Resource reservation for real-time self-suspending tasks

The generalized multiframe task model (GMF) extends the sporadic task model and multiframe task model. Each frame in the GMF model contains an execution time, a relative deadline, and a minimum inter-arrival time. These parameters are fixed after task specification time in the GMF model. However, multimedia and adaptive control systems may be overloaded and no longer stabilized when the task parameters in such systems are not flexible. In order to address this problem, deadlines and periods of frames may change to alleviate temporal overload, e.g., in the parameter adaptation and elastic scheduling model. In this paper, we propose a new model GMF-PA (the GMF model with parameter adaptation). This model allows task parameters to be flexible in arbitrary-deadline systems. A necessary schedulability test based on mixed-integer linear programming is given to check the schedulability under EDF scheduling and optimally assign frame deadlines and periods at the same time. We also prove that the test is a sufficient and necessary schedulability test when frame deadlines and periods must be integers. An approximation algorithm is also deployed to reduce computational running time and indicates a sufficient schedulability test in general. The speed-up factor of our approximation algorithm is $$1+\epsilon $$ where $$\epsilon $$ can be arbitrarily small, with respect to the exact schedulability test of GMF-PA tasks under EDF. We also apply the GMF model to self-suspending tasks. By extending recent work on scheduling self-suspending tasks, we remove the assumption that frame deadlines are equally assigned in self-suspending tasks, and the system is extended from constrained-deadline systems to arbitrary-deadline systems. We have done extensive experiments to show that the schedulability ratio is improved using our techniques in our GMF-PA model.

The generalized multiframe task model (GMF) extends the sporadic task model and multiframe task model. Each frame in the GMF model contains an execution time, a relative deadline, and a minimum inter-arrival time. These parameters are fixed after task specification time in the GMF model. However, multimedia and adaptive control systems may be overloaded and no longer stabilized when the task parameters in such systems are not flexible. In order to address this problem, deadlines and periods may change to alleviate temporal overload, for example in the parameter adaption and elastic scheduling model. In this paper, we propose a new model GMF-PA (the GMF model with parameter adaption). This model allows task parameters to be flexible in arbitrary-deadline systems. A necessary schedulability test based on mixed-integer linear programming (MILP) is given to check the schedulability under EDF scheduling and optimally assign deadlines and periods at the same time. We also prove that the test is a sufficient and necessary schedulability test when task parameters must be integers. An approximation algorithm is also deployed to reduce computational running time. The speed-up factor of our approximation algorithm is 1+a#x0395; where a#x0395; can be arbitrarily small, with respect to the exact schedulability test of GMF-PA tasks under EDF. We also apply the GMF model to self-suspending tasks. By extending recent work on scheduling self-suspending tasks, we remove the assumption that deadlines are equally assigned in self-suspending tasks, and the system is extended from constrained-deadline systems to arbitrary-deadline systems. We have done exhaustive experiments to show that the schedulability ratio is improved using our techniques in our GMF-PA model.

Generally speaking, real-time system is considered to be able to influence the environment by receiving and processing data, and returning calculation results rapid enough, so as to control the environment. In computer science, real-time system describes the software and hardware system affected by time constraints, and its correctness relies on the logical correctness of the function and the time when the result is generated. According to the main characteristics of real-time operating system, such as time constraint, predictability and reliability, it puts forward higher requirements for the time accuracy and reliability of real-time operating system. This paper first introduces the real-time system from its main characteristics, related concepts and scheduling algorithm. Then five classical graph based task models of real-time system are introduced. Finally, this paper introduces the directed graph realtime task model from two aspects of definition and semantics. As an extension of realtime system task model, directed graph real-time task model is considered to be able to provide real-time systems with stronger expressive power and support the formal study of time constraint problems.

In this paper we study the scheduling of parallel and real-time recurrent tasks on multiprocessor platforms. Firstly, we propose a new parallel task model which allows recurrent tasks to be composed of several phases, each one composed of several threads. Each thread requires a single processor for execution and can be scheduled simultaneously. We then propose an algorithm to transpose popular Fork-Join task model to our MPMT task model. Secondly, we define several kinds of real-time schedulers that can be applied to our parallel task model. We distinguish between two scheduling classes: Hierarchical schedulers and Global Thread schedulers. We present and prove correct an exact schedulability test for each class. Lastly, we also evaluate the performance of our scheduling paradigm in comparison with Gang scheduling by means of simulations. In this work we extend the work of Lupu and Goossens in Scheduling of hard real-time multi-thread periodic tasks (Real-Time and Network Systems, 2011) which considers mono-phase multi-thread task model. We extend their previous results to a Multi-Phase Multi-Thread task model.

