Priority Assignment Policy Research Articles

A shared ride matching approach to low-carbon and electrified ridesplitting

Ridesplitting has the potential to enhance transportation efficiency and reduce CO2 emissions from ridesourcing services by matching multiple shared rides to the same trip. However, an inappropriate matching can significantly diminish the environmental performance of ridesplitting. This study aims to propose a shared ride matching approach to low-carbon and electrified ridesplitting. Firstly, feasible ridesplitting trips within time constraints are identified and represented using a shareability network. Subsequently, the CO2 emission reduction of each feasible ridesplitting trip is calculated by comparing it with its corresponding single ride alternative. To maximize the total CO2 emission reduction from ridesplitting trips, an optimization model for shared ride matching is established and solved using a graph-theoretic algorithm. Additionally, a priority assignment policy is designed to preferentially assign available electric vehicles to feasible ridesplitting trips that have greater potential for emission reductions. Comparative experiments conducted in Chengdu, China show that the proposed approach can achieve higher levels of CO2 emission reduction without compromising service quality in ridesplitting operations. Furthermore, significant improvements in emission reductions can be achieved by implementing the priority assignment policy instead of a random assignment policy for electrified ridesplitting trips, particularly when electric vehicles are limited. Finally, the effects of maximum tolerable delay and electric vehicle penetration are further analyzed to provide valuable insights for the government and ridesourcing companies aiming to enhance both the economic and environmental benefits associated with ridesplitting.

Polaris: Enabling Transaction Priority in Optimistic Concurrency Control

Transaction priority is a critical feature for real-world database systems. Under high contention, certain classes of transactions should be given a higher chance to commit than others. Such a prioritization mechanism is commonly implemented in locking-based concurrency control protocols as some lock scheduling mechanisms, but it is rarely supported in the world of optimistic concurrency control. We present Polaris, an optimistic concurrency control protocol that supports multiple priority levels. To enforce priority, Polaris introduces a minimal amount of pessimism through a lightweight reservation mechanism. The protocol is fully optimistic among transactions within the same priority level and preserves the high throughput advantage of optimistic protocols. Our evaluation with YCSB workload shows that Polaris can make the p999 tail latency of high-priority transactions 13x lower than that of low-priority ones. With an abort-aware priority assignment policy, Polaris can deliver 1.9x higher throughput and 17x lower tail latency compared to Silo for high-contention workloads.

A framework for multi-core schedulability analysis accounting for resource stress and sensitivity

Timing verification of multi-core systems is complicated by contention for shared hardware resources between co-running tasks on different cores. This paper introduces the Multi-core Resource Stress and Sensitivity (MRSS) task model that characterizes how much stress each task places on resources and how much it is sensitive to such resource stress. This model facilitates a separation of concerns, thus retaining the advantages of the traditional two-step approach to timing verification (i.e. timing analysis followed by schedulability analysis). Response time analysis is derived for the MRSS task model, providing efficient context-dependent and context independent schedulability tests for both fixed priority preemptive and fixed priority non-preemptive scheduling. Dominance relations are derived between the tests, along with complexity results, and proofs of optimal priority assignment policies. The MRSS task model is underpinned by a proof-of-concept industrial case study. The problem of task allocation is considered in the context of the MRSS task model, with Simulated Annealing shown to provide an effective solution.

A hybrid multi-criteria decision making model for elective admission control in a Chinese public hospital

In healthcare service systems, patients are not always served in the order they arrive, but are ranked with respect to their relative “importance” and “urgency” to the service system. We consider such a system where elective admission requests backlogged on a list wait to be assigned inpatient beds. To consolidate the performance of Classified Diagnose and Treatment in China, determining an optimal admission priority assignment policy for all waiting patients is vital. It is a complicated multi-criteria decision making (MCDM) problem involving both qualitative and quantitative criteria. Evaluating the admission priority of each patient is based on vague information or uncertain data in which significant dependence and feedback between the evaluation dimensions and criteria may exist. This paper applies a hybrid MCDM model that integrates the 2-tuple DEMATEL technique and the fuzzy VIKOR method to the elective admission control problem. It makes use of the modified 2-tuple DEMATEL to determine the relative weights of the evaluation criteria and the fuzzy VIKOR method to assess the alternatives (waiting patients) over the criteria. An empirical case in West China Hospital is presented to demonstrate the applicability of the proposed approach. Sensitivity analysis of the results by the proposed hybrid MCDM model and comparative analysis with other different approaches are presented. The results show that the proposed model is effective and provides insightful implications for hospital managers to refer.

