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     

针对移动云计算任务迁移的快速高效调度算法

计算量较大的应用程序由于需要大量的能耗,因此在电池容量有限的移动设备上运行时十分受限。云计算迁移技术是保证此类应用程序在资源有限的设备上运行的主流方法。针对无线网络中应用程序任务图的调度和迁移问题,提出了一种快速高效的启发式算法。该算法将能够迁移到云端的任务都安排在云端完成这种策略作为初始解,通过逐次计算可迁移任务在移动端运行的能耗节省量,依次将节省量最大的任务迁移到移动端,并依据任务间的通讯时间及时更新各个任务的能耗节省量。为了寻找全局最优解,构造了适用于此问题的禁忌搜索算法,给出了相应的编码方法、禁忌表、邻域解以及算法终止准则。构造的禁忌搜索算法以提出的启发式解为初始解进行全局搜索,并实现对启发解的进一步优化。通过 实验 将所提方法与无迁移、随机迁移、饱和迁移3类算法进行对比,结果表明提出的启发式算法能够快速有效地给出能耗更小的解。例如,在宽度为10的任务图上,当深度为8时,无迁移、随机迁移与饱和迁移的能耗分别为5461、3357和2271能量单位,而给出的启发解对应的能耗仅为2111。在此基础上禁忌搜索算法又将其能耗降低到1942, 这进一步说明了提出的启发式算法能够产生高质量的近似解。     

Energy efficient scheduling of parallel tasks on multiprocessor computers

In this paper, scheduling parallel tasks on multiprocessor computers with dynamically variable voltage and speed are addressed as combinatorial optimization problems. Two problems are defined, namely, minimizing schedule length with energy consumption constraint and minimizing energy consumption with schedule length constraint. The first problem has applications in general multiprocessor and multicore processor computing systems where energy consumption is an important concern and in mobile computers where energy conservation is a main concern. The second problem has applications in real-time multiprocessing systems and environments where timing constraint is a major requirement. Our scheduling problems are defined such that the energy-delay product is optimized by fixing one factor and minimizing the other. It is noticed that power-aware scheduling of parallel tasks has rarely been discussed before. Our investigation in this paper makes some initial attempt to energy-efficient scheduling of parallel tasks on multiprocessor computers with dynamic voltage and speed. Our scheduling problems contain three nontrivial subproblems, namely, system partitioning, task scheduling, and power supplying. Each subproblem should be solved efficiently, so that heuristic algorithms with overall good performance can be developed. The above decomposition of our optimization problems into three subproblems makes design and analysis of heuristic algorithms tractable. A unique feature of our work is to compare the performance of our algorithms with optimal solutions analytically and validate our results experimentally, not to compare the performance of heuristic algorithms among themselves only experimentally. The harmonic system partitioning and processor allocation scheme is used, which divides a multiprocessor computer into clusters of equal sizes and schedules tasks of similar sizes together to increase processor utilization. A three-level energy/time/power allocation scheme is adopted for a given schedule, such that the schedule length is minimized by consuming given amount of energy or the energy consumed is minimized without missing a given deadline. The performance of our heuristic algorithms is analyzed, and accurate performance bounds are derived. Simulation data which validate our analytical results are also presented. It is found that our analytical results provide very accurate estimation of the expected normalized schedule length and the expected normalized energy consumption and that our heuristic algorithms are able to produce solutions very close to optimum.     

基于异构CMP的改进蚁群优化任务调度策略

为提高异构CMP任务调度执行效率,充分发挥异构CMP的异构性和并行能力,提出一种基于异构CMP的改进蚁群优化任务调度算法--IACOTS。IACOTS算法首先建立任务调度模型、路径选择规则和信息素更新规则,使蚁群算法能够适用于异构CMP任务调度问题。同时通过采用动态信息素更新、相遇并行搜索策略和引入遗传算法中的变异因子对基本的蚁群算法进行优化,克服蚁群算法搜索时间过长和“早熟”现象。通过仿真实验获得的结果表明,IACOTS算法执行效率优于现有的遗传算法,完成相同的任务需要的迭代次数最少,能有效降低程序执行时间,适用于异构CMP等大规模并行环境的任务调度。     

