Dynamic Scheduling Approach Research Articles

Intelligent controllers for bi-objective dynamic scheduling on a single machine with sequence-dependent setups

This article addresses the problem of dynamic job scheduling on a single machine with Poisson arrivals, stochastic processing times and due dates, in the presence of sequence-dependent setups. The objectives of minimizing mean earliness and mean tardiness are considered. Two approaches for dynamic scheduling are proposed, a Reinforcement Learning-based and one based on Fuzzy Logic and multi-objective evolutionary optimization. The performance of the two scheduling approaches is tested against the performance of 15 dispatching rules in four simulation scenarios with different workload and due date pressure conditions. The scheduling methods are compared in terms of Pareto optimal-oriented metrics, as well as in terms of minimizing mean earliness and mean tardiness independently. The experimental results demonstrate the merits of the proposed methods.

Dynamic Scheduling Approaches to Wafer Test Scheduling with Unpredictable Error

Scheduling of semiconductor wafer testing processes can be seen as a resource constraint project scheduling problem (RCPSP). However, it includes uncertainties caused by human factors, wafer errors and so on. Because some uncertainties are not simply quantitative, range estimation of the parameters would not be very useful. Considering such uncertainties, finding a good situation-dependent dispatching rule would be more suitable than solving the RCPSP under uncertainties. In this paper we apply the Pitts approach, one of the genetic algorithms, to the situation-dependent dispatching rule acquisition. We compare the obtained rule with the simple dispatching rules and examine the effectiveness and usefulness of the obtained rule in the problems with unpredictable wafer errors.

A software-based dynamic-warp scheduling approach for load-balancing the Viola–Jones face detection algorithm on GPUs

Face detection is a key component in applications such as security surveillance and human–computer interaction systems, and real-time recognition is essential in many scenarios. The Viola–Jones algorithm is an attractive means of meeting the real time requirement, and has been widely implemented on custom hardware, FPGAs and GPUs. We demonstrate a GPU implementation that achieves competitive performance, but with low development costs. Our solution treats the irregularity inherent to the algorithm using a novel dynamic warp scheduling approach that eliminates thread divergence. This new scheme also employs a thread pool mechanism, which significantly alleviates the cost of creating, switching, and terminating threads. Compared to static thread scheduling, our dynamic warp scheduling approach reduces the execution time by a factor of 3. To maximize detection throughput, we also run on multiple GPUs, realizing 95.6 FPS on 5 Fermi GPUs.

A New Approach for Dynamic Job Scheduling in a Volunteer Desktop Grid

Nodes in a volunteer desktop grid are based on the volunteer participation of desktop nodes owned by the individual users. The volunteer nodes contribute their idle resources for public execution during their free time and withdraw during their busy time due to high priority node owner’s private execution. This property makes desktop grid dynamic in nature. Job scheduling is mainly influenced by two factors: node dynamism and heterogeneity. The job generation rate at each node is different from other nodes in the desktop Grid and hence the load at each node changes with time. This situation leads to increased computational demands at some nodes than from others and makes the grid often to get into unbalanced environment. Job migration to remote nodes involves job transmission latency. Since grid is a dynamic environment, when the job reaches to the remote node for execution the node might become busy and the selected target node may not complete the execution of the job at the expected speed. Therefore, the selection of a target node for job migration plays an important role in improving the overall performance of the desktop Grid. In this paper we present a new approach for dynamic job scheduling that considers node dynamism and job transmission latency into account for making scheduling decisions. The algorithm is compared against the Resource Exclusion and non migration algorithms and the simulation results shows that the proposed algorithm has got considerable improvement over the other two.

