RAPS: a rule-based language for specifying resource allocation andtime-tabling problems

A general language for specifying resource allocation and time-tabling problems is presented. The language is based on an expert system paradigm that was developed previously by the authors and that enables the solution of resource allocation problems by using experts' knowledge and heuristics. The language enables the specification of a problem in terms of resources, activities, allocation rules, and constraints, and thus provides a convenient knowledge acquisition tool. The language syntax is powerful and allows the specification of rules and constraints that are very difficult to formulate with traditional approaches, and it also supports the specification of various control and backtracking strategies. We constructed a generalized inference engine that runs compiled resource allocation problem specification language (RAPS) programs and provides all necessary control structures. This engine acts as an expert system shell and is called expert system for resource allocation (ESRA). The performance of RAPS combined with ESRA is demonstrated by analyzing its solution of a typical resource allocation problem     

Security-Aware Resource Allocation for Real-Time Parallel Jobs on Homogeneous and Heterogeneous Clusters

Security is increasingly becoming an important issue in the design of real-time parallel applications, which are widely used in the industry and academic organizations. However, existing resource allocation schemes for real-time parallel jobs on clusters generally do not factor in security requirements when making allocation and scheduling decisions. In this paper, we develop two resource allocation schemes, called task allocation for parallel applications with deadline and security constraints (TAPADS) and security-aware and heterogeneity-aware resource allocation for parallel jobs (SHARP), by taking into account applications' timing and security requirements in addition to precedence constraints. We consider two types of computing platforms: homogeneous clusters and heterogeneous clusters. To facilitate the presentation of the new schemes, we build mathematical models to describe a system framework, security overhead, and parallel applications with deadline and security constraints. The proposed schemes are applied to heuristically find resource allocations that maximize the quality of security and the probability of meeting deadlines for parallel applications running on clusters. Extensive experiments using real-world applications and traces, as well as synthetic benchmarks, demonstrate the effectiveness and practicality of the proposed schemes.     

Temporal linear relaxation in IBM ILOG CP Optimizer

IBM ILOG CP Optimizer is a constraint solver that implements a model-and-run paradigm. For scheduling problems, CP Optimizer provides a relatively simple but very expressive modeling language based on the notion of interval variables. This paper presents the temporal linear relaxation (TLR) used to guide the automatic search when solving scheduling problems that involve temporal and resource allocation costs. We give the rationale of the TLR, describe its integration in the automatic search of CP Optimizer, and present the relaxation of most of the constraints and expressions of the model. An experimental study on a set of classical scheduling benchmarks shows that using the TLR is essential for problems with irregular temporal costs and generally helps for problems with resource allocation costs.     

Scheduling security-critical multimedia applications in heterogeneous networks

Security is becoming an increasingly important issue in the design of multimedia applications, which are widely used in the industry and academic organizations. However, existing scheduling schemes for real-time multimedia service in heterogeneous networks generally do not take into account security requirements when making allocation and control decisions. In this paper, we develop and evaluate a security-critical multimedia scheduling scheme in the framework of heterogeneous networks. At first, we construct a general media distortion model according to the observed parameters in each network, as well as each application’s characteristic. After that, we exploit a scalable graph-based authentication method which achieves a good trade-off between flexibility and efficiency. Furthermore, a security-critical scheduling scheme is proposed by taking into account applications’ timing and security requirements in addition to precedence constraints. The proposed scheme is applied to heuristically find resource allocations, which maximize the quality of security and the probability of meeting deadlines for all the multimedia applications running on heterogeneous networks. Extensive simulations are provided to demonstrate the effectiveness and feasibility of the proposed scheme.     

