Efficient maximum reaction time analysis for data chains of real-time tasks in multiprocessor systems

Scheduling of real-time tasks in multiprocessor systems is a NP-hard problem. Recently, swarm intelligence algorithms have been efficiently applied for this problem. Real-time tasks can be classified into hard real-time tasks and soft real-time tasks. The aim of hard real-time task scheduling algorithms is to meet all tasks deadline constraints. However, slight violation is not critical, in the case of soft real-time tasks. In this paper, a new algorithm based on artificial bee colony (ABC) is proposed for scheduling of soft real-time tasks. In this method, a hybrid neighborhood search mechanism is introduced to improve the convergence of ABC. Experimental results demonstrate the effectiveness of proposed algorithm for scheduling of soft realtime tasks in heterogeneous multiprocessor systems.

A new scheduling approach supporting different fault-tolerant techniques for real-time multiprocessor systems

The paper introduces improvements in partitioning schemes for multiprocessor real time systems which allow higher processor utilization and enhanced schedulability by using exact feasibility tests to evaluate the schedulability limit of a processor. The paper analyzes how to combine these tests with existing bin packing algorithms for processor allocation and provides new variants which are exhaustively evaluated using two assumptions: variable and fixed number of processors. The problem of evaluating these algorithms is complex, since metrics are usually based on comparing the performance of a given algorithm to an optimal one, but determining optimals is often NP hard on multiprocessors. This problem has been overcome by defining lower or upper bounds on the performance of an optimal algorithm and then defining metrics with respect these bounds. The evaluation has shown that the algorithms exhibit extremely good behavior and they can be considered very close to the maximum achievable utilization. It is also shown that dynamic priority policies produce significantly better results than fixed priority policies when task sets require high processor utilizations.

Many complex real-time applications involve combined scheduling of hard and soft real-time tasks. In this paper, we propose a combined scheduling algorithm, called Emergency Algorithm, for multiprocessor real-time systems. The primary goal of the algorithm is to maximize the schedulability of soft tasks without jeopardizing the schedulability of hard tasks. The algorithm has the inherent feature of dropping already scheduled soft tasks by employing a technique that switches back and forth between two modes of operation: regular mode, in which all tasks (hard and soft) are considered for scheduling without distinction; emergency mode, in which one or more already scheduled soft tasks can be dropped in order to accommodate a hard task in the schedule. The proposed algorithm has been evaluated by comparing it with the iterative server rate adjustment algorithm (ISRA) [6]. The simulation results show that emergency algorithm outperforms ISRA by offering better schedulability for soft tasks while maintaining lower scheduling overhead.KeywordsMultimedia ServerMultiprocessor SystemHard TaskRegular ModeMultimedia StreamThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Integrated dynamic scheduling of hard and QoS degradable real-time tasks in multiprocessor systems

Many time critical applications require predictable performance and tasks in these applications have deadlines to be met. For tasks with hard deadlines, a deadline miss can be catastrophic, while for QoS degradable tasks (soft real time tasks) timely approximate results of poorer quality or occasional deadline misses are acceptable. Imprecise computation and (m,k) firm guarantee are two workload models that quantify the trade off between schedulability and result quality. We propose dynamic scheduling algorithms for integrated scheduling of real time tasks, represented by these workload models, in multiprocessor systems. The algorithms aim at improving the schedulability of tasks by exploiting the properties of these models in QoS degradation. We also show how the proposed algorithms can be adapted for integrated scheduling of multimedia streams and hard real time tasks, and demonstrate their effectiveness in quantifying QoS degradation. Through simulation, we evaluate the performance of these algorithms using the metrics-success ratio (measure of schedulability) and quality. Our simulation results show that one of the proposed algorithms, multilevel degradation algorithm, outperforms the others in terms of both the performance metrics.

As multiprocessor systems are increasingly used in automotive real-time environments, scheduling and synchronization analysis of these platforms receive growing attention. Upcoming multicore ECUs allow the integration of previously separated functionality for body electronics or sensor fusion onto a single unit, and allow the parallelization of complex computations over multiple cores. The application of multiple CPUs turns an ECU into a highly integrated ldquonetworked systemrdquo microcosm, in which complex interdependencies can be observed due to the use of shared resources even in partitioned scheduling. To deliver predictable performance, resource arbitration protocols are required and have been proposed in literature. This paper presents an novel analytical approach to provide the worst-case response time for real-time tasks in multiprocessor systems with shared resources. The method supports realistic, event- or time-driven task activation schemes and allows to calculate tight bounds on the estimated system performance.

A distributed protocol is proposed for the synchronization of real-time tasks that have variable resource requirements. The protocol is simple to implement and is intended for large-scale distributed or parallel systems in which processes communicate by message passing. Critical sections, even when nested, may be executed on any processor. Thus, given an adequate number of processors, the execution of critical sections can be completely distributed. More significantly, since the protocol enables the distributed allocation of critical sections, the benefits of various allocations can be analyzed and the system optimized to provide minimal blocking. This has important application in global optimization techniques for allocating large numbers of hard real-time tasks in multiprocessor systems.

