Dynamic Scheduling Approach Research Articles

A dynamic scheduling approach for optimizing the material handling operations in a robotic cell

This paper investigates a real-time dynamic job-shop scheduling problem in a robotic cell, in which multiple jobs enter into the cell with unexpected arriving rates. Different from classical flow-shop and job-shop scheduling problems, the jobs’ transportation handled by a robot must be considered. Another characteristic is that the jobs’ processing times are not constant values but confined in time-window constraints. To efficiently solve this problem in real time, the original schedule is restricted to zero changes. The problem is formulated as a sophisticated Mixed Integer Programming (MIP) model in which the new jobs’ processing and transportation operations are inserted into the available time intervals of the original schedule. To strengthen the MIP model, speed-up constraints are added by taking advantage of specific relationships between the available time intervals arranged for a job's processing and transportation operations. Furthermore, an exact iterative algorithm is proposed, which starts with a relaxed solution of the MIP model and iteratively adds essential robot handling capacity constraints back to the relaxed MIP model until an optimal solution is found. Computational results validate effectiveness and efficiency of the strengthened MIP model and the iterative algorithm.

A dynamic task scheduling framework based on chicken swarm and improved raven roosting optimization methods in cloud computing

Scheduling means devoting tasks among computational resources, considering specific goals. Cloud computing is facing a dynamic and rapidly evolving situation. Devoting tasks to the computational resources could be done in numerous different ways. As a consequence, scheduling of tasks in cloud computing is considered as a NP-hard problem. Meta-heuristic algorithms are a proper choice for improving scheduling in cloud computing, but they should, of course, be consistent with the dynamic situation in the field of cloud computing. One of the newest bio-inspired meta-heuristic algorithms is the chicken swarm optimization (CSO) algorithm. This algorithm is inspired by the hierarchical behavior of chickens in a swarm for finding food. The diverse movements of the chickens create a balance between the local and the global search for finding the optimal solution. Raven roosting optimization (RRO) algorithm is inspired by the social behavior of raven and the information flow between the members of the population with the goal of finding food. The advantage of this algorithm lies in using the individual perception mechanism in the process of searching the problem space. In the current work, an ICDSF scheduling framework is proposed. It is a hybrid (IRRO-CSO) meta-heuristic approach based on the improved raven roosting optimization algorithm (IRRO) and the CSO algorithm. The CSO algorithm is used for its efficiency in satisfying the balance between the local and the global search, and IRRO algorithm is chosen for solving the problem of premature convergence and its better performance in bigger search spaces. First, the performance of the proposed hybrid IRRO-CSO algorithm is compared with other imitation-based swarm intelligence methods using benchmark functions (CEC 2017). Then, the capabilities of the proposed scheduling hybrid algorithm (IRRO-CSO) are tested using the NASA-iPSC parallel workload and are compared with the other available algorithms. The obtained results from the implementation of the hybrid IRRO-CSO algorithm in MATLAB show an improvement in the average best fitness compared with the following algorithms: IRRO, RRO, CSO, BAT and PSO. Finally, simulation tests performed in cloud computing environment show improvements in terms of reduction of execution time, reduction of response time and the increase in throughput by using the proposed hybrid IRRO-CSO approach for dynamic scheduling.

Dynamic Temperature Aware Scheduling for CPU-GPU 3D Multicore Processor with Regression Predictor

The 3D stacked integration of CPU, GPU and DRAM dies is a rising horizon in chip fabrication, where dies are vertically interconnected by TSVs (Through-Silicon Vias) to achieve high bandwidth, low latency and power consumption. However, thinned substrate, high power density and low thermal conductivity of inter-layer dielectric material cause thermal management a crucial problem. Moreover, the vertically stacked dies are susceptible to tight thermal correlations. High temperatures which tend to show higher spatial/temporal localities can make a negative impact on the IC’s reliability and lifetime. To mitigate such problems on CPU-GPU 3D heterogeneous processors, a novel dynamic temperature-aware task scheduling approach for compute workloads using OpenCL framework is proposed in this work. The proposed scheduler predicts the future temperature of each core from a regression model based on its current temperature, the neighbors’ temperatures and the execution profile of each workgroup. The scheduler then selects a core to assign workgroups from task queue based on their predicted temperature to keep the 3D chip below certain threshold temperature. Our experimental results demonstrate that the proposed scheduling technique is a viable solution to address the hotspots and heat dissipation issue of 3D stacked heterogeneous processors under reasonable performance tradeoffs.

