Hierarchical Scheduling Strategy Research Articles

A Reverse Order Hierarchical Integrated Scheduling Algorithm Considering Dynamic Time Urgency Degree of the Process Sequences

Aiming at the general integrated scheduling problem of tree-structured complex single-product machining and assembling, a reverse order hierarchical integrated scheduling algorithm (ROHISA) is proposed by considering the dynamic time urgency degree (TUD) of process sequences (PSs). The strategy of process sorting is put forward, and the TUD of PS is defined. The process tree is reversed using leaf alignment, and according to the order from leaf to root, the scheduling order of leaf nodes in the same layer is determined layer by layer according to the TUD values of the PSs to which the leaf nodes belong. In turn, the sorted leaf nodes in each layer are stored in a corresponding layered array (LA). Finally, the elements in each LA are reversed, and the LAs’ arranging order is reversed. A reverse order hierarchical scheduling strategy is proposed. Starting from the root node, every LA is taken as a unit to conduct trial scheduling each time. Under the condition of meeting the craft constraints, a set of quasi-scheduling schemes of same-layer processes (QSSSLP) is obtained, and the one with the minimum end time is selected from it as the scheduling scheme of the same layer processes (SSSLP). If it is not unique, the QSSSLP that machines all the same layer processes (SLP) as early as possible is selected. The research shows that the ROHISA optimizes the integrated scheduling results of single-product manufacturing enterprises and improves its production efficiency.

Hierarchical management strategy for electric vehicles charging schedule considering the scarcity of charging resources

Charging resources are scarce due to the limited capacity of charging stations and the congestion of distribution networks. Here, a hierarchical scheduling framework that considers the privacy of participants is established to optimize the charging power with a fair payment mechanism. The charging resource allocation model is established considering the scarcity of charging resources. Furthermore, the model is divided into three sub problems, including the congestion management problem of distribution network operator, the energy management problem of charging station and the charging mode problem of electric vehicle. Then the collaborative optimization algorithms are proposed to ensure the privacy of participants. Finally, a hierarchical payment mechanism based on the opportunity cost theory is proposed. The performance of the proposed hierarchical scheduling framework is evaluated using the Roy Billiton Test System with 2000 EVs. On the premise of ensuring distribution network economic operation, the proposed hierarchical scheduling strategy can satisfy the requirement for privacy protection and eliminate the overload problem caused by electric vehicle charging. The simulation results have shown that the additional capacity cost has an obvious levelling effect. With the application of the payment mechanism, charging resources is equitably allocated. Also, the equilibrium of a market and the stable operation of power system can be guaranteed.

A spatio-temporal constrained hierarchical scheduling strategy for multiple warehouse mobile robots under industrial cyber–physical system

The industrial cyber–physical system (ICPS) framework can reduce the computational load and improve task efficiency. This paper studies the ICPS-based scheduling strategy for multi-warehouse mobile robots (multi-WMRs). First of all, the possible congestion problem is considered in topological map modeling, which is transformed into a new path time cost index. Second, each robot independently executes the path planning algorithm, which realizes distributed path computation and takes time cost and destination distance into account. The improved task assignment strategy includes task evaluation and decision-making, which are considered part of the planning and help to improve task efficiency. Finally, the complete scheduling process is applied to the novel ICPS architecture, including cost evaluation, path planning, task assignment, and collision avoidance. In numerical experiments, the task efficiency has been increased by 24.8% to recent research and 14.59% to previous work. The average congestion time is reduced by 28.41%, and the planning time is reduced to 10.13% of the traditional method.

Hierarchical scheduling for multi-composite tasks in cloud manufacturing

Cloud manufacturing (CMfg) is a new manufacturing mode formed by the integration of information technology and communication technology with manufacturing. As a core role in CMfg, the CMfg platform is responsible for decomposing a large number of tasks from demander and allocating them to available services. The scheduling requires comprehensive consideration of the relevance, complexity and dynamics of task and service. When the decomposable task is multi-composite, how to allocate the optimum services to multi-composite tasks is a tricky and important problem. To solve the issue, a hierarchical scheduling model for multi-composite tasks is proposed, which is divided into user-level scheduling and sublevel scheduling to reduce the scale and difficulty of scheduling. User-level scheduling achieves two-way matching between demander and provider based on various attributes. For the sublevel scheduling, an improved firefly genetic algorithm is created for multi-objective optimisation. A detailed analysis of the hierarchical scheduling strategy is performed by testing several different instances. Experimental results indicate that this strategy reduces the complexity than collective scheduling; and has a better comprehensive balance effect on multiple optimisation goals than sequential scheduling.

