Paper Info
Title
Resource Central: Understanding and Predicting Workloads for Improved Resource Management in Large Cloud Platforms.
Abstract
Cloud research to date has lacked data on the characteristics of the production virtual machine (VM) workloads of large cloud providers. A thorough understanding of these characteristics can inform the providers' resource management systems, e.g. VM scheduler, power manager, server health manager. In this paper, we first introduce an extensive characterization of Microsoft Azure's VM workload, including distributions of the VMs' lifetime, deployment size, and resource consumption. We then show that certain VM behaviors are fairly consistent over multiple lifetimes, i.e. history is an accurate predictor of future behavior. Based on this observation, we next introduce Resource Central (RC), a system that collects VM telemetry, learns these behaviors offline, and provides predictions online to various resource managers via a general client-side library. As an example of RC's online use, we modify Azure's VM scheduler to leverage predictions in oversubscribing servers (with oversubscribable VM types), while retaining high VM performance. Using real VM traces, we then show that the prediction-informed schedules increase utilization and prevent physical resource exhaustion. We conclude that providers can exploit their workloads' characteristics and machine learning to improve resource management substantially.
Year
DOI
Venue
2017
10.1145/3132747.3132772
SOSP '17: ACM SIGOPS 26th Symposium on Operating Systems Principles Shanghai China October, 2017
Keywords
Field
DocType
Cloud workloads, machine learning, predictive management
Resource management,Virtual machine,Software deployment,Computer science,Workload,Server,Exploit,Real-time computing,Schedule,Operating system,Cloud computing,Distributed computing
Conference
ISBN
Citations 
PageRank 
978-1-4503-5085-3
48
1.21
References 
Authors
21
6
Name
Order
Citations
PageRank
Eli Cortez116610.96
Anand Bonde2511.95
Alexandre Muzio3482.23
Mark Russinovich426011.89
Marcus Fontoura5111661.74
Ricardo Bianchini62706172.32