Non-preemptive Scheduling Research Articles

Timing-aware analysis of shared cache interference for non-preemptive scheduling

AbstractIn multi-core architectures, the last-level cache (LLC) is often shared between cores. Sharing the LLC leads to inter-core interference, which impacts system performance and predictability. This means that tasks running in parallel on different cores may experience additional LLC misses as they compete for cache space. To compute a task’s worst-case execution time (WCET), a safe bound on the inter-core cache interference has to be determined. We propose an interference analysis for set-associative shared least-recently-used caches. The analysis leverages timing information to establish tight bounds on the worst-case interference and classifies individual accesses as either cache hits or potential cache misses. We evaluated the analysis performance for systems containing 2 and 4 cores using shared caches up to 64 KB. The evaluation shows an average WCET reduction of up to 23.3% for dual-core systems and 8.5% for quad-core systems.

A novel exact formulation for parallel machine scheduling problems

Machine scheduling is one of the most studied problems due to its technical challenges and prevalence in real life. In the literature, continuous- and discrete-time formulations are the two most known formulations for scheduling problems. However, continuous-time formulations often suffer from weak linear relaxations, while discrete-time formulations struggle with large numbers of variables. In contrast, the bucket-indexed formulation is an alternative that mitigates both issues by working with partial time discretization. We propose a mixed-integer linear programming model based on a bucket-indexed formulation to solve a nonpreemptive scheduling problem of identical parallel machines considering release dates, deadlines, precedence, eligibility, and machine availability constraints. We evaluate the proposed formulation against real-world instances comprising more than 400 jobs and 100 machines, comparing its performance against equivalent continuous- and discrete-time formulations. Remarkably, our formulation can be solved to optimality for all instances, outperforming both continuous- and discrete-time formulations.

EPTAS for parallel identical machine scheduling with time restrictions

We consider the non-preemptive scheduling problem on identical machines where there is a parameter B and each machine in every unit length time interval can process up to B different jobs. The goal function we consider is the makespan minimization and we develop an EPTAS for this problem. Prior to our work a PTAS was known only for the case of one machine and constant values of B, and even the case of non-constant values of B on one machine was not known to admit a PTAS.

Bicriteria fabrication scheduling of two-component jobs on a single machine

This paper studies the bicriteria problem of non-preemptively scheduling n jobs, each of which is associated with a due date and comprises a standard and a specific component, on a single fabrication machine to minimize makespan and maximum lateness simultaneously. The specific components are processed individually and the standard components are grouped into batches for processing. A setup time is required before each batch of standard components is processed. A standard component is available (i.e., ready for delivery to the next production stage) only when the batch it belongs to is totally completed, whereas a specific component is available on completion of its processing. The completion time of a job is defined as the moment when both its two components have been processed and are available. An O(n2logn)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$O(n^2\\log n)$$\\end{document}-time algorithm with linear memory requirements is presented which can generate all Pareto optimal points and find a corresponding Pareto optimal schedule for each Pareto optimal point.

Methods to Realize Preemption in Phased Execution Models

Phased execution models are a good solution to tame the increased complexity and contention of commercial off-the-shelf (COTS) multi-core platforms, e.g., Acquisition-Execution-Restitution (AER) model, PRedictable Execution Model (PREM). Such models separate execution from access to shared resources on the platform to minimize contention. All data and instructions needed during an execution phase are copied into the local memory of the core before starting to execute. Phased execution models are generally used with non-preemptive scheduling to increase predictability. However, the blocking time in non-preemptive systems can reduce schedulability. Therefore, an investigation of preemption methods for phased execution models is warranted. Although, preemption for phased execution models must be carefully designed to retain its execution semantics, i.e., the handling of local memory during preemption becomes non-trivial. This paper investigates different methods to realize preemption in phased execution models while preserving their semantics. To the best of our knowledge, this is the first paper to explore different approaches to implement preemption in phased execution models from the perspective of data management. We introduce two strategies to realize preemption of execution phases based on different methods of handling local data of the preempted task. Heuristics are used to create time-triggered schedules for task sets that follow the proposed preemption methods. Additionally, a schedulability-aware preemption heuristic is proposed to reduce the number of preemptions by allowing preemption only when it is beneficial in terms of schedulability. Evaluations on a large number of synthetic task sets are performed to compare the proposed preemption models against each other and against a non-preemptive version. Furthermore, our schedulability-aware preemption heuristic has higher schedulability with a clear margin in all our experiments compared to the non-preemptive and fully-preemptive versions.

