Low-level Scheduling Research Articles

A Bi-Level Optimized Dispatching Method for Grids with High Penetration of New Energy Considering Ancillary Service Costs

To address the issue of high auxiliary service costs caused by the grid connection of new energy generation, a dual layer optimization scheduling method for high penetration new energy power grids is proposed, taking into account the auxiliary service costs of grid connection. The correlation between the output fluctuations and load fluctuations of new energy generation is analyzed, and a calculation model for auxiliary service costs caused by grid connection of new energy generation is established. Taking into account the timescale division requirements for grid energy trading and auxiliary service market transactions, a new high penetration energy grid two-level optimization scheduling model is established. The upper level scheduling model aims to minimize the sum of grid energy procurement costs and auxiliary service costs caused by new energy generation and completes the optimization scheduling of the grid for several hours. The lower level scheduling model aims to closely follow the upper level scheduling plan for new energy power plants/groups. Optimization and scheduling of the power grid for tens of minutes is completed, taking into account the balanced allocation of wind and solar losses among various power plants within the new energy power plant group. The simulation results show that the method proposed in this paper can reduce the energy transaction costs and auxiliary service transaction costs of the power grid during low and peak load periods.

Integrated modeling and optimization of production planning and scheduling in hybrid flow shop for order production mode

This paper studies the integration of production planning and scheduling in the forging process of small batch orders. Firstly, the demand for orders and the processing constraints of forging equipment are comprehensively considered. Then by integrating periodic requirements for orders, the cycle of production is assigned based on production status and inventory, and each cycle of production is independently scheduled, a two-level decision-making model for production planning and adjustment of hybrid flow shop for forging is established. Secondly, in order to realize the closed-loop integration of high-level planning and low-level scheduling, a particle swarm-genetic hybrid algorithm is proposed to obtain the feasible integrated solution of production planning and scheduling. Thirdly, through a large number of numerical experiments, the genetic algorithm parameters of scheduling layer are optimized and its optimization ability is verified, and the optimal results also show that the parameters improve the overall algorithm performance. Lastly, a case of hybrid flow workshop for forging is presented to demonstrate the practicability of the proposed model and algorithm.

Bi-level robust scheduling for multi-terminal DC distribution networks considering uncertainties of loads and renewable energies

The randomness of loads and renewable energies affects the security of DC distribution networks. In this paper, a bi-level robust scheduling method is proposed to improve the security and economy of droop-controlled DC distribution networks. The upper-level scheduling aims to minimize the variance of node voltage deviation from rated value with the optimization of voltage source converter (VSC) operation reference power. The lower-level scheduling aims to minimize the power purchase cost with the optimization of energy storage systems (ESSs). Based on the linear power flow model of droop-controlled DC distribution networks, the upper-level scheduling model is converted to a least square programming model and the lower-level scheduling model is converted to a linear programming model, which can be solved with high computation efficiency. In the scheduling model of VSC operation reference power, the uncertain scenarios are directly established based on the linear power flow model of droop-controlled DC distribution networks to improve the calculation speed of robust algorithm. The adjustable confidence intervals of load variations are used to coordinate the robustness and conservatism. The VSC operation reference power and ESS charging/discharging power are coordinated through iterative optimization. The simulation results of two DC distribution networks show that the proposed scheduling method can effectively improve the security and economy of DC distribution networks.

Drum buffer rope-based heuristic for multi-level rolling horizon planning in mixed model production

In recent years mixed model production industries are highly interested to apply Industry 4.0 and internet of things. The existing planning and scheduling methods are not efficient enough to make intelligent plans. Therefore, there is a strong need to develop planning and scheduling methods which can timely update the medium level and lower level schedules and can be utilised for Industry 4.0. Drum buffer rope (DBR) is a direct application of theory of constraint, is utilised here to make an efficient plan. Current research proposed a DBR-based heuristic algorithm (DBR-HA) for multi-level planning considering shifting bottleneck resource to make efficient schedule in rolling horizon in mixed model production environment and utilise capacity constraint resource (CCR) at maximum. The proposed DBR-HA identifies the drum, i.e. CCR, and make an efficient schedule on it in each lower level scheduling period and utilise a feedback method to update customer orders in each medium level planning horizon. The proposed method is useful to implement Industry 4.0 in mixed model industries and update their plan and schedule in real time. The performance of the proposed DBR-HA algorithm is measured and compared with the performance of the basic scheduling rules used in the Case Company based on a Case Company problem data. Results indicate that the proposed method is significant to reduce the gap between medium level planning and lower level schedules and gives an efficient medium level plan and lower level schedule in each planning horizon as compared to the other methods.