Investigating and Classifying the Applications of Flexible Manufacturing Systems in Customization

The recent manufacturing environment is characterized as having diverse products due to mass customization, short production lead-time, and unstable customer demand. Today, the need for flexibility, quick responsiveness, and robustness to system uncertainties in production scheduling decisions has increased significantly. In traditional job shops, tooling is usually assumed as a fixed resource. However, when tooling resource is shared among different machines, a greater product variety, routing flexibility with a smaller tool inventory can be realized. Such a strategy is usually enabled by an automatic tool changing mechanism and tool delivery system to reduce the time for tooling setup, hence allows parts to be processed in small batches. In this research, a dynamic scheduling problem under flexible tooling resource constraints is studied. An integrated approach is proposed to allow two levels of hierarchical, dynamic decision making for job scheduling and tool flow control in Automated Manufacturing Systems. It decomposes the overall problem into a series of static sub-problems for each scheduling window, handles random disruptions by updating job ready time, completion time, and machine status on a rolling horizon basis, and considers the machine availability explicitly in generating schedules. Two types of manufacturing system models are used in simulation studies to test the effectiveness of the proposed dynamic scheduling approach. First, hypothetical models are generated using some generic shop flow structures (e.g. flexible flow shops, job shops, and single-stage systems) and configurations. (Insup Um, Hyeonjae Cheon, Hongchul Lee, 2009; Stefan Bock, 2008). They are tested to provide the empirical evidence about how well the proposed approach performs for the general automated manufacturing systems where parts have alternative routings. Second, a model based on a real industrial flexible manufacturing system was used to test the effectiveness of the proposed approach when machine types, part routing, tooling, and other production parameters closely mimic to the real flexible manufacturing operations.

Adaptive co-scheduling for periodic application and update transactions in real-time database systems

In this paper, we study the co-scheduling problem of periodic application transactions and update transactions in real-time database systems for surveillance of critical events. To perform the surveillance functions effectively, it is important to meet the deadlines of the application transactions and maintain the quality of the real-time data objects for their executions. Unfortunately, these two goals are conflicting and difficult to be achieved at the same time. To address the co-scheduling problem, we propose a real-time co-scheduling algorithm, called Adaptive Earliest Deadline First Co-Scheduling (AEDF-Co). In AEDF-Co, a dynamic scheduling approach is adopted to adaptively schedule the update and application jobs based on their deadlines. The performance goal of AEDF-Co is to determine a schedule for given sets of periodic application and update transactions such that the deadline constraints of all the application transactions are satisfied and at the same time the quality of data (QoD) of the real-time data objects is maximized. Extensive simulation experiments have been performed to evaluate the performance of AEDF-Co. The results show that by adaptively adjusting the release times of update jobs and scheduling the update and application jobs dynamically based on their urgencies, AEDF-Co is effective in achieving the performance goals and maximizing the overall system performance.

A weighted-fair-queuing (WFQ)-based dynamic request scheduling approach in a multi-core system

A popular website is expected to simultaneously deal with a large number of dynamic requests in the reasonable mean response time. The performance of websites mainly depends on hardware performance and the processing strategy of dynamic requests. In order to improve the hardware performance, more and more web servers are adopting multi-core CPUs. Moreover, the scheduling algorithm of requests on the first-come–first-served (FCFS) basis is still utilized. Although FCFS is a reasonable and fair strategy for request sequences, it takes into account neither the distribution of the dynamic request service times nor the characteristics of multi-core CPUs. In the present paper, in order to solve the above-mentioned problems, a new dynamic request scheduling approach is proposed. The new scheduling approach, according to the distribution of the dynamic request service time, schedules the dynamic requests based on a weighted-fair-queuing (WFQ) system, and exploits the performance of multi-core CPUs by means of the hard affinity method in the O/S. Simulation experiments have been done to evaluate the new scheduling approach, and the results obtained prove that the new scheduling approach could eliminate the ping-pong effect and efficiently reduce the mean response time.

