Ismael Ripoll

Improvement in feasibility testing for real-time tasks

Scheduling analysis in real-time systems require an off-line feasibility (schedulability) test to guarantee the response time of critical tasks. There are fast and efficient tests for static schedulers. However, when a dynamic scheduler is required, the available tests are not as feast and efficient as static ones. In this paper, two different characterizations of feasible task sets are presented. These characterizations lead to an new feasibility algorithm. The proposed algorithm has an worst-case exponential complexity, but experimental results indicated that it runs on pseudo-polynomial time for a very large percentage of task sets. The algorithm also provides a sufficient condition for feasible asynchronous task sets. One of the main contributions of this work is the theoretical approach used to obtain the new feasibility test. The results of a large number of simulations, as well as, the theoretical demonstrations point out the improvements reached over previous tests.

TLSF: a new dynamic memory allocator for real-time systems

Dynamic storage allocation (DSA) algorithms play an important role in the modern software engineering paradigms and techniques (such as object oriented programming). Using DSA increases the flexibility and functionalities of applications. There exists a large number of references to this particular issue in the literature. However, the use of DSA has been considered a source of indeterminism in the real-time domain, due to the unconstrained response time of DSA algorithms and the fragmentation problem. Nowadays, real-time applications require more flexibility: the ability to adjust system configuration in response to workload changes and application reconfiguration. This aspect adds value to the definition and implementation of dynamic storage allocation algorithms. Considering these reasons, DSA algorithms with a bounded and acceptable timing behaviour must be developed to be used by real-time operating systems (RTOSs). In this paper a DSA algorithm called two-level segregated fit memory allocator (TLSF), developed specifically to be used by RTOS, is introduced. The TLSF algorithm provides explicit allocation and deallocation of memory blocks with a temporal cost /spl Theta/(1).

An optimal algorithm for scheduling soft aperiodic tasks in dynamic-priority preemptive systems

The paper addresses the problem of jointly scheduling tasks with both hard and soft real time constraints. We present a new analysis applicable to systems scheduled using a priority preemptive dispatcher, with priorities assigned dynamically according to the EDF policy. Further, we present a new efficient online algorithm (the acceptor algorithm) for servicing aperiodic work load. The acceptor transforms a soft aperiodic task into a hard one by assigning a deadline. Once transformed, aperiodic tasks are handled in exactly the same way as periodic tasks with hard deadlines. The proposed algorithm is shown to be optimal in terms of providing the shortest aperiodic response time among fixed and dynamic priority schedulers. It always guarantees the proper execution of periodic hard tasks. The approach is composed of two parts: an offline analysis and a run time scheduler. The offline algorithm runs in pseudopolynomial time O(mn), where n is the number of hard periodic tasks and m is the hyperperiod/min deadline.

RT control scheduling to reduce control performance degrading

In the framework of real-time digital control, two fundamental parameters are defined, the control effort and the control action interval. The first one is related to the strength of the control that, due to the intersampling open-loop control, determines the degrading of performances under unexpected delays. The second one refers to the unavoidable delays in the multitasking environment due to interactions among the tasks. As a consequence, the scheduling policy should consider not only the tasks delays but also their influence in the control loop behavior, being calculated to minimize the overall degrading of performances.

Reducing Delays in RT Control: The Control Action Interval

Abstract Industrial application of digital control requires the synergy between well designed control algorithms and carefully implemented control systems. The design of digital controllers considers different parameters when selecting the appropriate algorithms. However, the control performances can be reduced if the system is implemented with several control loops (tasks) which are not considered in the design phase. In this paper, we analyze the control theory and the effects of the multitasking control loops from the data acquisition and output actuation. The paper shows how the output actuation can vary significantly and proposes a method to reduce the jitter effects and the parameters to schedule the tasks. This reduced action interval (Control Action Interval) can be considered in the control design phase in order to properly adjust the control algorithm.

Optimal deadline assignment for periodic real-time tasks in dynamic priority systems

Real-time systems are often designed using a set of periodic tasks. Task periods are usually set by the system requirements, but deadlines and computation times can be modified in order to improve system performance. Sensitivity analysis in real-time systems has focused on changes in task computation times using fixed priority analysis. Only a few studies deal with the modification of deadlines in dynamic priority scheduling. The aim of this work is to provide a sensitivity analysis for task deadlines in the context of dynamic-priority, pre-emptive, uniprocessor scheduling. In this paper, we present a deadline minimisation method that achieves the maximum reduction. As undertaken in other studies concerning computation times, we also define and calculate the critical scaling factor for task deadlines. Our proposal is evaluated and compared with other works in terms of jitter. The deadline minimisation can be used to strongly reduce jitter of control tasks, in a real-time control application

A Task Model to Reduce Control Delays

Industrial control applications are usually developed in two phases: control design and real-time system implementation. In the control design stage a regulator is obtained and later it is translated into an algorithm in the implementation phase. Traditionally, these two phases have been developed in separate ways. Recently, some works have pointed out the necessity of the integration of the control design and its implementation. One of these works reduce the delay variance of control tasks (defined as the control action interval (CAI) and data acquisition interval (DAI) parameters) splitting every task into three parts. The CAI reduction method highly reduces the delay variance and improves the control performance. This work shows how to evaluate these delays under static and dynamic scheduling policies. A new task model is proposed in order to reduce the CAI and DAI parameters, which implies an improvement in the control performance. The new task model will be implemented in a real process, and the experimental measurements will show how, effectively, the control performance is highly improved with the methods presented in this paper.

Control loop timing analysis using truetime and jitterbug

A modern control system is typically implemented as a multitasking software application executing in a real-time operating system. If the computer load is high, the controller will experience delays and jitter, which in turn degrade the control performance. Arguing for an integrated design approach, the paper describes two computer tools for implementation-aware control analysis: TrueTime and Jitterbug. An example is given where the tools are used together to evaluate the performance of various control task implementations

Minimum Deadline Calculation for Periodic Real-Time Tasks in Dynamic Priority Systems

Real-time systems are often designed using a set of periodic tasks. Task periods are usually set by the system requirements, but deadlines and computation times can be modified in order to improve system performance. Sensitivity analysis in real-time systems has focused on changes in task computation times using fixed priority analysis. Only a few studies deal with the modification of deadlines in dynamic-priority scheduling. The aim of this work is to provide a sensitivity analysis for task deadlines in the context of dynamic-priority, preemptive, uniprocessor scheduling. In this paper, we present a deadline minimization method that computes the shortest deadline of a periodic task. As undertaken in other studies concerning computation times, we also define and calculate the critical scaling factor for task deadlines. Our proposal is evaluated and compared with other works. The deadline minimization proposed strongly reduces jitter and the response time of control tasks, which can lead to a significant improvement in system performance.

A constant-time dynamic storage allocator for real-time systems

Dynamic memory allocation has been used for decades. However, it has seldom been used in real-time systems since the worst case of spatial and temporal requirements for allocation and deallocation operations is either unbounded or bounded but with a very large bound.In this paper, a new allocator called TLSF (Two Level Segregated Fit) is presented. TLSF is designed and implemented to accommodate real-time constraints. The proposed allocator exhibits time-bounded behaviour, O(1), and maintains a very good execution time. This paper describes in detail the data structures and functions provided by TLSF. We also compare TLSF with a representative set of allocators regarding their temporal cost and fragmentation.Although the paper is mainly focused on timing analysis, a brief study and comparative analysis of fragmentation incurred by the allocators has been also included in order to provide a global view of the behaviour of the allocators.The temporal and spatial results showed that TLSF is also a fast allocator and produces a fragmentation close to that caused by the best existing allocators.