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Cooperating CoScheduling: A Coscheduling Proposal Aimed at Non-Dedicated Heterogeneous NOWs

机译:合作协同计划:针对非专用异构NOW的协同计划建议

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Implicit coscheduling techniques applied to non-dedicated homogeneous Networks Of Workstations (NOWs) have shown they can perform well when many local users compete with a single parallel job. Implicit coscheduling deals with minimizing the communication waiting time of parallel processes by identifying the processes in need of coscheduling through gathering and analyzing implicit runtime information, basically communication events. Unfortunately, implicit coscheduling techniques do not guarantee the performance of local and parallel jobs, when the number of parallel jobs competing against each other is increased. Thus, a low efficiency use of the idle computational resources is achieved. In order to solve these problems, a new technique, named Cooperating CoScheduling (CCS), is presented in this work. Unlike traditional implicit coscheduling techniques, under CCS, each node takes its scheduling decisions from the occurrence of local events, basically communication, memory, Input/Output and CPU, together with foreign events received from cooperating nodes. This allows CCS to provide a social contract based on reserving a percentage of CPU and memory resources to ensure the progress of parallel jobs without disturbing the local users, while coscheduling of communicating tasks is ensured. Besides, the CCS algorithm uses status information from the cooperating nodes to balance the resources across the cluster when necessary. Experimental results in a non-dedicated heterogeneous NOW reveal that CCS allows the idle resources to be exploited efficiently, thus obtaining a satisfactory speedup and provoking an overhead that is imperceptible to the local user.
机译:应用于非专用同构工作站网络(NOW)的隐式协同调度技术表明,当许多本地用户竞争单个并行作业时,它们可以发挥良好的性能。隐式协同调度通过收集和分析隐式运行时信息(基本上是通信事件)来识别需要进行协同调度的进程,从而最大限度地减少了并行进程的通信等待时间。不幸的是,当并行作业相互竞争的数量增加时,隐式协同调度技术不能保证本地作业和并行作业的性能。因此,实现了空闲计算资源的低效率使用。为了解决这些问题,在这项工作中提出了一种称为协作协同计划(CCS)的新技术。与传统的隐式协同调度技术不同,在CCS中,每个节点都根据本地事件(基本上是通信,内存,输入/输出和CPU)以及从协作节点接收到的外部事件的发生来做出调度决策。这使CCS可以基于预留一定比例的CPU和内存资源来提供社会契约,以确保并行作业的进行而不会打扰本地用户,同时确保了通信任务的协同安排。此外,CCS算法在必要时使用来自协作节点的状态信息来平衡整个集群中的资源。非专用异构NOW的实验结果表明,CCS可以有效地利用空闲资源,从而获得令人满意的加速效果并引起本地用户无法察觉的开销。

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