首页> 外文会议>Proceedings of the IASTED international conferences on informatics >DRAS: FAST DYNAMIC RESCHEDULING SCHEME ELIMINATING REDUNDANT COMPUTATION
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DRAS: FAST DYNAMIC RESCHEDULING SCHEME ELIMINATING REDUNDANT COMPUTATION

机译:DRAS:快速动态重排方案,消除冗余计算

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Task scheduling is very important for efficient execution of large-scale workflows. Static scheduling schemes achieve high performance when executing workflows in stable environments. However, the scheduling costs are very high for large-scale workflows and they may perform poorly if the system performance is changed dynamically. Dynamic rescheduling schemes support the dynamic performance changes because tasks are rescheduled using algorithms from static scheduling schemes when the performance is changed. Thus, better performance can be achieved in workflow applications, although the scheduling costs will increase if the system performance is frequently changed. Therefore, we propose a dynamic recursive adaptive scheduling scheme (DRAS) which can reduces the computational cost. DRAS uses RAS, which is a static scheme with low cost, as rescheduling algorithm. However, if DRAS uses RAS algorithm with no change, the cost increases since RAS is called many times. Thus, we improved the RAS to eliminate the redundant computation. Furthermore, we modified RAS to improve the makespan when used for the rescheduling since DRAS may perform poorly in that case. The evaluation using an simulation shows the makespan of DRAS decreases by 30% compared with a dynamic rescheduling scheme which uses the conventional RAS algorithm. Moreover, DRAS considerably reduces the scheduling time, achieving approximately 6 times speedup for workflows consisting of 1,000 tasks when the dynamic performance is frequently changed.
机译:任务计划对于有效执行大规模工作流非常重要。在稳定的环境中执行工作流时,静态调度方案可实现高性能。但是,对于大型工作流,​​调度成本非常高,并且如果动态更改系统性能,它们的性能可能会很差。动态重新计划方案支持动态性能更改,因为当性能发生变化时,将使用静态计划方案中的算法对任务进行重新计划。因此,尽管如果频繁更改系统性能会增加调度成本,但是在工作流应用程序中可以实现更好的性能。因此,我们提出了一种动态递归自适应调度方案(DRAS),可以降低计算成本。 DRAS使用RAS(一种低成本的静态方案)作为重新计划算法。但是,如果DRAS使用RAS算法不做任何更改,则由于RAS被多次调用,因此成本会增加。因此,我们改进了RAS以消除冗余计算。此外,由于在这种情况下DRAS的性能可能不佳,因此我们对RAS进行了修改以改进制造周期。通过仿真评估,与使用常规RAS算法的动态重新计划方案相比,DRAS的有效期减少了30%。此外,DRAS大大减少了调度时间,当动态性能经常变化时,由1,000个任务组成的工作流的速度提高了大约6倍。

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