On parallelizing the multiprocessor scheduling problem

Existing heuristics for scheduling a node and edge weighted directed task graph to multiple processors can produce satisfactory solutions but incur high time complexities, which tend to exacerbate in more realistic environments with relaxed assumptions. Consequently, these heuristics do not scale well and cannot handle problems of moderate sizes. A natural approach to reducing complexity, while aiming for a similar or potentially better solution, is to parallelize the scheduling algorithm. This can be done by partitioning the task graphs and concurrently generating partial schedules for the partitioned parts, which are then concatenated to obtain the final schedule. The problem, however, is nontrivial as there exists dependencies among the nodes of a task graph which must be preserved for generating a valid schedule. Moreover, the time clock for scheduling is global for all the processors (that are executing the parallel scheduling algorithm), making the inherent parallelism invisible. In this paper, we introduce a parallel algorithm that is guided by a systematic partitioning of the task graph to perform scheduling using multiple processors. The algorithm schedules both the tasks and messages, and is suitable for graphs with arbitrary computation and communication costs, and is applicable to systems with arbitrary network topologies using homogeneous or heterogeneous processors. We have implemented the algorithm on the Intel Paragon and compared it with three closely related algorithms. The experimental results indicate that our algorithm yields higher quality solutions while using an order of magnitude smaller scheduling times. The algorithm also exhibits an interesting trade-off between the solution quality and speedup while scaling well with the problem size     

A Hyper-Heuristic Framework for Lifetime Maximization in Wireless Sensor Networks With A Mobile Sink

Maximizing the lifetime of wireless sensor networks(WSNs) is an important and challenging research problem. Properly scheduling the movements of mobile sinks to balance the energy consumption of wireless sensor network is one of the most effective approaches to prolong the lifetime of wireless sensor networks. However, the existing mobile sink scheduling methods either require a great amount of computational time or lack effectiveness in finding high-quality scheduling solutions. To address the above issues, this paper proposes a novel hyperheuristic framework, which can automatically construct high-level heuristics to schedule the sink movements and prolong the network lifetime. In the proposed framework, a set of low-level heuristics are defined as building blocks to construct high-level heuristics and a set of random networks with different features are designed for training. Further, a genetic programming algorithm is adopted to automatically evolve promising high-level heuristics based on the building blocks and the training networks. By using the genetic programming to evolve more effective heuristics and applying these heuristics in a greedy scheme, our proposed hyper-heuristic framework can prolong the network lifetime competitively with other methods, with small time consumption. A series of comprehensive experiments, including both static and dynamic networks,are designed. The simulation results have demonstrated that the proposed method can offer a very promising performance in terms of network lifetime and response time.     

Agent-based airline scheduling

The development of an airline schedule can be defined as the art of developing system-wide flight patterns that deliver optimum service to the public in terms of quantity as well as quality. The development of the schedule is market driven with maintenance and crew requirements as constraints. This paper deals with an integrated agent-based approach for the airline scheduling problem. A bidding protocol is used to generate a market based schedule. FIFO and genetic algorithms are used to develop a crew schedule. An expert system combined with the Q-learning algorithm assist operational schedulers in resolving operational conflicts such as delays.     

Simple and integrated heuristic algorithms for scheduling tasks with time and resource constraints

We consider the problem of scheduling a set of n tasks in a system having r resources. Each task has an arbitrary, but known, processing time and a deadline, and may request use of a number of resources. A resource can be used either in shared mode or exclusive mode. In this article, we study algorithms used for determining whether or not a set of tasks is schedulable in such a system, and if so, determining a schedule for it. This scheduling problem is known to be NP-complete and hence we methodically study a set of heuristics that can be used by such an algorithm. Due to the complexity of the problem, simple heuristics do not perform satisfactorily. However, an algorithm that uses combinations of these simple heuristics works very well compared to an optimal algorithm that takes exponential time complexity. For the combination that performs the best, we also determine the scheduling costs as a function of the size of the task set scheduled.     

Flexible job shop scheduling using hybrid differential evolution algorithms

This paper proposes hybrid differential evolution (HDE) algorithms for solving the flexible job shop scheduling problem (FJSP) with the criterion to minimize the makespan. Firstly, a novel conversion mechanism is developed to make the differential evolution (DE) algorithm that works on the continuous domain adaptive to explore the problem space of the discrete FJSP. Secondly, a local search algorithm based on the critical path is embedded in the DE framework to balance the exploration and exploitation by enhancing the local searching ability. In addition, in the local search phase, the speed-up method to find an acceptable schedule within the neighborhood structure is presented to improve the efficiency of whole algorithms. Extensive computational results and comparisons show that the proposed algorithms are very competitive with the state of the art, some new best known solutions for well known benchmark instances have even been found.     

