分布式测量系统服务窗口动态调度方法研究

在网络制造环境下, 动态时间性能是测量系统的重要指标. 针对基于 CORBA (Common object request broker architecture) 和尺寸测量接口标准 (Dimensional measurement interface standard, DMIS) 的分布式测量系统 (Distributed measurement system, DMS), 根据多用户非抢占优先排队网络静态性能模型, 提出基于无穷小摄动分析的样本轨道划分方法, 建立测量系统服务窗口的动态调度算法, 实现测量系统的时间性能调优. 通过在一个制造工厂中进行的应用实验, 证明了此方法的有效性.     

On scheduling and clustering in hierarchical TH-PPM UWB wireless ad hoc networks

Ultra wideband (UWB) systems are considered as the key wireless infrastructure platforms for efficient short-range communications. In particular, the UWB based mobile computing systems are envisioned to be attractive solutions to various ad hoc networking applications. However, due to UWB’s unique physical characteristics, the traditional resource management schemes for ad hoc networks cannot be applied to UWB based systems directly. In this paper, we consider the bandwidth scheduling problem in a UWB based hierarchical wireless ad hoc network, which is typically used in an enterprise-scale mobile computing environment. Based on the mathematical analysis and the computer simulations, it is demonstrated that our proposed scheduling scheme exhibits close-to-optimal performance governed by the proportional fairness (PF) constraint. Moreover, a novel self-organized clustering method is designed to improve the system throughput while meeting the PF constraint. Simulation results suggest that the proposed clustering method is effective under various system configurations.

在μC/OS-II中消除优先级反转

使用实时内核,优先级反转是实时系统中出现最多的问题。为了防止这种现象的发生,内核必须能够自动变换任务的优先级,目前比较有效的方法有优先级继承和优先级顶置等。而作为一个优秀而应用广泛的实时内核,μC/OS-II没有防优先级反转的机制。基于此,首先分析了优先级反转及解决方法,然后提出如何对μC/OS-II的调度算法进行扩展,使其支持优先级顶置协议,从而良好解决了该实时系统中的优先级反转问题,提高了系统的实时性能。     

Stability of size-based scheduling disciplines in resource-sharing networks

Size-based scheduling strategies such as Shortest Remaining Processing Time first (SRPT) and Least Attained Service first (LAS) provide popular mechanisms for improving the overall delay performance by favoring smaller service requests over larger ones. The performance gains from these disciplines have been thoroughly investigated for single-server systems, and have also been experimentally demonstrated in web servers for example. In the present paper we explore the fundamental stability properties of size-based scheduling strategies in multi-resource systems, such as bandwidth-sharing networks, where users require service from several shared resources simultaneously. In particular, we establish the exact stability conditions for the SRPT and LAS disciplines in various limiting regimes. The results indicate that size-based scheduling strategies may fail to use the available resources efficiently, and in fact cause instability effects, even at arbitrarily low traffic loads, and will therefore not yield optimal delay performance. The qualitative findings confirm the tendency for users with long routes and large service requirements to experience severe performance degradation.     

Sensitivity analysis in the single-machine scheduling problem with max–min criterion

In this paper, the single‐machine scheduling problem 1∣prec∣f_max is considered. It is one of the most general scheduling problems for which an efficient, polynomial algorithm has been developed. It is always possible to calculate quickly one optimal solution (a sequence of jobs) in that problem. However, the set of all optimal solutions may contain a lot of other sequences, so it is important to give a full characterization of that set. This paper consists of two parts. In the first part, some sufficient and necessary conditions of optimality of a given solution to the problem 1∣prec∣f_max are proved. In the second part, an application of these conditions to the sensitivity analysis is presented.     

Applying a hybrid job shop procedure to a Belgian manufacturing company producing industrial wheels and castors in rubber

In this paper, several methods for job shop scheduling are combined, adjusted and successfully applied to a real-world scheduling problem at a Belgian manufacturer producing industrial wheels and castors in rubber. The procedure is an extension of a hybrid shifting bottleneck procedure with a tabu search algorithm while incorporating various company specific constraints. The various extensions to cope with the company specific constraints have a strong similarity with the complex job shop problem formulation of Mason, Fowler, and Carlyle (2002). The new procedure is used as a simulation engine to test the relevance of various scenarios in order to improve the current planning approach of the company. A detailed computational experiment highlights the main contribution of the novel procedure for the company.     