Revenue maximisation for scheduling deadline-constrained mouldable jobs on high performance computing as a service platforms

Traditionally, high-performance computing (HPC) systems usually deal with the so-called best-effort jobs which do not have deadlines and are scheduled in an as-quick-as-possible manner. Recently the concept of HPC as a service (HPCaaS) was proposed, aiming to transform HPC facilities and applications into a more convenient and accessible service model. To achieve that goal, there will be new issues to explore, such as scheduling jobs with deadlines and maximising the revenue of service providers. This paper presents a reservation-based dynamic scheduling approach for scheduling deadline-constrained mouldable jobs with the aim of maximising a service provider's revenue. The proposed approach has been evaluated with a series of simulation experiments. The experimental results indicate that our scheduling approach can achieve significantly higher revenue than previous methods. In the experiments, we also explored several research issues, including waiting queue sequencing, processor allocation decisions on time and space, admission control, and partial rescheduling.

A Q-learning-based memetic algorithm for multi-objective dynamic software project scheduling

Software project scheduling is the problem of allocating employees to tasks in a software project. Due to the large scale of current software projects, many studies have investigated the use of optimization algorithms to find good software project schedules. However, despite the importance of human factors to the success of software projects, existing work has considered only a limited number of human properties when formulating software project scheduling as an optimization problem. Moreover, the changing environments of software companies mean that software project scheduling is a dynamic optimization problem. However, there is a lack of effective dynamic scheduling approaches to solve this problem. This work proposes a more realistic mathematical model for the dynamic software project scheduling problem. This model considers that skill proficiency can improve over time and, different from previous work, it considers that such improvement is affected by the employees’ properties of motivation and learning ability, and by the skill difficulty. It also defines the objective of employees’ satisfaction with the allocation. It is considered together with the objectives of project duration, cost, robustness and stability under a variety of practical constraints. To adapt schedules to the dynamically changing software project environments, a multi-objective two-archive memetic algorithm based on Q-learning (MOTAMAQ) is proposed to solve the problem in a proactive-rescheduling way. Different from previous work, MOTAMAQ learns the most appropriate global and local search methods to be used for different software project environment states by using Q-learning. Extensive experiments on 18 dynamic benchmark instances and 3 instances derived from real-world software projects were performed. A comparison with seven other meta-heuristic algorithms shows that the strategies used by our novel approach are very effective in improving its convergence performance in dynamic environments, while maintaining a good distribution and spread of solutions. The Q-learning-based learning mechanism can choose appropriate search operators for the different scheduling environments. We also show how different trade-offs among the five objectives can provide software managers with a deeper insight into various compromises among many objectives, and enabling them to make informed decisions.

Dynamic Scheduling of a Semiconductor Production Line Based on a Composite Rule Set

Various factors and constraints should be considered when developing a manufacturing production schedule, and such a schedule is often based on rules. This paper develops a composite dispatching rule based on heuristic rules that comprehensively consider various factors in a semiconductor production line. The composite rule is obtained by exploring various states of a semiconductor production line (machine status, queue size, etc.), where such indicators as makespan and equipment efficiency are used to judge performance. A model of the response surface, as a function of key variables, is then developed to find the optimized parameters of a composite rule for various production states. Furthermore, dynamic scheduling of semiconductor manufacturing is studied based on support vector regression (SVR). This approach dynamically obtains a composite dispatching rule (i.e., parameters of the composite dispatching rule) that can be used to optimize production performance according to real-time production line state. Following optimization, the proposed dynamic scheduling approach is tested in a real semiconductor production line to validate the effectiveness of the proposed composite rule set.