基于遗传禁忌算法的多目的间歇过程调度问题求解

由于组合爆炸特性,多目的厂的调度问题很难求解大规模甚至中等规模的问题,本文采用一种新的随机性优化技术一基于禁忌技术的遗传算法点(Tabu-genetic algorithm,TGA)来对该问题进行求解,引入新的选择策略和变异方法．并以零等待的多目的间歇过程调度为实例,计算表明同已有的方法相比,该方法求解效率高、收敛速度快、使用简单方便,可有效的克服计算负荷和求解质量之间的冲突,是一种求解多目的厂间歇过程调度问题的有效算法。     

Building a retargetable local instruction scheduler

While high-performance architectures have included some Instruction-Level Parallelism (ILP) for at least 25 years, recent computer designs have exploited ILP to a significant degree. Although a local scheduler is not sufficient for generation of excellent ILP code, it is necessary as many global scheduling and software pipelining techniques rely on a local scheduler. Global scheduling techniques are well-documented, yet practical discussions of local schedulers are notable in their absence. This paper strives to remedy that disparity by describing a list scheduling framework and several important practical details that, taken together, allow implementation of an efficient local instruction scheduler that is easily retargetable for ILP architectures. The foundation of our machine-independent instruction scheduler is a timing model that allows easy retargetability to a wide range of architectures. In addition to describing how a general list-scheduler can be implemented within the framework of our timing model, experimental results indicate that lookahead scheduling can profoundly improve a scheduler's ability to produce a legal schedule. Further experimental data shows that deciding to schedule a data dependence DAG (DDD) in forward or reverse order depends significantly upon that target architecture, suggesting the possibility of scheduling in each direction and using the best of the two schedules. In contrast, experiments demonstrate little difference in code quality for schedules generated by either instruction-driven or operation-driven schedulers. Thus, the inherent flexibility of operation-driven methods suggests including that approach in a retargetable instruction scheduler. List scheduling is, of course, a heuristic scheduling method. A variety of scheduling heuristics are presented. In addition, the paper describes a method, using a genetic algorithm search, to ‘fine-tune’ the weights of twenty-four individual heuristics to form a DDD-node heuristic tuned to a specific architecture. © 1998 John Wiley & Sons, Ltd.     

A genetic algorithm for multiprocessor scheduling

The problem of multiprocessor scheduling can be stated as finding a schedule for a general task graph to be executed on a multiprocessor system so that the schedule length can be minimized. This scheduling problem is known to be NP-hard, and methods based on heuristic search have been proposed to obtain optimal and suboptimal solutions. Genetic algorithms have recently received much attention as a class of robust stochastic search algorithms for various optimization problems. In this paper, an efficient method based on genetic algorithms is developed to solve the multiprocessor scheduling problem. The representation of the search node is based on the order of the tasks being executed in each individual processor. The genetic operator proposed is based on the precedence relations between the tasks in the task graph. Simulation results comparing the proposed genetic algorithm, the list scheduling algorithm, and the optimal schedule using random task graphs, and a robot inverse dynamics computational task graph are presented     

A study on local search neighborhoods for the job shop scheduling problem with total weighted tardiness objective

In this paper we consider the job shop scheduling problem with total weighted tardiness objective (JSPTWT). This objective reflects the goal to achieve a high service level which is of increasing importance in many branches of industry. The paper concentrates on a class of baseline heuristics for this problem, known as neighborhood search techniques. An approach based on disjunctive graphs is developed to capture the general structure of neighborhoods for the JSPTWT. Existing as well as newly designed neighborhoods are formulated and analyzed. The performance and search ability of the operators (as well as combinations thereof) are compared in a computational study. Although no dominant operator is identified, a transpose-based perturbation on multiple machines turns out as a promising choice if applied as the only operator. Combining operators improves the schedule quality only slightly. But, the implementation of operators within a meta-heuristic enables to produce a higher schedule quality. A structural classification of neighborhood operators and some new analytical results are presented as well.     

