Real-time scheduling algorithms like RMA or EDF and their corresponding schedulability test have proven to be powerful tools for developing predictable real-time systems. However, the traditional interrupt management model presents multiple inconsistencies that break the assumptions of many of the real-time scheduling tests, diminishing its utility. In this article, we analyze these inconsistencies and present a model that resolves them by integrating interrupts and tasks in a single scheduling model. We then use the RMA theory to calculate the cost of the model and analyze the circumstances under which it can provide the most value. This model was implemented in a kernel module. The portability of the design of our module is discussed in terms of its independence from both the hardware and the kernel. We also discuss the implementation issues of the model over conventional PC hardware, along with its cost and novel optimizations for reducing the overhead. Finally, we present our experimental evaluation to show evidence of its temporal determinism and overhead.

In this paper we analyze the traditional model of interrupt management and its inability to incorporate the reliability and temporal predictability demanded by real-time systems. As a result of this analysis, we propose a model that integrates interrupts and tasks handling. We introduce a novel implementation of this model that uses an adaptation of the optimistic interrupt protection technique for achieving predictability and low overhead. The detailed design of a flexible and portable kernel interrupt subsystem for this integrated optimistic model is presented. We make a schedulability analysis to evaluate the optimistic integrated model and perform experiments to verify its deterministic behavior and its overhead

We address power-aware scheduling of periodic tasks to reduce CPU energy consumption in hard real-time systems through dynamic voltage scaling. Our intertask voltage scheduling solution includes three components: 1) a static (offline) solution to compute the optimal speed, assuming worst-case workload for each arrival, 2) an online speed reduction mechanism to reclaim energy by adapting to the actual workload, and 3) an online, adaptive and speculative speed adjustment mechanism to anticipate early completions of future executions by using the average-case workload information. All these solutions still guarantee that all deadlines are met. Our simulation results show that our reclaiming algorithm alone outperforms other recently proposed intertask voltage scheduling schemes. Our speculative techniques are shown to provide additional gains, approaching the theoretical lower-bound by a margin of 10 percent.

In this paper, we propose a novel scheduling framework for a dynamic real-time environment with energy constraints. This framework dynamically adjusts the CPU voltage/frequency so that no task in the system misses its deadline and the total energy savings of the system are maximized. In this paper, we consider only realistic, discrete-level speeds.Each task in the system consumes a certain amount of energy, which depends on a speed chosen for execution. The process of selecting speeds for execution while maximizing the energy savings of the system requires the exploration of a large number of combinations, which is too time consuming to be computed online. Thus, we propose an integrated heuristic methodology, which executes an optimization procedure in a low computation time. This scheme allows the scheduler to handle power-aware real-time tasks with low cost while maximizing the use of the available resources and without jeopardizing the temporal constraints of the system. Simulation results show that our heuristic methodology is able to generate power-aware scheduling solutions with near-optimal performance.

The need for supporting dynamic real-time environments where changes in workloads occur frequently requires a scheduling framework that: (1) explicitly addresses overload conditions, (2) allows the system to achieve graceful degradation while guaranteeing the deadlines of the most critical tasks in the system, and (3) supports an efficient runtime selection mechanism capable of determining the load to be shed from the system to handle the overload. In this paper, we propose a novel scheduling framework for a real-time environment that experiences dynamic workload changes. This framework is capable of adjusting the system workload in incremental steps under overloaded conditions such that the most critical tasks in the system are always scheduled and the total value of the system is maximized. Each task has an assigned criticality value and consists of two parts, a mandatory part and an optional part. A timely answer is available after the mandatory part completes execution and its value may be improved by executing the entire optional part. The process of selecting tasks (mandatory or optional parts) to discard while maximizing the value of the system requires the exploration of a potentially large number of combinations. Since an optimal solution is too time-consuming to be computed online, an approximate algorithm is executed incrementally whenever the processor would otherwise be idle, progressively refining the quality of the solution. This scheme allows the scheduler to handle overloads with low cost while maximizing the use of the available resources and without jeopardizing the temporal constraints of the most critical tasks in the system. Simulation results show that few stages of the algorithm need to be executed for achieving a performance with near-optimal results.

