Optimizing Cloud Virtual Machine Migration: Minimizing Downtime and Migration Time Using Machine Learning

Abstract

Cloud computing has revolutionized the way services are delivered to users, offering unparalleled flexibility and scalability. However, cloud services can become temporarily unavailable due to maintenance, resource allocation, load balancing, cyberattacks, power management, fault tolerance, and various other factors. To ensure a seamless experience for clients during these periods, live virtual machine migration (LVM) emerges as an indispensable choice. LVM involves relocating virtual machines (VMs) from source to destination with minimal disruption to client activities. While the pre-copy method is the most commonly used live migration technique due to its reliability, it faces challenges such as extended downtime and migration time caused by a large number of dirty pages generated in each iteration. To optimize these migration metrics, numerous solutions have been developed. Nevertheless, a recurring issue in most techniques is the use of static stopping conditions. If the dirty rate exceeds the network throughput, the number of dirty pages retransmitted in each iteration increases, and the hypervisor cannot complete the dirty page transfer in a specific iteration. Moreover, there is no universal stopping condition suitable for all VMs. As a result, the source VM needs to be suspended for a longer time to complete the migration, increasing downtime. Extended migration downtime causes service interruptions and affects the performance of running applications. Therefore, optimizing the memory dirty rate to minimize downtime and total migration time is the primary challenge in the pre-copy approach. To address these challenges, we conducted a thorough analysis of the critical factors influencing live migration performance. Subsequently, we devised an algorithm to identify these critical features and leveraged them to build a machine-learning model capable of intelligently predicting the optimal time to transition into the stop and copy phase, reducing reliance on static stopping conditions. Our proposed machine learning method was rigorously evaluated through experiments conducted on a dedicated testbed using KVM/QUEM technology, involving different VM sizes and memory-intensive workloads. A comparative analysis against proposed pre-copy methods and existing techniques reveals a remarkable improvement, with an average 61.24% reduction in downtime for different RAM configurations in high-write-intensive workloads, along with an average reduction in total migration time of approximately 85.81%. Furthermore, we examined the security concerns surrounding live migration, particularly in domains handling critical applications such as banking, healthcare, etc. Many organizations in these sectors are hesitant to employ live migration due to security risks. To address this, we introduced a selective encryption approach for protecting sensitive information during migration. Our experimental results highlight that the selective encryption method enhances our proposed machine learning model to reduce downtime and total migration time while preserving the privacy of sensitive data.