Task Priority Assignment Research Articles

Service-Aware Hierarchical Fog–Cloud Resource Mappingfor e-Health with Enhanced-Kernel SVM

Fog–cloud-based hierarchical task-scheduling methods are embracing significant challenges to support e-Health applications due to the large number of users, high task diversity, and harsher service-level requirements. Addressing the challenges of fog–cloud integration, this paper proposes a new service/network-aware fog–cloud hierarchical resource-mapping scheme, which achieves optimized resource utilization efficiency and minimized latency for service-level critical tasks in e-Health applications. Concretely, we develop a service/network-aware task classification algorithm. We adopt support vector machine as a backbone with fast computational speed to support real-time task scheduling, and we develop a new kernel, fusing convolution, cross-correlation, and auto-correlation, to gain enhanced specificity and sensitivity. Based on task classification, we propose task priority assignment and resource-mapping algorithms, which aim to achieve minimized overall latency for critical tasks and improve resource utilization efficiency. Simulation results showcase that the proposed algorithm is able to achieve average execution times for critical/non-critical tasks of 0.23/0.50 ms in diverse networking setups, which surpass the benchmark scheme by 73.88%/52.01%, respectively.

The Executive-Function-Related Cognitive–Motor Dual Task Walking Performance and Task Prioritizing Effect on People with Parkinson’s Disease

To safely walk in a community environment requires dual cognitive-walking ambulation ability for people with Parkinson's disease (PD). A past study showed inconsistent results on cognitive-walking performance for PD patients, possibly due to the various cognitive tasks used and task priority assignment. This study designed cognitive-walking tests that used executive-related cognitive tasks to evaluate patients with early-stage Parkinson's disease who did not have obvious cognitive deficits. The effect of assigning task prioritization was also evaluated. Sixteen individuals with PD (PD group) and 16 individuals without PD (control group) underwent single cognitive tests, single walking tests, dual walking tests, and prioritizing task tests. Three types of cognitive, spatial memory, Stroops, and calculation tasks were employed. The cognitive performance was evaluated by response time, accuracy, and speed-accuracy trade off composite score. The walking performance was evaluated by the temporal spatial gait characteristics and variation in gait. The results showed that the walking performance of the PD group was significantly worse than the control group in both single and dual walking conditions. The group difference in cognitive performance was shown in composite score under the dual calculation walking task but not under the single task. While assigning priority to walking, no group difference in walking was observed but the response accuracy rate of PD groups declined. This study concluded that the dual task walking test could sharpen the cognitive deficits for early-stage PD patients. The task priority assignment might not be recommended while testing gait deficits since it decreased the ability to discriminate group differences.

Isolation of Shared Resources for Mixed-Criticality AUTOSAR Applications

Temporal isolation without consideration of spatial isolation has been attained for mixed-criticality systems, while the need for spatial isolation is urgently required in the automotive industry. Moreover, tasks with different criticality levels sharing the same resources are a common requirement for safety-critical automotive applications. Such tasks are more challenging to spatially isolate due to context sharing to access the same resources. Nevertheless, safety certification cannot be issued without addressing spatial isolation. This paper argues that traditional real-time locking solutions are unsuitable for mixed-criticality applications within the automotive open system architecture (AUTOSAR). We adopted the server task named resource server for spatial isolation within AUTOSAR limitations. We formalized a software component model for reducing design space and proposed the mapping algorithms. Properties of resource servers within AUTOSAR were formally analyzed for blocking delays, task priority assignment, and utilization analysis. Case studies in a powertrain domain of an electric vehicle were carried out to assess the proposed solutions.

Interval type-2 fuzzy logic based radar task priority assignment method for detecting hypersonic-glide vehicles

Interval type-2 fuzzy logic based radar task priority assignment method for detecting hypersonic-glide vehicles

Energy-aware primary/backup scheduling of periodic real-time tasks on heterogeneous multicore systems

For real-time embedded systems, energy management and fault tolerance are both critical. However these two objectives are often at odds, because extra resources needed to tolerate faults significantly increase the energy consumption. In this paper, we consider energy-aware and fault-tolerant scheduling of periodic real-time tasks. Our target platform is a heterogeneous multicore system. We reduce the energy consumption by both applying DVFS to scale the primary tasks, and maximizing the opportunities to cancel the back-up tasks in fault-free execution scenarios. To tolerate both transient and permanent faults, primary and backup copies of tasks are scheduled on different cores. Our framework consists of offline and online phases to manage energy and fault-tolerant scheduling of periodic tasks in tandem. The latter objective is achieved through an explicit task priority assignment phase, coupled with a dual queue based back-up delaying algorithm. In particular, we propose a scheme called Reverse Preference-Oriented Priority Assignment (RPPA) which is experimentally shown to be very effective to reduce the energy consumption. RPPA, when coupled with the dual-queue based delaying algorithm, outperforms other schemes and approaches the energy performance of a theoretical lower bound. All the proposed schemes satisfy the stringent timing and fault tolerance requirements of periodic real-time tasks while managing the energy consumption dynamically.

