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Multiprocessor and multicore architectures are fast becoming the platform of choice for deploying workloads, as they have higher computing capabilities and energy efficiency than traditional architectures. In addition to time constraints, a number of real-time applications are required to operate in systems working with limited power supplies, which also imposes tight energy constraints on their execution. Therefore, it is desirable for the system to minimize its energy consumption while still achieving a satisfactory performance. Several energy-aware scheduling techniques addressing this issue have been proposed over the past few years. Unfortunately, few aspects of implementation are seldom considered in theoretical work, and only a tiny fraction of these techniques have been implemented in an actual hardware platform and evaluated by analytical methods. The work presented in this paper thus attempts to provide a prototyping and evaluation framework in which energy-aware multiprocessor scheduling algorithms can translate into full-fledged practical realizations, where their power consumption profiles can be properly measured.

Diabetic retinopathy (DR) is the primary cause of blindness in developing and developed countries. Early-stage DR detection reduces the risk of blindness in Diabetes Mellitus (DM) patients. There has been a sharp rise in the prevalence of DM in recent years, especially in low- and middle-income countries. In this context, automated artificial intelligence-based DM screening is a crucial tool to help classify the considerable amount of Retinal Fundus Images (RFI). However, retinal image quality assessment has shown to be fundamental in real-world DR screening processes to avoid out-of-distribution data, drift, and images lacking relevant anatomical information. This work analyzes the spatial domain features and image quality assessment metrics for carrying out Deep Learning (DL) classification and detecting notable features in RFI. In addition, a novel lightweight convolutional neural network is proposed specifically for binary classification at a low computational cost. The training results are comparable to state-of-the-art neural networks, which are widely used in DL applications. The implemented architecture achieves 98.6% area under the curve, and 97.66%, and 98.33% sensitivity and specificity, respectively. Moreover, the object detection model trained achieves 94.5% mean average precision. Furthermore, the proposed approach can be integrated into any automated RFI analysis system.

In this paper, we present a novel approach that aims to solve one of the main challenges in hand gesture recognition tasks in static images, to compensate for the accuracy lost when trained models are used to interpret completely unseen data. The model presented here consists of two main data-processing stages. A deep neural network (DNN) for performing handshape segmentation and classification is used in which multiple architectures and input image sizes were tested and compared to derive the best model in terms of accuracy and processing time. For the experiments presented in this work, the DNN models were trained with 24,000 images of 24 signs from the American Sign Language alphabet and fine-tuned with 5200 images of 26 generated signs. The system was real-time tested with a community of 10 persons, yielding a mean average precision and processing rate of 81.74% and 61.35 frames-per-second, respectively. As a second data-processing stage, a bidirectional long short-term memory neural network was implemented and analyzed for adding spelling correction capability to our system, which scored a training accuracy of 98.07% with a dictionary of 370 words, thus, increasing the robustness in completely unseen data, as shown in our experiments.

The sheer complexity of Web applications and inadequate approaches to analyze, design or update such projects generate incorrect environments, which are the source of potential risks. Poor applications reflect low availability, maintenance difficult and questionable efficiency in the services they provide. At present the quality assurances schemes still performed manually. A group of evaluators are based on a test matrix to run tests, and thus according to the degree of experience is that the results are interpreted by each evaluator. This process requires much time and effort, since the goal is to test the system thoroughly to identify as many potential errors in the system. This proposal focuses on the development of an assessment tool with the ability to emulate a group of virtual testers in real contexts of tests for applications on the Internet. This system generates controlled testing using virtual evaluators that emulate the activity of people atmosphere. The results have been satisfactory, currently we worked on the proposal to create a factory abstract of test cases to extend the functionality tool.

Mobility and handoff management is a key problem of the Future Internet. Current solutions provide mobility services, such as seamless mobility, adaptive mobility, and always-best-connected (ABC) mobility. The problem is these services achieve different purposes, work separately, and ignore conflicts between them; thus, they may improve one purpose and worsen others, yielding an erratic global behavior. In this article, we propose a new multipurpose mobility service that integrates multiple mobility services and supplies a fair balance between all the objectives to meet. As a proof-of-concept, we integrate the seamless, ABC, and adaptive mobility services, which have objectives in conflict. We formulate a multi-objective handoff optimization problem, which grades as NP-Hard. We develop a heuristic handoff algorithm, which provides near-optimal and balanced solutions. Finally, we evaluate the algorithm through random samples of simulated handoff scenarios, which provide hit rates over 90%.

