RT Conference Proceedings
T1 Towards a Performance Interference-aware Virtual Machine Placement Strategy for Supporting Soft Real-time Applications in the Cloud
A1 Caglar, Faruk
A1 Shekhar, Shashank
A1 Gokhale, Aniruddha
A2 Cucinotta, Tommaso
A2 Pautet, Laurent
A2 García Valls, Marisol
AB It is standard practice for cloud service providers (CSPs) to overbook physical system resources to maximize the resource utilization and make their business model more profitable. Resource overbooking can lead to performance interference, however, among the virtual machines (VMs) hosted on the physical resources causing performance un-predictability for soft real-time applications hosted in the VMs, which is unacceptable to these applications. Balancing these conflicting requirements needs a careful design of the placement strategies for hosting soft real-time applications such that the performance interference effects are minimized while still allowing resource overbooking. These placement decisions cannot be made offline because workloads change at run time. Moreover, satisfying the priorities of collocated VMs may require VM migrations, which require an online solution. This paper presents a machine learning-based, online placement solution to this problem where the system is trained using a publicly available trace of a large data center owned by Google. Our approach first classifies the VMs based on their historic mean CPU and memory usage, and performance features. Subsequently, it learns the best patterns of collocating the classified VMs by employing machine learning techniques. These extracted patterns are those that provide the lowest performance interference level on the specified host machines making them amenable to hosting soft real-time applications while still allowing resource overbooking.
PB Universidad Carlos III de Madrid
SN 978-84-697-1736-3
YR 2011
FD 2011-11
LK https://hdl.handle.net/10016/19687
UL https://hdl.handle.net/10016/19687
LA eng
NO REACTION 2014. 3rd International Workshop on Real-time and Distributed Computing in Emerging Applications. Rome, Italy. December 2nd, 2014.
NO This work was supported in part by the National Science Foundation CAREER CNS 0845789 and AFOSR DDDAS FA9550-13-1-0227.
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RD 17 may. 2024