Characterization of real-time computers

A real-time system consists of a computer controller and controlled processes. Despite the synergistic relationship between these two components, they have been traditionally designed and analyzed independently of and separately from each other; namely, computer controllers by computer scientists/engineers and controlled processes by control scientists. As a remedy for this problem, in this report real-time computers are characterized by performance measures based on computer controller response time that are: (1) congruent to the real-time applications, (2) able to offer an objective comparison of rival computer systems, and (3) experimentally measurable/determinable. These measures, unlike others, provide the real-time computer controller with a natural link to controlled processes. In order to demonstrate their utility and power, these measures are first determined for example controlled processes on the basis of control performance functionals. They are then used for two important real-time multiprocessor design applications - the number-power tradeoff and fault-masking and synchronization

Imprecise Computation Model, Synchronous Periodic Real-time Task Sets and Total Weighted Error

This paper proposes two scheduling approaches, one-level and two-level scheduling, for synchronous periodic real-time task sets based on the Imprecise Computation Model. The imperative of real-time systems is a reaction on an event within a limited amount of time. Sometimes the available time and resources are not enough for the computations to complete within the deadlines, but still enough to produce approximate results. The Imprecise Computation Model is motivated by this idea, which gives the flexibility to trade off precision for timeliness. In this model a task is logically decomposed into a mandatory and optional subtask. Only the mandatory subtask is required to complete by its deadline, while the optional subtask may be left unfinished. Usually, different scheduling policies are used for the scheduling of mandatory and optional subtasks. For both proposed approaches the earliest deadline first and rate monotonic scheduling algorithms are used for the scheduling of mandatory subtasks, whereas the optional subtasks are scheduled in a way that the total weighted error is minimized. The basic idea of one-level scheduling is to extend the mandatory execution times, while in two-level scheduling the mandatory and optional subtasks are separately scheduled. The single preemptive processor model is assumed

YARTISS: A Tool to Visualize, Test, Compare and Evaluate Real-Time Scheduling Algorithms

International audienceIn this paper, we present a free software written in Java, YARTISS, which is a real-time multiprocessor scheduling simulator. It is aimed at comparing user-customized algorithms with ones from the literature on real-time scheduling. This simulator is designed as an easy-to-use modular tool in which new modules can be added without the need to decompress, edit nor recompile existing parts. It can simulate the execution of a large number of concurrent periodic independent task sets on multiprocessor systems and generate clear visual results of the scheduling process (both schedules and tunable metrics presentations). Other task models are already implemented in the simulator, like graph tasks with precedence constraints and it is easily extensible to other task models. Moreover, YARTISS can simulate task sets in which energy consumption is a scheduling parameter in the same manner as Worst Case Execution Time (WCET)

Optimal Time Utility Based Scheduling Policy Design for Cyber-Physical Systems

Classical scheduling abstractions such as deadlines and priorities do not readily capture the complex timing semantics found in many real-time cyber-physical systems. Time utility functions provide a necessarily richer description of timing semantics, but designing utility-aware scheduling policies using them is an open research problem. In particular, optimal utility accrual scheduling design is needed for real-time cyber-physical domains. In this paper we design optimal utility accrual scheduling policies for cyber-physical systems with periodic, non-preemptable tasks that run with stochastic duration. These policies are derived by solving a Markov Decision Process formulation of the scheduling problem. We use this formulation to demonstrate that our technique improves on existing heuristic utility accrual scheduling policies

Analysis Literatures of Machine Learning and Neural Networks for Real Time Scheduling

Real time scheduling problems are present in every aspect of software development. An optimized real time scheduling scheme would determine the performance of an operating system. There are many different approaches that real time scheduling researchers developed to tackle scheduling problems in many computer systems that have great important roles in keeping our modern society running smoothly. Neural-network real time scheduling is one of those approaches that can solve many computer scheduling problems. As computing technology advanced, more and more real time scheduling problems arise that need new solutions to keep up with the demand of faster computer systems. In this literature review, we analyze four research papers that promote some great solutions for some particular scheduling problems. The first one is “A Neurodynamic Approach for Real Time Scheduling via Maximizing Piecewise Linear utility” by Zhishan Gou and Sanjoy K. Baruah (2016). The second paper is “Scheduling Multiprocessor Job with Resource and Timing Constraints Using Neural Networks” by Y. Huang and R. Chen (1999). The third paper is “Solving Real Time Scheduling Problems Using Hopfield-Types Neural Networks” by M. Silva, C. Cardeira, and Z. Mammeri (1997). Finally, the last one is “Neural Network for Multiprocessor Real Time Scheduling” by C. Cardiera and Z. Mammeri (1994)