Two-state dynamic gain scheduling control applied to an F16 aircraft model

The feasibility and benefits of applying a novel multi-variable dynamic gain scheduling (DGS) approach to a complex ‘industry-scale’ aircraft model are investigated; the latter model being a non-linear representation of the intrinsically unstable F16 aircraft incorporating detailed aerodynamic data. DGS is a novel control approach, which involves scheduling controller gains with one (or more) of the system states whilst accounting for the ‘hidden coupling terms’ ensuring a near-ideal response. It is effective for non-linear systems exhibiting rapid dynamic changes between operating points. Recently, this approach has been extended to a multi-variable and multi-input context. Hence, unlike previous DGS work on realistic aircraft models, relevant feedback gains are to be scheduled with all (i.e. two) state variables in order to demonstrate the ability of the approach to compensate for non-linearity during rapid manoeuvres and consequently achieving better flying qualities over a range of conditions than standard gain scheduling. Time history simulations are used to draw comparisons with the more traditional ‘static’ gain scheduling and input gain scheduling methods.

Exploring decentralized dynamic scheduling for grids and clouds using the community-aware scheduling algorithm

Job scheduling strategies have been studied for decades in a variety of scenarios. Due to the new characteristics of the emerging computational systems, such as the grid and cloud, metascheduling turns out to be an important scheduling pattern because it is responsible for orchestrating resources managed by independent local schedulers and bridges the gap between participating nodes. Equally, to overcome issues such as bottleneck, single point failure, and impractical unique administrative management, which are normally led by conventional centralized or hierarchical schemes, the decentralized scheduling scheme is emerging as a promising approach because of its capability with regards to scalability and flexibility.In this work, we introduce a decentralized dynamic scheduling approach entitled the community-aware scheduling algorithm (CASA). The CASA is a two-phase scheduling solution comprised of a set of heuristic sub-algorithms to achieve optimized scheduling performance over the scope of overall grid or cloud, instead of individual participating nodes. The extensive experimental evaluation with a real grid workload trace dataset shows that, when compared to the centralized scheduling scheme with BestFit as the metascheduling policy, the use of CASA can lead to a 30%–61% better average job slowdown, and a 68%–86% shorter average job waiting time in a decentralized scheduling manner without requiring detailed real-time processing information from participating nodes.

An Approach of Chunk-based Task Runtime Prediction for Self-Scheduling on Multi-core Desk Grid

Self-Scheduling is a dynamic and adaptive loop scheduling approach to reduce the total execution time for a task running in the cluster or grid environment. This paper focuses on how to use and optimize Self-scheduling technologies to allocate tasks reasonable and achieve better parallel performance. It introduces the prediction algorithms and proposes a novel Chunk-based Task Runtime Prediction (CTRP) algorithm according to the characters of desk grid and multi-core environment. Our experimental results show that our approach can predict the execution time more accurate and achieve better load balancing than that of others when most slave nodes’ load is changing frequently and rulelessly in the multi-core desk grid.

A multi-agent based approach for dynamic multi-project scheduling

The earlier research in the area of multi-project scheduling is primarily focused on developing efficient priority rules for setting of due dates for new projects, resource allocation to the active projects, and activity scheduling. The priority rule-based heuristics suffer from the drawbacks of providing poor quality schedules and inflexibility. Recently, the artificial intelligence techniques such as multi-agent technology have been applied for scheduling the static multi-project problems. This paper proposes a multi-agent based framework to solve a distributed resource constrained dynamic multi-project scheduling problem. The architecture of the system is developed and the cooperative negotiation framework of various self-managing agents is described.

A multi-objective dynamic scheduling approach using multiple attribute decision making in semiconductor manufacturing

This paper proposes a multi-objective dynamic scheduling approach that combines three attributes based on a hybrid multiple attribute decision making (MADM) technique. With consideration of two kinds of uncertainties, three advanced attributes are specifically designed to optimize four key performance measures. Each attribute is converted into a relative closeness to the ideal alternative, and collaborate with the others at each scheduling instant. Furthermore, a cutoff method is used to make the proposed approach adapt to full-scale semiconductor manufacturing systems (SMSs) better. Results in two SMS models show that the proposed approach is validated for the real-time multi-objective scheduling problem.