Intelligent Road Management System for Autonomous, Non-Autonomous, and VIP Vehicles

Currently, autonomous vehicles, non-autonomous vehicles, and VIP (emergency) autonomous cars are using intelligent road management techniques to interact with one another and enhance the effectiveness of the traffic system. All sorts of vehicles are managed and under control using the intersection management unit approach. This study focuses on transportation networks where VIP cars are a major disruption, accounting for 40% of accidents and 80% of delays. Intelligent Mobility (IM) is a strategy promoted in this study that proposes setting up intelligent channels for all vehicle communication. As part of its function, the IM unit keeps tabs on how often each junction is used so that it may notify drivers on traffic conditions and ease their workload. The suggested layout may drastically cut average wait times at crossings, as shown in SUMO simulations. The entrance of a VIP car should disrupt all traffic, but the IM (intersection management) unit effectively manages all traffic by employing preemptive scheduling and non-preemptive scheduling techniques for all types of vehicles. We are employing Nishtar roads, the M4 motorway, Mexico, and Washington roads in our scenario. In comparison to all other routes, the simulation results demonstrate that the Washington road route is better able to manage all vehicle kinds. Washington’s traffic delays for 50 cars of all sorts are 4.02 s for autonomous vehicles, 3.62 s for VIP autonomous vehicles, and 4.33 s for non-autonomous vehicles.

EPTAS for load balancing problem on parallel machines with a non-renewable resource

The problem considered is the non-preemptive scheduling of independent jobs that consume a resource (which is non-renewable and replenished regularly) on parallel uniformly related machines. The input defines the speed of machines, size of jobs, the quantity of the resource required by the jobs, the replenished quantities, and replenishment dates of the resource. Every job can start processing only after the required quantity of the resource is allocated to the job. The objective function is a generalization of makespan minimization and minimization of the lp-norm of the vector of loads of the machines. We present an EPTAS for this problem. Prior to our work only a PTAS was known in this non-renewable resource settings only for the special case of our problem of makespan minimization on identical machines.

A NOTE ON HARDNESS OF MULTIPROCESSOR SCHEDULING WITH SCHEDULING SOLUTION SPACE TREE

We study the computational complexity of the non-preemptive scheduling problem of a listof independent jobs on a set of identical parallel processors with a makespan minimizationobjective. We make a maiden attempt to explore the combinatorial structure showing theexhaustive solution space of the problem by defining the Scheduling Solution Space Tree(SSST) data structure. The properties of the SSST are formally defined and characterizedthrough our analytical results. We develop a unique technique to show the problemNP using the SSST and the Weighted Scheduling Solution Space Tree (WSSST) datastructures. We design the first non-deterministic polynomial-time algorithm named MagicScheduling (MS) for the problem based on the reduction framework. We also define anew variant of multiprocessor scheduling by including the user as an additional inputparameter. We formally establish the complexity class of the variant by the reductionprinciple. Finally, we conclude the article by exploring several interesting open problemsfor future research investigation.

Algorithms for two-agent unbounded serial-batch scheduling with makespan and maximum lateness objectives

<abstract><p>We study the problem of non-preemptively scheduling jobs from two agents on an unbounded serial-batch machine. Agents $ A $ and $ B $ have $ n_A $ and $ n_B $ jobs. The machine can process any number of jobs sequentially as a batch, and the processing time of the batch is equal to the total processing time of the jobs in it. Each batch requires a setup time before it is processed. Compatibility means that the jobs from different agents can be processed in a common batch; Otherwise, the jobs from different agents are incompatible. Both the compatible and incompatible models are considered, under both the batch availability and item availability assumptions. Batch availability means that any job in a batch is not available until all the jobs in this batch are completed. Item availability means that a job in a batch becomes available immediately after it is completed processing. The completion time of a job is defined to be the moment when it is available. The goal is to minimize the makespan of agent $ A $ and the maximum lateness of agent $ B $ simultaneously. For the compatible model with batch availability, an $ O(n_A+n_B^2\log n_B) $-time algorithm is presented which improves the existing $ O(n_A+n_B^4\log n_B) $-time algorithm. A slight modification of the algorithm solves the incompatible model with batch availability in $ O(n_A+n_B^2\log n_B) $ time, which has the same time complexity as the existing algorithm. For the compatible model with item availability, the analysis shows that it is easy and admits an $ O(n_A+n_B\log n_B) $-time algorithm. For the incompatible model with item availability, an $ O(n_A+n_B\log n_B) $-time algorithm is also obtained which improves the existing $ O(n_A+n_B^2) $-time algorithm. The algorithms can generate all Pareto optimal points and find a corresponding Pareto optimal schedule for each Pareto optimal point.</p></abstract>

Energy efficient non-preemptive scheduling of imprecise mixed-criticality real-time tasks

