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Net-Cohort: Detecting and Managing VM Ensembles in Virtualized Data Centers
Proceedings of the 9th International Conference on Autonomic Computing
  • Liting Hu
  • Karsten Schwan
  • Ajay Gulati
  • Junjie Zhang, Wright State University - Main Campus
  • Chengwei Wang
Document Type
Conference Proceeding
Publication Date
9-1-2012
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Abstract
Bi-section bandwidth is a critical resource in today’s data centers because of the high cost and limited bandwidth of higher-level network switches and routers. This problem is aggravated in virtualized environments where a set of virtual machines, jointly implementing some service, may run across multiple L2 hops. Since data center administrators typically do not have visibility into such sets of communicating VMs, this can cause inter-VM traffic to traverse bottlenecked network paths. To address this problem, we present `Net-Cohort’, which offers lightweight system-level techniques to (1) discover VM ensembles and (2) collect information about intra-ensemble VM interactions. Net- Cohort can dynamically identify ensembles to manipulate entire services/applications rather than individual VMs, and to support VM placement engines in co-locating communicating VMs in order to reduce the consumption of bi-section bandwidth. An implementation of Net-Cohort on a Xen-based system with 15 hosts and 225 VMs shows that its methods can detect VM ensembles at low cost and with about 90.0% accuracy. Placements based on ensemble information provided by Net- Cohort can result in an up to 385% improvement in application throughput for a RUBiS instance, a 56.4% improvement in application throughput for a Hadoop instance, and a 12.76 times improvement in quality of service for a SIPp instance.
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Presented at the 9th ACM International Conference on Autonomic Computing, San Jose, CA.

DOI
10.1145/2371536.2371540
Citation Information
Liting Hu, Karsten Schwan, Ajay Gulati, Junjie Zhang, et al.. "Net-Cohort: Detecting and Managing VM Ensembles in Virtualized Data Centers" Proceedings of the 9th International Conference on Autonomic Computing (2012) p. 3 - 12 ISSN: 978-1-4503-1520-3
Available at: http://works.bepress.com/junjie_zhang/6/