Design of Fast and Efficient Energy-Aware Gradient-Based Scheduling Algorithms Heterogeneous Embedded Multiprocessor Systems

In this paper, we present two heuristic energy-aware scheduling algorithms (EGMS and EGMSIV) for scheduling task precedence graphs in an embedded multiprocessor system having processing elements with dynamic voltage scaling capabilities. Unlike most energy-aware scheduling algorithms that consider task ordering and voltage scaling separately from task mapping, our algorithms consider them in an integrated way. EGMS uses the concept of energy gradient to select tasks to be mapped onto new processors and voltage levels. EGM-SIV extends EGMS by introducing intra-task voltage scaling using a Linear Programming (LP) formulation to further reduce the energy consumption. Through rigorous simulations, we compare the performance of our proposed algorithms with a few approaches presented in the literature. The results demonstrate that our algorithms are capable of obtaining energy-efficient schedules using less optimization time. On the average, our algorithms produce schedules which consume 10% less energy with more than 47% reduction in optimization time when compared to a few approaches presented in the literature. In particular, our algorithms perform better in generating energy-efficient schedules for larger task graphs. Our results show a reduction of up to 57% in energy consumption for larger task graphs compared to other approaches.     

Scheduling with dynamic voltage/speed adjustment using slack reclamation in multiprocessor real-time systems

The high power consumption of modern processors becomes a major concern because it leads to decreased mission duration (for battery-operated systems), increased heat dissipation, and decreased reliability. While many techniques have been proposed to reduce power consumption for uniprocessor systems, there has been considerably less work on multiprocessor systems. In this paper, based on the concept of slack sharing among processors, we propose two novel power-aware scheduling algorithms for task sets with and without precedence constraints executing on multiprocessor systems. These scheduling techniques reclaim the time unused by a task to reduce the execution speed of future tasks and, thus, reduce the total energy consumption of the system. We also study the effect of discrete voltage/speed levels on the energy savings for multiprocessor systems and propose a new scheme of slack reservation to incorporate voltage/speed adjustment overhead in the scheduling algorithms. Simulation and trace-based results indicate that our algorithms achieve substantial energy savings on systems with variable voltage processors. Moreover, processors with a few discrete voltage/speed levels obtain nearly the same energy savings as processors with continuous voltage/speed, and the effect of voltage/speed adjustment overhead on the energy savings is relatively small.     

Real-time task scheduling by multiobjective genetic algorithm

Real-time tasks are characterized by computational activities with timing constraints and classified into two categories: a hard real-time task and a soft real-time task. In hard real-time tasks, tardiness can be catastrophic. The goal of hard real-time tasks scheduling algorithms is to meet all tasks’ deadlines, in other words, to keep the feasibility of scheduling through admission control. However, in the case of soft real-time tasks, slight violation of deadlines is not so critical.In this paper, we propose a new scheduling algorithm for soft real-time tasks using multiobjective genetic algorithm (moGA) on multiprocessors system. It is assumed that tasks have precedence relations among them and are executed on homogeneous multiprocessor environment.The objective of the proposed scheduling algorithm is to minimize the total tardiness and total number of processors used. For these objectives, this paper combines adaptive weight approach (AWA) that utilizes some useful information from the current population to readjust weights for obtaining a search pressure toward a positive ideal point. The effectiveness of the proposed algorithm is shown through simulation studies.     

多核系统中基于Global EDF 的在线节能实时调度算法

随着多核系统能耗问题日益突出,在满足时间约束条件下降低系统能耗成为多核实时节能调度研究中亟待解决的问题之一.现有研究成果基于事先已知实时任务属性的假设,而实际应用中,只有当任务到达之后才能够获得其属性.为此,针对一般任务模型,不基于任何先验知识提出一种多核系统中基于Global EDF在线节能硬实时任务调度算法,通过引入速度调节因子,利用松弛时间,结合动态功耗管理和动态电压/频率调节技术,降低多核系统中任务的执行速度,达到实时约束与能耗节余之间的合理折衷.所提出的算法仅在上下文切换和任务完成时进行动态电压/频率调节,计算复杂度小,易于在实时操作系统中实现.实验结果表明,该算法适用于不同类型的片上动态电压/频率调节技术,节能效果始终优于Global EDF算法,最多可节能15％～20％,最少可节能5％～10％.     

