Aug 2026· ACM Transactions on Architecture and Code Optimization (TACO)· 0 citations· 62 references
TL;DR
Xtream designs a mechanism for collaborative cold and hot data migration to ensure timely responses to VM I/O requests and identifies a macro-level locality pattern, characterized as multi-stripe disk access pattern, and develops a stripe-oriented prefetching algorithm in Xtream to improve I/O hit rate.
Abstract
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.
Serverless platforms commonly colocate many diverse workloads, each in a fast-booting, memory-lean virtual machine (VM), to improve deployment density. Overprovisioning each VM for its peak protects tail latency during traffic bursts but hurts density; maintaining high density while effectively protecting tail latency requires the infrastructure to be able to shift physical cores, at a microsecond timescale, to whichever latency-sensitive VM is bursting and reclaim them as the burst subsides. No VM substrate delivers this: conventional VMs resize a guest's cores only through a millisecond-scale vCPU hot-plug path, Firecracker fixes a VM's core count at boot, and the ultralight VMs that boot fastest drop multicore execution entirely. We present HyperFlux, a commodity-KVM ultralight VM substrate that makes a VM's parallelism width (the number of physical cores backing it) elastic at runtime. We show that HyperFlux can move a core across VMs in merely 13$\mu$s, even when forcibly reclaiming it from a busy donor, orders of magnitude faster than vCPU hot-plug. A HyperFlux VM incurs only a 3.2MB memory footprint and can cold-boot in 1.37ms, on par with the fastest-booting ultralight VMs, while uniquely supporting multicore parallelism. Under colocation, it can reduce high-priority VMs'tail latency by up to 10x under high load compared to static core-sharing with Firecracker and Cloud Hypervisor, and deliver a lower and more stable tail latency compared to using cgroup and vCPU hot-plug under changing load bursts.
The results place page-cache attacks within the broader class of OS-mediated microarchitectural timing channels and motivate coordinated hardware, virtualization, and OS support for timing isolation.
Alon Abudraham, Xing-Yu Chen, Itamar Levi et al.· arXiv.org· 0 citations
Latency-critical cloud services place growing pressure on memory while requiring isolation, fairness, and predictable QoS. Near-data processing (NDP) reduces data movement by executing requests close to memory, and prior systems further improve locality through caching and replication. However, writes make replica maintenance expensive, while uneven compute and memory traffic can overload a few NDP units and increase tail latency. Existing throughput-oriented schedulers do not fully address these challenges for co-located cloud services. We present Gutenberg, a CPU+NDP for mutable, latency-critical cloud services. Gutenberg stages subpage updates in a CPU-resident delta buffer, allowing hot writable pages to remain replicated without eager full-page synchronization. It also adopts CPU helper cores to assist request execution when NDP execution or replica maintenance becomes costly. An online controller jointly decides page placement, replication, CPU/NDP execution, and routing using access patterns, queue pressure, and feedback from prior decisions. The system further enforces isolation and fair resource allocation across services. We also model-check CPU--NDP coordination protocol for correctness. We evaluate on TailBench using ZSim with Ramulator-calibrated memory timing. Across evaluated services, Gutenberg outperforms prior systems, reducing average and p99 latency by up to 80.4% and 85.8%. It also improves isolation and fairness while adapting to changing workload behaviors.
Qi Lin, Phillip B. Gibbons, Jovan Stojkovic et al.· 0 citations
Abstract —Today, Infrastructure-as-a-service providers are trying to minimize the cost of data center operations, while maintaining the Service Level Agreements. This can be achieved by one of the advanced state-of-the-art services of virtualization - the live migration capability. Live migration is defined as the process of transferring an active virtual machine from one physical machine to another without any disconnection. This is achieved by transferring all of the encapsulated states of the VM from one host to another. It has become an essential tool for efficient management of resources in a data center by enabling server consolidation and load balancing. There are two classical migration techniques, namely - pre-copy and post-copy, which employ different memory transfer mechanism during the offloading of a VM. In this paper, we propose a novel hybrid live migration technique by combining the existing pre-copy and post-copy approaches. Compare to its counterparts, our hybrid technique is a fast, efficient and a reliable migration technique.
P. Gangadhar· International Journal of Mac...· 0 citations
Hierarchical Data Storage Systems (HSSs) provide a cost-effective architecture that balances capacity and performance through internal data migration. Prior work has primarily focused on optimizing individual migration tasks, either within or across HSS tiers, or on exploiting device bandwidth to improve overall throughput. These approaches treat migration tasks in isolation, and the performance implications of executing heterogeneous migration tasks concurrently remain largely unexplored despite their prevalence in real-world HSS deployments. The growing adoption of Direct Data Access (DDA) architectures, in which accelerators access storage without CPU mediation, further amplifies this problem by removing a natural bandwidth arbiter from the I/O path.
This paper presents an in-depth analysis of data migration behavior in commercial HSSs, uncovering substantial performance variability when multiple migration tasks execute concurrently. To mitigate this issue, we propose PASCAL, a system-level bandwidth orchestration framework that improves performance robustness in production-grade HSSs. Inspired by hydraulic systems, PASCAL adapts pressure/backpressure-style coordination to the multi-task migration setting: it treats each tier as a pressurized vessel and uses pressure gradients to allocate bandwidth across cache flush, tiering, garbage collection, and DDA flows. We evaluate PASCAL on a commercial OceanStor HSS across three hardware configurations and eight workloads spanning database, AI training, AI inference, and production traces. PASCAL achieves up to 20% higher throughput, 67% lower tail latency, and 79% reduced throughput jitter compared to local state-of-the-art controllers, while also stabilizing the performance jitter introduced by DDA architectures.
Ji Zhang, Li Liu, André Brinkmann et al.· Proceedings of the VLDB Endo...· 0 citations
Function-as-a-Service (FaaS) is a cloud-native model where small pieces of application logic execute as isolated functions within virtual machines (VMs). Cloud Service Providers (CSPs) aim to maximize the number of VMs per system. However, idle VMs, i.e., VMs without active functions, continue to consume memory, limiting overall VM density. Rather than using prior disk-based snapshotting solutions to reduce memory usage, we propose to place the pages of an idle VM in a compressed memory pool. Doing so, however, incurs high execution latency upon new invocations due to page decompression during on-demand page faults. To address this, we propose MRT. MRT generates memory restoration templates from the learned sequence of pages that incur page faults during a warm start and uses them to bulk-restore pages using Intel's In-Memory Analytics Accelerator (IAA), avoiding costly on-demand faults. MRT lowers single-instance function execution latency from 2.63× to 1.62× relative to a hot start, eliminating 62% of the overhead that on-demand page-fault-based restoration adds over a hot start. MRT further reduces the memory footprint of an idle FaaS VM instance by 57% (geometric mean) by placing its pages in the compressed memory pool, allowing CSPs to host more active VMs per system. At scale, MRT reduces 95th- and 99th-percentile function execution latency by 70% and 58%, respectively, relative to on-demand IAA decompression.
Aravinda Prasad, S. Subramoney, Sandeep Kumar et al.· Proceedings of the 4th Works...· 0 citations
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