Midterm Scheduling for the Production of a Stack and Folding-Type Battery Using a Hierarchical Method

The medium-term production scheduling of a stack and folding battery process is modeled using a state–task network and a hierarchical framework. The hierarchical framework comprises two mathematical models: an upper-level planning model and a lower-level scheduling model. The upper-level model is implemented to assign the corresponding product demands for each short horizon. The lower-level scheduling model then determines specific equipment operation in each short-term period. The mathematical models are formulated as mixed-integer linear programming problems. The formulation is based on a discrete-time representation considering shift operations in real plants. Two case studies with a real industrial-sized plant are presented to demonstrate the effectiveness of the proposed models and method.

Hierarchical Scheduling Optimization Scheme in Hybrid Cloud Computing Environments

The paper proposes hierarchical scheduling optimization scheme in hybrid cloud. Our proposed hierarchical scheduling takes advantage of the interaction of cloud users, private cloud and public cloud. For high level optimization in hybrid cloud, the objective of public cloud provider optimization is to maximize the revenue of providing virtual machines (VMs) and minimize the energy cost. The private cloud users' applications give the unique optimal payment to public cloud providers under deadline and cost constraint to maximize the satisfaction of private cloud user applications. The objective of low-level scheduling optimization is to minimize the cost and execution time of private cloud application. From the simulation results, the revenue, execution success ratio and resource utilization of our proposed hierarchical scheduling algorithm are better than other related works.

Description of a Low Level Virtual-Resource QoS CPU Manager

We present the description of a QoS aware dynamic virtualization manager. The proposal is a set of definitions for the low-level framework, named QoS CPU manager, which is part of a Local Resource Manager (LRM). In this work, we give a formal specification of the internals of our LRM. We give the relationships of the CPU capacity management component that is in charge of managing and mapping each application goal to low-level scheduler parameters, and the VM CPU consumption component which allows the LRM to offer adaptive management through the lifetime of the VM.

User-defined schedulers for real-time concurrent objects

Scheduling concerns the allocation of processors to processes, and is traditionally associated with low-level tasks in operating systems and embedded devices. However, modern software applications with soft real-time requirements need to control application-level performance. High-level scheduling control at the application level may complement general purpose OS level scheduling to fine-tune performance of a specific application, by allowing the application to adapt to changes in client traffic on the one hand and to low-level scheduling on the other hand. This paper presents an approach to express and analyze application-specific scheduling decisions during the software design stage. For this purpose, we integrate support for application-level scheduling control in a high-level object-oriented modeling language, Real-Time ABS, in which executable specifications of method calls are given deadlines and real-time computational constraints. In Real-Time ABS, flexible application-specific schedulers may be specified by the user, i.e., developer, at the abstraction level of the high-level modeling language itself and associated with concurrent objects at creation time. Tool support for Real-Time ABS is based on an abstract interpreter that supports simulations and measurements of systems at the design stage.

User-oriented hierarchical bandwidth scheduling for ethernet passive optical networks

Ethernet passive optical networks (EPONs) are being designed to deliver different quality of service (QoS) to carry heterogeneous traffic of end users. For this purpose, hierarchical scheduling is needed for upstream bandwidth allocation, with high-level scheduling for inter-optical network unit (ONU) allocation and low-level scheduling for intra-ONU distribution. In this paper, we propose new User-oriented Hierarchical bandwidth Scheduling Algorithms (UHSAs) that support differentiated services and guaranteed fairness among end users. For inter-ONU scheduling, we adopt an improved hybrid cycle approach that separates a frame into a static part for high priority traffic and an adaptive dynamic part for low priority traffic. For intra-ONU scheduling, we propose credit-based scheduling approach to guarantee fairness among end users. To improve scheduling efficiency and lower queue management complexity, we design a novel credit-based common queue (CCQ) for each traffic class to enhance scheduling architecture and minimize average number of queues in the ONU. On the other hand, we propose a transmission priority scheme for different queue groups, which together with CCQ mechanism serves the objective of improving delay and delay variation performance of high priority traffic, guaranteeing throughput for bandwidth sensitive medium priority traffic, as well as providing fairness and throughput protection among different users. The UHSAs exhibit a feature of multiple transmission opportunities (M-opportunities) per-cycle for high priority traffic that is significant in improving delay and delay variation performance for high priority traffic as compared with previous solutions of single transmission opportunity (S-opportunity) per-cycle. Detailed simulation experiments are conducted to study the performance and validate the effectiveness of the proposed protocols.