多处理器系统的在线节能调度算法

随着多处理器系统计算性能的提高,能耗管理已变得越来越重要,如何满足实时约束并有效降低能耗成为实时调度中的一个重要问题。基于多处理器计算系统,针对随机到达的任务,提出一种在线节能调度算法(OLEAS)。该算法在满足任务截止期限的前提下,尽量将任务调度到产生能耗最少的处理器,当某个任务在所有处理器上都不能满足截止期限要求时,则调整处理器之间的部分任务,使之尽量满足截止期限要求。同时,OLEAS尽量使单个处理器上的任务按平均电压/频率执行,以降低能耗,只有当新到任务不满足截止期限要求时,才逐个调高前面任务的电压/频率。模拟实验比较了OLEAS、最早完成时间优先(EFF)、最高电压节能(HVEA)、最低电压节能(LVEA)、贪心最小能耗(MEG)和最小能耗最小完成时间(ME-MC)的性能,结果表明OLEAS在满足任务截止期限和节省能耗方面具有明显的综合优势     

一种用于PFSP节能优化的混合禁忌搜索算法

为了追求节能减排与净利润最大化,建立一种置换流水车间订单接受与调度模型。禁忌搜索是一类启发式全局搜索算法,传统禁忌搜索对初始解依赖较大,没有对考虑能效的置换流水车间调度问题进行更深入的优化。鉴于问题的复杂性,提出了一种节能混合禁忌搜索算法,结合了NEH构造启发式算法的优势,并在该算法中设计了订单接受与拒绝编码方式、能耗调整与交货期配置策略。最后采用大量随机实例对性能进行分析。实验结果表明,通过上述改进,改善了算法的全局搜索能力与解决复杂模型的寻优能力,节能混合禁忌搜索较单一算法而言性能更优,可以有效增加企业总净利润,降低能源消耗。     

一种满足可靠性和能效的云工作流调度方法

为了同步解决云工作流调度时的失效和高能耗问题,提出一种基于可靠性和能效的工作流调度算法。算法为了在截止时间的QoS约束下最大化系统可靠性并最小化调度能耗,将工作流调度过程划分为四个阶段:计算任务优先级、工作流任务聚簇、截止时间子分配和任务调度。算法在满足执行次序的情况下对任务进行拓扑排序,并以通信代价最小为目标对任务进行聚簇;将截止时间在任务间进行子分割;以合适的频率/电压等级对聚簇后的任务进行调度,在确保可靠性的前提下最小化系统能耗。通过随机任务图和高斯消除任务图进行综合仿真测试,结果表明算法在降低总体能耗和提高工作流调度可靠性方面均优于对比算法。     

异构多核处理器的任务分配及能耗的研究*

异构多核处理器采用不同的任务分配与调度算法,会导致不同的时间消耗与能量消耗,采用合适的任务分配与调度算法能节省较多的能耗。目前普遍认为最有发展前途的任务分配与调度技术是先用启发式方法进行分组,然后再用遗传算法进行调度。在改进任务分组后,又首次提出了用遗传算法解决能耗问题。实验结果表明在实时要求不高的情况下,能以较小的时间代价来节省较多的能耗。     

开销敏感的多处理器最优节能实时调度算法

嵌入式多处理器系统的能耗问题变得日益重要,如何减少能耗同时满足实时约束成为多处理器系统节能实时调度中的一个重要问题.目前绝大多数研究基于关键速度降低处理器的频率以减少动态能耗,采用关闭处理器的方法减少静态能耗.虽然这种方法可以实现节能,但是不能保证最小化能耗.而现有最优的节能实时调度未考虑处理器状态切换的时间和能量开销,因此在切换开销不可忽视的实际平台中不再是最优的.文中针对具有独立动态电压频率调节和动态功耗管理功能的多处理器系统,考虑处理器切换开销,提出一种基于帧任务模型的最优节能实时调度算法.该算法根据关键速度来判断系统负载情况,确定具有最低能耗值的活跃处理器个数,然后根据状态切换开销来确定最优调度序列.该算法允许实时任务在处理器之间任意迁移,计算复杂度小,易于实现.数学分析证明了该算法的最优性.     

