Intelligent, Performance Interference-Aware Resource Management for IoT Cloud Backends

Emerging Internet of Things (IoT) applications often demonstrate unpredictable Big Data processing workloads at the cloud backends making it hard for cloud service providers (CSPs) to employ existing resource overbooking schemes effectively. Ad hoc approaches to resource overbooking can lead to performance interference among the virtual machines (VMs) hosted on the physical resources causing performance unpredictability for VM-hosted performance-sensitive IoT applications. Balancing these conflicting needs requires an intelligent strategy for hosting applications such that the performance interference effects are minimized while still allowing resource overbooking. Such a strategy must be online because application workloads may change at run time. To address these challenges, this paper presents iSensitive, which is an intelligent, performance interference-aware resource management scheme for IoT cloud backends. iSensitive first classifies the VMs based on their historic mean CPU, memory, and network usage features. Subsequently, it learns the desired VM patterns of collocating the classified VMs by employing machine learning techniques. These extracted patterns document the lowest performance interference level on the specified host machines making them amenable to hosting performance-sensitive applications while still allowing resource overbooking. Our approach is validated by emulating a publicly available usage trace of a large data center owned by Google and benchmark tools running real-world applications. Experimental results evaluating iSensitive illustrates its advantages in deploying VMs to aptly-suited host machine compared to traditional schemes, such as first-fit bin packing.