A Multi-Budget-Based Approach to Enhance the Responsiveness of Aperiodic Task for a Bandwidth-Preserving Server in Real-Time Systems

Real-time applications may be comprised of both soft and hard tasks, the former being related to streaming-based or other non-critical services. For these applications one is interested in minimizing the response time of soft tasks, for which quality of service is at stake, while not jeopardizing hard deadlines. In this paper, we propose a framework for multiprocessor real-time systems, that can schedule hard and soft tasks with unconstrained deadlines according to two EDF-based configurations. Our solution relies on two special servers, which are responsible for providing temporal isolation and slack reclaiming. These strategies are combined so that the average response time of soft tasks are improved. Results are evaluated via extensive simulation, which indicates that: the available processing capacity can be effectively utilized; average response time of aperiodic tasks is significantly reduced and hard deadlines are preserved while soft deadline miss ratio is kept low.

본 논문에서는 주기 및 비주기 태스크의 효율적인 관리를 제공하는 실시간 센서 노드 플랫폼을 설계하고 구현하였다. 기존 센서 노드의 소프트웨어 플랫폼은 제한된 센서 노드의 자원을 효율적으로 사용하기 위하여 메모리 및 전력 소비량의 최소화에만 초점을 두었기 때문에 태스크의 실시간성과 빠른 평균 응답시간을 보장하는 실시간 센서 노드의 소프트웨어 플랫폼에는 적합하지 않다. 이에 본 논문에서는 센서 노드의 소프트웨어 플랫폼으로 많이 사용되고 있는 TinyOS 기반에서 태스크의 실시간성과 빠른 평균 응답시간을 보장할 수 있는 기법과 한계를 분석하였으며, 모든 주기 태스크가 마감시한 내에 실행이 완료되는 것을 보장하고 비주기 태스크의 응답시간을 최소화하는 실시간 센서 노드 플랫폼을 제안하였다. 본 논문에서 제안한 플랫폼은 Atmel사의 초경량 8비트 마이크로프로세서인 Atmega128L이 탑재된 센서 보드에서 구현되었다. 구현된 실시간 센서 플랫폼의 성능을 분석한 결과, 모든 주기 태스크의 마감시한 보장을 제공함과 동시에 향상된 비주기 태스크의 평균 응답시간과 낮은 시스템의 평균 처리기 이용률을 확인할 수 있었다. In this paper, we propose a real-time sensor node platform that efficiently manages periodic and aperiodic tasks. Since existing sensor node platforms available in literature focus on minimizing the usage of memory and power consumptions, they are not capable of supporting the management of tasks that need their real-time execution and fast average response time. We first analyze how to structure periodic or aperiodic task decomposition in the TinyOS-based sensor node platform as regard to guaranteeing the deadlines of ail the periodic tasks and aiming to providing aperiodic tasks with average good response time. Then we present the application and efficiency of the proposed real-time sensor node platform in the sensor node equipped with a low-power 8-bit microcontroller, an IEEE802.15.4 compliant 2.4GHz RF transceiver, and several sensors. Extensive experiments show that our sensor node platform yields efficient performance in terms of three significant, objective goals: deadline miss ratio of periodic tasks, average response time of aperiodic tasks, and processor utilization of periodic and aperiodic tasks.

This paper presents multi budget bandwidth preserving server (MBBPS) to improve the quality of service in terms of better responsiveness of the aperiodic task by utilizing the concept of multi budget and multi priority of the server while ensure the feasibility of periodic task at the same time. Feasibility analysis is done at offline by assigning the priority of periodic tasks as well as server to service the aperiodic tasks by fixed priority rate monotonic first algorithm. Theorem has been formulated to ensure the feasibility of the periodic tasks with enhanced budget. Besides providing better responsiveness to the aperiodic tasks, this work also improves the quality of service (QoS) by accepting aperiodic tasks that were rejected by the existing deferrable server. The complexity of proposed MBBPS algorithm is same as deferrable server. The extensive examples and simulation results illustrate that our approach can effectively reduce the average response time of aperiodic tasks as well as reduce the rejection ratio while guaranteeing the periodic tasks at the same time.

In general, two different types of low-end sensor node platforms are currently considered: event driven and multi-tasking operating systems. It is commonly assumed that event driven operating systems are more suited to WSN (Wireless Sensor Networks) as they use less memory and resources. Hence one of event driven operating systems, TinyOS incorporating a non-preemptive scheduling policy, is quickly deployed for WSN today. While the TinyOS can keep the memory overhead down, it has been shown that scheduling periodic and aperiodic non-preemptive tasks on the TinyOS is NP-hard. In this paper, we present a technique that makes it possible to guarantee the real-time scheduling of periodic tasks and minimize the average response time of aperiodic tasks for low-end sensor node platforms. This paper considers the following two perspectives. First, sensor node platforms available in the literature have not addressed the concept of scheduling for hybrid task sets where both types of periodic and aperiodic tasks exist. Second, each system service in sensor node platforms is decomposed to either a periodic or an aperiodic task in order to provide fully optimal real-time scheduling for given real-time requirements of WSN applications. A case study shows that the proposed technique yields efficient performance in terms of guaranteeing the completion of all the periodic tasks within their deadlines and aiming to provide aperiodic tasks with good average response time.

