Title
Scalable System Scheduling for HPC and Big Data.
Abstract
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 performance 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.
Year
DOI
Venue
2018
10.1016/j.jpdc.2017.06.009
Journal of Parallel and Distributed Computing
Keywords
DocType
Volume
Scheduler,Resource manager,Job scheduler,High performance computing,Data analytics
Journal
111
ISSN
Citations 
PageRank 
0743-7315
11
0.65
References 
Authors
37
11
Name
Order
Citations
PageRank
Albert Reuther133537.32
Chansup Byun218019.21
William Arcand317517.77
David Bestor418119.08
Bill Bergeron516816.57
Matthew Hubbell619220.93
Michael J. Jones711341927.21
Peter Michaleas820120.93
Andrew Prout918218.78
Antonio Rosa1017017.67
Jeremy Kepner1160661.58