Efficient trip scheduling algorithms for groups

Given the source and destination locations of $n$ group members and a set of required point of interest (POI) types such as restaurants and shopping centers, a Group Trip Scheduling (GTS) query schedules $n$ individual trips such that each POI type is included in exactly one trip and an aggregate trip overhead distance for visiting the required POI types is minimized. Each trip starts at a member’s source location, goes through some POIs, and ends at the member’s destination location. The trip distance of a group member is the distance from her source to destination via the POIs that the group member visits, and the trip overhead distance of the group member is measured by subtracting the distance between her source and destination locations (without visiting any POI type) from her trip distance. The aggregate trip overhead distance is either the summation or the maximum of the trip overhead distances of the group members for visiting the POIs. A GTS query enables a group to schedule independent trips for its members in order to perform a set of tasks with the minimum travel cost. For example, family members normally have many outdoor tasks to perform within a short time for the proper management of home. The members may need to go to a bank to withdraw or deposit money, a pharmacy to buy medicine, or a supermarket to buy groceries. Similarly, organizers of an event may need to visit different POI types to perform many tasks. These scenarios motivate us to introduce a GTS query, a novel query type in spatial databases. We develop an efficient approach to process GTS queries and variants for the Euclidean space and road networks. By exploiting geometric properties, we refine the POI search space and prune POIs, which in turn reduce the query processing overhead significantly. In addition, we propose a dynamic programming technique to eliminate the trip combinations that cannot be part of the optimal query answer. We show that processing a GTS query is NP-hard and propose an approximation algorithm to further reduce the query processing overhead. We perform extensive experiments using real and synthetic datasets and show that our approach outperforms a straightforward approach with a large margin.     

Application Layer Scheduling in Cloud: Fundamentals,Review and Research Directions

The cloud computing paradigm facilitates a finite pool of on-demand virtualized resources on a pay-per-use basis. For large-scale heterogeneous distributed systems like a cloud, scheduling is an essential component of resource management at the application layer as well as at the virtualization layer in order to deliver the optimal Quality of Services (QoS). The cloud scheduling, in general, is an NP-hard problem due to large solution space, thus, it is difficult to find an optimal solution within a reasonable time. In application layer scheduling, the tasks are mapped to logical resources (i.e., virtual machines), aiming to optimize one or more QoS parameters, and conforming to several constraints. Various algorithms have been proposed in the literature for application layer scheduling, where each of them is based on some fundamental design techniques like simple heuristics, meta-heuristics, and most recently hybrid heuristics. Although ample literature survey exists for cloud scheduling algorithms, none of them present their study exclusively for the application layer. In this survey paper, we present a study on task scheduling algorithms used only at the application layer of the cloud. We classify our study according to various fundamental techniques used in designing such scheduling algorithms. One of the main features of our study is that it covers numerous application type e.g., a set of independent tasks, simple workflow, scientific workflow, and MapReduce jobs. We also provide a comparative analysis of existing algorithms on various parameters like makespan, cost, resource utilization, etc. In the end, research directions for future work have been provided.     

Couple-based particle swarm optimization for short-term hydrothermal scheduling

A novel couple-based particle swarm optimization (CPSO) is presented in this paper, and applied to solve the short-term hydrothermal scheduling (STHS) problem. In CPSO, three improvements are proposed compared to the canonical particle swarm optimization, aimed at overcoming the premature convergence problem. Dynamic particle couples, a unique sub-group structure in maintaining population diversity, is adopted as the population topology, in which every two particles compose a particle couple randomly in each iteration. Based on this topology, an intersectional learning strategy using the partner learning information of last iteration is employed in every particle couple, which can automatically reveal useful history information and reduce the overly rapid evolution speed. Meanwhile, the coefficients of each particle in a particle couple are set as distinct so that the particle movement patterns can be described and controlled more precisely. In order to demonstrate the effectiveness of our proposed CPSO, the algorithm is firstly tested with four multimodal benchmark functions, and then applied to solve an engineering multimodal problem known as STHS, in which two typical test systems with four different cases are tested, and the results are compared with those of other evolutionary methods published in the literature.