A new real time disk-scheduling method based on GSR algorithm

Disk scheduling has an important role in QOS guarantee of soft real-time environments such as video-on-demand and multimedia servers. Since now, some disk-scheduling algorithms have been proposed to schedule real-time disk requests. One of the most recent algorithms is global seek-optimizing real-time (GSR) that schedules the disk requests with different ready times by a global regrouping scheme. In the present paper, we propose a real-time disk-scheduling algorithm based on GSR that is called IGSR (improved GSR). IGSR creates the scan-groups of the requests and tries to find a good feasible schedule by optimized grouping with considering another chance for tasks that miss their deadlines at initial grouping. With regard to the admission policy of tasks, two different version of proposed method are presented: the first one has been designed for the case that all the disk requests available simultaneously and second one has been designed for the case that requests are admitted dynamically (GSR does not support the second one). It means that in the second case, the request queue may change when a task is running but in the first one it does not change. Simulation results showed IGSR outperformed GSR and some other related works in terms of maximum supportable streams, number of missed deadlines, and disk throughput.     

一种实时系统中的多任务可预测调度算法

通过研究实时系统中可能出现的任务类型，提出了一种实时系统任务模型和基于这个模型的多任务调度算法，分析了该算法下所有周期任务和非周期任务可调度的条件。该算法结合了DMS(Dcadlinc Monotonic Scheduling)和DD(Deadline Driven)算法，在保证系统中周期任务执行的前提下,也使非周期任务获得了，较好的响应时间。     

Multi-Dimensional Scheduling for Real-Time Tasks on Heterogeneous Clusters

Multiple performance requirements need to be guaranteed in some real-time applications such as multimedia data processing and real-time signal processing in addition to timing constraints.Unfortunately,most conventional scheduling algorithms only take one or two dimensions of them into account.Motivated by this fact,this paper investigates the problem of providing multiple performance guarantees including timeliness,QoS,throughput,QoS fairness and load balancing for a set of independent tasks by dynamic ...     

基于TinyOS的HRRF调度策略

针对传感器网络操作系统TinyOS采用非剥夺的先来先服务调度策略,系统紧急任务不能得到及时响应及节点吞吐量下降的情况,提出了一种可抢占HRRF(Highest-Response-Ratio First)作业调度策略。HRRF算法采用对于实时性较强的任务优先调度策略,满足了系统对实时任务的响应,提高了处理器的响应速度;对于软实时任务采用高响应比(任务等待时间/需运行时间)调度策略,提高了系统的效率。在TinyOS上的测试表明,HRRF策略在不影响TinyOS原有性能的情况下极大改善了传感器网络承担实时性任务的运行效果。     

基于滚动优化的密码云实时任务阈值调度方法

针对当前云任务调度算法在密码云环境中无法实现任务实时处理的问题,提出一种基于滚动优化窗口的实时阈值调度方法。首先,将密钥调用环节融入密码任务流程中,提出一种密码云服务架构;其次,为实现任务的实时调度,构建基于滚动窗口的密码任务调度器模型和吞吐量分析模型,用于获得实时的吞吐量数据;最后,为满足云租户对高速密码服务的客观需求,提出吞吐量阈值调度算法,从而根据实时吞吐量相对于吞吐量阈值的变化情况实时迁移虚拟密码机。仿真结果表明,该方法与未采用滚动优化窗口或虚拟机迁移技术的方法相比,具有任务完成时间短、CPU占用率低的特点,且实时吞吐量能够持续保持在网络带宽的70%~85%,从而验证了其在密码云环境中的有效性和实时性。     

一种可行的DMS可调度性分析

固定优先级任务的可调度性判定是实时系统调度理论研究的核心问题之一。提出了一种可行的DMS(Deadline Monotonic Scheduling,简称DMS)可调度性判定方法—确切性判定方法(precised schedulability test algorithm简称PSTA),利用DMS调度的充要条件,保证任何任务集均可被判定,并且判定结果是确切的。首先给出了DMS调度模型,介绍了可调度性判定的基本思想,然后通过实验提出并验证了PSTA相关的结论。     

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.     

TinyOS中DSA调度策略的研究

针对TinyOS任务调度采用非剥夺的先来先服务调度策略,而产生的系统紧急任务不能及时得到响应及节点吞吐量下降情况,该文提出一种新的可抢占时限短作业调度策略——DSA。在绝对时限前执行硬实时任务,满足了系统对实时任务的响应要求,提高处理器的响应速度,对软实时任务实行短作业优先调度策略,提高系统的吞吐量。在TinyOS上测试表明,DSA策略在不影响TinyOS原有性能的情况下,改进了传感器网络承担实时性任务的运行效果。     

云计算环境下基于蜜蜂觅食行为的任务负载均衡算法

针对云计算环境下的任务调度程序通常需要较多响应时间和通信成本的问题,提出了一种基于蜜蜂行为的负载均衡(HBB-LB)算法。首先,利用虚拟机(VM)进行负载平衡来最大化吞吐量;然后,对机器上任务的优先级进行平衡;最后,将平衡重点放在减少VM等待序列中任务的等待时间上,从而提高处理过程的整体吞吐量和优先级。利用CloudSim工具模拟云计算环境进行仿真实验,结果表明,相比粒子群优化(PSO)、蚁群算法(ACO)、动态负载均衡(DLB)、先入先出(FIFO)和加权轮询(WRR)算法, HBB-LB算法的平均响应时间分别节省了5%、13%、17%、67%、37%,最大完成时间分别节省了20%、23%、18%、55%、46%,可以更好地平衡非抢占式独立任务,适用于异构云计算系统。     

A non-preemptive scheduling algorithm for soft real-time systems

Real-time systems are often designed using preemptive scheduling and worst-case execution time estimates to guarantee the execution of high priority tasks. There is, however, an interest in exploring non-preemptive scheduling models for real-time systems, particularly for soft real-time multimedia applications. In this paper, we propose a new algorithm that uses multiple scheduling strategies for efficient non-preemptive scheduling of tasks. Our goal is to improve the success ratio of the well-known Earliest Deadline First (EDF) approach when the load on the system is very high and to improve the overall performance in both underloaded and overloaded conditions. Our approach, known as group-EDF (gEDF) is based on dynamic grouping of tasks with deadlines that are very close to each other, and using Shortest Job First (SJF) technique to schedule tasks within the group. We will present results comparing gEDF with other real-time algorithms including, EDF, Best-effort, and Guarantee, by using randomly generated tasks with varying execution times, release times, deadlines and tolerance to missing deadlines, under varying workloads. We believe that grouping tasks dynamically with similar deadlines and utilizing a secondary criteria, such as minimizing the total execution time (or other metrics such as power or resource availability) for scheduling tasks within a group, can lead to new and more efficient real-time scheduling algorithms.