A Heuristic Algorithm for Task Scheduling Based on Mean Load on Grid

Efficient task scheduling is critical to achieving high performance on grid computing environment. The task scheduling on grid is studied as optimization problem in this paper. A heuristic task scheduling algorithm satisfying resources load balancing on grid environment is presented. The algorithm schedules tasks by employing mean load based on task predictive execution time as heuristic information to obtain an initial scheduling strategy. Then an optimal scheduling strategy is achieved by selecting two machines satisfying condition to change their loads via reassigning their tasks under the heuristic of their mean load. Methods of selecting machines and tasks are given in this paper to increase the throughput of the system and reduce the total waiting time. The efficiency of the algorithm is analyzed and the performance of the proposed algorithm is evaluated via extensive simulation experiments. Experimental results show that the heuristic algorithm performs significantly to ensure high load balancing and achieve an optimal scheduling strategy almost all the time. Furthermore, results show that our algorithm is high efficient in terms of time complexity.     

Multi-objective list scheduling of workflow applications in distributed computing infrastructures

Executing large-scale applications in distributed computing infrastructures (DCI), for example modern Cloud environments, involves optimization of several conflicting objectives such as makespan, reliability, energy, or economic cost. Despite this trend, scheduling in heterogeneous DCIs has been traditionally approached as a single or bi-criteria optimization problem. In this paper, we propose a generic multi-objective optimization framework supported by a list scheduling heuristic for scientific workflows in heterogeneous DCIs. The algorithm approximates the optimal solution by considering user-specified constraints on objectives in a dual strategy: maximizing the distance to the user’s constraints for dominant solutions and minimizing it otherwise. We instantiate the framework and algorithm for a four-objective case study comprising makespan, economic cost, energy consumption, and reliability as optimization goals. We implemented our method as part of the ASKALON environment (Fahringer et al., 2007) for Grid and Cloud computing and demonstrate through extensive real and synthetic simulation experiments that our algorithm outperforms related bi-criteria heuristics while meeting the user constraints most of the time.     

作业车间调度问题的一种改进的转换瓶颈算法

描述了一种解决作业车间调度最短完工时间问题有效的启发式算法。该算法是对Adams等人的转换瓶颈算法的改进,算法中用了改进的Calier单机调度方法以克服原Calier算法的不足。从对一组问题基准实例的实验计算结果看,该算法在合理的计算时间内,对多个实例得到比原转换瓶颈算法和Beam搜索算法更好的结果;从实验结果看,算法也优于ISB算法。     

作业车间调度问题的一种启发式算法

论文描述了解决作业车间调度最短完工时间问题的一种快速有效的启发式算法。该算法基于一种优先指派规则,并利用了往前看的思想。从对一组问题标准实例的实验计算结果看,该算法在很短的计算时间内,对多个实例得到最优解或近优解。     

独立任务调度的启发式算法

任务调度是一个NP-hard问题,而且是并行与分布式计算中一个必不可少的组成部分,特别是在网格计算环境下任务调度更加复杂。该文提出了满足负载均衡的一个启发式任务调度算法。给出了选择处理机和任务的方法,以提高算法的效率。实验表明该算法是一个高效率的调度算法,并且几乎总是找到了最优调度方案。     

Scheduling precedence constrained task graphs with non-negligibleintertask communication onto multiprocessors

The multiprocessor scheduling problem is the problem of scheduling the tasks of a precedence constrained task graph (representing a parallel program) onto the processors of a multiprocessor in a way that minimizes the completion time. Since this problem is known to be NP-hard in the strong sense in all but a few very restricted eases, heuristic algorithms are being developed which obtain near optimal schedules in a reasonable amount of computation time. We present an efficient heuristic algorithm for scheduling precedence constrained task graphs with nonnegligible intertask communication onto multiprocessors taking contention in the communication channels into consideration. Our algorithm for obtaining satisfactory suboptimal schedules is based on the classical list scheduling strategy. It simultaneously exploits the schedule-holes generated in the processors and in the communication channels during the scheduling process in order to produce better schedules. We demonstrate the effectiveness of our algorithm by comparing with two competing heuristic algorithms available in the literature     

