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UVirtio introduces a device-profile-based virtual hardware abstraction layer that minimizes performance overhead, and implements a live migration mechanism using differential packing, providing a scalable and agile virtualization solution for the ubiquitous computing frontier.
Virtual machine (VM) cross-cloud migration refers to the transfer of VMs between different cloud environments, enabling users to deploy their business with greater flexibility. However, VM cross-cloud migration faces many challenges in production due to the high latency and limited bandwidth of cross-cloud networks. These challenges are pronounced in disk migrations, where poor I/O performance persists and significantly constrains the migration process, even leads to migration failures. This drives the demand for VM migration drills, but existing solutions fail to effectively address it. We present Xtream, a production-level cross-cloud disk streaming migration system. By analyzing the business disk I/O patterns, we identify a macro-level locality pattern, characterized as multi-stripe disk access pattern, and develop a stripe-oriented prefetching algorithm in Xtream to improve I/O hit rate. Xtream designs a mechanism for collaborative cold and hot data migration to ensure timely responses to VM I/O requests. The evaluation demonstrates that Xtream reduces I/O latency by 88.2%, shortens cross-cloud VM startup time by 77.7%, and maintains an over 95% disk hit rate during migration, compared to the state-of-the-art streaming migration. In production, Xtream completes cross-cloud VM drills in 7–22 minutes depending on configuration, significantly reducing migration risks.
With the rapid growth of artificial intelligence (AI) and high-performance computing (HPC), GPUs and other accelerators have become a shared computing substrate for a wide range of workloads. However, many shared accelerator clusters still rely on coarse device-level allocation, which often leads to low effective utilization, resource fragmentation, and long queueing delays. Although pooling technologies offer a promising direction, existing approaches remain limited in supporting fine-grained, low overhead sharing across heterogeneous accelerators and diverse co-located workloads. This paper presents gPooling, a hardware-agnostic accelerator pooling framework based on driver-level interception. gPooling creates elastic virtual devices on demand and extends fine-grained sharing across heterogeneous accelerators through a unified control path. We evaluate gPooling using benchmarks derived from real cluster traces and through deployment in a production GPU cluster. Results show that gPooling improves accelerator utilization, reduces user waiting time, and increases the overall efficiency of shared accelerator environments.
