Device drivers are integral components of operating systems. The computational workloads imposed by device drivers tend to be aperiodic and unpredictable because they are triggered in response to events that occur in the device, and may arbitrarily block or preempt other time-critical tasks. This characteristic poses significant challenges in real-time systems, where schedulability analysis is essential to guarantee system-wide timing constraints. At the same time, device driver workloads cannot be ignored. Demand-based schedulability analysis is a technique that has been successful in validating the timing constraints in both single and multiprocessor systems. In this paper we present two approaches to demand-based schedulability analysis of systems that include device drivers. First, we derive load-bound functions using empirical measurement techniques. Second, we modify the scheduling of network device driver tasks in Linux to implement an algorithm for which a load-bound function can be derived analytically. We demonstrate the practicality of our approach through detailed experiments with a network device under Linux. Our results show that, even though the network device driver does not conform to conventional periodic or sporadic task models, it can be successfully modeled using hyperbolic load-bound functions that are fitted to empirical performance measurements

There is a constant battle to break-even between continuing improvements in DRAM capacities and the demands for even more memory by modern memory-intensive high-performance applications. Such applications do not take long to hit the physical memory limit and start paging to disk, which in turn considerably slows down their performance. We tackle this problem in the Adaptive Network Memory Engine (Anemone) project by pooling together the distributed memory resources of multiple machines across a gigabit network based cluster. Anemone is a distributed memory virtualization system that can dramatically improve application performance by paging over the gigabit network to the unused memory of remote clients. Anemone provides clients with completely transparent access to a potentially unlimited amount of collective memory pool. Earlier research efforts in this area advocate significant modifications to client systems, either in terms of a specific programming interface for applications, or in terms of extensive changes to the operating systems and device drivers. In contrast, Anemone requires no modifications to either the client system or the memory-intensive application.