Research on optimization of spare parts inventory policy considering maintenance priority

Aiming at multi-echelon inventory optimization of spare parts, the process of equipment support under preemptive maintenance and non-preemptive maintenance were analyzed. The support time of spare parts in each maintenance sites was calculated and a model of expected backorder was established. The marginal effect algorithm was utilized to calculate the maintenance priority assignment and inventory policy of spare parts. Finally, through a numerical example, the inventory project of spare parts obtained by the proposed model can greatly reduce the cost of the spare parts while ensuring the availability of the equipment, and which can improve the military and economic benefits of the equipment support commendably.

Exact speedup factors for linear-time schedulability tests for fixed-priority preemptive and non-preemptive scheduling

In this paper, we investigate the quality of several linear-time schedulability tests for preemptive and non-preemptive fixed-priority scheduling of uniprocessor systems. The metric used to assess the quality of these tests is the resource augmentation bound commonly known as the processor speedup factor. The speedup factor of a schedulability test corresponds to the smallest factor by which the processing speed of a uniprocessor needs to be increased such that any task set that is feasible under an optimal preemptive (non-preemptive) work-conserving scheduling algorithm is guaranteed to be schedulable with preemptive (non-preemptive) fixed priority scheduling if this scheduling test is used, assuming an appropriate priority assignment. We show the surprising result that the exact speedup factors for Deadline Monotonic (DM) priority assignment combined with sufficient linear-time schedulability tests for implicit-, constrained-, and arbitrary-deadline task sets are the same as those obtained for optimal priority assignment policies combined with exact schedulability tests. Thus in terms of the speedup-factors required, there is no penalty in using DM priority assignment and simple linear schedulability tests.

A review of priority assignment in real-time systems

It is over 40 years since the first seminal work on priority assignment for real-time systems using fixed priority scheduling. Since then, huge progress has been made in the field of real-time scheduling with more complex models and schedulability analysis techniques developed to better represent and analyse real systems. This tutorial style review provides an in-depth assessment of priority assignment techniques for hard real-time systems scheduled using fixed priorities. It examines the role and importance of priority in fixed priority scheduling in all of its guises, including: pre-emptive and non-pre-emptive scheduling; covering single- and multi-processor systems, and networks. A categorisation of optimal priority assignment techniques is given, along with the conditions on their applicability. We examine the extension of these techniques via sensitivity analysis to form robust priority assignment policies that can be used even when there is only partial information available about the system. The review covers priority assignment in a wide variety of settings including: mixed-criticality systems, systems with deferred pre-emption, and probabilistic real-time systems with worst-case execution times described by random variables. It concludes with a discussion of open problems in the area of priority assignment.

Fixed-priority global scheduling for mixed-criticality real-time systems

There has been a growing interest in recent years in mixed-criticality real-time systems in which tasks are attributed different levels of criticality based on the degree to which their deadlines must be assured. While most of the initial efforts on mixed-criticality systems targeted single-processor systems, the research community has recently started to investigate multiprocessor mixed-criticality real-time systems. In this paper we investigate how global, fixed-priority algorithms can be applied in the context of multiprocessor mixed-criticality systems. We identify the two key dimensions of the problem – priority assignment and schedulability testing, and consider candidate algorithms for each dimension. We also propose a new and simple priority assignment policy called CPRatio that considers both task criticality and timing constraints to improve the schedulability of mixed-criticality task sets. We experimentally evaluate the performance of priority assignment strategies and schedulability tests in the global multiprocessor mixed-criticality scheduling context.

Schedulability analysis for Controller Area Network (CAN) with FIFO queues priority queues and gateways

Controller Area Network (CAN) is widely used in automotive applications. Existing schedulability analysis for CAN is based on the assumption that the highest priority message ready for transmission at each node on the network will be entered into arbitration on the bus. However, in practice, some CAN device drivers implement FIFO rather than priority-based queues invalidating this assumption. In this paper, we introduce response time analysis and optimal priority assignment policies for CAN messages in networks where some nodes use FIFO queues while other nodes use priority queues. We show, via a case study and experimental evaluation, the detrimental impact that FIFO queues have on the real-time performance of CAN. Further, we show that in gateway applications, if it is not possible to implement a priority queue, then it is preferable to use multiple FIFO queues each allocated a small number of messages with similar transmission deadlines.

Dynamic scheduling algorithm for parallel real-time graph tasks

In this paper, we propose a dynamic global scheduling algorithm for a previously-presented specific model of real-time tasks called "Parallel Graphs" [1], based on the Least Laxity First priority assignment policy "LLF", we apply LLF policy on each subtask in the graphs individually, taking in consideration their precedence constraints. This model of tasks is a combination of graphs and parallelism, in which each subtask in the graph can execute sequentially or parallel according to its number of processors defined by the model. So we study parallelism possibilities in order to find the best structure of the tasks according to the practical specifications of the system.