基于图匹配的多处理机调度算法

本文对具有高通讯延迟的多处理机系统（机群系统）上的任务调度算法进行了研究，与以往算法主要考虑任务图的关键路径不同，本文给出了任务图的调度与其偶图匹配的对应关系，并由此提出了一种新的启发式算法，通过模拟试验显示本算法具有较好的调度效果。     

基于两阶段随机仿真优化算法的体检顾客预约调度

针对体检机构顾客排队等待时间长的问题，研究随机服务时间下的体检顾客调度，采用多人时间槽预约策略，并在预约调度策略的基础上优化每位顾客的体检项目顺序，提出一种包含粗糙仿真评估和精确仿真评估两阶段随机仿真优化算法。运用序优化思想将基于亲和度评估的多种群遗传算法作为迭代优化策略，并利用改进的最优计算量分配方法排除超级个体的影响，形成仿真资源的全局和自适应优化分配机制。实验结果表明，与不进行任何调度及使用体检顺序启发式调度规则的离散事件结果相比，该算法获得了更好的调度解。     

A novel fuzzy decision-making system for CPU scheduling algorithm

In this research article, we present a novel fuzzy decision-making system to improve CPU scheduling algorithm of a multitasking operating system. We add intelligence to the existing scheduling algorithms by incorporating fuzzy techniques in the selection of a process to be run on CPU, which result in improved waiting and turn-around times. We implement our proposed algorithm as a simulator using C language. The simulator implements our fuzzy scheduling algorithm, reads the required parameters of all the ready to run processes from a file, and finally computes a dynamic priority (dpi) value for each process. The run queue is sorted according to each process’s dpi, and the process at the head of the queue is selected to run on CPU. Finally, we compare our results with some existing proposed fuzzy CPU scheduling (PFCS) algorithms as well as with some standard CPU schedulers. Our results show improvements as compared to the work of Ajmani’s PFCS (Ajmani and Sethi in BVICAM’s Int J Inf Technol 5:583–588, 2013), as well as from Behera’s improved fuzzy-based CPU scheduling algorithm (Behera et al. in Int J Soft Comput Eng 2:326–331, 2012). Our efforts contribute to the overall efforts of the community contributing to the fuzzification of different operating system modules. These efforts finally result in an operating system that gives convenience to its users in both certain and uncertain environments and at the same time efficiently utilize the underlying hardware and software under precise as well as fuzzy conditions (Kandel et al. in Fuzzy Sets Syst 99:241–251, 1988).     

A steganographic method based upon JPEG and particle swarm optimization algorithm

In this paper, a novel steganographic method, based on JPEG and Particle Swarm Optimization algorithm (PSO), is proposed. In order to improve the quality of stego-images, an optimal substitution matrix for transforming the secret messages is first derived by means of the PSO algorithm. The standard JPEG quantization table is also modified to contain more secret messages. The transformed messages are then hidden in the DC-to-middle frequency components of the quantized DCT coefficients of the cover-image. Finally, a JPEG file with secret messages is generated through JPEG entropy coding. We compare our algorithm with Chang et al.’s JPEG-based steganographic algorithm. The experimental results show that our proposed method has larger message capacity and better image quality than Chang et al.’s. In addition, our method also has a high security level.     

分时EDF算法及其在多媒体操作系统中的应用

提出了一种新的CPU调度算法－－分时EDF（Earliest Deadine First)算法,该算法能保证硬实时任务不丢失死线,并易于在分时系统中实现。以分时EDF算法为基础,提出一种新的CPU层次调度算法－－HRFSFQ,该算法用于多媒体操作系统时能保证各类任务的QoS。最后通过大量实验证明了上述算法的有效性和正确性。     

A scheduling algorithm based on the singular value decomposition heuristic method in a distributed manufacturing system