Uncertainty in Unit Commitment in Power Systems: A Review of Models, Methods, and Applications

The unit commitment problem (UCP) is one of the key and fundamental concerns in the operation, monitoring, and control of power systems. Uncertainty management in a UCP has been of great interest to both operators and researchers. The uncertainties that are considered in a UCP can be classified as technical (outages, forecast errors, and plugin electric vehicle (PEV) penetration), economic (electricity prices), and “epidemics, pandemics, and disasters” (techno-socio-economic). Various methods have been developed to model the uncertainties of these parameters, such as stochastic programming, probabilistic methods, chance-constrained programming (CCP), robust optimization, risk-based optimization, the hierarchical scheduling strategy, and information gap decision theory. This paper reviews methods of uncertainty management, parameter modeling, simulation tools, and test systems.

A hierarchical task scheduling strategy in mobile edge computing

Mobile Edge Computing (MEC) has attracted much attention from the global researchers because it supports the task migration from the cloud server to the mobile edge devices, which greatly improves the computation performance. Under MEC, this letter proposes a hierarchical task scheduling strategy. To be specific, the system framework includes the edge computing layer and the frog computing layer, in which the former is responsible for processing the simple tasks (outputting the sketchy result) and the latter is used to addressing the complex tasks (outputting the precise result). In particular, the tasks after going through the edge computing layer, they are allocated with the priorities; on this basis, the frog computing layer embeds a priority‐based task scheduling strategy. The experimental results demonstrate that the proposed hierarchical scheduling strategy not only is feasible but also has better delay performance and load balance performance than the benchmarks.

Research on Partition Parameter Design Method for Integrated Modular Avionics Based on MOEA/D-ADV

In the avionics industry, Integrated Modular Avionics (IMA) which introduces the concept of partition has been widely adopted for its isolating capability. However, the real-time performance of the IMA system mainly depends on the partition parameters. This leads to the question of how to design the partition parameters for satisfying the timing requirements of real-time applications. In this paper, the problem of partition parameter design for multiprocessors system is investigated. Firstly, the hierarchical scheduling strategy of IMA is analyzed, and a new schedulability analysis method is proposed to judge the schedulability of the partitions according to the partition period and execution time. Then, an approximation algorithm is developed to minimize the allocated bandwidth of the partitions while simultaneously guaranteeing tasks schedulability within the partitions. The harmonic period partitions, which are used as the constraint of partition parameter design, are realized by considering the scheduling mechanism of intra-partition and inter-partition. The total required bandwidth and the system overhead caused by partition scheduling are regarded as the optimization objective functions. Moreover, Multi-objective Evolutionary Algorithm Based on Decomposition (MOEA/D) method is improved by applying the Adjustment for the Direction Vectors (ADV) algorithm. Constrained Dominance Principle (CDP) is embedded into the improved algorithm to solve the constrained optimization problem. Consequently, simulation results show that the presented algorithm can achieve better coverage and uniformity than the compared algorithms while obtaining the partition parameters, and the system overhead and total required bandwidth can also be reduced.

Deep Reinforcement Scheduling for Mobile Crowdsensing in Fog Computing

Mobile crowdsensing becomes a promising technology for the emerging Internet of Things (IoT) applications in smart environments. Fog computing is enabling a new breed of IoT services, which is also a new opportunity for mobile crowdsensing. Thus, in this article, we introduce a framework enabling mobile crowdsensing in fog environments with a hierarchical scheduling strategy. We first introduce the crowdsensing framework that has a hierarchical structure to organize different resources. Since different positions and performance of fog nodes influence the quality of service (QoS) of IoT applications, we formulate a scheduling problem in the hierarchical fog structure and solve it by using a deep reinforcement learning–based strategy. From extensive simulation results, our solution outperforms other scheduling solutions for mobile crowdsensing in the given fog computing environment.

Mission Scheduling in Space Network With Antenna Dynamic Setup Times

Due to the constantly increasing mission demands for the space network (SN), the technical issue of high-efficient scheduling has attracted continuous attention in recent years. Most of the previous scheduling algorithms have generally ignored two critical factors, i.e., the dynamic property of single access (SA) antenna's setup times and the time-space distribution characteristics of users’ requested missions. In this paper, we build a mathematical model for the heterogeneous intersatellite link antenna (ILA) pointing route problem with the consideration of constraints on visibility windows and dynamic setup times. Specifically, the ILA's pointing mechanics cost and the missions’ time-space distribution are first taken into account into the scheduling model. Meanwhile, a new two-stage heuristic algorithm with hierarchical scheduling strategies is proposed to solve this optimization model. Finally, we employ the SN dataset to verify our proposed model by comparing three algorithms, the existing greedy randomized adaptive search procedure (GRASP) algorithm with static setup times, our improved GRASP algorithm with dynamic setup times, and our proposed two-stage heuristic algorithm. Experimental results show that our improved GRASP can schedule 3.72%, 10.36%, 12.51% more missions and can consume 53.18%, 49.95%, and 49.32% less setup times of SA antennas than the original Rojanasoonthon's GRASP for the mission scale of 200, 400, and 600, respectively. Moreover, compared with the improved GRASP algorithm, our proposed two-stage heuristic algorithm is more effective, which can further enhance the total number of scheduled missions by 2.41%, 4.43%, and 6.02% and reduce the setup times of SA antennas by 11.84%, 10.38%, and 9.54%.

