Phurti: application and network-aware flow scheduling for MapReduce
Cai, Xiao
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https://hdl.handle.net/2142/78595
Description
Title
Phurti: application and network-aware flow scheduling for MapReduce
Author(s)
Cai, Xiao
Issue Date
2015-04-03
Department of Study
Computer Science
Discipline
Computer Science
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
M.S.
Degree Level
Thesis
Keyword(s)
Network
MapReduce
Abstract
Traffic for a typical MapReduce job in a datacenter consists of multiple network flows. Traditionally, network resources have been allocated to optimize network-level metrics such as flow completion time or throughput. Some recent schemes propose using application-aware scheduling which can reduce the average job completion time. However, most of them treat the core network as a black box with sufficient capacity. Even if only one network link in the core network becomes a bottleneck, it can hurt application performance.
We design and implement a centralized flow scheduling framework called Phurti with the goal of decreasing the completion time for Hadoop MapReduce jobs. Phurti communicates both with the Hadoop framework to retrieve job-level network traffic information and the OpenFlow-based switches to learn about network topology. Phurti implements a novel heuristic called Smallest Maximum Sequential-traffic First (SMSF) that uses collected application and network information to perform traffic scheduling for MapReduce jobs. Our evaluation with real Hadoop workloads shows that compared to application and network-agnostic scheduling strategies, Phurti improves job completion time for 95% of the jobs, decreases average job completion time by 20% and tail job completion time by 13%.
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