Android is a mobile operating system developed by Google [85], based on a modified version of the Linux kernel and other open source software and designed primarily for touchscreen mobile devices such as smartphones, tablets, TV’s and other devices.

An alternative to a two level interrupt handling scheme is interrupt handling in asynchronous concurrent activities (threads, tasks or processes). The general idea of this scheme is that the operating system kernel is responsible for placing a generic low-level interrupt handler, which activates an asynchronous activity (thread, task or process) to handle the interrupt. From this scheme two variants can be identified: interrupt signals as inter-process communication events (IPC), common in systems with microkernel architectures and interrupts as kernel threads, such as those in Solaris 2.0 [46] and Real-Time Linux [4].

In this chapter, the differences between the general purpose and real-time operating systems are analyzed. Each architecture is compared in terms of their mutual interferences (described in previous chapter), temporal predictability, event response latency, overhead and overload control.

Traditional computer applications specific to time-sharing systems accept input, process it and then produce some output. These type of computer systems has been called transformational systems [26]. Unlike these, embedded and real-time systems are characterized as being reactive or event-driven because they spend a lot of time in standby waiting for events or stimuli to which to react. Upon completion of the actions for managing of these events, such systems return to standby to wait for the next event [61].

Most time-sharing systems are based on the concept of interrupts. In this chapter we describe the best examples of such operating systems which are the classic UNIX systems (Unix Sixth Edition AT & T [43], the Berkeley BSD 4.2 [54]) and the Network Operating Systems (VMS 1.0+ [33], BSD 4.2+ [54], Windows NT [65] and Linux [7]).

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.

Research in wireless sensor networks (WSNs) has experienced a significant growth in recent years. One topic of special interest is the use of WSNs in the detection of forest fires. In this paper, we propose two algorithms for forest fire detection. The proposed algorithms are based on information fusion techniques. The first algorithm uses a threshold method and nodes equipped with temperature, humidity and light sensors. The second algorithm uses the DempsterShafer theory and assumes that the nodes use temperature and humidity sensors. Evaluation results show that both methods are able to efficiently detect fires in their initial stages. c � 2012 Published by Elsevier Ltd.

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.

Starting from 2008, more than half of the world's population now lives in urban areas, and this number is expected to grow for the next decades. To the extent that the population of a city grows, new problems arise, which include scarcity of resources, pollution, and traffic congestion. One of the most important problems of big cities are road traffic injuries, which is the eighth leading cause of death globally, and the main cause of death for young people, mainly in middle and low income countries. Vulnerable road users (VRUs) are among the users at higher risks of traffic accidents. In order to cope with the problems of the growing urban communities, the concept of smart cities has emerged. A smart city is based on the use of smart computing technologies, such as Intelligent Transportation Systems and Vehicular Ad hoc Networks. In this paper, we propose a model to be used in smart cities, to detect if a VRU intends to cross a road in a risky zone, and to issue alerts to the vehicles nearby. The proposed model is cost effective, and is able to detect a VRU at risk in a short period of time. The evaluation of the proposed model shows that it performs correctly.

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.

One of the most appealing applications of wireless sensor networks (WSNs) is in human detection and tracking. The aim of these applications is to detect if a person is in an area of interest, and to keep track of his location at every instant of time. In recent years, we have seen a growing interest in the development of proposals for the use of WSNs in detection and tracking applications for healthcare. In the particular case of a patient suffering from dementia, it is very important to detect him and keep track of his location at every time, to avoid that the patient may enter to a zone of risk without supervision. When an event of interest is detected, such as wandering, an action may be taken by sending out a notification to the caregiver personnel. In this chapter, we review the most important proposals regarding the use of WSNs for human detection and tracking in healthcare applications. Moreover, we introduce a model for detection of patients suffering from dementia, based on a WSN that uses binary sensors. The proposed model is able to detect if a patient leaves a secure zone without supervision, and to emit alerts directed to caregivers.

In diverse industrial and academic environments, the quality of the software has been evaluated using different analytic studies. The contribution of the present work is focused on the development of a methodology in order to improve the evaluation and analysis of the reliability of web-based software applications. The Personal Software Process (PSP) was introduced in our methodology for improving the quality of the process and the product. The Evaluation + Improvement (Ei) process is performed in our methodology to evaluate and improve the quality of the software system. We tested our methodology in a web-based software system and used statistical modeling theory for the analysis and evaluation of the reliability. The behavior of the system under ideal conditions was evaluated and compared against the operation of the system executing under real conditions. The results obtained demonstrated the effectiveness and applicability of our methodology., 17 pages, 7 figures,IEEE Fourth International Conference on Quality Software QSIC 04