Performance Evaluation of Scheduling Algorithms for Real Time Cloud Computing Systems

Cloud computing shares data and oers services transparently among its users. With the increase in number of users of cloud the tasks to be scheduled increases. The performance of cloud depends on the task scheduling algorithms used in the scheduling components or brokering components. Scheduling of tasks on cloud computing systems is one of the research problem, Where the matching of machines and completion time of the tasks are considered. Tasks matching of machines problem is that, assume number of active hosts are Y, number of VMs in each host are Z. Maximum number of possible Virtual Machines(VMs) to schedule a single task is (y*z). If we need to schedule X tasks, number of possibilities are (y *z)^x. So scheduling of tasks is NP Hard problem. NP Hard means this scheduling of tasks on VMs not having polynomial time complexity, but it may have algorithm for verifying solution. Fault-tolerance becomes an important key to establish dependability in cloud computing system. In task scheduling, if task not completed in it's deadline ,then it is one type of fault in scheduling of tasks. In this thesis this type of faults are taken and try to overcome it. In this thesis we present a non-preemptive scheduling algorithm, By inserting the ideal time for postponing the task by ensuring the other task will completes its execution with in the deadline. In simulation the proposed algorithm maximizes the prot of 25%, throughput of 25% and minimizes the penalty of 20% over EDF

Analyzing the effect of gain time on soft task scheduling policies in real-time systems

In hard real-time systems, gain time is defined as the difference between the Worst Case Execution Time (WCET) of a hard task and its actual processor consumption at runtime. This paper presents the results of an empirical study about how the presence of a significant amount of gain time in a hard real-time system questions the advantages of using the most representative scheduling algorithms or policies for aperiodic or soft tasks in fixed-priority preemptive systems. The work presented here refines and complements many other studies in this research area in which such policies have been introduced and compared. This work has been performed by using the authors' testing framework for soft scheduling policies, which produces actual, synthetic, randomly generated applications, executes them in an instrumented Real-Time Operating System (RTOS), and finally processes this information to obtain several statistical outcomes. The results show that, in general, the presence of a significant amount of gain time reduces the performance benefit of the scheduling policies under study when compared to serving the soft tasks in background, which is considered the theoretical worst case. In some cases, this performance benefit is so small that the use of a specific scheduling policy for soft tasks is questionable. © 2012 IEEE.This work is partially funded by research projects PROMETEO/2008/051, CSD2007-022, and TIN2008-04446.Búrdalo Rapa, LA.; Terrasa Barrena, AM.; Espinosa Minguet, AR.; García Fornes, AM. (2012). Analyzing the effect of gain time on soft task scheduling policies in real-time systems. IEEE Transactions on Software Engineering. 38(6):1305-1318. https://doi.org/10.1109/TSE.2011.95S1305131838

Real-time task attributes and temporal constraints

Real-time tasks need attributes for monitoring their execution and performing recovery actions in case of failures. Temporal constraints are a class of real-time task attributes where the constraints relate the status of the task to temporal entities. Violating temporal constraints can produce consequences of unknown severity. This paper is part of our on-going research on real-time multi agent systems constraints. We discuss the importance of temporal constraints and present a task model that explicitly represents temporal constraints. We also present our preliminary results from our initial implementation in the domain of Meeting Schedules Management involving multiple users assisted by agents

Cooperative Game Theory within Multi-Agent Systems for Systems Scheduling

Research concerning organization and coordination within multi-agent systems continues to draw from a variety of architectures and methodologies. The work presented in this paper combines techniques from game theory and multi-agent systems to produce self-organizing, polymorphic, lightweight, embedded agents for systems scheduling within a large-scale real-time systems environment. Results show how this approach is used to experimentally produce optimum real-time scheduling through the emergent behavior of thousands of agents. These results are obtained using a SWARM simulation of systems scheduling within a High Energy Physics experiment consisting of 2500 digital signal processors.Comment: Fourth International Conference on Hybrid Intelligent Systems (HIS), Kitakyushu, Japan, December, 200