并行任务图的优化调度算法

科学与工程计算中的很多复杂应用问题需要使用科学工作流技术,超算领域中的科学工作流常以并行任务图建模,并行任务图的有效调度对应用的高效执行有重要意义。给出了资源限制条件下并行任务图的调度模型;针对Fork-Join类并行任务图给出了若干最优化调度结论;针对一般并行任务图提出了一种新的调度算法,该算法考虑了数据通信开销对资源分配和调度性能的影响,并对已有的CPA算法在特定情况下进行了改进。通过实验与常用的CPR和CPA算法做比较,验证了提出的新算法能够获得很好的调度效果。本文提出的调度算法和得到的最优调度结论对工作流应用系统的高性能调度功能开发具有借鉴意义。     

无线城域网综合资源调度策略设计与实现

宽带无线城域网是当今无线通信领域中的一个热点,而其中的资源调度策略直接关系到系统性能的优劣。本文针对IEEE802.16标准下的宽带无线接入系统设计了一套能够保证用户QoS的资源调度和分配方案,该方案由接纳控制方案、请求机会分配方案及带宽分配方案三部分组成,它的双层调度机制能够针对不同QoS等级的业务提供不同的调度方案,满足用户的QoS要求。     

无线网络信道队列状态感知资源调度算法

资源调度时实现资源的有效利用、保证业务的QoS发挥着重要作用.对跨层资源调度问题的研究状况进行了概述.(1)根据所采用的技术体制和约束类型,介绍了单载波系统和OFDM系统的多用户机会调度问题和算法,分析了单一类型和多种类型资源共享约束下的机会调度算法;(2)分别从队列调度、子栽波分配和功率控制3个方面,对基于队列和信道状态信息的资源调度算法进行了分析和比较;最后指出了需要进一步研究的问题.     

A robust approach for multi-agent natural resource allocation based on stochastic optimization algorithms

Natural resource allocation is a complex problem that entails difficulties related to the nature of real world problems and to the constraints related to the socio-economical aspects of the problem. In more detail, as the resource becomes scarce relations of trust or communication channels that may exist between the users of a resource become unreliable and should be ignored. In this sense, it is argued that in multi-agent natural resource allocation settings agents are not considered to observe or communicate with each other. The aim of this paper is to study multi-agent learning within this constrained framework. Two novel learning methods are introduced that operate in conjunction with any decentralized multi-agent learning algorithm to provide efficient resource allocations. The proposed methods were applied on a multi-agent simulation model that replicates a natural resource allocation procedure, and extensive experiments were conducted using popular decentralized multi-agent learning algorithms. Experimental results employed statistical figures of merit for assessing the performance of the algorithms with respect to the preservation of the resource and to the utilities of the users. It was revealed that the proposed learning methods improved the performance of all policies under study and provided allocation schemes that both preserved the resource and ensured the survival of the agents, simultaneously. It is thus demonstrated that the proposed learning methods are a substantial improvement, when compared to the direct application of typical learning algorithms to natural resource sharing, and are a viable means of achieving efficient resource allocations.     

Tabu search based resource allocation in radiological examination process execution

Efficient resource scheduling and allocation in radiological examination process (REP) execution is a key requirement to improve patient throughput and radiological resource utilization and to manage unexpected events that occur when resource scheduling and allocation decisions change due to clinical needs. In this paper, a Tabu search based approach is presented to solve the resource scheduling and allocation problems in REP execution. The primary objective of the approach is to minimize a weighted sum of average examination flow time, average idle time of the resources, and delays. Unexpected events, i.e., emergent or absent examinations, are also considered. For certain parameter combinations, the optimal solution of radiological resource scheduling and allocation is found, while considering the limitations such as routing and resource constraints. Simulations in the application case are performed. Results show that the proposed approach makes efficient use of radiological resource capacity and improves the patient throughput in REP execution.     

