Fixed-priority Real-time Systems Research Articles

Impact of priority assignment on schedule-based attacks in real-time embedded systems

In this paper, we investigate the impact of priority assignment on schedule-based attacks in fixed-priority real-time systems. Through the schedule-based attacks, attackers may manipulate the input and output buffers of victim tasks, cause instability, and/or steal hidden information. The success of those attacks highly depends on the proper positioning of the malicious task with respect to the victim tasks in the actual schedule. Firstly, we develop an optimal priority assignment algorithm that incorporates the user specified priority preferences that reflect task types (anterior task, posterior task, or victim task), while still meeting all the deadlines. Secondly, we propose a dynamic delaying algorithm to further reduce posterior schedule-based attacks at run-time. Finally, we derive and compare the performance of six attack-aware priority-assignment policies as well as their dynamic extensions through comprehensive simulations. Our results suggest that the well-known RMS algorithm can be easily outperformed in terms of reducing schedule-based attacks, by properly specifying the relative priorities of malicious, anterior, and posterior tasks through our optimal algorithm. In particular, the AMP assignment, which assigns the anterior, malicious, and posterior tasks execution priorities in decreasing order, is shown to offer rather robust and consistent performance even at high system utilizations. Our framework explicitly incorporates the logical execution time and attack effective window constraints to model schedule-based attacks in realistic manner.

Response time analysis for fixed priority real-time systems with energy-harvesting

This paper introduces sufficient schedulability tests for fixed-priority pre-emptive scheduling of a real-time system under energy constraints. In this problem, energy is harvested from the ambient environment and used to replenish a storage unit or battery. The set of real-time tasks is decomposed into two different types of task depending on whether their rate of energy consumption is (i) more than or (ii) no more than the storage unit replenishment rate. We show that for this task model, where execution may only take place when there is sufficient energy available, the worst-case scenario does not necessarily correspond to the synchronous release of all tasks. We derive sufficient schedulability tests based on the computation of worst-case response time upper and lower bounds. We show that these tests are sustainable with respect to decreases in the energy consumption of tasks, and increases in the storage unit replenishment rate. Further, we show that Deadline Monotonic priority assignment is optimal with respect to the derived tests. We examine both the effectiveness and the tightness of the bounds, via an empirical investigation.

Slack computation for DVS algorithms in fixed-priority real-time systems using fluid slack analysis

This work presents a scheduling algorithm to reduce the energy of hard real-time tasks with fixed priorities assigned in a rate-monotonic policy. Sets of independent tasks running periodically on a processor with dynamic voltage scaling (DVS) are considered as well. The proposed online approach can cooperate with many slack-time analysis methods based on low-power work demand analysis (lpWDA) without increasing the computational complexity of DVS algorithms. The proposed approach introduces a novel technique called low-power fluid slack analysis (lpFSA) that extends the analysis interval produced by its cooperative methods and computes the available slack in the extended interval. The lpFSA regards the additional slack as fluid and computes its length, such that it can be moved to the current job. Therefore, the proposed approach provides the cooperative methods with additional slack. Experimental results show that the proposed approach combined with lpWDA-based algorithms achieves more energy reductions than do the initial algorithms alone.

Energy efficient DVS schedule for fixed-priority real-time systems

Energy consumption has become an increasingly important consideration in designing many real-time embedded systems. Variable voltage processors, if used properly, can dramatically reduce such system energy consumption. In this paper, we present a technique to determine voltage settings for a variable voltage processor that utilizes a fixed-priority assignment to schedule jobs. By exploiting more efficiently the processor slack time, our approach can be more effective in reducing the execution speed for real-time tasks when necessary. Our approach also produces the minimum constant voltage needed to feasibly schedule the entire job set. With both randomly generated and practical examples, our heuristic approach can achieve the dynamic energy reduction very close to the theoretically optimal one (within 2%) with much less computation cost.