Previous studies focus on the classical mixed-criticality task model with preemptive dynamic priority scheduling in which all low-criticality (LO) tasks are dropped in the high-criticality mode (HI-mode). This paper focuses on the imprecise mixed-criticality (IMC) tasks model in which LO tasks reduce their execution budgets instead of being dropped. So far, the energy minimization problem of non-preemptive dynamic priority scheduling has received little attention. In this paper, we first develop schedulability tests of non-preemptive earliest deadline first with virtual deadline (NP-EDFVD) for IMC tasks, which is the first attempt for non-preemptive dynamic priority scheduling in single processor IMC systems. Subsequently, the uniform single speed (USS) algorithm based on the schedulability tests of NP-EDFVD is proposed to reduce energy consumption while protecting the IMC correctness. Finally, the experimental results show that the USS algorithm saves an average of 25.62% energy compared to NP-EDFVD.

An improved greedy algorithm for stochastic online scheduling on unrelated machines

Most practical scheduling applications involve some uncertainty about the arriving times and lengths of the jobs. Stochastic online scheduling is a well-established model capturing this. Here the arrivals occur online, while the processing times are random. For this model, Gupta, Moseley, Uetz, and Xie recently devised an efficient policy for non-preemptive scheduling on unrelated machines with the objective to minimize the expected total weighted completion time. We improve upon this policy by adroitly combining greedy job assignment with αj-point scheduling on each machine. In this way we obtain a (3+5)(2+Δ)-competitive deterministic and an (8+4Δ)-competitive randomized stochastic online scheduling policy, where Δ is an upper bound on the squared coefficients of variation of the processing times. We also give constant performance guarantees for these policies within the class of all fixed-assignment policies. The αj-point scheduling on a single machine can be enhanced when the upper bound Δ is known a priori or the processing times are known to be δ-NBUE for some δ≥1. This implies improved competitive ratios for unrelated machines but may also be of independent interest.

Scheduling for Urban Air Mobility using Safe Learning

This work considers the scheduling problem for Urban Air Mobility (UAM) vehicles travelling between origin-destination pairs with both hard and soft trip deadlines. Each route is described by a discrete probability distribution over trip completion times (or delay) and over inter-arrival times of requests (or demand) for the route along with a fixed hard or soft deadline. Soft deadlines carry a cost that is incurred when the deadline is missed. An online, safe scheduler is developed that ensures that hard deadlines are never missed, and that average cost of missing soft deadlines is minimized. The system is modelled as a Markov Decision Process (MDP) and safe model-based learning is used to find the probabilistic distributions over route delays and demand. Monte Carlo Tree Search (MCTS) Earliest Deadline First (EDF) is used to safely explore the learned models in an online fashion and develop a near-optimal non-preemptive scheduling policy. These results are compared with Value Iteration (VI) and MCTS (Random) scheduling solutions.

Preemptive and non-preemptive scheduling on two unrelated parallel machines

Preemptive and non-preemptive scheduling on two unrelated parallel machines

Energy-Aware Nonpreemptive Scheduling of Mixed-Criticality Real-Time Task Systems

Energy-aware real-time scheduling for mixed-criticality (MC) systems with different criticality levels has drawn many researchers’ attentions. However, most of the studies focus on the preemptive MC task model and few studies consider the nonpreemptive MC task model, in which all jobs cannot be preempted until completion. In this article, we address the energy minimization problem for MC systems with nonpreemptive dynamic priority scheduling. First, we develop schedulability test of nonpreemptive earliest deadline first (NP-EDF) in single processor MC systems. Second, we extend the results to nonpreemptive earliest deadline first with virtual deadline (NP-EDFVD), which is the first attempt for nonpreemptive dynamic priority scheduling in single processor MC systems. Third, the energy-aware nonpreemptive scheduling algorithm (EANPS) based on NP-EDFVD is proposed to solve the energy minimization problem for MC systems with nonpreemptive dynamic priority scheduling. Finally, an industrial use-case and extensive simulations are used to validate the performance of the proposed algorithm, and the experimental results show that the EANPS algorithm consumes average 25.72% less energy than that of NP-EDFVD.

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.

