iPlace: An Intelligent and Tunable Power- and Performance-Aware Virtual Machine Placement Technique for Cloud-Based Real-Time Applications

Power and performance tradeoffs are critical and challenging issues faced by cloud service providers (CSPs) while managing their data centers. On the one hand, CSPs strive to reduce power consumption of their data centers to not only decrease their energy costs but to also reduce adverse impact on the environment. On the other hand, CSPs must deliver performance expected by the applications hosted in their cloud in accordance with predefined Service Level Agreements (SLAs). Not doing so will lead to loss of customers and thereby major revenue losses for the CSPs. Addressing these dual set of challenges is hard for the CSPs because power management and performance assurance are conflicting objectives, particularly in the context of multi-tenant cloud systems where multiple virtual machines (VMs) may be hosted on a single physical server. The problem becomes even harder when real-time applications are hosted in these VMs. To address these challenges and make appropriate tradeoffs, we present iPlace, which is an intelligent and tunable power- and performance-aware VM placement middleware. The placement strategy is based on a two-level artificial neural network which predicts (1) CPU usage at the first level, and (2) power consumption and performance of a host machine at the second level that uses the predicted CPU usage. The efficacy of iPlace is evaluated in the context of a VM consolidation algorithm that is applied to running virtual machines and host machines in a private cloud.