Formal Verification Method for Configuration of Integrated Modular Avionics System Using MARTE

The configuration information of Integrated Modular Avionics (IMA) system includes almost all details of whole system architecture, which is used to configure the hardware interfaces, operating system, and interactions among applications to make an IMA system work correctly and reliably. It is very important to ensure the correctness and integrity of the configuration in the IMA system design phase. In this paper, we focus on modelling and verification of configuration information of IMA/ARINC653 system based on MARTE (Modelling and Analysis for Real-time and Embedded Systems). Firstly, we define semantic mapping from key concepts of configuration (such as modules, partitions, memory, process, and communications) to components of MARTE element and propose a method for model transformation between XML-formatted configuration information and MARTE models. Then we present a formal verification framework for ARINC653 system configuration based on theorem proof techniques, including construction of corresponding REAL theorems according to the semantics of those key components of configuration information and formal verification of theorems for the properties of IMA, such as time constraints, spatial isolation, and health monitoring. After that, a special issue of schedulability analysis of ARINC653 system is studied. We design a hierarchical scheduling strategy with consideration of characters of the ARINC653 system, and a scheduling analyzer MAST-2 is used to implement hierarchical schedule analysis. Lastly, we design a prototype tool, called Configuration Checker for ARINC653 (CC653), and two case studies show that the methods proposed in this paper are feasible and efficient.

Rule-based scheduling of multi-stage multi-product batch plants with parallel units

Abstract A novel rule-based model for multi-stage multi-product scheduling problem (MMSP) in batch plants with parallel units is proposed. The scheduling problem is decomposed into two sub-problems of order assignment and order sequencing. Firstly, hierarchical scheduling strategy is presented for solving the former sub-problem, where the multi-stage multi-product batch process is divided into multiple sequentially connected single process stages, and then the production of orders are arranged in each single stage by using forward order assignment strategy and backward order assignment strategy respectively according to the feature of scheduling objective. Line-up competition algorithm (LCA) is presented to find out optimal order sequence and order assignment rule, which can minimize total flow time or maximize total weighted process time. Computational results show that the proposed approach can obtain better solutions than those of the literature for all scheduling problems with more than 10 orders. Moreover, with the problem size increasing, the solutions obtained by the proposed approach are improved remarkably. The proposed approach has the potential to solve large size MMSP.

Design of a Fuzzy Networked Control Systems. Priority Exchange Scheduling Algorithm

This work presents a supervisory control strategy for Networked Control Systems (NCSs). This shows the identification and control of the plant using fuzzy theory. The fuzzy model incorporates the delay dynamics within the fuzzy rules based upon a real-time hierarchical scheduling strategy. A hierarchical scheduling Priority Exchange algorithm is used based upon codesign strategy following mutual correlation among control and network algorithms in order to bounded time delays. A system of magnetic levitation is presented as a case study.

Hierarchical Unit Commitment With Uncertain Wind Power Generation

A hierarchical unit commitment (HUC) model is presented with the objective of maintaining system security by scheduling reserves in power systems with high wind penetration. The reserves in the HUC model include generation reserve, ramping reserve, and transmission reserve. These three reserves are proposed to guarantee system security with uncertain wind power. Moreover, the simplifications for the transmission reserve are applied to reduce computational burden. The hierarchical scheduling strategy, which considers both conventional and emergency operations, divides the wind power output into two intervals based on confidence levels, and uses different scheduling strategies for different intervals of wind power output. Finally, case studies compare HUC with RUC and SUC, which shows the effectiveness in system safety enhancement and the effectiveness in practical systems of the proposed HUC model.

A hierarchical scheduling and control strategy for thermal energy storage systems

Energy storage in buildings is an important component of peak shifting and load leveling strategies devised to improve the operation of the electric grid. Maximizing the load-leveling benefits afforded by both active and passive thermal energy storage (TES) requires coordinating the charge/discharge events with external factors, such as weather, occupancy, and time-of-use pricing. The development of such controllers (typically following the model predictive control (MPC) paradigm) is challenging owing to the dimensions and multiple time scale nature of the problem. In this work, we propose a hierarchical control strategy, comprising a dynamic scheduling problem for managing active TES in the slow time scale, and a control scheme with a shorter horizon for managing passive TES in the fast time scale. The approach is demonstrated through an application to a TES system with phase change material (PCM), which presents unique modeling challenges. The proposed formulation and solution strategy allow for obtaining solutions in real time. Moreover, it is shown that the proposed approach leads to significant cost savings, outperforming (reasonable) operating heuristics even under uncertainty in forecasting building loads.

