An energy-aware virtual machine scheduling method for service QoS enhancement in clouds over big data

摘要

Because of the strong demands of physical resources of big data, it is an effective and efficient way tornstore and process big data in clouds, as cloud computing allows on-demand resource provisioning. Withrnthe increasing requirements for the resources provisioned by cloud platforms, the Quality of Service (QoS)rnof cloud services for big data management is becoming significantly important. Big data has the characterrnof sparseness, which leads to frequent data accessing and processing, and thereby causes huge amount ofrnenergy consumption. Energy cost plays a key role in determining the price of a service and should be treatedrnas a first-class citizen as other QoS metrics, because energy saving services can achieve cheaper servicernprices and environmentally friendly solutions. However, it is still a challenge to efficiently schedule VirtualrnMachines (VMs) for service QoS enhancement in an energy-aware manner. In this paper, we propose anrnenergy-aware dynamic VM scheduling method for QoS enhancement in clouds over big data to address thernabove challenge. Specifically, the method consists of two main VM migration phases where computationrntasks are migrated to servers with lower energy consumption or higher performance to reduce service pricesrnand execution time. Extensive experimental evaluation demonstrates the effectiveness and efficiency of ourrnmethod.