Using Frequency Scaling on Virtualized Memory in Cloud Datacenters

As the increasing of IT-infrastructure in cloud platforms, rapidly growth of energy consumption becomes a critical problem in many cloud datacenters. Conventionally, most of studies on energy-efficiency optimization concentrate on CPU related energy costs instead of memory subsystem, since CPU often dominates the total energy consumption in modern servers. However, such a situation is gradually changing as more and more cloud datacenters are equipped with larger and larger memory systems for dealing with data intensive applications. In this paper, we present a novel mechanism, namely frequency scaling on virtualized memory (FSVM), which applies DVFS technology on memory subsystem based on the characteristics of active VM instances. Comparing with previous studies, our approach provides a fine-grained memory energy consumption conservation mechanism for virtualized servers. Extensive experiments are conducted to investigate the effectiveness and performance of our FSVM, and the results indicate that it can significantly improve the energy-efficiency of memory subsystem in virtualized servers.