In this paper, we provide an efficient solution for periodic real-time tasks with (potentially) different power consumption characteristics. We show that a task T/sub i/ can run at a constant speed S/sub i/ at every instance without hurting optimality. We sketch an O(n/sup 2/ log n) algorithm to compute the optimal S/sub i/ values. We also prove that the EDF (Earliest Deadline First) scheduling policy can be used to obtain a feasible schedule with these optimal speed values.

In this paper we address power-aware scheduling of periodic hard real-time tasks using dynamic voltage scaling. Our solution includes three parts: (a) a static (off-line) solution to compute the optimal speed, assuming worst-case workload for each arrival, (b) an on-line speed reduction mechanism to reclaim energy by adapting to the actual workload, and (c) an online, adaptive and speculative speed adjustment mechanism to anticipate early completions of future executions by using the average-case workload information. All these solutions still guarantee that all deadlines are met. Our simulation results show that the reclaiming algorithm saves a striking 50% of the energy, over the static algorithm. Further our speculative techniques allow for an additional approximately 20% savings over the reclaiming algorithm. In this study, we also establish that solving an instance of the static power-aware scheduling problem is equivalent to solving an instance of the reward-based scheduling problem [1, 4] with concave reward functions.

Reward-based scheduling refers to the problem in which there is a reward associated with the execution of a task. In our framework, each real-time task comprises a mandatory and an optional part. The mandatory part must complete before the task's deadline, while a nondecreasing reward function is associated with the execution of the optional part, which can be interrupted at any time. Imprecise computation and Increased-Reward-with-Increased-Service models fall within the scope of this-framework. In this paper, we address the reward-based scheduling problem for periodic tasks. An optimal schedule is one where mandatory-parts complete in a timely manner and the weighted average reward is maximized. For linear and concave reward functions, which are most common, we 1) show the existence of an optimal schedule where the optional service time of a task is constant at every instance and 2) show how to efficiently compute this service time. We also prove the optimality of Rate Monotonic Scheduling (with harmonic periods), Earliest Deadline First, and Least Laxity First policies for the case of uniprocessors when used with the optimal service times we computed. Moreover, we extend our result by showing that any policy which can fully utilize all the processors is also optimal for the multiprocessor periodic reward-based scheduling. To show-that our optimal solution is pushing the limits of reward-based scheduling, we further prove that, when the reward functions are convex, the problem becomes NP-Hard. Our static optimal solution, besides providing considerable reward improvements over the previous suboptimal strategies, also has a major practical benefit. Run-time overhead is eliminated and existing scheduling disciplines may be used without modification with the computed optimal service times.

Proposes a novel scheduling framework for a real-time environment that experiences dynamic changes. This framework is capable of adjusting the system workload in incremental steps under overloaded conditions such that the most critical tasks in the system are always scheduled and the total value of the system is maximized. Each task has an assigned criticality value and consists of two parts: a mandatory part and an optional part. A timely answer is available after the mandatory part completes execution and its value may be improved by executing the entire optional part. Optional parts can be discarded in overloaded conditions. The process of selecting optional parts to discard while maximizing the value of the system requires the exploration of a potentially large number of combinations. Since this process is too time-consuming to be computed online, an approximate algorithm is executed incrementally whenever the processor would otherwise be idle, progressively refining the quality of the solution. This criterion allows the scheduler to handle overloads with low cost while maximizing the use of the available resources and without jeopardizing the temporal constraints of the most critical tasks in the system. Simulation results show that few stages of the algorithm need to be executed to achieve a performance with near-optimal results.

Reward-based scheduling refers to the problem in which there is a reward associated with the execution of a task. In our framework, each real-time task comprises a mandatory and an optional part, with which a nondecreasing reward function is associated. Imprecise Computation and Increased-Reward-with-Increased-Service models fall within the scope of this framework. In this paper we address the reward-based scheduling problem for periodic tasks. For linear and concave reward functions we show: (a) the existence of an optimal schedule where the optional service time of a task is constant at every instance and (b) how to efficiently compute this service time. We also prove that RMS-h (RMS with harmonic periods), EDF and LLF policies are optimal when used with the optimal service times we computed, and that the problem becomes NP-Hard, when the reward functions are convex. Further, our solution eliminates run-time overhead, and makes possible the use of existing scheduling disciplines.