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     

Greed in resource scheduling

We examine the worst-case performance of a class of heuristic scheduling algorithms commonly referred to as priority-driven or list-scheduling algorithms. It is well known that these algorithms have anomalous, unpredictable performance when used to schedule nonpreemptive tasks with precedence constraints. We present a general method for deriving the worst-case performance of these algorithms. This method is easy to use, yet powerful enough to yield tight performance bounds for many classes of scheduling problems. We demonstrate the method for several problems to show it has wide applicability. We also present several task systems for which list-scheduling algorithms exhibit unavoidable worst-case performance and discuss the general characteristics of these task systems. These task systems are sometimes overlooked in simulation studies; consequently, the results of these studies may be overly optimistic.This work was partially supported by the US Navy. ONR Contract No. NVY N0001489 J1181     

网格环境下基于启发式智能算法的任务调度研究

网格环境中的资源和任务情况异常复杂,因此计算任务在各种资源之间的调度成为了一个关键的问题,启发式智能算法被证明是解决这类问题的有效算法.本文提出将遗传算法和改进的蚂蚁算法融合起来解决网格环境下的任务调度.     

A comparison of multiprocessor task scheduling algorithms with communication costs

Both parallel and distributed network environment systems play a vital role in the improvement of high performance computing. Of primary concern when analyzing these systems is multiprocessor task scheduling. Therefore, this paper addresses the challenge of multiprocessor task scheduling parallel programs, represented as directed acyclic task graph (DAG), for execution on multiprocessors with communication costs. Moreover, we investigate an alternative paradigm, where genetic algorithms (GAs) have recently received much attention, which is a class of robust stochastic search algorithms for various combinatorial optimization problems. We design the new encoding mechanism with a multi-functional chromosome that uses the priority representation—the so-called priority-based multi-chromosome (PMC). PMC can efficiently represent a task schedule and assign tasks to processors. The proposed priority-based GA has show effective performance in various parallel environments for scheduling methods.     

Methods of resource management in problem-oriented computing environment

One of the important classes of computational problems is problem-oriented workflow applications executed in distributed computing environment. A problem-oriented workflow application can be represented by a directed graph whose vertices are tasks and arcs are data flows. For a problem-oriented workflow application, we can get a priori estimates of the task execution time and the amount of data to be transferred between the tasks. A distributed computing environment designed for the execution of such tasks in a certain subject domain is called problem-oriented environment. To efficiently use resources of the distributed computing environment, special scheduling algorithms are applied. Nowadays, a great number of such algorithms have been proposed. Some of them (like the DSC algorithm) take into account specific features of problem-oriented workflow applications. Others (like Min–Min algorithm) take into account many-core structure of nodes of the computational network. However, none of them takes into account both factors. In this paper, a mathematical model of problem-oriented computing environment is constructed, and a new problem-oriented scheduling (POS) algorithm is proposed. The POS algorithm takes into account both specifics of the problem-oriented jobs and multi-core structure of the computing system nodes. Results of computational experiments comparing the POS algorithm with other known scheduling algorithms are presented.     

Scheduling algorithm for real-time tasks using multiobjective hybrid genetic algorithm in heterogeneous multiprocessors system

The scheduling problem for real-time tasks on multiprocessor is one of the NP-hard problems. This paper proposes a new scheduling algorithm for real-time tasks using multiobjective hybrid genetic algorithm (mohGA) on heterogeneous multiprocessor environment. In solution algorithms, the genetic algorithm (GA) and the simulated annealing (SA) are cooperatively used. In this method, the convergence of GA is improved by introducing the probability of SA as the criterion for acceptance of new trial solution.     

Reflection and time-dependent computing: Experiences with the R2 architecture

In this paper we present an application of computational reflection in the programming oftime-dependent systems. A time-dependent system performs its tasks according to timing specifications specified within the system or imposed from outside the system. Reflective techniques can be applied to programming time-dependent systems because (1) some application programs require the introduction of a new language construct for specifying timing requirements and (2) different applications may require domain-specific scheduling algorithms. To allow a programmer to add or modify language constructs or scheduling algorithms, however, a clear reflective architecture and program interfaces must be provided. This paper proposes a concurrent object-based reflective architecture (R ² architecture) for time-dependent computing. This architecture is based on anindividual reflection scheme and introduces new meta-level objects (real-time meta objects) that are responsible for time-dependent capabilities. An alarm-clock object and a scheduler object are introduced, and message protocols between them and real-time meta objects are defined. We implemented this architecture on ABCL/R2 and created the Sampled Sound Player program as an application. With this application we provided three different scheduler objects and measured the impact of different scheduling algorithms on sound playback. The measured results show that a scheduler with more complex computations at the meta level exhibited less scheduling overhead, thus was capable of better sound playback. The other example, Time-dependent Graceful Degradation Scheme, demonstrates the programming of functionality degradation triggered by failure to satisfy timing specifications.     

