Utility-based scheduling for grid computing under constraints of energy budget and deadline

With recent advances in computing and communication technologies enabling mobile devices more powerful, the scope of Grid computing has been broadened to include mobile and pervasive devices. Energy has become a critical resource in such devices. So, battery energy limitation is the main challenge towards enabling persistent mobile grid computing. In this paper, we address the problem of energy constrained scheduling scheme for the grid environment. There is a limited energy budget for grid applications. The paper investigates both energy minimization for mobile devices and grid utility optimization problem. We formalize energy aware scheduling using nonlinear optimization theory under constraints of energy budget and deadline. The paper also proposes distributed pricing based algorithm that is used to tradeoff energy and deadline to achieve a system wide optimization based on the preference of the grid user. The simulations reveal that the proposed energy constrained scheduling algorithms can obtain better performance than the previous approach that considers both energy consumption and deadline.     

Energy-efficient real-time scheduling for two-type heterogeneous multiprocessors

We propose three novel mathematical optimization formulations that solve the same two-type heterogeneous multiprocessor scheduling problem for a real-time taskset with hard constraints. Our formulations are based on a global scheduling scheme and a fluid model. The first formulation is a mixed-integer nonlinear program, since the scheduling problem is intuitively considered as an assignment problem. However, by changing the scheduling problem to first determine a task workload partition and then to find the execution order of all tasks, the computation time can be significantly reduced. Specifically, the workload partitioning problem can be formulated as a continuous nonlinear program for a system with continuous operating frequency, and as a continuous linear program for a practical system with a discrete speed level set. The latter problem can therefore be solved by an interior point method to any accuracy in polynomial time. The task ordering problem can be solved by an algorithm with a complexity that is linear in the total number of tasks. The work is evaluated against existing global energy/feasibility optimal workload allocation formulations. The results illustrate that our algorithms are both feasibility optimal and energy optimal for both implicit and constrained deadline tasksets. Specifically, our algorithm can achieve up to 40% energy saving for some simulated tasksets with constrained deadlines. The benefit of our formulation compared with existing work is that our algorithms can solve a more general class of scheduling problems due to incorporating a scheduling dynamic model in the formulations and allowing for a time-varying speed profile.     

一种考虑截止期的METCD业务组合调度方法

海洋设备检定、校准和检测（Marine Equipment Testing, Calibrate & Detection, METCD）业务规模大、紧急情况多,如何对业务进行合理的调配是海洋计量检定行业亟待解决的问题。本文提出了一种考虑截止期的任务组合METCD业务调度方法。在建立业务调度问题数学模型的基础上,采用最早截止时间优先-蚁群算法(EDF-PACO)对模型求解,在最早截止日期的约束条件下对任务组合处理的最优调度方案,达到降低任务总完成时间和减少执行空间浪费双重优化目标。为了验证方法的可行性,以国家海洋局东海标准技术中心的业务为实例,将EDF-PACO算法与传统的最早截止时间优先算法和蚁群算法进行比较,结果表明本文所提出的调度方法在满足截止期的约束条件下能高效地对海洋设备的计量检定业务进行组合调度。     

基于Linux的多核实时任务调度算法改进

嵌入式实时系统通常被实现为多任务系统,以满足多个外部输入的响应时间的最后期限约束。Linux内核中已经实现了基于EDF(Earliest Deadline First)调度算法的DL调度器,使得实时任务能在截止期限内运行完成。但对于多核处理器,由于实时任务在EDF算法下会出现Dhall效应,论文对 Linux内核中实时任务调度算法进行了改进。在EDF算法的基础上,实现LLF(Least Laxity First)调度算法并对其加以改进,通过降低任务上下文切换频率以及减少松弛度的计算来减小调度过程中的颠簸现象。实验证明该方法既避免了Dhall效应,又减少了任务上下文切换带来的系统开销,并使得任务能在截止期限内完成调度,取得了较好的调度性能。     

A real-time scheduling framework for embedded systems with environmental energy harvesting

Real-time scheduling refers to the problem in which there is a deadline associated with the execution of a task. In this paper, we address the scheduling problem for a uniprocessor platform that is powered by a renewable energy storage unit and uses a recharging system such as photovoltaic cells. First, we describe our model where two constraints need to be studied: energy and deadlines. Since executing tasks require a certain amount of energy, classical task scheduling like earliest deadline is no longer convenient. We present an on-line scheduling scheme, called earliest deadline with energy guarantee (EDeg), that jointly accounts for characteristics of the energy source, capacity of the energy storage as well as energy consumption of the tasks, and time. In order to demonstrate the benefits of our algorithm, we evaluate it by means of simulation. We show that EDeg outperforms energy non-clairvoyant algorithms in terms of both deadline miss rate and size of the energy storage unit.     

