A Toolset for Detecting Containerized Application's Dependencies in CaaS Clouds

Pinchao Liu, Liting Hu, Hailu Xu, Zhiyuan Shi, Jason Liu, Qingyang Wang, Jai Dayal, Yuzhe Tang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

There has been a dramatic increase in the popularity of Container as a Service (CaaS) clouds. The CaaS multi-tier applications could be optimized by using network topology, link or server load knowledge to choose the best endpoints to run in CaaS cloud. However, it is difficult to apply those optimizations to the public datacenter shared by multi-tenants. This is because of the opacity between the tenants and the datacenter providers: Providers have no insight into tenant's container workloads and dependencies, while tenants have no clue about the underlying network topology, link, and load. As a result, containers might be booted at wrong physical nodes that lead to performance degradation due to bi-section bandwidth bottleneck or co-located container interference. We propose 'DocMan', a toolset that adopts a black-box approach to discover container ensembles and collect information about intra-ensemble container interactions. It uses a combination of techniques such as distance identification and hierarchical clustering. The experimental results demonstrate that DocMan enables optimized containers placement to reduce the stress on bi-section bandwidth of the datacenter's network. The method can detect container ensembles at low cost and with 92% accuracy and significantly improve performance for multi-tier applications under the best of circumstances.

Original languageEnglish (US)
Title of host publicationProceedings - 2018 IEEE International Conference on Cloud Computing, CLOUD 2018 - Part of the 2018 IEEE World Congress on Services
PublisherIEEE Computer Society
Pages194-201
Number of pages8
ISBN (Electronic)9781538672358
DOIs
StatePublished - Sep 7 2018
Event11th IEEE International Conference on Cloud Computing, CLOUD 2018 - San Francisco, United States
Duration: Jul 2 2018Jul 7 2018

Publication series

NameIEEE International Conference on Cloud Computing, CLOUD
Volume2018-July
ISSN (Print)2159-6182
ISSN (Electronic)2159-6190

Other

Other11th IEEE International Conference on Cloud Computing, CLOUD 2018
CountryUnited States
CitySan Francisco
Period7/2/187/7/18

Keywords

  • Clustering
  • Dependency Analysis
  • Virtualization

ASJC Scopus subject areas

  • Artificial Intelligence
  • Information Systems
  • Software

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