OpenMP task scheduling strategies for multicore NUMA systems

The recent addition of task parallelism to the OpenMP shared memory API allows programmers to express concurrency at a high level of abstraction and places the burden of scheduling parallel execution on the OpenMP run-time system. Efficient scheduling of tasks on modern multi-socket multicore shared memory systems requires careful consideration of an increasingly complex memory hierarchy, including shared caches and non-uniform memory access (NUMA) characteristics. In order to evaluate scheduling strategies, we extended the open source Qthreads threading library to implement different scheduler designs, accepting OpenMP programs through the ROSE compiler. Our comprehensive performance study of diverse OpenMP task-parallel benchmarks compares seven different task-parallel run-time scheduler implementations on an Intel Nehalem multi-socket multicore system: our proposed hierarchical work-stealing scheduler, a per-core work-stealing scheduler, a centralized scheduler, and LIFO and FIFO versions of the Qthreads round-robin scheduler. In addition, we compare our results against the Intel and GNU OpenMP implementations. Our hierarchical scheduling strategy leverages different scheduling methods at different levels of the hierarchy. By allowing one thread to steal work on behalf of all of the threads within a single chip that share a cache, the scheduler limits the number of costly remote steals. For cores on the same chip, a shared LIFO queue allows exploitation of cache locality between sibling tasks as well as between a parent task and its newly created child tasks. In the performance evaluation, our Qthreads hierarchical scheduler is competitive on all benchmarks tested. On five of the seven benchmarks, it demonstrates speedup and absolute performance superior to both the Intel and GNU OpenMP run-time systems. Our run-time also demonstrates similar performance benefits on AMD Magny Cours and SGI Altix systems, enabling several benchmarks to successfully scale to 192 CPUs of an SGI Altix.

Hierarchical scheduling strategies for parallel tasks and advance reservations in grids

Recently, the advance reservation functionality gained high importance in grids due to increasing popularity of modern applications that require interactive tasks, co-allocation of multiple resources, and performance guarantees. However, simultaneous scheduling, both advance reservations and batch tasks affects the performance. Advance reservations significantly deteriorate flow time of batch tasks and the overall resource utilization, especially in hierarchical scheduling structures. This is a consequence of unknown batch task processing times and the lack of possibility of altering allocations of advance reservations. To address these issues we present a common model for scheduling both computational batch tasks and tasks with advance reservation requests. We propose simple on-line scheduling policies and generic advices that reduce negative impact of advance reservations on a schedule quality. We also propose novel data structures and algorithms for efficient scheduling of advance reservations. A comprehensive experimental analysis is presented to show the influence of advance reservations on resource utilization, mean flow time, and mean tardiness—the criteria significant for administrators, users submitting batch tasks, and users requesting advance reservations, respectively. All experiments were performed with a well-known real workload using the GSSIM simulator.

A market-oriented hierarchical scheduling strategy in cloud workflow systems

A cloud workflow system is a type of platform service which facilitates the automation of distributed applications based on the novel cloud infrastructure. One of the most important aspects which differentiate a cloud workflow system from its other counterparts is the market-oriented business model. This is a significant innovation which brings many challenges to conventional workflow scheduling strategies. To investigate such an issue, this paper proposes a market-oriented hierarchical scheduling strategy in cloud workflow systems. Specifically, the service-level scheduling deals with the Task-to-Service assignment where tasks of individual workflow instances are mapped to cloud services in the global cloud markets based on their functional and non-functional QoS requirements; the task-level scheduling deals with the optimisation of the Task-to-VM (virtual machine) assignment in local cloud data centres where the overall running cost of cloud workflow systems will be minimised given the satisfaction of QoS constraints for individual tasks. Based on our hierarchical scheduling strategy, a package based random scheduling algorithm is presented as the candidate service-level scheduling algorithm and three representative metaheuristic based scheduling algorithms including genetic algorithm (GA), ant colony optimisation (ACO), and particle swarm optimisation (PSO) are adapted, implemented and analysed as the candidate task-level scheduling algorithms. The hierarchical scheduling strategy is being implemented in our SwinDeW-C cloud workflow system and demonstrating satisfactory performance. Meanwhile, the experimental results show that the overall performance of ACO based scheduling algorithm is better than others on three basic measurements: the optimisation rate on makespan, the optimisation rate on cost and the CPU time.