On using adversary simulators to evaluate global fixed-priority and FPZL scheduling of multiprocessors

There are many real-time systems where it is useful to have an estimate for the worst-case response time of each task. Simulators can be used to establish a lower bound on the worst-case response time. But classic simulators apply arrival patterns originally conceived for uniprocessor and fail to achieve a good estimate for the worst-case response time when multiprocessors are used. An adversary simulator generates arrival patterns to stress the processing capacity of the system and, in this way, to obtain tighter estimates. In this paper we present a new heuristic for adversary simulators specifically designed for fixed-priority zero-laxity (FPZL) scheduling. This new adversary algorithm is simple and fast, and it works with both deadline monotonic (DMPO) and deadline minus computation monotonic (DCMPO) priority assignment policies. The evaluation shows that the adversary simulator proposed in this paper is more effective when FPZL scheduling is used. We also compare four scheduling approaches (FP-DMPO, FP-DCMPO, FPZL-DMPO and FPZL-DCMPO) using an appropriate adversary simulator for each one.

Improved priority assignment for global fixed priority pre-emptive scheduling in multiprocessor real-time systems

This paper is an extended version of a paper that appeared in the proceedings of the IEEE Real-Time Systems Symposium 2009. This paper has been updated with respect to advances made in schedulability analysis, and contains a number of significant additional results. The paper addresses the problem of priority assignment in multiprocessor real-time systems using global fixed task-priority pre-emptive scheduling. We prove that Audsley's Optimal Priority Assignment (OPA) algorithm, originally devised for uniprocessor scheduling, is applicable to the multiprocessor case, provided that three conditions hold with respect to the schedulability tests used. Our empirical investigations show that the combination of optimal priority assignment policy and a simple compatible schedulability test is highly effective in terms of the number of tasksets deemed to be schedulable. We also examine the performance of heuristic priority assignment policies such as Deadline Monotonic, and an extension of the TkC priority assignment policy called DkC that can be used with any schedulability test. Here we find that Deadline Monotonic priority assignment has relatively poor performance in the multiprocessor case, while DkC priority assignment is highly effective.

Distributed real time database systems: background and literature review

Today's real-time systems (RTS) are characterized by managing large volumes of dispersed data making real-time distributed data processing a reality. Large business houses need to do distributed processing for many reasons, and they often must do it in order to stay competitive. So, efficient database management algorithms and protocols for accessing and manipulating data are required to satisfy timing constraints of supported applications. Therefore, new research in distributed real-time database systems (DRTDBS) is needed to investigate possible ways of applying database systems technology to real-time systems. This paper first discusses the performance issues that are important to DRTDBS, and then surveys the research that has been done so far on the issues like priority assignment policy, commit protocols and optimizing the use of memory in non-replicated/replicated environment pertaining to distributed real time transaction processing. In fact, this study provides a foundation for addressing performance issues important for the management of very large real time data and pointer to other publications in journals and conference proceedings for further investigation of unanswered research questions.

Controller Area Network (CAN) schedulability analysis: Refuted, revisited and revised

Controller Area Network (CAN) is used extensively in automotive applications, with in excess of 400 million CAN enabled microcontrollers manufactured each year. In 1994 schedulability analysis was developed for CAN, showing how worst-case response times of CAN messages could be calculated and hence guarantees provided that message response times would not exceed their deadlines. This seminal research has been cited in over 200 subsequent papers and transferred to industry in the form of commercial CAN schedulability analysis tools. These tools have been used by a large number of major automotive manufacturers in the design of in-vehicle networks for a wide range of cars, millions of which have been manufactured during the last decade.This paper shows that the original schedulability analysis given for CAN messages is flawed. It may provide guarantees for messages that will in fact miss their deadlines in the worst-case. This paper provides revised analysis resolving the problems with the original approach. Further, it highlights that the priority assignment policy, previously claimed to be optimal for CAN, is not in fact optimal and cites a method of obtaining an optimal priority ordering that is applicable to CAN. The paper discusses the possible impact on commercial CAN systems designed and developed using flawed schedulability analysis and makes recommendations for the revision of CAN schedulability analysis tools.

A Feasible Schedulability Analysis for Fault-Tolerant Real-Time Systems

Based on the worst-case response time schedulability analysis for fault-tolerant real-time systems, a new fault-tolerant priority assignment algorithm is proposed. This algorithm can be used, together with the schedulability analysis, to effectively improve system fault resilience when the two traditional fault-tolerant priority assignment policies can't improve system fault resilience. A fault-tolerant priority configuration search algorithm is also presented for the proposed analysis. The effectiveness of the proposed approach is evaluated by simulation.

