Scheduling soft aperiodic tasks in adaptable fixed-priority systems

In a real-time system with both hard deadline periodic tasks and soft deadline aperiodic tasks, hard deadline tasks must be guaranteed to meet their deadlines and soft deadline tasks are serviced in a best-effort manner. To schedule soft deadline aperiodic tasks in dynamic priority systems, one can assign a virtual deadline like the TBS algorithm or use capacity servers like the DPE algorithm. Both approaches have their own advantages, and we cannot say which one is better in terms of response time. In some cases, TBS provides shorter response time than DPE server. In other cases, the reverse may happen. In this paper, we propose a combined approach to improve the response time of soft deadline aperiodic tasks. The proposed method combines the deadline assignment and the capacity server approach. When there is enough capacity available for aperiodic tasks, the aperiodic tasks are served by capacity servers. Otherwise, our method assigns a deadline and schedule aperiodic task together with periodic tasks by EDF algorithm. In the worst case, the proposed method provides response time no later than TBS. Experimental results show that the proposed approach improves the responsiveness of soft deadline aperiodic tasks.

In hard real-time task systems where periodic and aperiodic tasks coexist, the object of task scheduling is to reduce the response time of the aperiodic tasks while meeting the deadline of periodic tasks. Total bandwidth server (TBS) and advanced TBS (ATBS) are used in dynamic priority systems. However, these methods are not optimal solutions because they use the worst-case execution time (WCET) or the estimation value of the actual execution time of the aperiodic tasks. This paper presents an online slack-stealing algorithm called SSML that can make significant response time reducing by modification of look-ahead earliest deadline first (laEDF) algorithm as the slack computation method. While the conventional slack-stealing method has a disadvantage that the slack amount of each frame must be calculated in advance, SSML calculates the slack when aperiodic tasks arrive. Our simulation results show that SSML outperforms the existing TBS based algorithms when the periodic task utilization is higher than 60%. Compared to ATBS with virtual release advancing (VRA), the proposed algorithm can reduce the response time up to about 75%. The performance advantage becomes much larger as the utilization increases. Moreover, it shows a small performance variation of response time for various task environments.

Real-time wireless sensor networking platform should satisfy requirements of sensor networking features - efficient exploitation of limited resources and energy efficiency, real-time characteristics and scalability. Existing studies on sensor networking platforms, such as TinyOS and DCOS, have been focused on solving the maximization problem of exploitation of limited resources and the energy efficiency. Envisioned application scenarios applied in sensor networking platforms need to perform real-time sensing activity as well as provide good response time with task communication, and support the scalability to improve productivity. The proposed method is able to support task scheduling which meets deadlines of all the periodic tasks and provides aperiodic tasks with good average response time. It also supports the scalability by dividing the functionalities in sensor node into periodic and aperiodic tasks. Experimental results show that our method improves real-time characteristics of sensor networking platforms by means of guaranteeing to complete the execution of all the periodic tasks within their deadlines and having a good response time for aperiodic tasks.

본 논문에서는 단일 처리기를 가지는 임베디드 시스템에서 실시간성과 결함 허용을 고려한 태스크 스케줄러를 설계하고 구현한다. RMS(Rate Monotonic Scheduling) 기법을 이용하여 주기적 태스크를 실행하고 태스크의 실행 마감 시간을 보장하며 잉여 시간을 관리함으로써 비주기적 태스크의 실행 및 완료 방법을 제시한다. 또한 백업 시간을 관리함으로써 일시적인 태스크의 단일 결함을 허용하기 위한 결함 허용 기법을 제공한다. 주기적 태스크와 비주기적 태스크의 응답시간을 조절할 수 있는 주기적 태스크의 중요도를 제시한다. 마지막으로 시뮬레이션을 통해 제시한 방법의 결과를 분석하고 평가한다. In this paper, we design and implement a task scheduler that considers real-time and fault tolerance in embedded system with a single processor. We propose a method how it can meet the deadlines of periodic tasks using RMS and complete the execution of aperiodic tasks by calculating surplus times from a periodic task set. And we describe a method how to recover of a transient fault task by managing backup time. We propose an important level of periodic tasks that can control the response time of periodic and aperiodic tasks. Finally, we analyse and evaluate the proposed methods by simulation.