Multi-processor systems consist of more than one processor and are mostly used for computationally intensive applications. Real-time systems are those systems that require completing execution of tasks within a pre-defined deadline. Traditionally, multiprocessor systems are given attention in periodic models, where tasks are executed at regular intervals of time. Gradually, as maturity in a multiprocessor design had increased; their usage has become very common for real-time systems to execute both periodic and aperiodic tasks. As the priority of an aperiodic task is usually but not essentially greater than the priority of a periodic task, they must be completed within the deadline. There is a lot of research works on multiprocessor systems with scheduling of periodic tasks, but the task scheduling is relatively remained unexplored for a mixed workload of both periodic and aperiodic tasks. Moreover, higher energy consumption is another main issue in multiprocessor systems. Although it could be reduced by using the energy-aware scheduling technique, the response time of aperiodic tasks still increases. In the literature, various techniques were suggested to decrease the energy consumption of these systems. However, the study on reducing the response time of aperiodic tasks is limited. In this paper, we propose a scheduling technique that: 1) executes aperiodic tasks at full speed and migrates periodic tasks to other processors if their deadline is earlier than aperiodic tasks-reduces the response time and 2) executes aperiodic tasks with lower speed by identifying appropriate processor speed without affecting the response time-reduces energy consumption. Through simulations, we demonstrate the efficiency of the proposed algorithm and we show that our algorithm also outperforms the well-known total bandwidth server algorithm.

In order to shorten the response times of aperiodic tasks in mixed task sets in real-time systems, this paper proposes an adaptive total bandwidth server (ATBS) algorithm based on aperiodic tasks with varying executing times. This method of reducing response times of aperiodic execution by using predictive execution times instead of worst-case execution times for deadline calculations in the total bandwidth server to obtain shorter deadlines, while ensuring the integrity of periodic tasks. Then, an adaptive total bandwidth server with resource reclaiming which can obtain much shorter response times of aperiodic tasks is proposed. In the evaluation by simulation, the proposed ATBS and ATBSRR techniques shorten average response times for aperiodic tasks, by up to 15.17% and 48.68% compared with the original total bandwidth server technique respectively.

This paper presents a preemption control based bandwidth preserving server approach to improve the quality of service in terms of better responsiveness to the aperiodic task and reduce their rejection ratio while ensuring the feasibility of periodic task at the same time. Feasibility analysis is done at offline by assigning the priority of periodic tasks as well as server to service the aperiodic tasks by fixed priority rate monotonic first algorithm. The proposed preemption control approach judiciously prevents preemption of the aperiodic task as results reduces losses due to the context switching and have better utilization of budget. The extensive examples and simulation results illustrate that our approach can effectively reduce the average response time of aperiodic tasks as well as reduce the rejection ratio while guaranteeing the periodic tasks at the same time.

Most existing scheduling algorithms for hard real-time systems apply either to periodic tasks or aperiodic tasks but not to both. In practice, real-time systems require an integrated, consistent approach to scheduling that is able to simultaneously meet the timing requirements of hard deadline periodic tasks, hard deadline aperiodic (alert-class) tasks, and soft deadline aperiodic tasks. This paper introduces the Deferrable Server (DS) algorithm which will be shown to provide improved aperiodic response time performance over traditional background and polling approaches. Taking advantage of the fact that, typically, there is no benefit in early completion of the periodic tasks, the Deferrable Server (DS) algorithm assigns higher priority to the aperiodic tasks up until the point where the periodic tasks would start to miss their deadlines. Guaranteed alert-class aperiodic service and greatly reduced response times for soft deadline aperiodic tasks are important features of the DS algorithm, and both are obtained with the hard deadlines of the periodic tasks still being guaranteed. The results of a simulation study performed to evaluate the response time performance of the new algorithm against traditional background and polling approaches are presented. In all cases, the response times of aperiodic tasks are significantly reduced (often by an order of magnitude) while still maintaining guaranteed periodic task deadlines.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

In this paper, we present a novel technique that makes it possible to guarantee the real-time scheduling of periodic tasks and minimize the average response time of aperiodic tasks through a task decomposition technique for low-end WSN system platforms. This paper considers the following two perspectives. First, WSN platforms available in literature have not addressed the concept of scheduling for hybrid task sets where both types of periodic and aperiodic tasks exist. Second, each system service in WSN platforms needs to be assigned to either a periodic or an aperiodic task in order to provide fully optimal real-time scheduling for given real-time requirements of WSN applications. A case study based on experiments is conducted to illustrate the application and efficiency of the proposed technique in WSN motes equipped with a low-power 8-bit microcontroller.