Scheduling Renewal of Water Pipes While Considering Adjacency of Infrastructure Works and Economies of Scale

Much research effort has been dedicated to the development of optimal strategies for rehabilitation and/or replacement of water mains. Some of the methods are intended for high-level planning of groups or cohorts of pipes, while others address low-level scheduling of individual water mains. This paper focuses on the latter aspect. An approach is proposed for the efficient scheduling of individual water mains for replacement in a short to medium predefined planning period and subject to various budgetary constraints. This approach also accounts for economies of scale considerations as well as harmonization with other known infrastructure works. A multiobjective genetic algorithm scheme is used as a tool to search a vast combinatorial solution space, comprising various combinations of pipe replacement schedules.

Design and Potential Performance of Goal-Oriented Job Scheduling Policies for Parallel Computer Workloads

To balance multiple scheduling performance requirements on parallel computer systems, traditional job schedulers use many parameters that can be configured to define job or queue priorities. Offering many parameters seems flexible, but in reality tuning the values for the parameters is highly challenging. To simplify the task of resource management, we propose goal-oriented policies, which allow system administrators to specify high-level performance objectives, rather than tuning low-level scheduling parameters. We study the design of goal-oriented policies, including (1) appropriate multi-objective models for specifying trade-offs between objectives, (2) efficient search algorithms for searching the best schedule at each scheduling decision point, and (3) appropriate performance measures to be optimized in the objectives with respect to two common performance requirements: preventing starvation and favoring shorter jobs. We compare goal-oriented policies with widely used backfill policies. Policies are evaluated by simulation using ten monthly workloads that ran on a Linux cluster (IA-64) from NCSA. Our results show that by automatically optimizing performance according to the given objectives through search, goal-oriented policies can simultaneously outperform FCFS-backfill and LXF-backfill, which are designed in favor of the maximum wait and average slowdown, respectively.

A Fast Job Scheduling System for a Wide Range of Bioinformatic Applications

Bioinformatic tools are often used by researchers through interactive Web interfaces, resulting in a strong demand for computational resources. The tools are of different kind and range from simple, quick tasks, to complex analyses requiring minutes to hours of processing time and often longer than that. Batteries of computational nodes, such as those found in parallel clusters, provide a platform of choice for this application, especially when a relatively large number of concurrent requests is expected. Here, we describe a scheduling architecture operating at the application level, able to distribute jobs over a large number of hierarchically organized nodes. While not contrasting and peacefully living together with low-level scheduling software, the system takes advantage of tools, such as SQL servers, commonly used in Web applications, to produce low latency and performance which compares well and often surpasses that of more traditional, dedicated schedulers. The system provides the basic functionality necessary to node selection, task execution and service management and monitoring, and may combine loosely linked computational resources, such as those located in geographically distinct sites.

Coordinating decentralized local schedulers in complex supply chain manufacturing

Motivated by scheduling challenges in back-end semiconductor manufacturing, we propose a framework to oversee and integrate local decentralized scheduling algorithms utilized in complex supply chain manufacturing networks. We fill the gap between higher-level production planning and lower-level scheduling by establishing short-term production targets and priority scores for each product at each step in the system. Given a target output schedule, target cycle times for each step, the process and product structure, and initial WIP status, short-term production targets for each product/step are set. These targets can be used to evaluate the system performance and guide decentralized schedulers to control the system so as to achieve desirable outputs in dynamic environments.

Storage performance virtualization via throughput and latency control

I/O consolidation is a growing trend in production environments due to increasing complexity in tuning and managing storage systems. A consequence of this trend is the need to serve multiple users and/or workloads simultaneously. It is imperative to ensure that these users are insulated from each other by virtualization in order to meet any service-level objective (SLO). Previous proposals for performance virtualization suffer from one or more of the following drawbacks: (1) They rely on a fairly detailed performance model of the underlying storage system; (2) couple rate and latency allocation in a single scheduler, making them less flexible; or (3) may not always exploit the full bandwidth offered by the storage system.This article presents a two-level scheduling framework that can be built on top of an existing storage utility. This framework uses a low-level feedback-driven request scheduler, called AVATAR, that is intended to meet the latency bounds determined by the SLO. The load imposed on AVATAR is regulated by a high-level rate controller, called SARC, to insulate the users from each other. In addition, SARC is work-conserving and tries to fairly distribute any spare bandwidth in the storage system to the different users. This framework naturally decouples rate and latency allocation. Using extensive I/O traces and a detailed storage simulator, we demonstrate that this two-level framework can simultaneously meet the latency and throughput requirements imposed by an SLO, without requiring extensive knowledge of the underlying storage system.