Spatio-temporal thermal-aware job scheduling to minimize energy consumption in virtualized heterogeneous data centers

Job scheduling in data centers can be considered from a cyber–physical point of view, as it affects the data center’s computing performance (i.e. the cyber aspect) and energy efficiency (the physical aspect). Driven by the growing needs to green contemporary data centers, this paper uses recent technological advances in data center virtualization and proposes cyber–physical, spatio-temporal (i.e. start time and servers assigned), thermal-aware job scheduling algorithms that minimize the energy consumption of the data center under performance constraints (i.e. deadlines). Savings are possible by being able to temporally “spread” the workload, assign it to energy-efficient computing equipment, and further reduce the heat recirculation and therefore the load on the cooling systems. This paper provides three categories of thermal-aware energy-saving scheduling techniques: (a) FCFS-Backfill-XInt and FCFS-Backfill-LRH, thermal-aware job placement enhancements to the popular first-come first-serve with back-filling (FCFS-backfill) scheduling policy; (b) EDF-LRH, an online earliest deadline first scheduling algorithm with thermal-aware placement; and (c) an offline genetic algorithm for SCheduling to minimize thermal cross-INTerference (SCINT), which is suited for batch scheduling of backlogs. Simulation results, based on real job logs from the ASU Fulton HPC data center, show that the thermal-aware enhancements to FCFS-backfill achieve up to 25% savings compared to FCFS-backfill with first-fit placement, depending on the intensity of the incoming workload, while SCINT achieves up to 60% savings. The performance of EDF-LRH nears that of the offline SCINT for low loads, and it degrades to the performance of FCFS-backfill for high loads. However, EDF-LRH requires milliseconds of operation, which is significantly faster than SCINT, the latter requiring up to hours of runtime depending upon the number and size of submitted jobs. Similarly, FCFS-Backfill-LRH is much faster than FCFS-Backfill-XInt, but it achieves only part of FCFS-Backfill-XInt’s savings.     

基于优先级表的实时调度算法及其实现

讨论了综合考虑任务的截止期和价值两个特征参数的优先级表设计方法,提出了EDV(earliest deadline value)与VED(value earliest deadline)两种不同的基于优先级表的实时任务调度算法,并且利用多重链表给出了这两种算法的实现,包括任务接收策略与任务完成/夭折策略的算法实现.这种优先级表设计方法及其基于多重链表的实现方法也适用于对任务的其他两种甚至3种不同特征参数之间的综合.基于累积实现价值率、加权截止期保证率与差分截止期保证率3个方面,分析了VED算法与EDV算法的性能,实验结果表明,在所有负载条件下VED算法与EDV算法相对于EDF(earliest deadline first)算法与HVF(highest value first)算法都有很大的性能改进.     

Real-time load balancing scheduling algorithm for periodic simulation models

A scheduling algorithm is crucial for real-time simulations because it guarantees that each model meets its deadline. Traditional online real-time scheduling algorithms such as Earliest Deadline First (EDF) introduce a high overhead when scheduling a large number of models. In this paper, a new algorithm called time-stepped load balancing (TLS) is proposed to address the real-time execution of a model set in a time-stepped simulation. A load balancing schedule table is generated before a simulation and rebalanced at runtime to dynamically schedule the changed model set. This table is organized by the execution periods of the models and balanced according to the load of each time step. Moreover, the slack time is distributed evenly among the steps to improve the real-time reliability. An extension to the algorithm for a multi-core environment is further studied to address those models with long execution times. Experimental results show that our scheduling algorithm outperforms the classical EDF approach. The highest performance improvement of TLS over EDF reaches 3–4% in terms of saving processor resources, and the jitter is about 4 times less when 90 entities are employed in a typical tank combat simulation scenario.     

