实时嵌入式异构环境下多优先级混合任务调度动态策略

针对现有异构环境下的调度策略,引入迫切密度和剩余价值密度,分析迫切密度和剩余价值密度调节任务执行紧急程度的影响、对优先级制定,通过构建单有向无环图（ DAG）系统模型实现了混合任务的动态调度。仿真实验结果表明：该调度策略在系统负载较高的情况下,仍有较优的任务执行效能和避免颠簸现象。     

异构分布系统的实时任务轮转式容错调度算法

建立了一个异构分布式系统实时调度模型,对异构分布式系统中的任务及不同处理机资源进行了形式化描述.结合基版本/副版本技术,给出了用于异构分布式系统的实时任务轮转式容错调度算法.实例分析表明,该算法有效提高了异构处理机环境下的资源利用率以及整体计算性能.     

Dynamically mapping tasks with priorities and multiple deadlines in a heterogeneous environment

In a distributed heterogeneous computing system, the resources have different capabilities and tasks have different requirements. To maximize the performance of the system, it is essential to assign the resources to tasks (match) and order the execution of tasks on each resource (schedule) to exploit the heterogeneity of the resources and tasks. Dynamic mapping (defined as matching and scheduling) is performed when the arrival of tasks is not known a priori. In the heterogeneous environment considered in this study, tasks arrive randomly, tasks are independent (i.e., no inter-task communication), and tasks have priorities and multiple soft deadlines. The value of a task is calculated based on the priority of the task and the completion time of the task with respect to its deadlines. The goal of a dynamic mapping heuristic in this research is to maximize the value accrued of completed tasks in a given interval of time. This research proposes, evaluates, and compares eight dynamic mapping heuristics. Two static mapping schemes (all arrival information of tasks are known) are designed also for comparison. The performance of the best heuristics is 84% of a calculated upper bound for the scenarios considered.     

Adaptive energy-efficient scheduling for real-time tasks on DVS-enabled heterogeneous clusters

Developing energy-efficient clusters not only can reduce power electricity cost but also can improve system reliability. Existing scheduling strategies developed for energy-efficient clusters conserve energy at the cost of performance. The performance problem becomes especially apparent when cluster computing systems are heavily loaded. To address this issue, we propose in this paper a novel scheduling strategy–adaptive energy-efficient scheduling or AEES–for aperiodic and independent real-time tasks on heterogeneous clusters with dynamic voltage scaling. The AEES scheme aims to adaptively adjust voltages according to the workload conditions of a cluster, thereby making the best trade-offs between energy conservation and schedulability. When the cluster is heavily loaded, AEES considers voltage levels of both new tasks and running tasks to meet tasks’ deadlines. Under light load, AEES aggressively reduces the voltage levels to conserve energy while maintaining higher guarantee ratios. We conducted extensive experiments to compare AEES with an existing algorithm–MEG, as well as two baseline algorithms–MELV, MEHV. Experimental results show that AEES significantly improves the scheduling quality of MELV, MEHV and MEG.     

PASTA: a power-aware solution to scheduling of precedence-constrained tasks on heterogeneous computing resources

Power efficiency is one of the main challenges in large-scale distributed systems such as datacenters, Grids, and Clouds. One can study the scheduling of applications in such large-scale distributed systems by representing applications as a set of precedence-constrained tasks and modeling them by a Directed Acyclic Graph. In this paper we address the problem of scheduling a set of tasks with precedence constraints on a heterogeneous set of Computing Resources (CRs) with the dual objective of minimizing the overall makespan and reducing the aggregate power consumption of CRs. Most of the related works in this area use Dynamic Voltage and Frequency Scaling (DVFS) approach to achieve these objectives. However, DVFS requires special hardware support that may not be available on all processors in large-scale distributed systems. In contrast, we propose a novel two-phase solution called PASTA that does not require any special hardware support. In its first phase, it uses a novel algorithm to select a subset of available CRs for running an application that can balance between lower overall power consumption of CRs and shorter makespan of application task schedules. In its second phase, it uses a low-complexity power-aware algorithm that creates a schedule for running application tasks on the selected CRs. We show that the overall time complexity of PASTA is $O(p.v^{2})$ where $p$ is the number of CRs and $v$ is the number of tasks. By using simulative experiments on real-world task graphs, we show that the makespan of schedules produced by PASTA are approximately 20 % longer than the ones produced by the well-known HEFT algorithm. However, the schedules produced by PASTA consume nearly 60 % less energy than those produced by HEFT. Empirical experiments on a physical test-bed confirm the power efficiency of PASTA in comparison with HEFT too.     

