Real-time scheduling theory offers constant-time schedulability tests for periodic and sporadic tasks based on utilization bounds. Unfortunately, the periodicity or the minimal interarrival-time assumptions underlying these bounds make them inapplicable to a vast range of aperiodic workloads such as those seen by network routers, Web servers, and event-driven systems. This paper makes several important contributions toward real-time scheduling theory and schedulability analysis. We derive the first known bound for schedulability of aperiodic tasks. The bound is based on a utilization-like metric we call synthetic utilization, which allows implementing constant-time schedulability tests at admission control time. We prove that the synthetic utilization bound for deadline-monotonic scheduling of aperiodic tasks is 1/1+/spl radic/1/2. We also show that no other time-independent scheduling policy can have a higher schedulability bound. Similarly, we show that EDF has a bound of 1 and that no dynamic-priority policy has a higher bound. We assess the performance of the derived bound and conclude that it is very efficient in hit-ratio maximization.

Utilization bounds for schedulability of aperiodic tasks are new in real-time scheduling literature. All aperiodic bounds known to date apply only to independent tasks. They either assume a liquid task model (one with infinitely many infinitesimal tasks) or are limited to deadline-monotonic and earliest-deadline first scheduling. In this paper, the authors make two important contributions. First, they derive the first aperiodic utilization bound that considers a task model with resource requirements. Second, the new bound is a function of a parameter called preemptable deadline ratio that depends on the scheduling policy. We show that many scheduling policies can be classified by this parameter allowing per-policy bounds to be derived. Simulation results demonstrating the applicability of aperiodic utilization bounds are presented.

Power management in data centers has become an increasingly important concern. Large server installations are designed to handle peak load, which may be significantly larger than in off-peak conditions. The increasing cost of energy consumption and cooling incurred in farms of high-performance Web servers make low-power operation during off-peak hours desirable. This paper investigates adaptive algorithms for dynamic voltage scaling in QoS-enabled Web servers to minimize energy consumption subject to service delay constraints. We implement these algorithms inside the Linux kernel. The instrumented kernel supports multiple client classes with per-class deadlines. Energy consumption is minimized by using a feedback loop that regulates frequency and voltage levels to keep the synthetic utilization around the aperiodic schedulability bound derived in an earlier publication. Enforcing the bound ensures that deadlines are met. Our evaluation of an Apache server running on the modifier Linux kernel shows that non-trivial off-peak energy savings are possible without sacrificing timeliness.