Sensitivity analysis for fixed-priority real-time systems

At early stages in the design of real-time embedded applications, the timing attributes of the computational activities are often incompletely specified or subject to changes. Later in the development cycle, schedulability analysis can be used to check the feasibility of the task set. However, the knowledge of the worst-case response times of tasks is often not sufficient to precisely determine the actions that would correct a non-schedulable design. In these situations, sensitivity analysis provides useful information for changing the implementation, by giving a measure of those computation times that must be reduced to achieve feasibility, or those that can be increased in case of a product extension, or providing the range of feasible periods for selecting the proper task activation rates. In this work, we exploit the concept of feasibility region to propose a faster and more concise solution to the sensitivity analysis problem with respect to existing techniques based on binary search. Furthermore, we show how the formalization of other problems in the feasibility domain, such as managing overloads through elastic scheduling, can be extended to the exact analysis.

Transition-overhead-aware voltage scheduling for fixed-priority real-time systems

Time transition overhead is a critical problem for hard real-time systems that employ dynamic voltage scaling (DVS) for power and energy management. While it is a common practice of much previous work to ignore transition overhead, these algorithms cannot guarantee deadlines and/or are less effective in saving energy when transition overhead is significant and not appropriately dealt with. In this article we introduce two techniques, one offline and one online, to correctly account for transition overhead in preemptive fixed-priority real-time systems. We present several DVS scheduling algorithms that implement these methods that can guarantee task deadlines under arbitrarily large transition time overheads and reduce energy consumption by as much as 40% when compared to previous methods.

Procrastination scheduling in fixed priority real-time systems

Procrastination scheduling has gained importance for energy efficiency due to the rapid increase in the leakage power consumption. Under procrastination scheduling, task executions are delayed to extend processor shutdown intervals, thereby reducing the idle energy consumption. We propose algorithms to compute the maximum procrastination intervals for tasks scheduled by either the fixed priority or the dual priority scheduling policy. We show that dual priority scheduling always guarantees longer shutdown intervals than fixed priority scheduling. We further combine procrastination scheduling with dynamic voltage scaling to minimize the total static and dynamic energy consumption of the system. Our simulation experiments show that the proposed algorithms can extend the sleep intervals up to 5 times while meeting the timing requirements. The results show up to 18% energy gains over dynamic voltage scaling.

Voltage-Clock Scaling for Low Energy Consumption in Fixed-Priority Real-Time Systems

Power and energy constraints are becoming increasingly prevalent in real-time embedded systems. Voltage-scaling is a promising technique to reduce energy and power consumption: clock speed tends to decrease linearly with supply voltage while power consumption goes down quadratically. We therefore have a tradeoff between the energy consumption of a task and the speed with which it can be completed. The timing constraints associated with real-time tasks can be used to resolve this tradeoff. In this paper, we present two algorithms for voltage-scaling. Assuming that a processor can operate in one of two modes: high voltage and low voltage, we show how to schedule the voltage settings so that deadlines are met while reducing the total energy consumed. We show that significant reductions can be made in energy consumption.

Components for the implementation of fixed priority real-time systems in ADA

In recent years fixed-priority scheduling has received a lot of attention. Indeed, it could be said that it has become a mature approach to engineering real-time systems. The Ada language includes the necessary mechanisms for implementing this kind of systems.This paper reports the work done in order to implement in Ada a set of basic components for building fixed-priority real-time systems. These components provide support for periodic and sporadic tasks, fault detection and recovery, intertask communication and mode change. With respect to mode change, an improved implementation approach has been developed, based on a simple protocol.

Scheduling soft aperiodic tasks in adaptable fixed-priority systems

This paper presents a new type of scheduling algorithm for servicing soft deadline aperiodic tasks in adaptable fixed-priority real-time systems. The major goals of our proposed task scheduling are not only to guarantee all the deadlines of periodic tasks and to obtain fast response time for aperiodic tasks, but also to retain considerable scheduling predictability. To achieve these goals, we have adopted a new aperiodic task scheduling principle in which a normal fixed-priority assignment strategy and the information on a preassignment table built off-line are properly mingled somewhat dynamically according to the aperiodic tasks' arrivals at runtime. The paper also shows some simulation results in terms of the average aperiodic response time, verifying that the new algorithm offers significant performance improvements over other conventional joint scheduling algorithms, especially under a heavy transient overload.