OFDM中继系统的传输策略选择与资源分配

提出传输策略选择结合资源分配的迭代算法,证明其收敛性。进一步将算法简化为顺序进行的三步：子信道指配,传输策略选择和中继的发射功率分配,源的发射功率分配。仿真结果表明,所提资源分配迭代方案和简化方案的频谱效率性能均优于已有的两种资源分配方案,简化方案更适合实际应用。     

Dynamic hinting: Collaborative real-time resource management for reactive embedded systems

The increasing complexity of today’s reactive embedded applications can rapidly result in reduced real-time capabilities of the underlying hard and software. As an example for this paper we’ll refer to the specific and growing demands on the severely resource constrained sensor nodes in sensor/actuator networks (SANet). While preemptive operating systems are one way to retain acceptable reactivity within highly dynamic environments, their concurrency paradigm commonly leads to severe resource management problems, caused by the coexistence of tasks with interfering and even varying requirements. To counteract these problems, we present the novel Dynamic Hinting approach for maintaining good reactivity in typically resource constrained sensor/actuator systems by efficient combination of preemptive task scheduling and collaborative resource allocation. With respect to task priorities, our technique significantly improves classical methods for handling priority inversions (and deadlocks where required) under both short- and long-term resource allocations. Furthermore, we facilitate compositional software design by providing independently developed tasks with runtime information for yet collaborative and reflective resource sharing – e.g. by means of time-utility-functions. In many cases this even allows to reduce blocking delays as otherwise imposed by bounded priority inversion.     

云工作流中基于分时虚拟机的任务层调度算法

云计算是新的一种面向市场的商业计算模式,向用户按需提供服务,云计算的商业特性使其关注向用户提供服务的服务质量。任务调度和资源分配是云计算中两个关键的技术,所使用的虚拟化技术使得其资源分配和任务调度有别于以往的并行分布式计算。目前主要的调度算法是借鉴网格环境下的调度策略,研究基于QoS的调度算法,存在执行效率较低的问题。我们对云工作流任务层调度进行深入研究,分析由底层资源虚拟化形成的虚拟机的特性,结合工作流任务的各类QoS约束,提出了基于虚拟机分时特性的任务层ACS调度算法。经过试验,我们提出的算法相比于文献[1]中的算法在对于较多并行任务的执行上存在较大的优势,能够很好的利用虚拟的分时特性,优化任务到虚拟机的调度。     

Internet scheduling environment with market-driven agents

This paper describes a new generation scheduling paradigm, the Internet scheduling environment. It is formed by a group of Internet scheduling agents which share computational resources to solve scheduling problems in a distributed and collaborative manner. We propose a migration scheme to transform existing standalone scheduling systems to Internet scheduling agents that can communicate with each other and solve problems beyond individual capabilities. To coordinate computational resource collaboration among agents, we introduce the market-based control mechanism in which self-interested agents initiate or participate in auctions to sell or buy scheduling problems. Efficient allocation of computational resources is achieved through the auctions. This paper also describes a prototype Internet scheduling environment named LekiNET, which is migrated from LEKIN/spl reg/, a flexible job shop scheduling system. The experiments on the LekiNET testbed demonstrate that the agent-based market-driven Internet scheduling environment is feasible and advantageous to future scheduling research and development.     

Dual scheduling and quantised control for networked control systems with communication constraints

A novel integrated design scheme of average dwell time scheduling strategy, dynamic bandwidth allocation policy and quantised control for a collection of networked control systems (NCSs) with time delay and communication constraints is proposed in this paper. A scheduling policy is presented to accommodate the limitation of communication capacity which depends on the convergence rate of closed-loop system and divergence rate of open-loop plant. Linear programming technique is adopted to dynamically allocate bit rate for each node and the strategy is used to make trade-offs between the network utilisation and the control performance which provides an effective way of optimising the quality of control (QoC) and the quality of service (QoS) for NCSs. Mid-tread uniform quantisers update the quantisation rules according to the assignment of the bit rate and convert the quantised state into a kind of input saturation with bounded disturbances. Taking into account the effect of dual scheduling strategy and quantisation, the NCSs are modelled as discrete-time switched systems with bounded disturbances. Furthermore, a scheduling and quantised feedback control co-design procedure is proposed for the simultaneous stabilisation of the collection of networked subsystems. Finally, a simulation example is given to illustrate the effectiveness of the proposed method.     