Because distributed manufacturing technology is the foundation of modernized production and traditional heuristic methods exhibit problems of high complexity and low efficiency, this paper designs a scheduling algorithm based on the singular value decomposition heuristic (SVDH) method. The algorithm uses the device distribution and the transportation relationship between devices in a distributed manufacturing system. The algorithm takes the sequence relationship between tasks and the distance between devices as the implicit relationship between the task and the device. The algorithm makes use of the implicit relationship to amend the processing time matrix of the task and corrects the processing time matrix that contains the transportation relationship. Singular value decomposition principal component analysis is performed on the corrected processing time to find the most suitable processing device for each process, and an initial solution matrix is established. The heuristic solution is used to optimize the initial solution to find the optimal scheduling result based on the initial solution matrix. The establishment of the initial solution can effectively reduce the computational complexity of the heuristic solution, realize a parallelizing solution, and improve the efficiency of the heuristic solutions. In addition, the SVDH scheduling result has a lower transfer time between devices due to the consideration of the topology of tasks and devices, that is, the transit time. In this paper, the experiments are conducted on the heuristic performance, scheduling results, and transportation time. The experimental results show the advantages of SVDH over general heuristic algorithms in terms of efficiency and transit time.     

An efficient heuristic for adaptive production scheduling and control in one-of-a-kind production

Even though research in flow shop production scheduling has been carried out for many decades, there is still a gap between research and application—especially in manufacturing paradigms such as one-of-a-kind production (OKP) that intensely challenges real time adaptive production scheduling and control. Indeed, many of the most popular heuristics continue to use Johnson's algorithm (1954) as their core. This paper presents a state space (SS) heuristic, integrated with a closed-loop feedback control structure, to achieve adaptive production scheduling and control in OKP. Our SS heuristic, because of its simplicity and computational efficiency, has the potential to become a core heuristic. Through a series of case studies, including an industrial implementation in OKP, our SS-based production scheduling and control system demonstrates significant potential to improve production efficiency.     

Minimizing total completion time for re-entrant flow shop scheduling problems

In this paper, we study re-entrant flow shop scheduling problems with the objective of minimizing total completion time. In a re-entrant scheduling problem, jobs may visit some machines more than once for processing. The problem is NP-hard even for machine number m=2. A heuristic algorithm is presented to solve the problem, in which an effective k-insertion technique is introduced as the improvement strategy in iterations. Computational experiments and analyses are performed to give guidelines of choosing parameters in the algorithm. We also provide a lower bound for the total completion time of the optimal solution when there are only two machines. Objective function values of the heuristic solutions are compared with the lower bounds to evaluate the efficiency of the algorithm. For randomly generated instances, the results show that the given heuristic algorithm generates solutions with total completion times within 1.2 times of the lower bounds in most of the cases.     

Multiprocessor task scheduling in multistage hybrid flow-shops: A parallel greedy algorithm approach

Hybrid flow shop scheduling problems have a special structure combining some elements of both the flow shop and the parallel machine scheduling problems. Multiprocessor task scheduling problem can be stated as finding a schedule for a general task graph to execute on a multiprocessor system so that the schedule length can be minimized. Hybrid Flow Shop Scheduling with Multiprocessor Task (HFSMT) problem is known to be NP-hard. In this study we present an effective parallel greedy algorithm to solve HFSMT problem. Parallel greedy algorithm (PGA) is applied by two phases iteratively, called destruction and construction. Four constructive heuristic methods are proposed to solve HFSMT problems. A preliminary test is performed to set the best values of control parameters, namely population size, subgroups number, and iteration number. The best values of control parameters and operators are determined by a full factorial experimental design using our PGA program. Computational results are compared with the earlier works of O?uz et al. [1], [3], and O?uz [2]. The results indicate that the proposed parallel greedy algorithm approach is very effective in terms of reduced total completion time or makespan (C_max) for the attempted problems.     