The Concept of Unschedulability Core for Optimizing Real-Time Systems with Fixed-Priority Scheduling

In the design optimization of real-time systems scheduled with fixed priority, schedulability analysis is used to define the feasibility region within which tasks meet their deadlines, so that optimization algorithms can find the best solution within the region. However, the complexity of schedulability analysis techniques often makes it difficult to leverage existing optimization frameworks and scale to large designs. In this paper, we propose the concept of unschedulability core, a compact representation of the schedulability conditions, and develop efficient algorithms for its calculation. We present a new optimization framework that leverages such a concept. We show that this concept is applicable to a range of optimization problems, for example, when the decision variables include the task priority assignment and the selection of mechanisms protecting shared buffers. Experimental results on two case studies demonstrate that the new optimization procedure maintains the optimality of the solutions, but is a few orders of magnitude faster than other exact algorithms (branch-and-bound, integer linear programming).

The concept of Maximal Unschedulable Deadline Assignment for optimization in fixed-priority scheduled real-time systems

This paper considers the problem of design optimization for real-time systems scheduled with fixed priority, where task priority assignment is part of the decision variables, and the timing constraints and/or objective function linearly depend on the exact value of task response times (such as end-to-end deadline constraints). The complexity of response time analysis techniques makes it difficult to leverage existing optimization frameworks and scale to large designs. Instead, we propose an efficient optimization framework that is three orders of magnitude (1000 times) faster than Integer Linear Programming (ILP) while providing solutions with the same quality. The framework centers around three novel ideas: (1) an efficient algorithm that finds a schedulable task priority assignment for minimizing the average worst-case response time; (2) the concept of Maximal Unschedulable Deadline Assignment (MUDA) that abstracts the schedulability conditions, i.e., a set of maximal virtual deadline assignments such that the system is unschedulable; and (3) a new optimization procedure that leverages the concept of MUDA and the efficient algorithm to compute it.

A hierarchal planning framework for AUV mission management in a spatiotemporal varying ocean

This paper provides a hierarchical dynamic mission planning framework for an AUV to accomplish task-assign process in a restricted time operating in uncertain undersea environment. A high-level reactive mission planner is developed for task priority assignment, guiding the vehicle toward a target of interest, and managing on-time mission completion. A low-level motion planner is also developed to handle unexpected changes of the dynamic terrain by re-generating optimal trajectories. The mission planner reactively re-arranges the tasks based on mission/terrain updates. As a result, the vehicle is able to undertake the maximum number of tasks with certain degree of maneuverability having situational awareness of the operating field. The Biogeography-Based Optimization (BBO) algorithm is used as the computational engine of the framework in both mission and motion planners. The simulations results indicate the significant potential of the proposed hierarchical framework in providing efficient solutions for mission success and its applicability for real-time implementation.

An exact schedulability test for fixed-priority preemptive mixed-criticality real-time systems

The current literature of fixed-priority scheduling algorithms relies on sufficient tests to determine if a set of mixed-criticality sporadic tasks is schedulable on a single processor. The drawback of these safe tests is their pessimism, a matter that could be solved if an exact schedulability analysis is used. However, because of the non-deterministic behavior of tasks in the mentioned setups, exact quantification of worst-case response times, needed for the test, is a difficult problem; more precisely, such a quantification needs evaluation of enormous sequences of job executions. The core problem is thus to merge such sequences to make the analysis practical. This paper, for the first time, gives an algorithm for exact worst-case response time characterization of mixed-criticality sporadic real-time tasks executing according to a given fixed-priority scheduler. We use a set of techniques which carefully consider the task properties and their relation to the worst scenarios to prune the analysis state space. We also show an interesting result that if an exact schedulability test is used, the Audsley's optimal priority assignment algorithm is not applicable to the mixed-criticality case. Accordingly, we need new priority assignment algorithms to work with the exact test; we give a simple task priority assignment algorithm to this aim. The performance of the proposed exact test (in terms of time complexity) is examined and the effectiveness of some heuristic priority assignment algorithms using the test (in terms of the ratio of task sets which are deemed schedulable) are compared.