Scheduler Modeling Based on the Controller Synthesis Paradigm

The controller synthesis paradigm provides a general framework for scheduling real-time applications. Schedulers can be considered as controllers of the applications; they restrict their behavior so that given scheduling requirements are met. We study a modeling methodology based on the controller synthesis paradigm. The methodology allows to get a correctly scheduled system from timed models of its processes in an incremental manner, by application of composability results which simplify schedulability analysis. It consists in restricting successively the system to be scheduled by application of constraints defined from scheduling requirements. The latter are a conjunction of schedulability requirements that express timing properties of the processes and policy requirements about resource management. The presented methodology allows a unified view of scheduling theory and approaches based on timing analysis of models of real-time applications.     

Constraint-based timetabling a case study

In this article we concentrate on a typical scheduling problem: the computation of a timetable for a German college. Like many other scheduling problems, this problem contains a variety of complex constraints and necessitates special-purpose search strategies. Techniques from operations research and traditional constraint logic programming are not able to express these constraints and search strategies on a sufficiently high level of abstraction. We show that the higher order concurrent constraint language Oz provides this high-level expressivity, and can serve as a useful programming tool for college time-tabling.     

基于资源约束的并行活动优化调度方法

针对并行产品开发过程,提出了一种基资源约束的优化调度算法,首先给出了并行活动的三个定义,提出了一个使算法能够实现优化调度的综合函数,并研究了并行工程中的两个关键问题－－角色分配和反馈修改,仿真实例表明了算法的可行性。     

Flow-shop path planning for multi-automated guided vehicles in intelligent textile spinning cyber-physical production systems dynamic environment

Aiming at the path planning and decision-making problem, multi-automated guided vehicles (AGVs) have played an increasingly important role in the multi-stage industries, e.g., textile spinning. We recast a framework to investigate the improved genetic algorithm (GA) on multi-AGV path optimization within spinning drawing frames to solve the complex multi-AGV maneuvering scheduling decision and path planning problem. The study reported in this paper simplifies the scheduling model to meet the drawing workshop's real-time application requirements. According to the characteristics of decision variables, the model divides into two decision variables: time-independent variables and time-dependent variables. The first step is to use a GA to solve the AGV resource allocation problem based on the AGV resource pool strategy and specify the sliver can's transportation task. The second step is to determine the AGV transportation scheduling problem based on the sliver can-AGV matching information obtained in the first step. One significant advantage of the presented approach is that the fitness function is calculated based on the machine selection strategy, AGV resource pool strategy, and the process constraints, determining the scheduling sequence of the AGVs to deliver can. Moreover, it discovered that double-path decision-making constraints minimize the total path distance of all AGVs, and minimizing single-path distances of each AGVs exerted. By using the improved GA, simulation results show that the total path distance was shortened.     

Resource allocation decisions under various demands and cost requirements in an unreliable flow network

This paper considers resource allocation decisions in an unreliable multi-source multi-sink flow network, which applies to many real-world systems such as electric and power systems, telecommunications, and transportation systems. Due to uncertainties of components in such an unreliable flow network, transmitting resources successfully and economically through the unreliable flow network is of concern to resource allocation decisions at resource-supplying (source) nodes. We study the resource allocation decisions in an unreliable flow network for a range of demand configurations constrained by demand-dependent and demand-independent cost considerations under the reliability optimization objective. Solutions to these problems can be obtained by computing the resource allocation for each demand configuration independently. In contrast, we pursue an updating scheme that eludes time-consuming enumeration of flow patterns, which is necessary in independent computation of resource allocations for different demand configurations. We show that updating is attainable under both demand-independent and demand-dependent cost constraints when demand incurs an incremental change, and demonstrate the proposed updating scheme with numerical examples.     

A model predictive control approach to the periodic implementation of the solutions of the optimal dynamic resource allocation problem

This paper proposes a model predictive control (MPC) approach to the periodic implementation of the optimal solutions of a class of resource allocation problems in which the allocation requirements and conditions repeat periodically over time. This special class of resource allocation problems includes many practical energy optimization problems such as load scheduling and generation dispatch. The convergence and robustness of the MPC algorithm is proved by invoking results from convex optimization. To illustrate the practical applications of the MPC algorithm, the energy optimization of a water pumping system is studied.