Parallel machine scheduling problems using memetic algorithms

In this paper, we investigate how to apply the hybrid genetic algorithms (the memetic algorithms) to solve the parallel machine scheduling problem. There are two essential issues to be dealt with for all kinds of parallel machine scheduling problems: job partition among machines and job sequence within each machine. The basic idea of the proposed method is that (a) use the genetic algorithms to evolve the job partition and then (b) apply a local optimizer to adjust the job permutation to push each chromosome climb to his local optima. Preliminary computational experiments demonstrate that the hybrid genetic algorithm outperforms the genetic algorithms and the conventional heuristics.     

Rolling-horizon and fix-and-relax heuristics for the multi-product multi-level capacitated lotsizing problem with sequence-dependent setups

This paper discusses the multi-product multi-level capacitated lotsizing and scheduling problem with sequence-dependent setups. An exact formulation of the problem is provided as a mixed-integer program which is impractical to solve in reasonable computing time for non-small instances. To solve non-small instances of the problem, MIP-based heuristics are provided. To test the accuracy of heuristics, two lower bounds are developed and compared against the optimal solution. The trade-offs between schedule quality and computational time of heuristics are also provided.     

列队竞争算法求解多产品间歇生产过程排序问题

由于组合爆炸特性,多产品厂的排序问题很难求解大规模甚至中等规模的问题,本文采用一种新的随机型进化搜索算法——列队竞争算法来对该问题进行求解,引入新的选择策略和变异方法。计算表明同已有的方法相比,该方法求解效率高、收敛速度快、使用简单方便,是一种求解多产品间歇过程排序问题的有效算法,为多目的厂间歇过程排序研究提供了新思路。     

On multiprocessor task scheduling using efficient state space search approaches

Obtaining an optimal schedule for a set of precedence-constrained tasks is a well-known NP-complete problem in its general form. In view of the intractability of the problem, most of the previous work relies on heuristics that try to find reasonably high quality solutions in an acceptable amount of time. While optimal polynomial-time algorithms are known only for a few simple cases (and in other cases can only be obtained through an exhaustive search with prohibitively high time complexity), they may be critically important for applications in which performance is the prime objective. Optimal solutions can also serve as a reference to test the performance of various heuristics. Moreover, an optimal schedule for a program at hand needs to be determined only once (and off-line) but the program using that schedule is in general executed several times. In this paper, we propose optimal algorithms for static scheduling of task graphs with arbitrary parameters to multiple homogeneous processors. The first algorithm is based on the A^* search technique and uses a computationally efficient cost function for guiding the search with reduced complexity. Additionally, we propose a number of effective state-pruning techniques to reduce the search space. For further lowering the complexity, we propose an efficient parallelization of the search algorithm. We parallelize the algorithm with reduced interprocessor communication as well as with static and dynamic load-balancing schemes to evenly distribute the search states to the processors. We also propose an approximate algorithm that guarantees a bounded deviation from the optimal solution but executes in a considerably shorter time. Based on an extensive experimental evaluation of the algorithms, we conclude that the parallel algorithm with pruning techniques is an efficient scheme for generating optimal solutions of reasonably large problems while the approximate algorithm is effective if slightly degraded solutions are acceptable.     

An improved constraint satisfaction adaptive neural network for job-shop scheduling

This paper presents an improved constraint satisfaction adaptive neural network for job-shop scheduling problems. The neural network is constructed based on the constraint conditions of a job-shop scheduling problem. Its structure and neuron connections can change adaptively according to the real-time constraint satisfaction situations that arise during the solving process. Several heuristics are also integrated within the neural network to enhance its convergence, accelerate its convergence, and improve the quality of the solutions produced. An experimental study based on a set of benchmark job-shop scheduling problems shows that the improved constraint satisfaction adaptive neural network outperforms the original constraint satisfaction adaptive neural network in terms of computational time and the quality of schedules it produces. The neural network approach is also experimentally validated to outperform three classical heuristic algorithms that are widely used as the basis of many state-of-the-art scheduling systems. Hence, it may also be used to construct advanced job-shop scheduling systems.     

Wingrid-面向参数扫描应用的网格计算系统

参数扫描应用在计算网格环境下扮演十分重要的角色。在Wingrid项目中,我们提出并实现了一种面向参数扫描的自适应调度机制。客户端,主节点和从节点的调度基础设施,以及基于领导节点的通信系统能够改善调度的效率。同时,我们比较了自适应workqueue算法和标准启发式调度算法。实验结果显示大网络延迟下,启发式调度算法效率高于workqueue算法,在各种启发式算法中,min-min启发式算法的任务完成时间最小。     