OEA-scheduler is a scheduler that generates a schedule of aperiodic tasks and periodic tasks by the principle of eliminating unnecessary context switches under the rate monotonic algorithm. This new scheduler will minimize the average response time of aperiodic tasks and also guarantee the hard deadlines of an arbitrary number of periodic tasks in hard real-time environments. In previous research, the OE-scheduler was developed to schedule periodic tasks only, and later improved to use object-oriented methods. Due to the object-oriented approach, the authors have been able to reuse the OE-scheduler algorithm and extend it to work with both periodic tasks and aperiodic tasks. An object oriented design of the scheduler will be presented, along with an implementation result

Scheduling periodic and aperiodic tasks to meet their time constraints has been an important issue in the design of real-time computing systems. Usually, the task scheduling algorithms in such systems must satisfy the deadlines of periodic tasks and provide fast response times for aperiodic tasks. A simple and efficient approach to scheduling real-time tasks is the use of a periodic server in a static preemptive scheduling algorithm. Periodic tasks, including the server, are scheduled at priori to meet their deadlines according to the knowledge of their periods and computation times. The scheduling of aperiodic tasks is then managed by the periodic server during its service time. In this paper, a new scheduling algorithm is proposed. The new algorithm creates a periodic server which will have the highest priority but not necessarily the shortest period. The server is suspended to reduce the overhead if there are no aperiodic tasks waiting, and is activated immediately upon the arrival of the next aperiodic task. After activated, the server performs its duty periodically until all waiting aperiodic tasks are completed. For a set of tasks scheduled by this algorithm, the deadlines of periodic tasks are guaranteed by a deterministic feasibility check, and the mean response time of aperiodic tasks are estimated using a queueing model. Based on the analytical results, we can determine the period and service time of the server producing the minimum mean response time for aperiodic tasks. The analytical results are compared with simulation results to demonstrate the correctness of our model.

Scheduling periodic and aperiodic tasks to meet their time constraints has been an important issue in the design of real-time computing systems. Usually, the task scheduling algorithms in such systems must satisfy the deadlines of periodic tasks and provide fast response times for aperiodic tasks. A simple and efficient approach to scheduling real-time tasks is the use of a periodic server in a static preemptive scheduling algorithm. Periodic tasks, including the server, are scheduled at priori to meet their deadlines according to the knowledge of their periods and computation times. The scheduling of aperiodic tasks is then managed by the periodic server during its service time. In this paper, a new scheduling algorithm is proposed. The new algorithm creates a periodic server which will have the highest priority but not necessarily the shortest period. The server is suspended to reduce the overhead if there are no aperiodic tasks waiting, and is activated immediately upon the arrival of the next aperiodic task. After activated, the server performs its duty periodically until all waiting aperiodic tasks are completed. For a set of tasks scheduled by this algorithm, the deadlines of periodic tasks are guaranteed by a deterministic feasibility check, and the mean response time of aperiodic tasks are estimated using a queueing model. Based on the analytical results, we can determine the period and service time of the server producing the minimum mean response time for aperiodic tasks. The analytical results are compared with simulation results to demonstrate the correctness of our model.

This paper presents a technique to integrate energy-aware real-time scheduling into sensor node platforms from the perspective of the combined periodic (or time-bounded) and a periodic (or time-unbounded) scheduling policy. In the scheduling policy, the first objective is to meet the timing constraints of periodic tasks as well as to improve the response time of a periodic tasks. The second objective is to minimize the energy consumption in a battery-powered sensor node without missing the timing constraints of periodic tasks. Experiments showed that the proposed scheduling technique achieved efficient performance in terms of the minimal deadline miss ratio of periodic tasks, a fast average response time of a periodic tasks, and low power consumption.

This paper presents a new aperiodic request server in a hard real-time system handling periodic and aperiodic tasks, using EDF (earliest deadline first) in order to schedule periodic tasks. The objective of the server is to reduce response times for aperiodic tasks and guaranteeing the schedulability of all periodic tasks (considered as critical tasks). This server improves the response time offered by TBS (total bandwidth server), because idle times, aperiodic and periodic tasks are considered for deadline assignment. The optimality of the server is also proved. Finally a performance evaluation is made by comparing the server with TBS in different periodic and aperiodic load levels

In spite of numerous inter-task dynamic voltage scaling (DVS) algorithms of real-time systems with either periodic tasks or aperiodic tasks, few of them were aimed at the mixed workload of both kind of tasks. A DVS algorithm for mixed workload real-time systems should not only focus on energy saving, but also should consider low response time of aperiodic tasks. In this paper, we develop an on-line energy efficient scheduling, called Slack Stealing for DVS (SS-DVS), to reduce CPU energy consumption for mixed workload real-time systems under the earliest deadline first (EDF) scheduling policy. The SS-DVS is based on the concept of slack stealing to serve aperiodic tasks and to save energy by using the dynamic reclaiming algorithm (DRA). Unlike other existing approaches, the SS-DVS does not need to know the workload and the worst case execution time of aperiodic tasks in advance. Experimental results show that the proposed SS-DVS obtains better energy reduction (17% - 22%) while maintaining the same response time compared to existing approaches.