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Performance Evaluation of Two New Lightweight Real-Time Scheduling Mechanisms for Ubiquitous and Mobile Computing Environments

机译:普适和移动计算环境中两种新型轻量级实时调度机制的性能评估

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摘要

In ubiquitous and mobile computing environments, most of user requests have timing constraints. Therefore, for their execution, real-time scheduling mechanisms should be employed. In such environments, some mechanisms, such as Earliest Deadline First and Least Slack Time Rate (LSTR), are the most used real-time scheduling algorithms today. However, these existing mechanisms cannot guarantee the execution of the most critical user tasks. Furthermore, the results of executed tasks should also be provided to users on time so as to not waste the resources processing these tasks. The existing scheduling mechanisms, such as Dynamic Adjustment with Time Constraint and Dynamic Priority-based Heuristic action (DPH), are not suitable for this due to some limitations. The current paper proposes two new lightweight real-time mechanisms for scheduling input tasks (received requests) and output tasks (execution results) and evaluates their performance. The first mechanism satisfies users based on the classification of similar tasks and the use of the LSTR algorithm. The second mechanism is an improved form of the combination of the LSTR and DPH algorithms, and aims provide users with the maximum number of services by considering a possibility matrix. Extensive simulation experiments on real workload data demonstrate the superiority of the proposed mechanisms over existing algorithms, in terms of the percentage of executed tasks, makespan, and average turnaround time criteria.
机译:在无处不在的移动计算环境中,大多数用户请求都具有时序约束。因此,对于它们的执行,应采用实时调度机制。在这样的环境中,某些机制(例如最早的截止期限优先和最小的松弛时间速率(LSTR))是当今最常用的实时调度算法。但是,这些现有机制不能保证执行最关键的用户任务。此外,还应将已执行任务的结果及时提供给用户,以免浪费处理这些任务的资源。由于某些限制,现有的调度机制(例如带时间约束的动态调整和基于动态优先级的启发式动作(DPH))不适合于此。本文提出了两种新的轻量级实时机制,用于计划输入任务(接收的请求)和输出任务(执行结果),并评估其性能。第一种机制基于相似任务的分类和LSTR算法的使用来满足用户。第二种机制是LSTR和DPH算法结合的改进形式,旨在通过考虑可能性矩阵为用户提供最大数量的服务。在实际工作负载数据上的大量仿真实验证明,在执行任务的百分比,有效期和平均周转时间标准方面,所提出的机制优于现有算法。

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