Efficient local search far DAG scheduling

Scheduling DAGs to multiprocessors is one of the key issues in high-performance computing. Most realistic scheduling algorithms are heuristic and heuristic algorithms often have room for improvement. The quality of a scheduling algorithm can be effectively improved by a local search. In this paper, we present a fast local search algorithm based on topological ordering. This is a compaction algorithm that can effectively reduce the schedule length produced by any DAG Scheduling algorithm. Thus, it can improve the quality of existing DAG scheduling algorithms. This algorithm can quickly determine the optimal search direction. Thus, it is of low complexity and extremely fast     

Platform impact on performance of parallel genetic algorithms: Design and implementation considerations

Many problems in the operations research field cannot be solved to optimality within reasonable amounts of time with current computational resources. In order to find acceptable solutions to these computationally demanding problems, heuristic methods such as genetic algorithms are often developed. Parallel computing provides alternative design options for heuristic algorithms, as well as the opportunity to obtain performance benefits in both computational time and solution quality of these heuristics. Heuristic algorithms may be designed to benefit from parallelism by taking advantage of the parallel architecture. This study will investigate the performance of the same global parallel genetic algorithm on two popular parallel architectures to investigate the interaction of parallel platform choice and genetic algorithm design. The computational results of the study illustrate the impact of platform choice on parallel heuristic methods. This paper develops computational experiments to compare algorithm development on a shared memory architecture and a distributed memory architecture. The results suggest that the performance of a parallel heuristic can be increased by considering the desired outcome and tailoring the development of the parallel heuristic to a specific platform based on the hardware and software characteristics of that platform.     

Solving the integrated scheduling of production and rail transportation problem by Keshtel algorithm

Nowadays, scheduling of production cannot be done in isolation from scheduling of transportation since a coordinated solution to the integrated problem may improve the performance of the whole supply chain. In this paper, because of the widely used of rail transportation in supply chain, we develop the integrated scheduling of production and rail transportation. The problem is to determine both production schedule and rail transportation allocation of orders to optimize customer service at minimum total cost. In addition, we utilize some procedures and heuristics to encode the model in order to address it by two capable metaheuristics: Genetic algorithm (GA), and recently developed one, Keshtel algorithm (KA). Latter is firstly used for a mathematical model in supply chain literature. Besides, Taguchi experimental design method is utilized to set and estimate the proper values of the algorithms’ parameters to improve their performance. For the purpose of performance evaluation of the proposed algorithms, various problem sizes are employed and the computational results of the algorithms are compared with each other. Finally, we investigate the impacts of the rise in the problem size on the performance of our algorithms.     

QoS guided Min-Min heuristic for grid task scheduling

Task scheduling is an integrated component of computing.With the emergence of Grid and ubiquitous computing,new challenges appear in task scheduling based on properties such as security,quality of service,and lack of central control within distributed administrative domains.A Grid task scheduling framework must be able to deal with these issues.One of the goals of Grid task scheduling is to achivev high system throughput while matching applications with the available computing resources.This matching of resources in a non-deterministically shared heterogeneous environment leads to concerns over Quality of Service (QoS).In this paper a novel QoS guided task scheduling algorithm for Grid computing is introduced.The proposed novel algorithm is based on a general adaptive scheduling heuristics that includes QoS guidance.The algorithm is evaluated within a simulated Grid environment.The experimental results show that the nwe QoS guided Min-Min heuristic can lead to significant performance gain for a variety of applications.The approach is compared with others based on the quality of the prediction formulated by inaccurate information.     

An Integrated Technique for Task Matching and Scheduling onto Distributed Heterogeneous Computing Systems

This paper presents a problem-space genetic algorithm (PSGA)-based technique for efficient matching and scheduling of an application program that can be represented by a directed acyclic graph, onto a mixed-machine distributed heterogeneous computing (DHC) system. PSGA is an evolutionary technique that combines the search capability of genetic algorithms with a known fast problem-specific heuristic to provide the best-possible solution to a problem in an efficient manner as compared to other probabilistic techniques. The goal of the algorithm is to reduce the overall completion time through proper task matching, task scheduling, and inter-machine data transfer scheduling in an integrated fashion. The algorithm is based on a new evolutionary technique that embeds a known problem-specific fast heuristic into genetic algorithms (GAs). The algorithm is robust in the sense that it explores a large and complex solution space in smaller CPU time and uses less memory space as compared to traditional GAs. Consequently, the proposed technique schedules an application program with a comparable schedule length in a very short CPU time, as compared to GA-based heuristics. The paper includes a performance comparison showing the viability and effectiveness of the proposed technique through comparison with existing GA-based techniques.