A Novel Hybrid-Copy Algorithm for Live Migration of Virtual Machine

Live migration of virtual machines is an important approach for dynamic resource scheduling in cloud environment. The hybrid-copy algorithm is an excellent algorithm that combines the pre-copy algorithm with the post-copy algorithm to remedy the defects of the pre-copy algorithm and the post-copy algorithm. Currently, the hybrid-copy algorithm only copies all memory pages once in advance. In a write-intensive workload, copy memory pages once may be enough. However, more iterative copy rounds can significantly reduce the page faults in a read-intensive workload. In this paper, we propose a new parameter to decide the appropriate time to stop the iterative copy phase based on real-time situation. We use a Markov model to forecast the memory access pattern. Based on the predicted results and the analysis of the actual situation, the memory page transfer order would be adjusted to reduce the invalid transfers. The novel hybrid-copy algorithm is implemented on the Xen platform. The experimental results demonstrate that our mechanism has good performance both on read-intensive workloads and write-intensive workloads.

Dynamic scheduling in RFID-driven discrete manufacturing system by using multi-layer network metrics as heuristic information

Since discrete manufacturing system (DMS) is a complicated dynamic network that comprises of processes, machines, and work in process, a coherent methodology for performance tracking and sustainable improvement at the system/network level is of great significance for manufacturers to respond rapidly in a mass customization paradigm. Fortunately, the radio frequency identification (RFID) technologies provide us the real-time tracking ability of the production process that suffers unpredictable and recessive disturbances. This paper proposes a dynamic scheduling approach based on multi-layer network metrics of RFID-driven DMS. Firstly, considering the elements of DMS (e.g., parts, manufacturing activities, and equipment) and relationships among them, a DMS model named complex manufacturing network (CMN) is proposed. Then, several multi-layer network metrics of the CMN are defined and analysed. The implications of these metrics lead to a better understanding of the current status and performance of DMS. Thirdly, a dynamic scheduling algorithm using these metrics as heuristic information is proposed to solve multi-resources and independent-task DMS. Finally, a Printing Machine manufacturing system is chosen as an example to illustrate the feasibility of the proposed approach.

Dynamic scheduling of a flow shop with on-site wind generation for energy cost reduction under real time electricity pricing

On-site renewable electricity generation represents an attractive option for manufacturing enterprises to deal with time varying electricity prices while reducing their carbon footprint. Production scheduling can be used to take full advantage of the installed renewable energy capacity for electricity cost reduction. A dynamic scheduling approach is proposed to minimize the electricity cost of a flow shop with a grid-integrated wind turbine. Time series models are used to provide updated wind speed and electricity prices as actual data becomes available. The production schedule and energy supply decisions are adjusted based on the new information. The approach is demonstrated using a case study.

An Effective Real-Time Dynamic Scheduling Approach for Periodic Tasks

An Effective Real-Time Dynamic Scheduling Approach for Periodic Tasks

Scheduling internet of things applications in cloud computing

Internet of Things (IoT) is one of the greatest technology revolutions in the history. Due to IoT potential, daily objects will be consciously worked in harmony with optimized performances. However, today, technology is not ready to fully bring its power to our daily life because of huge data analysis requirements in instant time. On the other hand, the powerful data management of cloud computing gives IoT an opportunity to make the revolution in our life. However, the traditional cloud computing server schedulers are not ready to provide services to IoT because IoT consists of a number of heterogeneous devices and applications which are far away from standardization. Therefore, to meet the expectations of users, the traditional cloud computing server schedulers should be improved to efficiently schedule and allocate IoT requests. There are several proposed scheduling algorithms for cloud computing in the literature. However, these scheduling algorithms are limited because of considering neither heterogeneous servers nor dynamic scheduling approach for different priority requests. Our objective is to propose dynamic dedicated server scheduling for heterogeneous and homogeneous systems to efficiently provide desired services by considering priorities of requests. Results show that the proposed scheduling algorithm improves throughput up to 40 % in heterogeneous and homogeneous cloud computing systems for IoT requests. Our proposed scheduling algorithm and related analysis will help cloud service providers build efficient server schedulers which are adaptable to homogeneous and heterogeneous environments by considering system performance metrics, such as drop rate, throughput, and utilization in IoT.