Best Speed Fit EDF Scheduling for Performance Asymmetric Multiprocessors

In order to improve the performance of a real-time system, asymmetric multiprocessors have been proposed. The benefits of improved system performance and reduced power consumption from such architectures cannot be fully exploited unless suitable task scheduling and task allocation approaches are implemented at the operating system level. Unfortunately, most of the previous research on scheduling algorithms for performance asymmetric multiprocessors is focused on task priority assignment. They simply assign the highest priority task to the fastest processor. In this paper, we propose BSF-EDF (best speed fit for earliest deadline first) for performance asymmetric multiprocessor scheduling. This approach chooses a suitable processor rather than the fastest one, when allocating tasks. With this proposed BSF-EDF scheduling, we also derive an effective schedulability test.

A novel versatile architecture for autonomous underwater vehicle’s motion planning and task assignment

Expansion of today's underwater scenarios and missions necessitates the requestion for robust decision making of the Autonomous Underwater Vehicle (AUV); hence, design an efficient decision making framework is essential for maximizing the mission productivity in a restricted time. This paper focuses on developing a deliberative conflict-free-task assignment architecture encompassing a Global Route Planner (GRP) and a Local Path Planner (LPP) to provide consistent motion planning encountering both environmental dynamic changes and a priori knowledge of the terrain, so that the AUV is reactively guided to the target of interest in the context of an unknown underwater environment. The architecture involves three main modules: The GRP module at the top level deals with the task priority assignment, mission time management, and determination of a feasible route between start and destination point in a large scale environment. The LPP module at the lower level deals with safety considerations and generates collision-free optimal trajectory between each specific pair of waypoints listed in obtained global route. Re-planning module tends to promote robustness and reactive ability of the AUV with respect to the environmental changes. The experimental results for different simulated missions, demonstrate the inherent robustness and drastic efficiency of the proposed scheme in enhancement of the vehicles autonomy in terms of mission productivity, mission time management, and vehicle safety.

Task partitioning and priority assignment for distributed hard real-time systems

In this paper, we propose the Distributed using Optimal Priority Assignment (DOPA) heuristic that finds a feasible partitioning and priority assignment for distributed applications based on the linear transactional model. DOPA partitions the tasks and messages in the distributed system, and makes use of the Optimal Priority Assignment (OPA) algorithm known as Audsley's algorithm, to find the priorities for that partition. The experimental results show how the use of the OPA algorithm increases in average the number of schedulable tasks and messages in a distributed system when compared to the use of Deadline Monotonic (DM) usually favoured in other works. Afterwards, we extend these results to the assignment of Parallel/Distributed applications and present a second heuristic named Parallel-DOPA (P-DOPA). In that case, we show how the partitioning process can be simplified by using the Distributed Stretch Transformation (DST), a parallel transaction transformation algorithm introduced in [1].

An Efficient Formulation of the Real-Time Feasibility Region for Design Optimization

In the design of time-critical applications, schedulability analysis is used to define the feasibility region of tasks with deadlines, so that optimization techniques can find the best design solution within the timing constraints. The formulation of the feasibility region based on the response time calculation requires many integer variables and is too complex for solvers. Approximation techniques have been used to define a convex subset of the feasibility region, used in conjunction with a branch and bound approach to compute suboptimal solutions for optimal task period selection, priority assignment, or placement of tasks onto CPUs. In this paper, we provide an improved and simpler real-time schedulability test that allows an exact and efficient definition of the feasibility region in Mixed Integer Linear Programming (MILP) optimization. Our method requires a significantly smaller number of binary variables and is viable for the treatment of industrial-size problem, as shown by the experiments.

Optimization of task allocation and priority assignment in hard real-time distributed systems

The complexity and physical distribution of modern active safety, chassis, and powertrain automotive applications requires the use of distributed architectures. Complex functions designed as networks of function blocks exchanging signal information are deployed onto the physical HW and implemented in a SW architecture consisting of a set of tasks and messages. The typical configuration features priority-based scheduling of tasks and messages and imposes end-to-end deadlines. In this work, we present and compare formulations and procedures for the optimization of the task allocation, the signal to message mapping, and the assignment of priorities to tasks and messages in order to meet end-to-end deadline constraints and minimize latencies. Our formulations leverage worst-case response time analysis within a mixed integer linear optimization framework and are compared for performance against a simulated annealing implementation. The methods are applied for evaluation to an automotive case study of complexity comparable to industrial design problems.