Performance evaluation of dynamic scheduling approaches in vehicle-based internal transport systems

This paper studies the performance of static and dynamic scheduling approaches in vehicle-based internal transport (VBIT) systems and is one of the first to systematically investigate under which circumstances, which scheduling method helps in improving performance. In practice, usually myopic dispatching heuristics are used, often using look-ahead information. We argue more advanced scheduling methods can help, depending on circumstances. We introduce three basic scheduling approaches (insertion, combined and column generation) for the static problem. We then extend these to a dynamic, real-time setting with rolling horizons. We propose two further real-time scheduling approaches: dynamic assignment with and without look-ahead. The performances of the above five scheduling approaches are compared with two of the best performing look-ahead dispatching rules known from the literature. The performance of the various approaches depends on the facility layout and work distribution. However, column generation, the combined heuristic, and the assignment approach with look-ahead consistently outperform dispatching rules. Column generation can require substantial calculation time but delivers very good performance if sufficient look-ahead information is available. For large scale systems, the combined heuristic and the dynamic assignment approach with look ahead are recommended and have acceptable calculation times.

Dynamic Airline Scheduling

Demand stochasticity is a major challenge for the airlines in their quest to produce profit maximizing schedules. Even with an optimized schedule, many flights on departure have empty seats while others suffer a lack of seats to accommodate passengers who desire to travel. We approach this challenge, recognizing that demand forecast quality for a particular departure date improves as it approaches, by developing a dynamic scheduling approach that reoptimizes elements of the flight schedule during the passenger booking process. The goal is to match capacity to demand given the many operational constraints that restrict possible assignments. We leverage flight retiming as a new dynamic scheduling mechanism and develop a reoptimization model that integrates both flight retiming and refleeting. Our reoptimization approach, redesigning the flight schedule at regular intervals, uses information from both revealed booking data and improved forecasts available at later reoptimizations. We conduct experiments using data from a major U.S. airline and demonstrate that significant potential profitability improvements are achieved.

Dynamic task scheduling with load balancing using parallel orthogonal particle swarm optimisation

This paper presents a novel approach for dynamic task scheduling using particle swarm optimisation. Particle swarm optimisation (PSO) is a population-based meta-heuristic method which can be used to solve np-hard problems. The algorithm has been developed to dynamically schedule heterogeneous tasks on to heterogeneous processors in a distributed setup. Load balancing which is a major issue in task scheduling is also considered. The nature of the tasks are independent and non pre-emptive. Different approaches using PSO has been tried namely PSO with fixed inertia, PSO with variable inertia, PSO with elitism, MPSO, parallel PSO, hybrid PSO, orthogonal PSO and parallel orthogonal PSO. The performance of PSO and its variants is also compared with the genetic algorithm concept. The objective of the algorithms is to minimise the make-span of the entire schedule. Benchmark problems have been taken and validated. The result depicts that the dynamic task scheduling implemented using parallel orthogonal particle swarm optimisation technique is cost-effective in nature when compared to the other algorithms tested.

Hardware Supported Task Scheduling on Dynamically Reconfigurable SoC Architectures

Dynamically reconfigurable system-on-a-chip (RSoC) technology features embedded microprocessors that are dispersed on the same die with significant amounts of programmable logic fabric. In this paper, we present a strategy to solve the recently emerging problem of how to utilize the flexible but still limited RSoC resources in an effective manner for a multi-task application. The major contribution of this paper is the development of a dynamic task scheduling algorithm that can be implemented in fixed or reconfigurable hardware that will perform the online scheduling of task systems onto the RSoC type architecture. The results from extensive simulations demonstrate the benefits of the proposed dynamic scheduling approach as compared with that of other static scheduling techniques taken from the technical literature.