A Dynamic Graph-Based Scheduling and Interference Coordination Approach in Heterogeneous Cellular Networks

To meet the demand of increasing mobile data traffic and provide better user experience, heterogeneous cellular networks (HCNs) have become a promising solution to improve both the system capacity and coverage. However, due to dense self-deployment of small cells in a limited area, serious interference from nearby base stations may occur, which results in severe performance degradation. To mitigate downlink interference and utilize spectrum resources more efficiently, we present a novel graph-based resource allocation and interference management approach in this paper. First, we divide small cells into cell clusters, considering their neighborhood relationships in the scenario. Then, we develop another graph clustering scheme to group user equipment (UE) in each cell cluster into UE clusters with minimum intracluster interference. Finally, we utilize a proportional fairness scheduling scheme to assign subchannels to each UE cluster and allocate power using water-filling method. To show the efficacy and effectiveness of our proposed approach, we propose a dual-based approach to search for optimal solutions as the baseline for comparisons. Furthermore, we compare the graph-based approach with the state of the art and a distributed approach without interference coordination. The simulation results show that our graph-based approach reaches more than 90% of the optimal performance and achieves a significant improvement in spectral efficiency compared with the state of the art and the distributed approach both under cochannel and orthogonal deployments. Moreover, the proposed graph-based approach has low computation complexity, making it feasible for real-time implementation.

Dynamic Scheduling of Irregular Stream Programs toward Many-Core Scalability

The stream programming model has received much interest because it naturally exposes task, data, and pipeline parallelism. However, most priorwork has focused on the static scheduling of regular stream programs. Therefore, irregular applications cannot be handled in static scheduling, and the load imbalance caused by static scheduling faces scalability limitations in many-core systems. In this paper, we introduce the DANBI programming model, which supports irregular stream programs, and propose dynamic scheduling techniques. Scheduling irregular stream programs is very challenging, and the load imbalance becomes a major hurdle to achieving scalability. Our dynamic load-balancing scheduler exploits producer-consumer relationships already expressed in the DANBI program to achieve scalability. Moreover, it effectively avoids the thundering-herd problem and dynamically adapts to load imbalance in a probabilistic manner. It surpasses prior static stream scheduling approaches which are vulnerable to load imbalance and also surpasses prior dynamic stream scheduling approaches which result in many restrictions on supported program types, on the scope of dynamic scheduling, and on data ordering preservation. Our experimental results on a 40-core server show that DANBI achieves an almost linear scalability and outperforms state-of-the-art parallel runtimes by up to 2.8 times.

The Dynamic Job Shop Scheduling Approach Based on Data-Driven Genetic Algorithm

Rapid development of the Internet of Things not only provides large amounts of data to the job-shop scheduling, but also proposes a great challenge for dynamic job shop scheduling. A dynamic job shop scheduling approach is proposed based on the data-driven genetic algorithm. Application examples suggest that this approach is correct, feasible and available. This approach can provide the technical support for the long-term development of enterprises in the field of intelligent production.

Dynamic Scheduling of Shuttle Robots in the Warehouse of a Polymer Plant Based on Dynamically Configured Timed Automata Models

The increasing demand for customized products and the pressure to produce them on time with low-cost upon customers’ demands not only call for efficient and economical production processes but also require high-performance logistic planning to make the best possible use of the available resources. The elements to be planned besides the production itself include preproduction material handling, such as raw material deliveries and storage, and the postproduction material handling, such as storage and distribution of products. Specialty products are usually stored in warehousing systems where the speed of the removal and the distribution of products can strongly affect the efficiency of the complete system. Hence, optimal scheduling of the distribution operations is required to meet throughput targets and lead to improved efficiency and financial gains. In this contribution, we deal with a bottleneck subsystem of an industrial polymer processing plant, the pallet removal system of a high-rise warehouse. A dynamic scheduling approach that uses optimization to solve the problem on a moving window is proposed. The shuttle management problem is modeled using timed automata and is solved by cost-optimal reachability analysis. The most challenging aspect of the problem is introduced by the a priori unknown arrival times of the pallets and their unknown routes. The proposed dynamic solution approach is compared to an enhanced version of the currently used first-come, first-served dispatching rule. The comparative study shows that the proposed approach leads to makespan reductions of about 30%.