DBC性价比资源调度算法

传统的DBC（Deadline and Budget Constrained）调度算法,比如时间最优调度算法、代价最优调度算法都是在时间（deadline）和代价（budget）的约束下,满足时间或代价单方面的QoS需求的极端情况。针对这一不足,提出了一种基于DBC的性价比资源调度算法,综合考虑了时间和代价的QoS需求,目的在于提高任务的完成量以及任务完成的性价比,并通过推理论证和仿真实验验证了该算法的有效性和优越性。     

A load index and a transfer policy for global scheduling tasks with deadlines

Two important components of a global scheduling algorithm are its transfer policy and its location policy. While the transfer policy determines whether a task should be transferred, the location policy determines where it should be transferred. Many global scheduling algorithms have been proposed to schedule tasks with deadline constraints. These algorithms try to transfer tasks only when task's deadlines cannot be met locally or local load is high (i.e. they take only corrective measures). However, a scheduling algorithm that takes preventive measures in addition to corrective measures can reduce potential deadline misses substantially. In this paper we present: (a) a load index which characterizes the system state and is more conducive to preventive and corrective measures; (b) a new transfer policy which takes preventive measures by doing anticipatory task transfers in addition to corrective measures. The proposed transfer policy adapts better to the workload by availing of the accurate system state made available by the proposed load index. An algorithm making use of the new transfer policy and the new load index is shown to reduce the number of deadline misses significantly when compared to algorithms taking only corrective measures.     

一种多重约束下确保成功率的云工作流调度方法

为提高多重约束下的调度成功率,提出一种满足期限和预算双重约束的云工作流调度算法。将可行工作流调度方案求解分解为工作流结构分层、预算分配、期限分配、任务选择和实例选择。工作流结构分层将所有工作流任务划分层次形成包任务,以提高并行执行程度;预算分配对整体预算在层次间进行分割;期限分配将全局期限在不同层次间分割;任务选择基于任务最早开始时间确定优先级,得到任务调度次序;实例选择根据时间和代价均衡因子,获取任务执行最佳实例。仿真结果证明,该算法在调度成功率、同步优化工作流执行时间与执行代价上相较对比算法更好。     

面向优先级用户的移动边缘计算任务调度策略

随着移动终端处理的数据量及计算规模不断增加,为降低任务处理时延、满足任务的优先级调度需求,结合任务优先级及时延约束,提出了基于任务优先级的改进min-min调度算法（task priority-based min-min,TPMM）。该算法根据任务的处理价值及任务的数据量计算任务的优先级,结合任务截止时间、服务器调度次数制定资源匹配方案,解决了边缘网络中服务器为不同优先级的用户进行计算资源分配的问题。仿真实验结果表明,该算法可以均衡服务器利用率,并有效降低计算处理的时延,提高服务器在任务截止处理时间内完成任务计算的成功率,相比min-min调度算法,TPMM算法最多可降低78.45%的时延,提高80%的计算成功率;相比max-min调度算法,TPMM算法最多可降低80.15%的时延并提高59.7%的计算成功率;相比高优先级（high priority first,HPF）调度算法,TPMM算法最多降低59.49%的时延,提高57.7%的计算成功率。     

基于至少连续满足弱硬实时限制的调度算法

对至少连续满足弱硬实时限制的性质进行了扩充，提出并证明了任务不满足子序列长度与任务连续满足的截止期限数之间的关系.在此基础上提出了改进的弱硬实时限制调度算法：MRA．MRA用于在弱硬实时系统中保证任务满足至少连续满足限制，是一种高效、易于实现的调度算法,仿真实验的结果表明，MRA调度算法在提高任务对限制的满足率和保证任务实时性方面优于同类算法．     

Applying pinwheel scheduling and compiler profiling for power-aware real-time scheduling

Energy consumption is a critical design issue in embedded systems, especially in battery-operated systems. Maintaining high performance while extending the battery life is an interesting challenge for system designers. Dynamic voltage scaling and dynamic frequency scaling allow us to adjust supply voltage and processor frequency to adapt to the workload demand for better energy management. Because of the high complexity involved, most solutions depend on heuristics for online power-aware real-time scheduling or offline time-consuming scheduling. In this paper, we discuss how we can apply pinwheel model to power-aware real-time scheduling so that task information, including start times, finish times, preemption times, etc, can be efficiently derived using pinwheel model. System predictability is thus increased and under better control on power-awareness. However, job execution time may be only a small portion of its worst case execution time and can only be determined at runtime. We implement a profiling tool to insert codes for collecting runtime information of real-time tasks. Worst case execution time is updated online for scheduler to perform better rescheduling according to actual execution. Simulations have shown that at most 50% energy can be saved by the proposed scheduling algorithm. Moreover, at most additional 33% energy can be saved when the profiling technique is applied. This paper is an extended version of the paper Power-Aware Real-Time Scheduling using Pinwheel Model and Profiling Technique that appeared in the 11th IEEE International Conference on Embedded and Real-Time Computing Systems and Applications.     