Hierarchical Dynamic Bandwidth Allocation Algorithm for Multimedia Services over Ethernet PONs

In this paper, we propose a new dynamic bandwidth allocation (DBA) algorithm for multimedia services over Ethernet PONs (passive optical networks). The proposed algorithm is composed of a low-level scheduler in the optical network unit (ONU) and a high-level scheduler in the optical line terminal (OLT). The hierarchical DBA algorithm can provide expansibility and efficient resource allocation in an Ethernet PON system in which the packet scheduler is separated from the queues. In the proposed DBA algorithm, the OLT allocates bandwidth to the ONUs in proportion to the weight associated with their class and queue length, while the ONU preferentially allocates its bandwidth to queues with a static priority order. The proposed algorithm provides an efficient resource utilization by reducing the unused remaining bandwidth caused by the variable length of the packets. We also define the service classes and present control message formats conforming to the multi-point control protocol (MPCP) over an Ethernet PON. In order to evaluate the performance, we designed an Ethernet PON system on the basis of IEEE 802.3ah “Ethernet in the first mile” (EFM) using OPNET and carried out simulations. The results are analyzed in terms of the channel utilization, queuing delay, and ratio of the unused remaining bandwidth.

Using Mobile Agents to Schedule a Manufacturing Chain on the Internet

This paper is mainly concerned with putting forward a novel manufacturing chain model and a new scheduling model for manufacturing chains in networked manufacturing systems. The manufacturing chain model is formed by adopting extensive activity-network-diagram workflow technique. The scheduling model is built upon mobile agent technologies, e.g. aglets. It includes two different scheduling levels. One is defined as the process scheduling which implements the scheduling logic for distributive manufacturing sites or manufacturing cells based on a specific manufacturing task. The other is the node scheduling which realizes the scheduling logic for manufacturing equipments based on specific working procedures or working paces. High-level scheduling aglet templates are created to support the operations at the levels of manufacturing sites or manufacturing cells. To complement, low-level scheduling aglet templates are created to support the operations such as working procedures or working paces over manufacturing equipments. As a result, the model is able to deal with dynamic manufacturing chains and their scheduling.

Convergent matching for model-based computer vision

We consider matching in model-based computer vision as a converging discrete iteration and give a basis for examining the convergence as the movement of the working point in a lattice. Because the matching is non-deterministic we discuss convergence in terms of completing sub-problems within a time slot. This form of low-level scheduling avoids effectively unlimited trials of sub-graphs, a phenomenon that we call the NP-trap. We define high-level scheduling as the need to test each reference class at least once and thereafter focus attention on the most promising candidates. Examples show the bounding of matching time with a time slot and focusing of attention guided by a figure of merit.

Massively parallel execution of logic programs: A static approach

A static model for the parallel execution of logic programs on MIMD distributed memory systems is presented where a refutation is implemented through a process network returned by the compilation of the logic program. The model supports Restricted-AND, OR and stream parallelism and it is integrated with a set of static analyses to optimise the process network. Altogether, the processes interact according to a static data driven model with medium grain operators. Data flowing in the network is tagged to distinguish bindings belonging to the same refutation. A scheduling strategy to integrate low level scheduling and message flow control has been defined. Performance figures are presented.

Algorithms for scheduling real-time tasks with input error and end-to-end deadlines

This paper describes algorithms for scheduling preemptive, imprecise, composite tasks in real-time. Each composite task consists of a chain of component tasks, and each component task is made up of a mandatory part and an optional part. Whenever a component task uses imprecise input, the processing times of its mandatory and optional parts may become larger. The composite tasks are scheduled by a two-level scheduler. At the high level, the composite tasks are scheduled preemptively on one processor, according to an existing algorithm for scheduling simple imprecise tasks. The low-level scheduler then distributes the time budgeted for each composite task across its component tasks so as to minimize the output error of the composite task.

How managers decide to use planning models

Recent published reports and surveys have shown that a growing number of corporations and government agencies are using decision models not only for lower level scheduling and resource allocation but also for short-range and long-range planning. Although the literature has described in some detail the types of models used and the ways in which they are used, limited attention has been paid to the ways in which managers decide whether or not to use these models. This paper, which is based on a series of case studies, suggests that the decision to use planning models is made not by performing a comprehensive cost benefit analysis, but by the use of a reference model—that is, an existing model, sometimes in a competitive organization, similar to the one being considered.