VMan: Virtual Machine
Management in Large-Scale Virtualized Computing Infrastructure
Background
Large-scale virtual computing
infrastructures have become important platforms for many real world
systems such as virtual computing lab (VCL), cloud computing, corporate
data centers, and multi-tier web servers. Those infrastructures are
expected to scale to tens of thousands of hosts and millions of virtual
machines (VMs) in the near future. However, existing virtual
infrastructures have inherent limitations pertaining to robustness,
performance, and scalaliblity. One important reason attributed to the
limitation is the absence of efficient distributed VM resource and
performance management mechanisms. The goal of this project is to
develop efficient and light-weight VM management techniques to greatly
improve the scalability and resource-efficiency of the distributed
virtual computing infrastructure. The proposed VM management system
provides four essential mechanisms: 1) continuous monitoring of
different VMs for detecting buggy or malicious guest VMs; 2)
sharing-aware VM placement that allows multiple VMs to efficiently
share host resources based on their load patterns; 3) adaptive VM
ensemble expansion and contraction that allows an overloaded VM to
clone itself into multiple instances for sharing workload or allows
multiple duplicated VM instances to merge themselves when the workload
drops; and 4) runtime VM migration that leverages live VM migration
technology to continuously optimize hosted distributed application
performance and balance resource utilization in the hosting
infrastructure.
People
Faculty
Students
Collaborator
Publications
- Zhenhuan Gong, Prakash Ramaswamy, Xiaohui Gu,
"Signature-Driven Virtual Machine Relocation for Efficient Cloud
Computing", NCSU Technical Report, June, 2009.
- Zhenhuan Gong, Prakash Ramaswamy, Xiaohui Gu, Xiaosong
Ma,"SigLM:
Signature-Driven Load Management for Cloud Computing
Infrastructures", Proc. of IEEE International Conference on Quality
of
Service (IWQoS), Charleston, South Carolina, July, 2009.
- Ying Zhao, Yongmin Tan, Zhenhuan Gong, Xiaohui Gu, Mike Wamboldt,
"Self-Correlating
Predictive Information Tracking for Large-Scale Production Systems",
IEEE International Conference on Autonomic Computing and Communications
(ICAC), Barcelona, Spain, June, 2009. (acceptance rate: 15/96 = 15.6%)
- Xiaohui Gu, Haixun Wang,"Online Anomaly
Prediction for Robust
Cluster Systems", IEEE International Conference on Data Engineering
(ICDE), Shanghai, China, April, 2009. (full paper, acceptance rate:
93/554 = 17%)
- Xiaohui Gu, Spiros Papadimitriou, Philip S. Yu, Shu-Ping Chang,"Toward
Predictive Failure Management for Distributed Stream Processing Systems",
IEEE International Conference on Distributed Computing Systems (ICDCS),
Beijing, China, June, 2008. (acceptance rate: 16%)
Related
Projects
Code
Release