Real-time systems have relied on multiprocessor architectures since some time ago. However, the proliferation of multi-core architectures, which have multiple processing units on a single chip, has increased the interest on such systems. As a consequence, scheduling techniques for multiprocessor architectures have received considerable attention in recent years. In the last decade, a lot of scheduling algorithms have been proposed. In many cases, the least upper bound for these algorithms is about 50% of the whole system utilization, which is very pessimistic. One alternative to verify the schedulability of a real-time task set with a larger system utilization on a multiprocessor system, is through the use of exhaustive simulation. In this paper, we introduce RealtssMP, a tool to perform scheduling analysis and simulation of multiprocessor real-time scheduling algorithms. The proposed tool has shown to be helpful in the evaluation of the performance of existing and new scheduling algorithms. Moreover, it can be be used as an educational tool.

Scheduling is a main issue of real-time systems because it involves meeting the deadlines. In this paper, we address the problem of scheduling a set of periodic tasks on m processors under EDF (Earliest Deadline First) using a partitioned scheme. The allocation problem is transformed into a binary integer linear program. Then, it is solved by applying Geoffrion's version of Balas' additive method, optimized for the real-time scheduling problem. In order to assess the feasibility of the approach for a small size practical problem, some experimental results are shown.

Vertical partitioning is a well known technique to improve query response time in relational databases. This consists in dividing a table into a set of fragments of attributes according to the queries run against the table. In dynamic systems the queries tend to change with time, so it is needed a dynamic vertical partitioning technique which adapts the fragments according to the changes in query patterns in order to avoid long query response time. In this paper, we propose an active system for dynamic vertical partitioning of relational databases, called DYVEP (DYnamic VErtical Partitioning). DYVEP uses active rules to vertically fragment and refragment a database without intervention of a database administrator (DBA), maintaining an acceptable query response time even when the query patterns in the database suffer changes. Experiments with the TPC-H benchmark demonstrate efficient query response time.

Border points are those instances located at the outer margin of dense clusters of samples. The detection is important in many areas such as data mining, image processing, robotics, geographic information systems and pattern recognition. In this paper we propose a novel method to detect border samples. The proposed method makes use of a discretization and works on partitions of the set of points. Then the border samples are detected by applying an algorithm similar to the presented in reference [8] on the sides of convex hulls. We apply the novel algorithm on classification task of data mining; experimental results show the effectiveness of our method.

In this paper we analyze the traditional model of interrupt management and its incapacity to incorporate reliability and temporal predictability demanded on realtime systems. As a result of this analysis, we propose a model that integrates interrupts and tasks handling. We make a schedulability analysis to evaluate and distinguish the circumstances under which this integrated model improves the traditional model. The design of a flexible and portable kernel interrupt subsystem for this integrated model is presented. In addition, we present the rationale for the implementation of our design over conventional PC hardware and the analysis of its overhead. Finally, experimental results are conducted to show the deterministic behavior of our integrated model.

In this paper, we analyze the difficulties of handling abnormal events. We introduce a framework that integrate the concepts of Design by Contract, exception safety and fault tolerance and from then, obtain a set of criterions for the design of a mechanism that integrate error code, exception and executable assertions for the handling of different types of abnormal events. From these criterions, a proposal for a novel exception mechanism adequate for C written embedded systems is presented. Finally, we analyze the advantages of our mechanism over the existing mechanisms and over other languages or previous extension to C.

In this paper we analyze the traditional model of interrupt management and its incapacity to incorporate reliability and the temporal predictability demanded on real-time systems. As a result of this analysis, we propose a model that integrates interrupts and tasks handling. We make a schedulability analysis to evaluate and distinguish the circumstances under which this integrated model improves the traditional model. The design of a flexible and portable kernel interrupt subsystem for this integrated model is presented. In addition, we present the rationale for the implementation of our design over conventional PC interrupt hardware and the analysis of its overhead. Finally, experimental results are conducted to demonstrate the deterministic behavior of our integrated model and to quantify its overhead.

In this paper, we propose an energy-based feedback control scheduling framework for power-aware soft real-time tasks executing in dynamic environments, where real-time parameters are not known a priori. We propose the use of a controller based on an energy savings ratio, which allows higher energy savings when higher missed deadlines are allowed to occur. The scheduler uses the energy feedback to calculate the amount of workload to be adjusted and provides the input for a variable voltage optimization algorithm (VVA). The VVA algorithm is a greedy algorithm that adjusts the workload to optimize power consumption by computing a near optimal solution for the tasks's voltage/speed selection problem. Extensive sets of tests are executed to simulate the performance of our energy feedback scheduling power-aware architecture under overload and underload conditions. Simulation results show that the proposed architecture is capable of handling real-time tasks with unknown arrivals and execution times, and derive a system in which power savings are maximized.