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

Recent smart devices have adopted heterogeneous multi-core processors which have high-performance big cores and low-power small cores. Unfortunately, the conventional task scheduler for heterogeneous multi-core processors does not provide appropriate amount of CPU resources for multimedia applications (whose QoS is important to users), resulting in energy waste; it often executes multimedia applications and non-multimedia applications on the same core. In this paper, we propose an advanced task scheduler for heterogeneous multi-core processors, which provides appropriate amount of CPU resources for multimedia applications. Our proposed task scheduler isolates multimedia applications from non-multimedia applications at runtime, exploiting the fact that multimedia applications have a specific thread for video/audio playback (to play video/audio, a multimedia application should use a function that generates the specific thread). Since multimedia applications usually require a smaller amount of CPU resources than non-multimedia applications due to dedicated hardware decoders, our proposed task scheduler allocates the former to the small cores and the latter to the big cores. In our experiments on an Android-based development board, our proposed task scheduler saves system-wide (not just CPU) energy consumption by 8.9 percent, on average, compared to the conventional task scheduler, preserving QoS of multimedia applications. In addition, it improves performance of non-multimedia applications by 13.7 percent, on average, compared to the conventional task scheduler.

Integrating real-time inter-task communication channels into hardware–software codesign

We propose power-saving real-time scheduling method for mixed task sets which consist of both time-based periodic and event-based aperiodic tasks in the automotive operating system. In order to consider power saving, it is necessary to estimate the slack time and minimize the response time of the aperiodic task simultaneously. However, because these two goals conflict with each other, It must compromise between the two goals, depending on the given application domain. In this paper, we find an adjustment factor that improves the response time of the aperiodic task with a slight increase in power consumption. We propose the lccEDF algorithm and balance the two goals by systematically adjusting the factors. Our simulation results show that our approach is excellent in various task sets.

An increasing number of DRTS (Distributed Real-Time Systems) are employing an end-to-end aperiodic task model. The key challenges of such DRTS are guaranteeing utilization on multiple processors to achieve overload protection, and meeting the end-to-end deadlines of aperiodic tasks. This paper proposes an end-to-end utilization control architecture and an IC-EAT (Integration Control for End-to-End Aperiodic Tasks) algorithm, which features a distributed feedback loop that dynamically enforces the desired utilization bound on multiple processors. IC-EAT integrates admission control with feedback control, which is able to dynamically determine the QoS (Quality of Service) of incoming tasks and guarantee the end-to-end deadlines of admitted tasks. Then an LQOCM (Linear Quadratic Optimal Control Model) is presented. Finally, experiments demonstrate that, for the end-to-end DRTS whose control matrix G falls into the stable region, the IC-EAT is convergeut and stable. Moreover, it is capable of providing better QoS guarantees for end-to-end aperiodic tasks and improving the system throughput.

In scheduling algorithms based on the Rate Monotonic (RM) method which are widely used in development of real-time systems, tasks with shorter periods have higher priorities. In contrast, ones with longer periods are likely to suffer from increased response times and jitter due to their lower priorities. We propose an Execution Right Delegation (ERD) method based on RM where a high-priority server for particular (or important) task is introduced to shorten response time and jitter of the task. In the evaluation, it is confirmed that response times and jitter of a particular (important) task are reduced. We also show Response Time Analysis (RTA), which assures worst-case response time of the task. This paper shows the algorithm and RTA of ERD and evaluates it by comparing it to a Deadline Monotonic method. The evaluation by simulation shows that ERD can reduce the average worst-case response time by 13.45% at maximum compared to the Deadline Monotonic scheduling. In addition, we confirm that the RTA provides a worst-case response time close to the simulation results.

In heterogeneous fault-tolerant system, especially high performance computer system, the issue of providing system with high availability assurance for real-time applications which have availability requirements has been widespread concerned. While, few research concentrates on combining real-time application availability requirement with scheduling algorithm. In this paper, an availability approached task scheduling algorithm is proposed. On the basis of heterogeneous fault-tolerant system scheduler and scheduling algorithm designation, we can improve the system availability without increasing additional hardware costs, and shorten task average response time, in addition, schedule task with high efficiency and reliability. Experiment results show that, such availability approached task scheduling algorithm has a system performance advantage over the traditional system task scheduling algorithms, it achieves the goal of balancing availability and task response time in heterogeneous fault-tolerant system, thus improve the system availability.

A wireless sensor network (WSN) is constructed based on the distributed cyber‐physical system for the indoor environment of smart buildings. The task allocation strategy in WSN is designed by considering computable complexity and dynamic scheduling for the dynamic task scheduling problem in the smart building. First, each task is divided as several subtasks which are calculated by the appropriate computing nodes. Then, the sub‐tasks are allocated according to a task scheduling sequence table which is constructed by the scheduling priority. The tasks are processed in the order in task scheduling sequence table. The simulations show that the proposed task scheduling can effectively reduce the communication time and waiting time of tasks in the distributed and computable WSN in the intelligent building indoor environment. Meanwhile, it can also improve the success rate of task scheduling. Finally, the proposed task scheduling can optimize the operating efficiency of the smart building system.