无线网络包调度算法综述

无线网络介质的广播特性使得相邻用户的业务传输产生竞争,并且网络所支持的支持业务也呈多样发展,而调度规则不仅可以解决竞争问题,还可以保证不同业务的QoS。该文概述了当前提出的用于无线网络的包调度算法,提出了还需解决的问题。     

高速IP路由器中输入排队调度算法综述

高速IP路由器一般采用基于定长信元的交换结构,其可扩展性和性能分别受排队策略和调度算法的影响.基于输入排队策略的路由器具有良好的可扩展性,但需要一个有效的调度算法的支持,才能保证吞吐率和延迟等性能.主要讨论输入排队调度算法,将现有的调度算法分为4类:最大(无权重)匹配、最大权重匹配、稳定婚姻匹配和确定型调度.对每一类算法,从技术特点和性能指标两个方面进行比较和分析.最后给出了输入排队调度算法的发展趋势.     

DP-Fair: a unifying theory for optimal hard real-time multiprocessor scheduling

We consider the problem of optimal real-time scheduling of periodic and sporadic tasks on identical multiprocessors. A number of recent papers have used the notions of fluid scheduling and deadline partitioning to guarantee optimality and improve performance. This article develops a unifying theory with the DP-Fair scheduling policy and examines how it overcomes problems faced by greedy scheduling algorithms. In addition, we present DP-Wrap, a simple DP-Fair scheduling algorithm which serves as a least common ancestor to other recent algorithms. The DP-Fair scheduling policy is extended to address the problem of scheduling sporadic task sets with arbitrary deadlines.     

Two-machine flowshop scheduling with job class setups to minimize total flowtime

This paper studies the two-machine flowshop scheduling problem with job class setups to minimize the total flowtime. The jobs are classified into classes, and a setup is required on a machine if it switches processing of jobs from one class to another class, but no setup is required if the jobs are from the same class. For some special cases, we derive a number of properties of the optimal solution, based on which we design heuristics and branch-and-bound algorithms to solve these problems. Computational results show that these algorithms are effective in yielding near-optimal or optimal solutions to the tested problems.     

Performance of local search heuristics on scheduling a class of pipelined multiprocessor tasks

This paper presents the evaluation of the solution quality of heuristic algorithms developed for scheduling multiprocessor tasks for a class of multiprocessor architectures designed to exploit temporal and spatial parallelism simultaneously. More specifically, we deal with multi-level or partitionable architectures where MIMD parallelism and multiprogramming support are the two main characteristics of the system. We investigate scheduling a number of pipelined multiprocessor tasks with arbitrary processing times and arbitrary processor requirements in this system. The scheduling problem consists of two interrelated sub-problems, which are finding a sequence of pipelined multiprocessor tasks on a processor and finding a proper mapping of tasks to the processors that are already being sequenced. For the solution of the second problem, various techniques are available. However, the problem remains of generating a feasible sequence for the pipelined operations. We employed three well-known local search heuristic algorithms that are known to be robust methods applicable to various optimization problems. These are Simulated Annealing, Tabu Search, and Genetic Algorithms. We then conduct computational experiments and evaluate the reduction achieved in completion time by each heuristic. We have also compared the results with well-known simple list-based heuristics.     

网格计算中任务调度算法的研究和改进

任务调度一直是网格计算中的热点问题,任务调度的目的是最优地分配任务,实现最佳的调度策略,以高效地完成计算任务。在网格环境中,资源的合理有效利用是实现任务调度的关键问题之一。本文首先论述静态任务调度算法和动态任务算法的原理和优缺点等,然后结合Min-min、Max-min算法的优点设计一种新的调度算法SA-MM,根据资源的使用情况自适应调度相应算法进行任务到资源的映射。最后,用GridSim模拟工具对网格计算中Min-min、Max-min和SA-MM任务调度算法进行仿真实验,分析和比较它们的调度长度(MakeSpan)和资源负载情况等影响任务调度效率的指标。     

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。     

具有线性恶化加工时间的调度问题

讨论了工件具有线性恶化加工时间的调度问题.在这类问题中,工件的恶化函数为线性 函数.对单机调度问题中目标函数为极小化最大完工时间加权完工时间和,最大延误以及最大费 用等问题分别给出了最优算法.对两台机器极小化最大完工时间的Flowshop问题,证明了利用 Johnson规则可以得到最优调度.对于一般情况,如果同一工件的工序的加工时间均相等,则 Flowshop问题可以转化为单机问题.     

