Adaptive Control of Resources in Virtualized Infrastructure


The primary motivation for this work is the poor utilization of resources in data centers. Consolidation is often used to avert this problem, but ad-hoc mechanisms produce more problems. Ironically, the consolidated servers still see poor utilization but fail to meet service levels when bursty load comes. consolidation.PNG


The goal of this project is to explore the use of dynamic, control-based allocation of resources (CPU cycles, memory, network bandwidth, storage bandwidth, etc.) to:

  • enable a resource sharing system to achieve application-level QoS goals, and
  • minimize the resource requirements for whole application stacks.

in a virtualized infrastructure.



Our approach is to specify the desired behavior of an application-stack in terms of a set of metrics, monitor these metrics, and control the configuration of the system so that the desired behavior is achieved. We explictly monitor behaviour indicators from multiple locations in an application stack including applications software, systems software layers, and hardware stack. These indicators are then used as inputs to a control element, which makes decisions for the settings of actuators — parameters that change behavior of a particular hardware or software layer.


System architecture

In our current system, we create a virtualized infrastructure using Xen and host two multi-tiered systems as shown in the Figure. The two multi-tiered systems are stressed using an e-commerce workload called RUBiS and TPC-W. Our adaptive controller tries maintain

  • Good performance (low response time and high throughput)
  • Good utilization (the VMs are never allocated more than they need)
  • QoS differentiation (when saturation happens)

The controller is developed using control theory techniques. Details of the controller are available in our EuroSys paper.





  • Pradeep Padala
  • Kang G. Shin
  • Xiaoyun Zhu (HP Labs)
  • Mustafa Uysal (HP Labs)
  • Zhikui Wang (HP Labs)
  • Sharad Singhal (HP Labs)
  • Arif Merchant (HP Labs)
  • Ira Cohen (HP Labs)
  • Ken Salem (University of Waterloo)



  • Xue Liu, Xiaoyun Zhu, Padala Pradeep, Zhikui Wang, Sharad Singhal. Optimal Multivariate Control for Differentiated Services on a Shared Hosting Platform. In the proceedings of the 46th IEEE Conference on Decision and Control (CDC’07), Dec 2007.
  • Pradeep Padala, Xiaoyun Zhu, Mustafa Uysal, Zhikui Wang, Sharad Singhal, Arif Merchant, Kenneth Salem and Kang G. Shin. Adaptive control of virutalized resources in utility computing environments. Proceedings of the EuroSys 2007.
  • Zhikui Wang, Xiaoyun Zhu, Pradeep Padala and Sharad Singhal. Capacity and Performance Overhead in Dynamic Resource Allocation to Virtual Containers. In the proceedings of the IFIP/IEEE Symposium on Integrated Management (IM’2007), May 2007.