Designing large-scale distributed systems is inherently complex. Typical systems have a large number of subsystems, processes, and threads developed in isolation from each other. The design is divided among groups with differing system performance goals, different schemes for determining priorities, and different approaches to runtime optimization.
This can be further compounded by product development in different geographic locations and time zones. Once all of these disparate subsystems are integrated into a common runtime environment, all parts of the system need to provide adequate response under all operating scenarios, such as:
Given the parallel development paths, system issues invariably arise when integrating the product. Typically, once a system is running, unforeseen interactions that cause serious performance degradations are uncovered. When situations such as this arise, there are usually very few designers or architects who can diagnose and solve these problems at a system level. Solutions often take considerable modifications (frequently, by trial and error) to get it right. This extends system integration, impacting the time to market.
Problems of this nature can take a week or more to troubleshoot, and several weeks to adjust priorities across the system, retest, and refine. If these problems can't be solved effectively, product scalability is limited.
This is largely due to the fact that there's no effective way to “budget” CPU use across these groups. Thread priorities provide a way to ensure that critical tasks run, but don't provide guaranteed CPU time for important, noncritical tasks, which can be starved in normal operations. In addition, a common approach to establishing thread priorities is difficult to scale across a large development team.
Adaptive partitioning using the thread scheduler lets architects maintain a reserve of resources for emergency purposes, such as a disaster-recovery system, or a field-debugging shell, and define high-level CPU budgets per subsystem, allowing development groups to implement their own priority schemes and optimizations within a given budget. This approach lets design groups develop subsystems independently and eases the integration effort. The net effect is to improve time-to-market and facilitate product scaling.