At present, most of the scheduling models for real-time periodic tasks are established based on independent fixed periodic tasks and few by considering the task model with period allowed to change and the processor model in the scheduling process. In this paper, a task model and processor model based on line tree(LT) for sporadic real-time periodic tasks are designed, and a transformation algorithm from task line tree(TLT) model to processor line tree(PLT) model is proposed. The algorithm takes the least common multiple of all real-time periodic tasks as the benchmark of layer number, and based on the complete job replacement rule, the minimum common multiple of all real-time periodic tasks is used as the reference of layer number. In order to achieve the optimal multiprocessor scheduling result, the method of replacing the empty node of non-migrating job with the node of migrating job and the condition of allowing job to publish tasks ahead of time with variable cycle are used. Experimental results show that comparing with PEDF, GEDF and RMFF, the present method not only has higher core utilization and lower time loss rate, but also reduces the number of context switching and migration.

One notable advantage of Model-Driven Architecture (MDA) method is that software developers could do sufficient analysis and tests on software models in the design phase, which helps construct high confidence on the expected software behaviors and performance, especially for safety-critical real-time software. Most existing literature of reliability analysis ignores the effects from those deadline requirements of tasks which are critical properties for real-time software and thus cannot be ignored. Considering the contradictory relationship between the deadline requirements and time costs of fault tolerance in real-time tasks, in this paper, we present a novel reliability model, which takes schedulability as one of the major factors affecting the reliability, to analyze reliability of the task execution model in real-time software design phase. The tasks in this reliability model has no restrictions on their distributions and thus could be distributed on a multiprocessor or on a distributed system. Furthermore, the tasks also define arrival rates of faults and fault-tolerant mechanisms to model the occurrences of non-permanent faults and the corresponding time costs of fault handling. By analyzing the probability of tasks still being schedulable in the worst-case execution scenario with faults occurring, reliability and schedulability are combined into an unified analysis framework, and two algorithms for reliability analysis are given. To make this reliability model more pragmatic, we also present an estimation technique for estimating the fault arrival rate of each task. We show through two case studies respectively the detailed derivation process under static-priority scheduling in a multiprocessor system and in the design process of avionics software, and then analyze the factors affecting the reliability analysis by setting up simulation experiments. When no assumptions of fault occurrences made on the task model, this reliability model regresses to a generic schedulability model.

Embedded control systems are often implemented in small microprocessors enabled with real-time technology. In this context, control laws are often designed according to discrete-time control systems theory and implemented as hard real-time periodic tasks. Standard discrete-time control theory mandates to periodically sample (input) and actuate (output). Depending on how input/output (I/O) operations are performed within the hard real-time periodic task, different control task models can be distinguished. However, existing task models present important drawbacks. They generate task executions prone to violate the periodic control demands, a problem known as sampling and latency jitter, or they impose synchronized I/O operations at each task job execution that produce a constant but artificially long I/O latency. In this paper, the one-shot task model for implementing control systems in embedded multitasking hard real-time platforms is presented. The novel control task model is built upon control theoretical results that indicate that standard control laws can be implemented considering only periodic actuation. Taking advantage of this property, the one-shot task model is shown to remove endemic problems for real-time control systems such as sampling and latency jitters. In addition, it can minimize the harmful effects that long I/O latencies have on control performance. Extensive simulations and real experiments show the feasibility and effectiveness of the novel task model, compared to previous real-time and/or control-based solutions.

This letter takes a first step towards the analysis of safety <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">and</i> performance critical computational tasks for autonomous robots. Our contribution is a safety-performance (SP) metric that ensures safety first and then rewards improved performance of real-time computational tasks, building on the notion of “nominal safety” which defines timely computation as critical to safety. To fully utilize the computing capacity of heterogeneous processing units (e.g., CPU + GPU), a computational task graph model called the Stochastic Heterogeneous Parallel Directed Acyclic Graph (SHP-DAG) is adopted to capture the uncertain nature of robotic applications and their required computation. Compared to state-of-the-art task models, SHP-DAG avoids the pessimism of deterministic worst-case execution time (WCET), instead modeling the execution times of tasks by probability distributions. Our SP metric is defined upon this task model, which allows us to apply the FIFO and CFS schedulers of the Linux kernel on complex robotic computational tasks and compare the SP metric with baseline metrics, average and worst-case makespan. Extensive experimental results on NVIDIA Jetson AGX Xavier hardware demonstrate that the proposed SP metric is appropriate for managing computational tasks in a manner that balances safety and performance in robotic systems.

In order to realize the fault tolerance of real-time tasks in scientific research data platform information platform, based on adaptive feedback equalization and symbol modulation technology, a fault-tolerant scheduling model of real-time tasks in scientific research data platform information is proposed in the paper. What is more, the platform transmission channel model of the scientific research data platform information under routing conflicts is first constructed to optimize the information transmission protocol of the scientific research data platform. Secondly, the fuzzy C-means clustering method is applied to perform the inspection robot information fusion. Meanwhile, the symbol modulation method is used for fault-tolerant scheduling of real-time tasks in the information platform. Finally, a simulation experiment is carried out. The results show that the method proposed in the paper has better fault tolerance for platform real-time task scheduling of scientific research data platform information, and the channel balance of the information platform is stronger, which improves the platform real-time task scheduling capability of scientific research data platform information.

