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Scalable system scheduling for HPC and big data
برنامه ریزی مقیاس پذیر برای HPC و داده های بزرگ-2018 In the rapidly expanding field of parallel processing, job schedulers are the ‘‘operating systems’’ of modern
big data architectures and supercomputing systems. Job schedulers allocate computing resources and
control the execution of processes on those resources. Historically, job schedulers were the domain of
supercomputers, and job schedulers were designed to run massive, long-running computations over
days and weeks. More recently, big data workloads have created a need for a new class of computations
consisting of many short computations taking seconds or minutes that process enormous quantities of
data. For both supercomputers and big data systems, the efficiency of the job scheduler represents a
fundamental limit on the efficiency of the system. Detailed measurement and modeling of the perfor
mance of schedulers are critical for maximizing the performance of a large-scale computing system. This
paper presents a detailed feature analysis of 15 supercomputing and big data schedulers. For big data
workloads, the scheduler latency is the most important performance characteristic of the scheduler. A
theoretical model of the latency of these schedulers is developed and used to design experiments targeted
at measuring scheduler latency. Detailed benchmarking of four of the most popular schedulers (Slurm,
Son of Grid Engine, Mesos, and Hadoop YARN) is conducted. The theoretical model is compared with data
and demonstrates that scheduler performance can be characterized by two key parameters: the marginal
latency of the scheduler ts and a nonlinear exponent αs. For all four schedulers, the utilization of the
computing system decreases to <10% for computations lasting only a few seconds. Multi-level schedulers
(such as LLMapReduce) that transparently aggregate short computations can improve utilization for these
short computations to >90% for all four of the schedulers that were tested.
Keywords: Scheduler ، Resource manager ، Job scheduler ، High performance computing ، Data analytics |
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