Live migration of virtual machines (VMs) can consume excessive time and resources, and may affect application performance significantly if VM memory pages get dirtied faster than their content can be transferred to the destination. Existing approaches to this problem transfer memory content faster with high-speed networks, slow down the dirtying of memory pages by throttling the execution of applications, or reduce the amount of memory content to be transferred, for example, using compression. However, these approaches incur high resource costs or application performance penalties. In this paper, we propose to skip the transfer of VM memory pages that need not be migrated for the execution of running applications at the destination, by exploiting applications' assistance. We have designed a generic framework for application-assisted live migration and then used it to build and evaluate JAVMM, which migrates VMs running various types of Java applications skipping the transfer of garbage in Java memory. Our experimental results show that JAVMM can reduce the completion time, the network traffic of transferring memory pages, and the application downtime of Java VM migration, all by up to over 90%, compared to the vanilla Xen VM migration, without incurring noticeable performance penalty to applications.

Virtualized data centers enable sharing of resources among hosted applications. However, it is difficult to satisfy service-level objectives(SLOs) of applications on shared infrastructure, as application workloads and resource consumption patterns change over time. In this paper, we present AutoControl, a resource control system that automatically adapts to dynamic workload changes to achieve application SLOs. AutoControl is a combination of an online model estimator and a novel multi-input, multi-output (MIMO) resource controller. The model estimator captures the complex relationship between application performance and resource allocations, while the MIMO controller allocates the right amount of multiple virtualized resources to achieve application SLOs. Our experimental evaluation with RUBiS and TPC-W benchmarks along with production-trace-driven workloads indicates that AutoControl can detect and mitigate CPU and disk I/O bottlenecks that occur over time and across multiple nodes by allocating each resource accordingly. We also show that AutoControl can be used to provide service differentiation according to the application priorities during resource contention.