基于差分进化算法多处理机任务调度研究

本文主要基于现代启发式差分算法讨论多处理机调度,多处理机调度是NP组合优化问题,目前多采用启发算法。差分进化算法是最近提出的进化算法,主要根据父代个体之间矢量差构造下一代,是一种全局优化搜索方式。本文考虑采用差分进化矢量优先级模型描述调度顺序进行调度,与模拟退火算法比较得到较好调度结果。     

改进蚁群算法求解同型机任务调度问题

针对并行与分布式系统中的同型机调度问题,提出了一种改进蚁群算法。结合问题具体特点,给出了蚂蚁分配方案的生成策略,设计了一种新颖的基于任务适合度的信息素表示方法,以实现信息素的有效累积;改进了状态转移规则,通过对阈值的自适应调整使算法能根据搜索进度确定查找区域;在对信息素全局更新前,对每轮迭代获得的最好解进行变邻域搜索,避免算法陷入局部最优,提高收敛速度。仿真结果表明,改进算法有较强的寻优能力和稳定的求解质量。     

粒子群优化多处理机任务调度算法

提出基于粒子群优化的多处理机调度算法,采用列表调度,同时把粒子群的矢量表达方式转换为基于调度优先级的模型。调度结果显示能提高全局搜索能力,加快进化速度,优于模拟退火等启发式算法结果。     

Performance Analysis of Power-Aware Task Scheduling Algorithms on Multiprocessor Computers with Dynamic Voltage and Speed

Task scheduling on multiprocessor computers with dynamically variable voltage and speed is investigated as combinatorial optimization problems, namely, the problem of minimizing schedule length with energy consumption constraint and the problem of minimizing energy consumption with schedule length constraint. The first problem has applications in general multiprocessor computing systems where energy consumption is an important concern and in mobile computers where energy conservation is a main concern. The second problem has applications in real-time multiprocessing systems where timing constraint is a major requirement. These problems emphasize the tradeoff between power and performance and are defined such that the power-performance product is optimized by fixing one factor and minimizing the other. It is found that both problems are equivalent to the sum of powers problem and can be decomposed into two subproblems, namely, scheduling tasks and determining power supplies. Such decomposition makes design and analysis of heuristic algorithms tractable. We analyze the performance of list scheduling algorithms and equal-speed algorithms and prove that these algorithms are asymptotically optimal. Our extensive simulation data validate our analytical results and provide deeper insight into the performance of our heuristic algorithms.     

Priority-Driven Scheduling of Periodic Task Systems on Multiprocessors

The scheduling of systems of periodic tasks upon multiprocessor platforms is considered. Utilization-based conditions are derived for determining whether a periodic task system meets all deadlines when scheduled using the earliest deadline first scheduling algorithm (EDF) upon a given multiprocessor platform. A new priority-driven algorithm is proposed for scheduling periodic task systems upon multiprocessor platforms: this algorithm is shown to successfully schedule some task systems for which EDF may fail to meet all deadlines.     

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.     

同构计算环境中一种快速有效的静态任务调度算法

快速有效的调度任务是多处理器计算环境中的一个关键问题．目前任务调度算法中刻画任务依赖关系最流行的模型是DAG,在以前的文献中,提出了一种新的更实际、更普遍的TTIG模型及其相应的MATE算法(基于同构计算环境)．延伸了TTIG模型,并提出基于同构系统的新的算法及两种启发式方法(GBHA1和GBHA2)．GBHA以组的形式尽量消除图中回路,因而能获得任务图的全局信息,具有更好的调度性能．在模拟实验中,将此算法与MATE和其他同构环境中基于DAG的有效调度算法,在不同测试条件下进行了比较,结果显示GBHA在性能上明显优于MATE,与基于DAG模型的调度算法比较而言,在性能方面各有千秋,但在算法时间复杂度方面具有显著的优势．