An integrated DBP for streams with (m, k)-firm real-time guarantee

(m, k)-firm real-time or weakly hard real-time (WHRT) guarantee is becoming attractive as it closes the gap between hard and soft (or probabilistic) real-time guarantee, and enables finer granularity of real-time QoS through adjusting m and k. For multiple streams with (m, k)-firm constraint sharing a single server, an on-line priority assignment policy based on the most recent k-length history of each stream called distance based priority (DBP) has been proposed to assign priority. In case of priority equality among these head-of-queue instances, Earliest Deadline First (EDF) is used. Under the context of WHRT schedule theory, DBP is the most popular, gets much attention and has many applications due to its straightforward priority assignment policy and easy implementation. However, DBP combined with EDF cannot always provide good performance, mainly because the initial DBP does not underline the rich information on deadline met/missed distribution, specially streams in various failure states which will travel different distances to restore success states. Considering how to effectively restore the success state of each individual stream from a failure state, an integrated DBP utilizing deadline met/missed distribution is proposed in this paper. Simulation results validated the performance improvement of this proposal.

An optimal fixed-priority assignment algorithm for supporting fault-tolerant hard real-time systems

The main contribution of this paper is twofold. First, we present an appropriate schedulability analysis, based on response time analysis, for supporting fault-tolerant hard real-time systems. We consider systems that make use of error-recovery techniques to carry out fault tolerance. Second, we propose a new priority assignment algorithm which can be used, together with the schedulability analysis, to improve system fault resilience. These achievements come from the observation that traditional priority assignment policies may no longer be appropriate when faults are being considered. The proposed schedulability analysis takes into account the fact that the recoveries of tasks may be executed at higher priority levels. This characteristic is very important since, after an error, a task certainly has a shorter period of time to meet its deadline. The proposed priority assignment algorithm, which uses some properties of the analysis, is very efficient. We show that the method used to find out an appropriate priority assignment reduces the search space from O(n!) to O(n/sup 2/), where n is the number of task recovery procedures. Also, we show that the priority assignment algorithm is optimal in the sense that the fault resilience of task sets is maximized as for the proposed analysis. The effectiveness of the proposed approach is evaluated by simulation.

Real-Time Transaction Scheduling: A Framework for SynthesizingStatic and Dynamic Factors

Real-time databases are poised to be an important component of complex embedded real-time systems. In real-time databases (as opposed to real-time systems), transactions must satisfy the ACID properties in addition to satisfying the timing constraints specified for each transaction (or task). Although several approaches have been proposed to combine real-time scheduling and database concurrency control methods, to the best of our knowledge, none of them provide a framework for taking into account the dynamic cost associated with aborts, rollbacks, and restarts of transactions. In this paper, we propose a framework in which both static and dynamic costs of transactions can be taken into account. Specifically, we present: i) a method for pre-analyzing transactions based on the notion of branch-points for data accessed up to a branch point and predicting expected data access to be incurred for completing the transaction, ii) a formulation of cost that includes static and dynamic factors for prioritizing transactions, iii) a scheduling algorithm which uses the above two, and iv) simulation of the algorithm for several operating conditions and workload. Our dynamic priority assignment policy (termed the cost conscious approach or CCA) adapts well to fluctuations in the system load without causing excessive numbers of transaction restarts. Our simulations indicate that i) CCA performs better than the EDF-HP algorithm for both soft and firm deadlines, ii) CCA is more fair than EDF-HP, iii) CCA is better than EDF-CR for soft deadline, even though CCA requires and uses less information, and iv) CCA is especially good for disk-resident data.

Optimal priority assignment for aperiodic tasks with firm deadlines in fixed priority pre-emptive systems

Abstract An optimal priority assignment policy is presented for independent aperiodic tasks with arbitrary ready times and firm deadlines, scheduled on a uniprocessor along with a set of hard periodic/sporadic tasks. The latter tasks are assumed to have been assigned unique fixed priorities according to some arbitrary policy and guaranteed, via off-line feasibility analysis, to meet their deadlines. In contrast, priority assignment and acceptance testing of aperiodic tasks must be carried out on-line. The priority assignment policy introduced is shown to be optimal both in terms of maximising the computation time which can be made available before the aperiodic deadline and with respect to guaranteeing subsequent aperiodic arrivals.

Multiclass query scheduling in real-time database systems

In recent years, a demand for real-time systems that can manipulate large amounts of shared data has led to the emergence of real-time database systems (RTDBS) as a research area. This paper focuses on the problem of scheduling queries in RTDBSs. We introduce and evaluate a new algorithm called Priority Adaptation Query Resource Scheduling (PAQRS) for handling both single class and multiclass query workloads. The performance objective of the algorithm is to minimize the number of missed deadlines, while at the same time ensuring that any deadline misses are scattered across the different classes according to an administratively-defined miss distribution. This objective is achieved by dynamically adapting the system's admission, memory allocation, and priority assignment policies according to its current resource configuration and workload characteristics. A series of experiments confirms that PAQRS is very effective for real-time query scheduling. >