In diverse industrial and academic environments, the quality of software has been evaluated (validated) using different analytic studies. It is common practice in these environments to use statistical models for the assurance, control and evaluation of the quality of a software product or process. A number of industries in the safety-critical sector are forced nowadays to use such processes by industry-specific standards (e.g., the DO-178B standard for airborne software systems). The contribution of the present work is focused on the development of a methodology for the evaluation and analysis of the reliability of Web-based software applications. We tested our methodology in a Web-based software system and used statistical modeling theory for the analysis and evaluation of the reliability. The behavior of the system under ideal conditions was evaluated and compared against the operation of the system executing under real conditions. The results obtained demonstrated the effectiveness and applicability of our methodology.

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 [12] 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

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.

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 paper introduces an integrated framework for the scheduling of non-deterministic workloads, demanded on time-critical recovery operations triggered by the detection of errors in a real time system. The framework will be developed within the context of fixed priority preemptive systems. A dynamic analysis for recovery workloads is introduced by developing a criterion for responsiveness of fault recovery operations. This is motivated by the need to verify the timing correctness of real time workloads under transient recovery workloads and provide graceful degradation to the real time workload during recovery. We hypothesize that a scheduler suited to this environment should dispatch tasks using only response time and slack as an admission control mechanism for recovery requests, as long as all deadlines can be met, and that in the presence of transient recovery overloads, the best the scheduler can do is criticality-driven load shedding. By temporarily eliminating less important tasks, the responsive algorithm developed is expected to service the more important tasks without harming less critical tasks unnecessarily. The performance of the responsive algorithm is measured quantitatively with simulations using synthetic task sets.

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.

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.

We introduce an exact schedulability analysis for the optional computation model under a specified failure hypothesis. From this analysis, we propose a solution for determining, before run-time, the degree of fault tolerance allowed in the system. This analysis will allow the system designer to verify if all the tasks in the system meet their deadlines and to decide which optional parts must be discarded if some deadlines would be missed. The identification of feasible options that satisfy some optimality criteria requires the exploration of a potentially large combinatorial space of possible optional parts to discard. Since this complexity is too high to be considered practical in dynamic systems, two heuristic algorithms are proposed for selecting which tasks must be discarded and for guiding the process of searching for feasible options. The performance of the algorithms is measured quantitatively with simulations using synthetic task sets.

Schedulability conditions are used in real-time systems to verify the fulfillment of the temporal constraints of task sets. In this paper, a performance analysis is conducted for the best-known real-time schedulability conditions that can be used in online admission control on uni-processor systems executing under the Rate-Monotonic scheduling policy. Since Liu and Layland introduced the Rate-Monotonic scheduling algorithm, many research studies have been conducted on the schedulability analysis of real-time periodic task sets. However, in most cases, the performance of the proposed schedulability conditions were compared only against the Liu and Layland test and not against the remaining schedulability tests. The goal of this paper is to provide guidelines for system designers in order to decide which schedulability condition provides better performance under different task characteristics. Extensive simulation experiments were conducted to evaluate the inexact schedulability conditions and compare their performance and computational complexity.ResumenLas condiciones de planificabilidad son utilizadas en los sistemas de tiempo real para verificar el cumplimiento de las restricciones temporales de los conjuntos de tareas. En este artículo se presenta un análisis del desempeño de las condiciones de planificabilidad mas conocidas y que pueden ser usadas como control de admisión en línea en sistemas monoprocesador que se ejecutan con la política de planificación rate-monotonic. Desde que Liu y Layland propusieron el algoritmo de planificación R-M, se han llevado a cabo muchos proyectos de investigación acerca del análisis de planificabilidad de conjuntos de tareas periódicas de tiempo real. Sin embargo, en la mayoría de los casos, el desempeño de las condiciones de planificabilidad ha sido comparado tan solo con la prueba de Liu y Layland y no consideran al resto de las condiciones de planificabilidad. El objetivo de este artículo es el de proporcionar una guía a los diseñadores de sistemas para que puedan decidir qué condición de planificabilidad presenta un mejor desempeño con diferentes características de las tareas. Se llevaron a cabo extensos experimentos de simulación para evaluar a las condiciones inexactas de planificabilidad, así como para comparar su desempeño y complejidad computacional.