The success of cloud computing makes an increasing number of real-time applications such as signal processing and weather forecasting run in the cloud. Meanwhile, scheduling for real-time tasks is playing an essential role for a cloud provider to maintain its quality of service and enhance the system’s performance. In this paper, we devise a novel agent-based scheduling mechanism in cloud computing environment to allocate real-time tasks and dynamically provision resources. In contrast to traditional contract net protocols, we employ a bidirectional announcement-bidding mechanism and the collaborative process consists of three phases, i.e., basic matching phase, forward announcement-bidding phase and backward announcement-bidding phase. Moreover, the elasticity is sufficiently considered while scheduling by dynamically adding virtual machines to improve schedulability. Furthermore, we design calculation rules of the bidding values in both forward and backward announcement-bidding phases and two heuristics for selecting contractors. On the basis of the bidirectional announcement-bidding mechanism, we propose an agent-based dynamic scheduling algorithm named ANGEL for real-time, independent and aperiodic tasks in clouds. Extensive experiments are conducted on CloudSim platform by injecting random synthetic workloads and the workloads from the last version of the Google cloud tracelogs to evaluate the performance of our ANGEL. The experimental results indicate that ANGEL can efficiently solve the real-time task scheduling problem in virtualized clouds.

The complexities of building educational software can be reduced by relying on appropriate tools for adapting and re-using similar, previous applications. Although researchers have been creating tools for automating some aspects of software design, most of these tools typically aim for generality, attempting to cover a broad class of interactive programs in many domains. However, this approach may not be effective for building complex interactive systems, and fails to re-apply the instruction and design expertise implicit within prior software applications. An alternative is to provide tools which use specialized, rather than general, task models, tailored specifically to a target category of software. In addition, an understanding of the authoring task itself can inform the design of such specialized authoring environments. This article presents a prototype authoring tool for interactive educational software, called IDLE-Tool. Investigate and Decide Learning Environments (IDLE) is introduced as the task model around which the tool is based, and a model of the authoring process, called Guided Case Adaptation, is offered as an approach to employing the model and an exemplar in tandem to effectively support the design process.

Embedded application is a hotspot at present and Linux gradually becomes the most important operating system for embedded applications. Aiming at the real-time problems of Linux and from five performance parameters of real-time operating system, this paper analyzes and concludes that scheduling latency and interrupt latency are the fundamental constraints for improving real-time performance of Linux 2.6 kernel, then designs and implements a new task model and new interrupt operations to solve the above problem. Hard real-time task scheduling algorithm which is named as priority bitmap algorithm, new interrupt response and new interrupt operations are emphasized and main codes are given out. Through real-feel testing, response time of real-time task is indicated to be shortened largely and meets the initial expectation.

As the size of data grows rapidly in modern IoT (Internet-of-Things) and CPS (Cyber-Physical System) applications, the memory power consumption of real-time embedded systems increases dramatically. Unlike general-purpose systems where memory consumes about 10&#x0025; of the CPU power consumption, modern real-time systems have the memory power of 20&#x2013;50&#x0025; of CPU power. This is because the memory size of a real-time system should be large enough to accommodate the entire task set, and thus DRAM refresh operations become a major source of power consumption. In this article, we present a new swap scheme for real-time systems, which aims at reducing memory power consumption. To support swap with real-time constraints, we adopt high-speed NVM storage and co-optimize power-savings in CPU and memory. Unlike traditional real-time task models that only consider the executions in CPU, we define an extended task model that characterizes memory and storage paths of tasks as well, and tightly evaluate the worst-case execution time by formulating the overlapped latency between CPU and memory. By optimizing the CPU supply voltage and the memory swap ratio of given task set, our scheme reduces the energy consumption of real-time systems by 31.1&#x0025; on average under various workload conditions.

Thermal management becomes a prominent issue in system design for both server systems and embedded systems. A system could fail if the peak temperature exceeds its thermal constraint. This research studies thermal-constrained scheduling for frame-based real-time tasks on a dynamic voltage/speed scaling system. Our objective is to design speed schedulers for real-time tasks by utilizing dynamic voltage/speed scaling to meet both timing and thermal constraints. Two approaches are proposed: One is based on the minimization of the response time under the thermal constraint, and the other is based on the minimization of the temperature under the timing constraint. We present detailed schedulability analysis for both proposed approaches. Our data show that our proposed proactive approaches outperform existing reactive ones.

Several task models have been introduced in the literature to describe the intrinsic parallelism of real-time activities, including fork/join, synchronous parallel, DAG-based, etc. Although schedulability tests and resource augmentation bounds have been derived for these task models in the context of multicore systems, they are still too pessimistic to describe the execution flow of parallel tasks characterized by multiple (and nested) conditional statements, where it is hard to decide which execution path to select for modeling the worst-case scenario. To overcome this problem, this paper proposes a task model that integrates control flow information by considering conditional parallel tasks (cp-tasks) represented by DAGs with both precedence and conditional edges. For this task model, a set of meaningful parameters are identified and computed by efficient algorithms and a response-time analysis is presented for different scheduling policies. Experimental results are finally reported to evaluate the efficiency of the proposed schedulability tests and their performance with respect to classic tests based on both conditional and non-conditional existing approaches.