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Automated Control of Multiple Virtualized Resources Pradeep Padala, Kai-Yuan Hou, Kang G. Shin, Xiaoyun Zhu, Mustafa Uysal, Zhikui Wang,
 

Summary: Automated Control of Multiple Virtualized Resources
Pradeep Padala, Kai-Yuan Hou, Kang G. Shin, Xiaoyun Zhu, Mustafa Uysal, Zhikui Wang,
Sharad Singhal, Arif Merchant
HP Laboratories
HPL-2008-123R1
Keyword(s):
virtualization, consolidation, service level objective, control, service differentiation
Abstract:
Virtualized data centers enable consolidation of multiple applications and sharing of multiple
resources among these applications. However, current virtualization technologies are inadequate
in achieving complex service level objectives (SLOs) for enterprise applications with time-
varying demands for multiple resources. In this paper, we present AutoControl, a resource
allocation system that automatically adapts to dynamic workload changes in a shared virtualized
infrastructure to achieve application SLOs. AutoControl is a combination of an online model
estimator and a novel multi-input, multi-output (MIMO) resource controller. The model
estimator captures the complex relationship between application performance and resource
allocations, while the MIMO controller allocates the right amount of resources to ensure
application SLOs. Our experimental results using RUBiS and TPC-W benchmarks along with
production-trace-driven workloads indicate that AutoControl can detect and adapt to CPU and
disk I/O bottlenecks that occur over time and across multiple nodes and allocate multiple

  

Source: Agrawal, Gagan - Department of Computer Science and Engineering, Ohio State University

 

Collections: Computer Technologies and Information Sciences