一种最小化绿色数据中心电费的负载调度算法

为了减少电费和碳排放,数据中心运营商开始建立就地绿色能源发电厂以进行供电.然而,负载的波动性、电价的时间差异性以及绿色能源的间歇性,给节约数据中心电费带来了挑战.针对以上问题,提出一种在线式负载调度算法,可以在不使用未来的负载、电价和绿色能源可用性信息的前提下,最小化数据中心的电费.首先,建立拥有就地绿色能源发电厂的数据中心的电费模型;然后,将数据中心电费最小化问题形式化为一个随机优化问题;最后,求解该优化问题得到相应的负载调度策略.基于真实数据的实验结果表明：该算法可以在保证负载性能的前提下,有效降低数据中心的电力成本.     

A hybrid heuristic workflow scheduling algorithm for cloud computing environments

Cloud computing is a relatively new concept in the distributed systems and is widely accepted as a new solution for high performance and distributed computing. Its dynamisms in providing virtual resources for organisations and laboratories and its pay-per-use policy make it very popular. A workflow models a process consisting of a series of steps that shape an application. Workflow scheduling is the method for assigning each workflow task to a processing resource in a way that specific workflow rules are satisfied. Some scheduling algorithms for workflows may assume some quality of service parameter such as cost and deadline. Some efforts have been done on workflow scheduling on cloud computing environments with different service level agreements. But most of them suffer from low speed. Here, we introduce a new hybrid heuristic algorithm based on particle swarm optimisation (PSO) and gravitation search algorithms. The proposed algorithm, in addition to processing cost and transfer cost, takes deadline limitations into account. The proposed workflow scheduling approach can be used by both end-users and utility providers. The CloudSim toolkit is used as a cloud environment simulator and the Amazon EC2 pricing is the reference pricing used. Our experimental result shows about 70% cost reduction, in comparison to non-heuristic implementations, 30% cost reduction in comparison to PSO, 30% cost reduction in comparison to gravitational search algorithm and 50% cost reduction in comparison to hybrid genetic-gravitational algorithm.     

基于自适应惩罚函数的云工作流调度协同进化遗传算法

云计算为大规模科学工作流应用的执行提供了更高效的运行环境。为了解决云环境中科学工作流调度的代价优化问题,提出了一种基于协同进化的工作流调度遗传算法CGAA。该算法将自适应惩罚函数引入严格约束的遗传算法中,通过协同进化的方法,自适应地调整种群个体的交叉与变异概率,以加速算法收敛并防止种群早熟。通过4种科学工作流的仿真实验结果表明,CGAA算法得到的调度方案在满足工作流调度截止时间约束与降低任务执行代价的综合性能方面优于同类型算法。     

提高软非周期任务响应性能的调度算法

实时环境中常常既包含硬周期任务,又包含软非周期任务,引入一种改进软非周期实时任务响应时间的算法.已有的解决混合任务调度问题的方法都是基于速率单调（Rate Monotonic）策略的,其中从周期任务“挪用时间”的算法被证明优于其他所有算法.但是,速率单调算法限制了处理器的使用率,从而使周期任务的可“挪用”时间受到限制.最后期限驱动（Deadline Driven）策略DD可使潜在的处理器利用率达到100%.新算法正是在周期任务的调度中适当加入了DD策略,从而使非周期任务的响应时间得以缩短.仿真实验的结果表明,这种算法的性能优于已有的所有算法,而由它所带来的额外开销却不算很高.     

结合价值密度和截止期的网格实时作业调度

在商业网格和云计算环境中,作业有到达时间、计算量、预算、截止期等参数,其中,预算是时间的函数。准确区分作业的重要性和紧迫性是作业调度系统的一个关键问题。综合利用这四个参数来定义作业的优先级,并提出基于价值密度和相对截止期的网格作业调度算法。分别对弱实时和强实时网格作业的调度进行仿真。仿真结果显示,所提出的调度算法的性能在两种情况下都优于所有对比算法的性能,且在强实时作业情况下优势更明显。     

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