Heuristics for scheduling file-sharing tasks on heterogeneous systems with distributed repositories

We consider the problem of scheduling an application on a computing system consisting of heterogeneous processors and data repositories. The application consists of a large number of file-sharing otherwise independent tasks. The files initially reside on the repositories. The processors and the repositories are connected through a heterogeneous interconnection network. Our aim is to assign the tasks to the processors, to schedule the file transfers from the repositories, and to schedule the executions of tasks on each processor in such a way that the turnaround time is minimized. We propose a heuristic composed of three phases: initial task assignment, task assignment refinement, and execution ordering. We experimentally compare the proposed heuristics with three well-known heuristics on a large number of problem instances. The proposed heuristic runs considerably faster than the existing heuristics and obtains 10–14% better turnaround times than the best of the three existing heuristics.     

异构环境中Fork-Join任务图的调度算法

目前已有的Fork-Join任务图的调度算法大多假定处理机为同构的,而没有考虑实际应用中处理机的异构性以及节省处理机的问题,导致算法在具体应用中效率较低.因此,对Fork-Join任务图的调度问题进行研究,提出了一个基于异构环境的贪心调度算法,该算法具有高的加速比和总体效率,其时间复杂度为O(v~2),其中,v表示任务集中任务的个数.实验结果表明,相比其它算法,该算法具有较短的调度长度、较短的完成时间,使用的处理机数较少,具有更强的实用性.     

On scheduling in a GT environment

Traditional research in scheduling has concentrated on developing optimal and optional tending heuristic algorithms for n x m job shop and flow shop problems. Relatively no work has been conducted in the area of scheduling within a GT cell. This paper examines the GT cell environment from a scheduling perspective in relation to a job and flow shop, and presents a modified approach to scheduling within a GT cell that implicitly takes advantage of common setups and part family coding structures.     

Quasi-static scheduling of communicating tasks

Good scheduling policies for distributed embedded applications are required for meeting hard real time constraints and for optimizing the use of computational resources. We study the quasi-static scheduling problem in which (uncontrollable) control flow branchings can influence scheduling decisions at run time. Our abstracted distributed task model consists of a network of sequential processes that communicate via point-to-point buffers. In each round, the task gets activated by a request from the environment. When the task has finished computing the required responses, it reaches a pre-determined configuration and is ready to receive a new request from the environment. For such systems, we prove that determining the existence of a scheduling policy that guarantees upper bounds on buffer capacities is undecidable. However, we show that the problem is decidable for the important subclass of “data-branching” systems in which control flow branchings are exclusively due to data-dependent internal choices made by the sequential components. This decidability result exploits ideas derived from the Karp and Miller coverability tree for Petri nets as well as the existential boundedness notion of languages of message sequence charts.     

Multi-queue scheduling of two tasks

Summary Scheduling in the central server queueing model, consisting of a CPU server and m I/O servers, is considered for the case of two customers. Optimal (maximal CPU utilization) CPU and I/O schedules are obtained. The best CPU schedule depends on the I/O schedule in effect; and is either Longest or Shortest-Expected-Remaining-Processing-Time-First. However, for certain I/O schedules the CPU schedule is immaterial. The best I/O schedule is always to process the longer CPU customer first.     

Recommendations in a heterogeneous service environment

This paper presents novel algorithms which are able to generate recommendations within a heterogeneous service environment. In this work explicitly set preferences as well as implicitly logged viewing behavior are employed to generate recommendations for Digital Video Broadcast (DVB) content. This paper also discusses the similarity between the DVB genres and YouTube categories. In addition it presents results to show the comparison between well known collaborative filtering methods. The outcome of this comparison study is used to identify the most suitable filtering method to use in the proposed environment. Finally the paper presents a novel Personal Program Guide (PPG), which is used as a tool to visualize the generated recommendations within a heterogeneous service environment. This PPG is also capable of showing the linear DVB content and the non-linear YouTube videos in a single view.     

Coordinated execution of tasks in a multiagent environment

This correspondence describes the application of discrete event control methods to provide conflict-free plan execution in a multiagent environment. This work uses planning methods to generate plans for multiple robots, and the plans are then compiled into Petri nets for analysis, execution, and monitoring. Supervisory control techniques are applied to the Petri net controller for the purpose of dealing with conflicts that arise due to the presence of shared resources. Furthermore, by preserving the state of the system replanning can occur at any time during execution to deal with unforeseen events.     

