Many IT professionals consider it important to build reserve capacity into hardware virtualization systems. This is part of overall planning for these distributed systems, in which resources are allocated dynamically.
The most fundamental reason to build in reserve capacity is that without it, a given system can be starved for resources. Virtual machines can lack the CPU or memory that they need to work well. Workload handling can be negatively affected. In general, this works on the same principle as any other system with any other resources. For instance, companies build reserve financial capacity into budgets in order to deal with upcoming or unanticipated capital projects. In the same way, reserve capacity in systems helps to provide for a range of situations and scenarios where more resources are needed.
One way to think about the capacity problem is by analyzing peak time for individual applications. Network engineers may spend significant amounts of time monitoring resource needs for applications in real time. Even setting aside the issue of scalability, all sorts of situations can trigger resource shortages in which users may experience high latency, lack of access to stored information and other problems.
In addition to building in reserve capacity, workload distribution is a major part of providing for these types of situations. In fact, there are arguments against excessive reserve capacity, arguments dealing with costs and inefficiency in systems. Part of the philosophy is that automated and highly evolved systems can practice workload distribution, as an alternative to having massive capacity reserves that the company must pay for.
In terms of scalability, there’s also the argument that engineers and planners should include extra capacity, or the ability to add extra capacity, early on, rather than scrambling to fix problems later, on the fly.
One of the best game plans for companies is to look at any new autonomic or automated resource allocation systems that can implement different kinds of workload handling which may eliminate the need for more reserve capacity or deflect a crisis around peak demand times.