An adaptive scheme for fault-tolerant scheduling of soft real-time tasks in multiprocessor systems

There are two major strategies to schedule real-time tasks in multiprocessor systems; partitioning and global scheduling. The partitioning approach has acceptable overhead but cannot guarantee to be optimal. The global approach can provide this guarantee but it has considerable overhead. Thus, a multiprocessor real-time scheduling approach for weakly hard real-time tasks is proposed that employs hybrid scheduling. Studies have shown that current multiprocessor scheduling of weakly hard real-time tasks used imprecise computation model based on iterative algorithms. This algorithm decomposed into two parts; mandatory and optional, unfortunately, the result analysis is precise only if its mandatory and optional parts are both executed. Even, the use of hierarchical scheduling algorithm, such as two-level scheduling under PFair algorithm may cause high overhead due to frequent preemptions and migrations. In this paper, an alternative scheduling approach will be proposed, which is, its combines elements of the two well-known multiprocessor scheduling approaches. It aims to employs benefits and advantages of the partitioning and global scheduling. Accordingly, the proposed hybrid multiprocessor real-time scheduling is use the best algorithm of each of partitioning and global approaches, R-BOUND-MP-NFRNS and RM-US (m/3m−2) with multiprocessor response time test. Schedulability experiments and simulation results using Matlab show the proposed hybrid multiprocessor scheduling approach to be effective for weakly hard real-time tasks.

The scheduling of real-time tasks with primary-backup based fault-tolerant requirements has been an important problem for several years. Most of the known scheduling schemes are non-adaptive in nature meaning that they do not adapt to the dynamics of faults and task’s parameters in the system. In this paper,we propose an adaptive faulttolerant scheduling scheme that has a mechanism to control the overlap interval between the primary and backup versions of tasks such that the overall performance of the system is improved. The overlap interval is determined based on the estimated primary fault probability and task’s soft laxity. We also propose a new performance index that integrates schedulability (S) and reliability (R) into a single metric, called SR index. To evaluate the proposed scheme, we have conducted analytical and simulation studies under different fault and deadline scenarios, and found that the proposed adaptive scheme adapts to system dynamics and offers better SR index than that of the non-adaptive schemes.KeywordsSchedule SchemeProcessor UtilizationFault RateFault ProbabilityExecution IntervalThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

End-to-end latency analysis is one of the key problems in the automotive embedded system design. In this paper, we propose an efficient worst-case end-to-end latency analysis method for data chains of periodic real-time tasks executed on multiprocessors under a partitioned fixed-priority preemptive scheduling policy. The key idea of this research is to improve the analysis efficiency by transforming the problem of bounding the worst-case latency of the data chain to a problem of bounding the releasing interval of data propagation instances for each pair of consecutive tasks in the chain. In particular, we derive an upper bound on the releasing interval of successive data propagation instances to yield the desired data chain latency bound by a simple accumulation. Based on the above idea, we present an efficient latency upper bound analysis algorithm with polynomial time complexity. Experiments with randomly generated task sets based on a generic automotive benchmark show that our proposed approach can obtain a relatively tighter data chain latency upper bound with lower computational cost.

The objective of the scheduling algorithm is to dynamically schedule as many tasks as possible such that each task meets its execution deadline while minimizing the total delay time of all of the tasks. The problem of scheduling of real-time tasks in multiprocessor systems is to determine when and on which processor a given task executes. In this paper we suggest a genetic algorithm for dynamic scheduling of real time tasks in multiprocessors system.The algorithm based on the use of a fixed size chromosome and repeatedly applying specific crossover (single point or double point) and mutation procedures with variable mutation rates (0.05 – 0.1) until all tasks are successfully scheduled.

This chapter presents applications of the developmental genetic programming (DGP) to design and optimize real-time computer-based systems. We show that the DGP approach may be efficiently used to solve the following problems: scheduling of real-time tasks in multiprocessor systems, hardware/software codesign of distributed embedded systems, budget-aware real-time cloud computing. The goal of optimization is to minimize the cost of the system, while all real-time constraints will be satisfied. Since the finding of the best solution is very complex, only efficient heuristics may be applied for real-life systems. Unlike the other genetic approaches where chromosomes represent solutions, in the DGP chromosomes represent system construction procedures. Thus, not the system architecture, but the synthesis process evolves. Finally, a tree describing the construction of a (sub-)optimal solution is obtained and the genotype-to-phenotype mapping is applied to create the target system. Some other ideas concerning other applications of the DGP for optimization of computer-based systems also are outlined.

This paper presents an energy-aware method to schedule multiple real-time tasks in multiprocessor systems that support dynamic voltage scaling (DVS). The key difference from existing approaches is that we consider the probabilistic distributions of the tasks' execution time to partition the workload for better energy reduction. We analyze the problem of energy-aware scheduling for multiprocessor with probabilistic workload information and derive its mathematical formulation. As the problem is NP-hard, we present a polynomial-time heuristic method to transform the problem into a probability-based load balancing problem that is then solved with worst-fit decreasing bin-packing heuristic. Simulation results with synthetic, multimedia, and stereo- vision tasks show that our method saves significantly more energy than existing methods.