Optimal scheduling of compute-intensive tasks on a network ofworkstations

In a network of high performance workstations, many workstations are underutilized by their owners. The problem of using these idle cycles for solving computationally intensive tasks by executing a large task on many workstations has been addressed before and algorithms with O(N²) time and O(N) space for choosing the optimal subset of workstations out of N workstations were presented. We improve these algorithms to reduce the running time to O(N log N), while keeping the space requirement the same. The proposed algorithms are particularly useful for SPMD parallelism where computation is the same for all workstations and the data space is partitioned between the workstations     

Aperiodic servers in a deadline scheduling environment

A real-time system may have tasks with soft deadlines, as well as hard deadlines. While earliest-deadline-first scheduling is effective for hard-deadline tasks, applying it to soft-deadline tasks may waste schedulable processor capacity or sacrifice average response time. Better average response time may be obtained, while still guaranteeing hard deadlines, with an aperiodic server. Three scheduling algorithms for aperiodic servers are described, and schedulability tests are derived for them. A simulation provides performance data for these three algorithms on random aperiodic tasks. The performances of the deadline aperiodic servers are compared with those of several alternatives, including background service, a deadline polling server, and rate-monotonic servers, and with estimates based on the M/M/1 queueing model. This adds to the evidence in support of deadline scheduling,versus fixed priority scheduling.     

Deterministic preemptive scheduling of real-time tasks

Algorithms for the preemptive scheduling of deterministic, real-time tasks can have applications in providing quality-of-service guarantees to packet flows in multichannel optical networks     

Multiprocessor online scheduling of hard-real-time tasks

The problems of hard-real-time task scheduling in a multiprocessor environment are discussed in terms of a scheduling game representation of the problem. It is shown that optimal scheduling without a priori knowledge is impossible in the multiprocessor case even if there is no restriction on preemption owing to precedence or mutual exclusion constraints. Sufficient conditions that permit a set of tasks to be optimally scheduled at run time are derived     

Online scheduling of moldable parallel tasks

In this paper, we study an online scheduling problem with moldable parallel tasks on m processors. Each moldable task can be processed simultaneously on any number of processors of a parallel computer, and the processing time of a moldable task depends on the number of processors allotted to it. Tasks arrive one by one. Upon arrival of each task, the scheduler has to determine both the number of processors and the starting time for the task. Moreover, these decisions cannot be changed in the future. The objective is to attain a schedule such that the longest completion time over all tasks, i.e., the makespan, is minimized. First, we provide a general framework to show that any \(\rho \)-bounded algorithm for scheduling of rigid parallel tasks (the number of processors for a task is fixed a prior) can be extended to yield an algorithm for scheduling of moldable tasks with a competitive ratio of \(4\rho \) if the ratio \(\rho \) is known beforehand. As a consequence, we achieve the first constant competitive ratio, 26.65, for the moldable parallel tasks scheduling problem. Next, we provide an improved algorithm with a competitive ratio of at most 16.74.     

Feedback fuzzy-DVS scheduling of control tasks

To consider the energy-aware scheduling problem in computer-controlled systems is necessary to improve the control performance, to use the limited computing resource sufficiently, and to reduce the energy consumption to extend the lifetime of the whole system. In this paper, the scheduling problem of multiple control tasks is discussed based on an adjustable voltage processor. A feedback fuzzy-DVS (dynamic voltage scaling) scheduling architecture is presented by applying technologies of the feedback control and the fuzzy DVS. The simulation results show that, by using the actual utilization as the feedback information to adjust the supply voltage of processor dynamically, the high CPU utilization can be implemented under the precondition of guaranteeing the control performance, whilst the low energy consumption can be achieved as well. The proposed method can be applied to the design in computer-controlled systems based on an adjustable voltage processor.     

Visualization and debugging in a heterogeneous environment

The authors' experiences with visualization and debugging of parallel virtual machine (PVM) applications and two of the tools they have devised to facilitate these tasks are described. One of the tools is a graphical monitoring package called Xab that can visually display PVM activities inside an application running across a network. The other is a graphical programming environment called Hence, which helps the user write, compile, execute, and trace heterogeneous distributed programs. The authors discuss their early work, the present research, and the future directions of these experimental projects     

Independent checkpointing in a heterogeneous grid environment

The EU-funded XtreemOS project implements an open-source grid operating system based on Linux. In order to provide fault tolerance and migration for grid applications, it integrates a distributed grid-checkpointing service called XtreemGCP. This service is designed to support various checkpointing protocols and different checkpointer packages (e.g. BLCR, LinuxSSI, OpenVZ, etc.) in a transparent manner through a uniform checkpointer interface. In this paper, we present the integration of a backward error recovery protocol based on independent checkpointing into the XtreemGCP service. The solution we propose is not checkpointer bound and thus can be transparently used on top of any checkpointer package.To evaluate the prototype we run it within a heterogeneous environment composed of single-PC nodes and a Single System Image (SSI) cluster. The experimental results demonstrate the capability of the XtreemGCP service to integrate different checkpointing protocols and independently checkpoint a distributed application within a heterogeneous grid environment. Moreover, the performance evaluation also shows that our solution outperforms the existing coordinated checkpointing protocol in terms of scalability.