall. Other benefits of private cloud include unrestricted configuration and allocation of resources, performance and availability assurance, and more.

Having a private cloud, however, does not necessarily require giving up the benefits of a public cloud. Customers that have implemented private clouds often look to use a public cloud as well for specific use cases – for example, enabling internal data centers to use external, cloud-hosted resources to handle peak loads, or the hosting of specific, standardized functionalities while maintaining core applications and business processes that handle sensitive data within the internal infrastructure.
This flexibility is achieved through the hybrid cloud model, which combines aspects of both private and public cloud computing. Hybrid cloud enables customers to keep running critical workloads on their on-premise datacenter or private clouds to achieve improved performance, security and control, as well as to comply with regulatory and data sovereignty laws that require sensitive information to be stored locally. At the same time, other workloads can be migrated to the public cloud to take advantage of benefits such as cost, scalability and getting rid of maintenance.  

Given the benefits of a hybrid cloud, it rarely serves as merely a transition phase but rather as a sustainable model that unites the advantages of both worlds. Still, hybrid cloud deployments involve some significant technological challenges. For example, to make this model work, cloud management technologies need to bridge private and public clouds while addressing security, performance, and availability issues. On top of this, there is a need for sophisticated policy engines to automate and control the movement of workloads between private and public clouds. Furthermore, multi-cloud has now become a primary strategy for organizations that wish to avoid cloud lock-in and take advantage of best-of-breed services, making it even more difficult to achieve effective management and control.

And things are only getting more complicated…

Distributed Computing Strikes Back

The rapid adoption of edge applications and multi-cloud will lead to increased distribution of workloads and data across multiple locations and environments outside the corporate data center or main public cloud infrastructure. To tackle the sprawl of complex workloads across hybrid, heterogeneous environments and maintain operational visibility and control, new distributed cloud models offer the ability to extend public cloud experiences to the edge and on-premise data centers. They enable organizations to leverage the ease-of-use, access to advanced services, scalability and flexibility of the cloud, and take advantage of low-latency, high availability and performance of on-premise data centers while achieving compliance with local data sovereignty laws.

Distributed cloud models are offered by different types of players. The major public cloud providers are offering solutions that enable organizations to deploy a cloud infrastructure in their own on-premises data centers, while the public cloud provider is in charge of managing and operating the cloud services in use. This model tackles some of the complexities involved in hybrid environments. Essentially, it provides a hybrid cloud as-a-service that enables customers to leverage cloud services on-premises to facilitate the management of applications (e.g. latency-sensitive edge applications) that cannot be moved to the public cloud.

On the flip side, organizations that require a distributed cloud infrastructure that spans across multiple locations may be dealing with significant connectivity costs as well as cloud lock-in concerns. Alternatively, other distributed cloud models are offered by specialized players.

For example, Ridge has developed a distributed cloud platform that enables application developers to deliver modern workloads locally from globally distributed networks of data centers and local cloud providers. Ridge federates data centers across the globe into a unified network hosting its platform, which can be leveraged to support the delivery of cloud services where data is generated, in proximity to end-users and end-devices.

Distributed cloud models along these lines will continue to evolve in the coming years in response to the increased number and diversity of advanced cloud-native services that require local delivery. Organizations should therefore plan their IT architectures accordingly and make them flexible enough to support the coexistence of different computing models, including the ability to leverage distributed cloud capabilities, where and when needed.

Distributed Cloud
Edge Cloud
edge computing
Managed Kubernetes
edge data center
data center providers
cloud service providers
on-prem data centers
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