Mélange: Multi-tenant scheduling with adaptive eviction for graph processing clusters
Mehar, Jayasi
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https://hdl.handle.net/2142/101212
Description
Title
Mélange: Multi-tenant scheduling with adaptive eviction for graph processing clusters
Author(s)
Mehar, Jayasi
Issue Date
2018-04-24
Director of Research (if dissertation) or Advisor (if thesis)
Gupta, Indranil
Department of Study
Computer Science
Discipline
Computer Science
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
M.S.
Degree Level
Thesis
Date of Ingest
2018-09-04T20:36:52Z
Keyword(s)
graph processing
multi-tenancy
eviction
scheduling
Abstract
Multi-tenancy is an important approach to resource consolidation in cluster management. In this thesis we design and evaluate Mélange, an efficient multi-tenant scheduler targeted towards graph processing jobs. Mélange supports job priorities and eviction, while attempting to avoid starvation. We propose novel ways of exploiting domain-specific knowledge to achieve better scheduling decisions for graph processing jobs. We evaluate static eviction policies and design Mélange to adapt to the cluster and job state at run time to reduce overhead costs during eviction. We have developed Mélange as a cross-layer scheduler built over Apache Giraph and YARN, and show experimental results with synthetic as well as production workloads.
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