Flexible scheduling of diagnostic tests in automotive manufacturing

The car of the future will be driven by software and offer a variety of customisation options. Enabling these customisation options forces modern automotive manufacturers to update their standardised scheduling concepts for testing and commissioning cars. A flexible scheduling concept means that every chosen customer configuration code must have its own testing procedure. This concept is essential to provide individual testing workflows where the time and resources are optimised for every car. Manual scheduling is complicated due to constraints on time, predecessor-successor relationships, mutual exclusion criteria, resources and status conditions on the car engineering and assembly line. Applied methods to handle the mathematical formulation for the corresponding industrial optimisation problem and its implementation are not yet available. This paper presents a procedure for automated and non-preemptive scheduling in the testing and commissioning of cars, which is built on a Boolean satisfiability problem on parallel and identical machines with temporal and resource constraints. The presented method is successfully implemented and evaluated on a variant assembly line of an automotive Original Equipment Manufacturer. This paper is the starting point for an automated workflow planning and scheduling process in automotive manufacturing.

Analysis of the feasibility to use metric approach for NP-hard makespan minimization problem

We consider the feasibility to find approximate non-preemptive schedules by metric approach for NP-complete problem of scheduling on two parallel identical machines with precedence delays for jobs or jobs of lengths 1 and 2 with makespan minimization. The execution of the job can be started only after the completion of any of its predecessors. We researched the application of methods looking for optimal solution for P2 | prec, pj = 1 | Cmax. We have tested Coffman's and Sethi's algorithms.

Wireless HART stack using multiprocessor technique with laxity algorithm

The use of a real-time operating system is required for the demarcation of industrial wireless sensor network (IWSN) stacks (RTOS). In the industrial world, a vast number of sensors are utilised to gather various types of data. The data gathered by the sensors cannot be prioritised ahead of time. Because all of the information is equally essential. As a result, a protocol stack is employed to guarantee that data is acquired and processed fairly. In IWSN, the protocol stack is implemented using RTOS. The data collected from IWSN sensor nodes is processed using non-preemptive scheduling and the protocol stack, and then sent in parallel to the IWSN's central controller. The real-time operating system (RTOS) is a process that occurs between hardware and software. Packets must be sent at a certain time. It's possible that some packets may collide during transmission. We're going to undertake this project to get around this collision. As a prototype, this project is divided into two parts. The first uses RTOS and the LPC2148 as a master node, while the second serves as a standard data collection node to which sensors are attached. Any controller may be used in the second part, depending on the situation. Wireless HART allows two nodes to communicate with each other.

Online Non-preemptive Scheduling on Unrelated Machines with Rejections

When a computer system schedules jobs there is typically a significant cost associated with preempting a job during execution. This cost can be incurred from the expensive task of saving the memory’s state or from loading data into and out of memory. Thus, it is desirable to schedule jobs non-preemptively to avoid the costs of preemption. There is a need for non-preemptive system schedulers for desktops, servers, and data centers. Despite this need, there is a gap between theory and practice. Indeed, few non-preemptive online schedulers are known to have strong theoretical guarantees. This gap is likely due to strong lower bounds on any online algorithm for popular objectives. Indeed, typical worst-case analysis approaches, and even resource-augmented approaches such as speed augmentation, result in all algorithms having poor performance guarantees. This article considers online non-preemptive scheduling problems in the worst-case rejection model where the algorithm is allowed to reject a small fraction of jobs. By rejecting only a few jobs, this article shows that the strong lower bounds can be circumvented. This approach can be used to discover algorithmic scheduling policies with desirable worst-case guarantees. Specifically, the article presents algorithms for the following three objectives: minimizing the total flow-time, minimizing the total weighted flow-time plus energy where energy is a convex function, and minimizing the total energy under the deadline constraints. The algorithms for the first two problems have a small constant competitive ratio while rejecting only a constant fraction of jobs. For the last problem, we present a constant competitive ratio without rejection. Beyond specific results, the article asserts that alternative models beyond speed augmentation should be explored to aid in the discovery of good schedulers in the face of the requirement of being online and non-preemptive.

Job-shop Scheduling Over a Heterogeneous Platform


 
 
 Real-time scheduling involves determining the allocation of platform resources in such a way tasks can meet their temporal restrictions.
 This work focuses on job-shop tasks model in which a task have a finite number of nonpreemptive different instances (jobs) that share a unique hard deadline and their time requirements are known until task arrival.
 Non-preemptive scheduling is considered because this characteristic is widely used in industry. Besides job-shop scheduling has direct impacts on the production efficiency and costs of manufacturing systems. So that the development of analysis for tasks with these characteristics is necessary.
 The aim of this work is to propose an online scheduling test able to guarantee the execution of a new arriving task, which is generated by human interaction with an embedded system, otherwise to discart it. An extension of the schedulability test proposed by Baruah in 2006 for non-preemptive periodic tasks over an identical platform is presented in this paper. Such extension is applied to non-preemptive tasks that have hard deadlines over a heterogeneous platform. To do that, some virtual changes over both the task set and the platform are effectuated.