We introduce a new set of dynamic scheduling algorithms for scheduling and guaranteeing the deadline compliance of a set of aperiodic real-time tasks on a uniprocessor architecture. The task model selected is that of non-preemptable tasks with arbitrary start times and deadlines. The proposed algorithms address a fundamental trade-off in dynamic scheduling between the cost of scheduling and the quality of the resulting schedules. The algorithms control the time allocated to scheduling explicitly, in order to obtain good-quality schedules in reasonable times. We show that taking into account the scheduling time is crucial for honoring the deadlines of scheduled real-time tasks. We provide an experimental evaluation of our algorithms via performance comparisons with existing landmark algorithms that were originally designed to address some similar issues. The results of our experiments show that our algorithms outperform the existing techniques in several parameter configurations.

Many modern parallel applications, such as desktop software and cloud-based web services, are service-oriented, long running, and perform frequent interactions with the external world (e.g., responding to user input). We want such interactive applications to provide fast response times because typically at the other end of the external interaction there is a user waiting for a response. Existing parallel platforms designed for multicore hardware do not work well for such interactive applications, because they are designed to maximize throughput (rather than responsiveness). Interactive applications may have a mixture of interactive and compute-intensive tasks occurring concurrently, and the scheduler must be able to discern and prioritize tasks so that tasks which require faster response are prioritized over background tasks. We present Interactive Cilk, or I-Cilk for short, a task parallel platform designed to schedule such parallel interactive applications. I-Cilk supports a C++-based templated library that allows the programmer to specify priorities for task-parallel code, and the underlying runtime schedules the computation so as to optimize for the response time of high-priority tasks. We show that the scheduling algorithm used by I-Cilk provides provably efficient response times for tasks at all levels of priorities, with better response time to high-priority tasks. We also empirically demonstrate that the scheduling algorithm can be implemented efficiently in practice with low scheduling overhead and provides fast response times for high-priority tasks.

Optical Grid that provides a way to integrate the geographical distributed resources for large- scale grid applications. This paper mainly focuses on the task scheduling issue in Optical Grid and proposes two task scheduling algorithms to mini-mize the total time or scheduling length required for a given task. A simulated optical grid has been addressed to demon-strate the effectiveness of the schedulin g approach. The performance tests and anal ysis of the two algorithms are investi-gated to show the effect of the routing policy on the schedulin g length. A more flexible policy is able to further improve scheduling length. Keywords: Optical Grid, Task Scheduling Algorithm, Communication Contention I INTRODUCTION Distributed computing, also known as computational Grid, is a technology that makes it possible to utilize the geographi-cal distributed computational resources to solve the intensive computation of large-scale data. Most Grid systems were built over the IP packet switched networks in the past. In the situation when some data-intensive Grid applications re-quire moving Gigabytes or even larger of data, the low speed of the network becomes the bottleneck of Grid system, which could not allow the remote resources to be used efficiently. Nowadays the optical network with Synchronous Digital Hierarchy (SDH), Wavelength-D ivision Multiplexing (WDM), and Automa tic Switched Optical Network (ASON) infrastructure can provide a much larger amount of bandwidth than the electronic network, such as IP packet switched network. Therefore, it is significant to use the optical network as the network infrastructure of the computational Grid. We refer to this infrastructure as Optical Grid, which employs optical network to interconnect grid computational re-sources (e.g. supercomputers, computer clusters, storage and display devices) with dynamically provisioned lightpaths [1]. It has been realized that task scheduling is a fundamental issue in achieving high performance in grid environments [6]. Task scheduling aims to optimize the overall performance of a system, e.g., minimizing the average job response time and maximizing the number of jobs exec uted in certain period of time. Optical grid task scheduling is quite different from classic grid task scheduling model. In Optical Grid system, we can treat the optical network as a kind of resource, the scheduling algorithm in Optical Grid system should schedule the tasks to appropriate resources as we ll as scheduling the communications between tasks to appropriate optical links. The objec-tive of task scheduling is to minimize the completion time (we called it as scheduling length or make span ) of an appli-cation. Usually, this problem is modeled and solved under several assumptions. Unless the assumptions are simplified enough, the problem has been proven to be NP-complete [8]. Therefore, many heuristic algorithms have been proposed. But these heuristics either are based on the assumption that the processors are fully connected, or just don’t take into ac-count the optical network’s characters, es pecially the characters of WDM optical network. While actually, in the Optical Grid system, the computational resources are more likely not fully connected, and the communication contention is an important factor that affects the completion time of a Grid application. Thus, a new heuris tic algorithm that can schedule tasks and also the communications are needed. In this paper, we mainly focus on the static scheduling problem, which means the character of the Grid applica tion, including the task execution times, task dependencies, task communications and synchronization are known a priori.