With a clear understanding of the existing resources, operational processes, traffic patterns, and how gaps and growths in each area will align with business goals, the project owner can start making decisions on geographical location of new resources, hosting, cloud vendors, and so on.
Factors Influencing the Scaling Process
As with anything related to IT infrastructure and architecture, a hybrid approach to scaling is probably best. It involves several key factors.
At every phase of scaling, insights and opinions of a diverse team of senior IT personnel from different roles (including system admins, data architects, and DevOps engineers) matters. They need to be fully aware of resource constraints, conduct a clear and transparent assessment of the current infrastructure, devise a roadmap for migration and upgrades, and be able to solve different architecture and implementation challenges as soon as they crop up.
A key decision is whether the scaling will be accomplished manually or if it will be automated. Barring certain advanced and complex vertical scaling scenarios where skilled human input is needed for configuration changes, manual scaling is prone to estimation errors and inefficiencies.
Automatic scaling (or autoscaling), on the other hand, enables dynamic addition or removal of CPU, memory, and storage resources whenever utilization rates go above or below pre-defined thresholds. The key advantages here are availability and efficient resource utilization. Based on user demands and traffic patterns, admins can schedule these scaling and provisioning tasks.
Emerging technology is always a key enabler in the efficient scaling. Infrastructure as Code (IaC) is one that facilitates the smooth provisioning of cloud resources, as well as migration of legacy systems to the cloud. It handles configuration and provisioning in the same way as application code, with templates for reproduction, automated scripts for configuration, and storage and access of config files with version control.
More than any other computing workload of the moment, those involving big data need high performance infrastructure the most. Bottlenecks in compute and storage can quickly cripple workloads that access and transfer large-scale datasets. Applications built for big data workloads need scalable but cost-effective storage solutions combined with superfast I/O, memory and processing power scaling capabilities. Companies that work with big data (who doesn’t?) therefore need a versatile cloud or on-premises scaling option that fluidly meet these storage performance needs.
HCI: Up, Up and Out
With the kind of cloud and data center resources available today, it’s not enough for any architecture to just scale up or out – it should be able to do both. Simultaneously. On demand.
This is where hyperconvergence is pushing the frontiers.
In the pre-hyperconvergence era, when vertical scaling was the norm, one or more physical servers or storage devices were added to the data center whenever an application needed to be scaled up. This 1:1 application-to-server or application-to-storage model was highly inefficient because either of the latter remained largely idle, owing to partial utilization or limited by memory or processing power (which was usually added later as and when budgets permitted). To compound the problem, management of the usually disparate hardware and software was painful for lack of unification and centralization.
Server virtualization technology brought considerable relief to this inefficiency – provisioning left room to add more VMs without the need to purchase physical hardware. Then, hyperconvergence arrived and took virtualization to the next level, enabling the clustering of all compute and storage resources into a single, shared resource pool. In fact, improved scalability is one of the top three benefits realized by organizations that have adopted a hyperconverged infrastructure (HCI).