Adaptive scheduling and tool flow control in flexible job shops

The recent manufacturing environment is characterized as having diverse products due to mass customization, short production lead-time, and ever-changing customer demand. Today, the need for flexibility, quick responsiveness, and robustness to system uncertainties in production scheduling decisions has dramatically increased. In traditional job shops, tooling is usually assumed as a fixed resource. However, when a tooling resource is shared among different machines, a greater product variety, routing flexibility with a smaller tool inventory can be realized. Such a strategy is usually enabled by an automatic tool changing mechanism and tool delivery system to reduce the time for tooling set-up, hence it allows parts to be processed in small batches. In this paper, a dynamic scheduling problem under flexible tooling resource constraints is studied and presented. An integrated approach is proposed to allow two levels of hierarchical, dynamic decision making for job scheduling and tool flow control in flexible job shops. It decomposes the overall problem into a series of static sub-problems for each scheduling horizon, handles random disruptions by updating job ready time, completion time, and machine status on a rolling horizon basis, and considers the machine availability explicitly in generating schedules. The effectiveness of the proposed dynamic scheduling approach is tested in simulation studies under a flexible job shop environment, where parts have alternative routings. The study results show that the proposed scheduling approach significantly outperforms other dispatching heuristics, including cost over time (COVERT), apparent tardiness cost (ATC), and bottleneck dynamics (BD), on due-date related performance measures. It is also found that the performance difference between the proposed scheduling approach and other heuristics tend to become more significant when the number of machines is increased. The more operation steps a system has, the better the proposed method performs, relative to the other heuristics.

Dynamic Scheduling Real-Time Task Using Primary-Backup Overloading Strategy for Multiprocessor Systems

The scheduling of real-time tasks with fault-tolerant requirements has been an important problem in multiprocessor systems. The primary-backup (PB) approach is often used as a fault-tolerant technique to guarantee the deadlines of tasks despite the presence of faults. In this paper we propose a dynamic PB-based task scheduling approach, wherein an allocation parameter is used to search the available time slots for a newly arriving task, and the previously scheduled tasks can be re-scheduled when there is no available time slot for the newly arriving task. In order to improve the schedulability we also propose an overloading strategy for PB-overloading and Backup-backup (BB) overloading. Our proposed task scheduling algorithm is compared with some existing scheduling algorithms in the literature through simulation studies. The results have shown that the task rejection ratio of our real-time task scheduling algorithm is almost 50% lower than the compared algorithms.

Interrupt modeling for efficient high-level scheduler design space exploration

Single Chip Heterogeneous Multiprocessors executing a wide variety of software are increasingly common in consumer electronics. Because of the mix of real-time and best effort software across the entire chip, a key design element of these systems is the choice of scheduling strategy. Without task migration, the benefits of single chip processing cannot be fully realized. Previously, high-level modeling environments have not been capable of modeling asynchronous events such as interrupts and preemptive scheduling while preserving the performance benefits of high level simulation. This paper shows how extensions to Modeling Environment for Software and Hardware (MESH) enable precise modeling of these asynchronous events while running more than 1000 faster than cycle-accurate simulation. We discuss how we achieved this and illustrate its use in modeling preemptive scheduling. We evaluate the potential of migrating running tasks between processors to improve performance in a multimedia cell phone example. We show that by allowing schedulers to rebalance processor loads as new tasks arrive significant performance gains can be achieved over statically partitioned and dynamic scheduling approaches. In our example, we show that system response time can be improved by as much as 1.96 times when a preemptive migratory scheduler is used, despite the overhead incurred by scheduling tasks across multiple processors and transferring state during the migration of running tasks. The contribution of this work is to provide a framework for evaluating preemptive scheduling policies and task migration in a high level simulator, by combining the new ability to model interrupts with dramatically increased efficiency in the high-level modeling of scheduling and commuincation MESH already provides.

Simulation Studies of Heuristic Approaches for Dynamic Scheduling of Container Terminal Operations

The growth of marine containers transportation along with increasing complex container terminal logistic system (CTLS) has called for more effective approaches for scheduling container terminal resources. This paper discusses the problem of scheduling quay crane, yard crane and container truck for improving the container terminal productivity. One dynamic scheduling method and specific heuristic scheduling rules for dynamically assigning quay crane, yard crane and container trucks are proposed according to stowage plans and storage plans. A simulation model of a container terminal in Shanghai is built by the application of WITNESS2002, a simulation software package, to evaluate the efficiency of the dynamic scheduling method. From simulation results, the production efficiency increased about 8% by the dynamic scheduling method than static one previously discussed in CTLS.