Developing a dynamic rolling-horizon decision strategy for yard crane scheduling

As a hub for land and marine transportation, container terminals play an important role in global trade. In today’s competitive environment, container terminals should improve their service quality, i.e., effective space resource handling and equipment resource scheduling, for their prosperity or even survival. Although intensive researches were attempted on yard crane scheduling, the solutions from these approaches likely reached a local optimum, and thereafter a rational strategy towards global optimum was still lacking. Accordingly, it became an imperative to explore a rational strategy for this purpose. To resolve this problem, a novel dynamic rolling-horizon decision strategy was proposed for yard crane scheduling in this study. Initially, an integer programming model was established to minimize the total task delaying at blocks. Due to the computational scale with regard to the yard crane scheduling problem, a heuristic algorithm, along with a simulation model, was then applied. In this fashion, the simulation model was next investigated to alternate the periods and evaluate the task delaying. Subsequently, a genetic algorithm was employed to optimize the initial solutions generated. Consequently, computational experiments were used to illustrate the proposed strategy for yard crane scheduling and verify the effectiveness and efficiency of the proposed approach.     

异构多核计算系统的Codelet任务调度策略

Codelet数据流计算模型在处理大规模并行计算任务时效果显著,但该模型目前缺少在异构多核环境中的任务调度策略。因此,提出了一种在异构多核环境下基于蚁群算法的Codelet任务调度策略。该调度策略将启发式算法与蚁群算法相融合,在发挥各自优势的同时克服了启发式算法不能得出最优解的缺陷以及蚁群算法初始信息匮乏的问题。实验结果表明,智能蚁群任务调度策略相比Codelet运行时系统中原生的动态调度和静态调度策略具有更高的执行效率。     

A hybrid instance-intensive workflow scheduling method in private cloud environment

Aiming to solve the problem of instance-intensive workflow scheduling in private cloud environment, this paper first formulates a scheduling optimization model considering the communication time between tasks. The objective of this model is to minimize the execution time of all workflow instances. Then, a hybrid scheduling method based on the batch strategy and an improved genetic algorithm termed fragmentation based genetic algorithm is proposed according to the characters of instance-intensive cloud workflow, where task priority dispatching rules are also taken into account. Simulations are conducted to compare the proposed method with the canonical genetic algorithm and two heuristic algorithms. Our simulation results demonstrate that the proposed method can considerably enhance the search efficiency of the genetic algorithm and is able to considerably outperform the compared algorithms, in particular when the number of workflow instances is high and the computational resource available for optimization is limited.

     

Dynamic Dispatching of Cyclic Real-Time Tasks with Relative Timing Constraints

In some hard real-time systems, relative timing constraints may be imposed on task executions, in addition to the release time and deadline constraints. Relative timing constraints such as separation or relative deadline constraints may be given between start or finish times of tasks (Gerber et al., 1995; Han and Lin, 1989; Han et al., 1992; Han and Lin, 1992; Han et al., 1996).One approach in real-time scheduling is to find a total order on a set of N tasks in a scheduling window, and cyclically use this order at run time to execute tasks. However, in the presence of relative timing constraints, if the task execution times are nondeterministic with defined lower and upper bounds, it is not always possible to statically assign task start times at pre-runtime for a given task ordering (Gerber et al., 1995).We develop a technique called dynamic cyclic dispatching as an extension of a parametric dispatching mechanism in (Gerber et al., 1995). An ordered set of N tasks is assumed to be given in a scheduling window and this schedule(ordering) is cyclically repeated at runtime in consecutive scheduling windows. Relative timing constraints between tasks may be defined across scheduling window boundaries as well as within one scheduling window. A task set is defined to be dispatchable if there exists any way in which the tasks can be dispatched with all their timing constraints satisfied. An off-line algorithm is presented to check the dispatchability of a task set and to obtain parametric lower and upper bound functions for task start times if the task set is dispatchable. These parametric bound functions are evaluated at runtime to obtain a valid time interval during which a task can be started. The complexity of this off-line component is shown to be O(n ² N ³) where n is the number of tasks in a scheduling window that have relative timing constraints with tasks in the next scheduling window. An online algorithm can evaluate these bounds in O(N) time.Unlike static approaches which assign fixed start times to tasks in the scheduling window, our approach allows us to flexibly manage the slack times at runtime without sacrificing the dispatchability of tasks. Also, a wider class of relative timing constraints can be imposed to the task set compared to the traditional approaches.