Schedulability analysis and task mapping for real-time on-chip communication

Priority-based wormhole switching with a priority share policy has been proposed as a possible solution for real-time on-chip communication. However, the blocking introduced by priority share complicates the analysis process. In this paper, we propose a new "per-priority" basis analysis scheme which computes the total time window at each priority level instead of each traffic-flow. By checking the release instance of each flow at the corresponding priority window, we can determine schedulability efficiently. Building on this static analysis, for a given set of tasks and network topology, we further propose a task mapping and priority assignment algorithm, in such a way that the hard time bounds are met with a reduced hardware overhead. Experiment results show that significant resource saving can be achieved with no performance degradation in terms of missed deadlines. By using this approach, a broad class of real-time communications with different QoS requirements can be explored and developed in a SoC/NoC communication platform.

Task Allocation and Optimization of Distributed Embedded Systems with Simulated Annealing and Geometric Programming

We consider the task model of periodic tasks running on a network of processor nodes connected by a bus based on the time-triggered protocol, an industry-standard bus protocol designed for safety-critical automotive and avionics distributed embedded systems, and present an integrated optimization framework that jointly considers one or more of the following attributes: task-to-processor allocation, task priority assignment, task period assignment and bus access configuration. We adopt a hierarchical optimization framework, where each possible task allocation and priority assignment is treated as one top-level coarse-grained state, which may contain many lower-level fine-grained states defined by different task period assignments and bus access configurations. Simulated annealing is used to explore the top-level states, which calls a geometric programming solver as a subroutine to explore the lower-level states contained within a given top-level state. Performance evaluation shows that our framework has good performance in terms of solution quality and scalability.

Task mapping and priority assignment for soft real-time applications under deadline miss ratio constraints

Both analysis and design optimisation of real-time systems has predominantly concentrated on considering hard real-time constraints. For a large class of applications, however, this is both unrealistic and leads to unnecessarily expensive implementations. This paper addresses the problem of task priority assignment and task mapping in the context of multiprocessor applications with stochastic execution times and in the presence of constraints on the percentage of missed deadlines. We propose a design space exploration strategy together with a fast method for system performance analysis. Experiments emphasize the efficiency of the proposed analysis method and optimisation heuristic in generating high-quality implementations of soft real-time systems with stochastic task execution times and constraints on deadline miss ratios.

Task reconstruction method for real-time singularity avoidance for robotic manipulators

The kinematic (KS) and algorithmic singularities (AS) in controlling robotic manipulators have been investigated intensively because they are not predictable or difficult to avoid. The problem with handling these singularities is an unnecessary performance reduction in the non-singular region and the difficulty in performance tuning. In this paper, we propose a method of avoiding KS and AS by applying a task reconstruction approach while maximizing the task performance by calculating singularity measures. The proposed method is implemented by removing the component approaching the singularity calculated by using a singularity measure in real-time. The outstanding feature of the proposed task reconstruction (TR) method is that it is based on a local TR as opposed to the local joint reconstruction of many other approaches. This method has a dynamic task priority assignment feature which ensures system stability under singular regions due to the change of task priority. The TR method enables us to increase the task controller gain to improve the task performance, whereas this increase can destabilize the system for conventional algorithms in real experiments. In addition, the physical meaning of tuning parameters is very straightforward. Hence, we can maximize task performance even near the singular region while simultaneously obtaining the singularity-free motion. The advantage of the proposed method is experimentally tested by using a 7-d. o. f. spatial manipulator and the result shows that the new method improves the performance several times over the existing algorithms.

로봇 매니플레이터의 실시간 특이점 회피를 위한 작업 재구성법: 동적 작업 우선도에 기초한 해석

There are several types of singularities in controlling robotic manipulators: kinematic singularity, algorithmic singularity, semi-kinematic singularity, semi-algorithmic singularity, and representation singularity. The kinematic and algorithmic singularities have been investigated intensively because they are not predictable or difficult to avoid. The problem with these singularities is an unnecessary performance reduction in non-singular region and the difficulty in performance tuning. Tn this paper, we propose a method of avoiding kinematic and algorithmic singularities by applying a task reconstruction approach while maximizing the task performance by calculating singularity measures. The proposed method is implemented by removing the component approaching the singularity calculated by using singularity measure in real time. The outstanding feature of the proposed task reconstruction method (TR-method) is that it is based on a local task reconstruction as opposed to the local joint reconstruction of many other approaches. And, this method has dynamic task priority assignment feature which ensures the system stability under singular regions owing to the change of task priority. The TR-method enables us to increase the task controller gain to improve the task performance whereas this increase can destabilize the system for the conventional algorithms in real experiments. In addition, the physical meaning of tuning parameters is very straightforward. Hence, we can maximize task performance even near the singular region while simultaneously obtaining the singularity-free motion. The advantage of the proposed method is experimentally tested by using the 7-dof spatial manipulator, and the result shows that the new method improves the performance several times over the existing algorithms.