Gain Scheduled Control of Linear Systems Subject to Actuator Saturation With Application to Spacecraft Rendezvous

This brief is concerned with gain scheduled approaches to the stabilization of linear systems with actuator saturation. For linear systems that are polynomially unstable, using the parametric Lyapunov equation-based and Riccati equation-based design, we propose gain scheduling approaches to increase the design parameter online so as to increase the convergence rates of the closed-loop systems. To apply the proposed gain scheduling approaches, only a scalar differential equation whose right-hand side is a function of the state vector is required to be integrated online. The closed-loop system is proven to be exponentially stable provided some parameters in the scheduling law are properly chosen. The established gain scheduling approaches are also extended to exponentially unstable linear systems with actuator saturation. As applications of the proposed dynamic gain scheduling approaches, the controller design of spacecraft rendezvous systems is revisited. Numerical simulation with the nonlinear model of a spacecraft rendezvous system shows the effectiveness of the proposed gain scheduling approaches.

Efficient schedulability analysis for mixed-criticality systems under deadline-based scheduling

Safety-critical avionics systems which become more complex and tend to integrate multiple functionalities with different levels of criticality for better cost and power efficiency are subject to certifications at various levels of rigorousness. In order to simultaneously guarantee temporal constraints at all different levels of assurance mandated by different criticalities, novel scheduling techniques are in need. In this paper, a mixed-criticality sporadic task model with multiple virtual deadlines is built and a certification-cognizant dynamic scheduling approach referred as earliest virtual-deadline first with mixed-criticality (EVDF-MC) is considered, which exploits different relative deadlines of tasks in different criticality modes. As for the corresponding schedulability analysis problem, a sufficient and efficient schedulability test is proposed on the basis of demand-bound functions derived in the mixed-criticality scenario. In addition, a modified simulated annealing (MSA)-based heuristic approach is established for virtual deadlines assignment. Experiments performing simulations with randomly generated tasks indicate that the proposed approach is computationally efficient and competes well against the existing approaches.

Exploring the Spectrum of Dynamic Scheduling Algorithms for Scalable Distributed-MemoryRay Tracing.

This paper extends and evaluates a family of dynamic ray scheduling algorithms that can be performed in-situ on large distributed memory parallel computers. The key idea is to consider both ray state and data accesses when scheduling ray computations. We compare three instances of this family of algorithms against two traditional statically scheduled schemes. We show that our dynamic scheduling approach can render data sets that are larger than aggregate system memory and that cannot be rendered by existing statically scheduled ray tracers. For smaller problems that fit in aggregate memory but are larger than typical shared memory, our dynamic approach is competitive with the best static scheduling algorithm.

The block planning approach for continuous time-based dynamic lot sizing and scheduling

AbstractThis paper calls for a change in paradigm in lot sizing and scheduling. Traditionally, a discrete time scale is chosen to model lot sizing and scheduling. As an alternative, the so-called block planning concept is proposed which is based on a continuous representation of time. A mixed-integer linear optimization model is presented that determines the size and the time phasing of the individual production lots in a single-stage production system under the objective of minimizing the makespan. The modelling approach presented here assumes the grouping of product variants into setup families and the production of product variants within a family in a pre-defined sequence. Numerical results demonstrate the practicability of this approach under experimental conditions which reflect typical settings from a leading company in the European beverage industry.

A multi-agent approach for dynamic production and distribution scheduling

In an age when enterprises are increasingly dependent on their suppliers and customers, individual companies should broaden their scope in order to include other organisations participating in the same value chain. Consequently, different parties of the chain ought to exchange information to make sound decisions in order to cut down global costs or improve customer service. This paper proposes a multi–agent approach for dynamic production and distribution scheduling in a simple supply chain. The approach is based on the continuous supervision of the active schedules and routes, in order to detect possible exceptions and apply corrective actions in a real–time and coordinated manner with other parties. Finally, the paper focuses on the design phase of the distribution stage, where the problem is mathematically formulated as a dynamic vehicle routing problem with time windows and backhauls (DVRP–TWB), and three combinatorial optimisation meta–heuristics are proposed as solving techniques.