Machine scheduling with earliness,tardiness and non-execution penalties

The study of scheduling problems with earliness–tardiness (E/T) penalties is motivated by the just-in-time (JIT) philosophy, which supports the notion that earliness, as well as tardiness, should be discouraged. In this work, we consider several scheduling problems. We begin by generalizing a known polynomial time algorithm that calculates an optimal schedule for a given sequence of tasks, on a single machine, assuming that the tasks have distinct E/T penalty weights, distinct processing times and distinct due dates. We then present new results to problems, where tasks have common processing times. We also introduce a new concept in E/T scheduling problems, where we allow the non-execution of tasks and consequently, are penalized for each non-executed task. The notion of task's non-execution, coincides with the JIT philosophy in that every violation or a breach of an agreement, should be penalized. We develop polynomial time algorithms for special cases in E/T scheduling problems with non-execution penalties.     

Task graph pre-scheduling, using Nash equilibrium in game theory

Prescheduling algorithms are targeted at restructuring of task graphs for optimal scheduling. Task graph scheduling is a NP-complete problem. This article offers a prescheduling algorithm for tasks to be executed on the networks of homogeneous processors. The proposed algorithm merges tasks to minimize their earliest start time while reducing the overall completion time. To this end, considering each task as a player attempting to reduce its earliest time as much as possible, we have applied the idea of Nash equilibrium in game theory to determine the most appropriate merging. Also, considering each level of a task graph as a player, seeking for distinct parallel processors to execute each of its independent tasks in parallel with the others, the idea of Nash equilibrium in game theory can be applied to determine the appropriate number of processors in a way that the overall idle time of the processors is minimized and the throughput is maximized. The communication delay will be explicitly considered in the comparisons. Our experiments with a number of known benchmarks task graphs and also two well-known problems of linear algebra, LU decomposition and Gauss–Jordan elimination, demonstrate the distinguished scheduling results provided by applying our algorithm. In our study, we consider ten scheduling algorithms: min–min, chaining, A ^?, genetic algorithms, simulated annealing, tabu search, HLFET, ISH, DSH with task duplication, and our proposed algorithm (PSGT).     

Sporadic task scheduling with overload conditions

Hard real-time task scheduling in a dynamic environment has been an important area of research, posing difficult problems. In an overloaded system where periodic and sporadic tasks have computational demands that are greater than the CPU time in that interval, the scheduler faces the question of which tasks must really make their deadlines. Assuming that periodic tasks have priority over sporadic ones, we end up with a system where some sporadic tasks may not make their deadlines. It is known that through the assignment of priorities to tasks based on the earliest deadline policy, there is no way to predict which sporadic task will miss the deadline and which will not. In order to prevent important sporadic tasks from missing their deadlines, we assign each task an importance function that is used by the scheduling algorithm. Generally, the summation of important function values must be maximized to allow the most important tasks to meet their timing constraints. We present two novel scheduling algorithms that try to maximize this summation. We show that these algorithms have better performance compared to related algorithms regarding complexity and benefit optimization.     

A Novel State Transition Method for Metaheuristic-Based Scheduling in Heterogeneous Computing Systems

Much of the recent literature shows a prevalance in the use of metaheuristics in solving a variety of problems in parallel and distributed computing. This is especially ture for problems that have a combinatorial nature, such as scheduling and load balancing. Despite numerous efforts, task scheduling remains one of the most challenging problems in heterogeneous computing environments. In this paper, we propose a new state transitionscheme , called the Duplication-based State Transition (DST) method specially designed for metaheuristics that can be used for the task scheduling problem in heterogeneous computing environments. State transition in metaheuristics is a key component that takes charge of generating variants of a given state. The DST method produces a new state by first overlapping randomly generated states with the current state and then the resultant state is refined by removing ineffectual tasks. The proposed method is incorporated into three different metaheuristics: genetic algorithms (GAs), simulated annealing (SA), and artificial immune system (AISs). They are experimentally evaluated and are also compared with existing algorithms. The experimental results confirm DST's promising impact on the performance of metaheuristics.     

遗传算法解柔性作业车间调度问题应用综述

柔性作业车间调度问题是典型的NP难问题,对实际生产应用具有指导作用。近年来,随着遗传算法的发展,利用遗传算法来解决柔性作业车间调度问题的思想和方法层出不穷。为了促进遗传算法求解柔性作业车间调度问题的进一步发展,阐述了柔性作业车间调度问题的研究理论,对已有改进方法进行了分类,通过对现存问题的分析,探讨了未来的发展方向。