Modern cloud servers achieve significant performance improvements by exploiting data processing units (DPUs) to offload virtualization overhead.Unlike traditional monolithic hypervisors, this offloading approach splits VM state and distributes hypervisor functions across the DPU and the Host, transforming the cloud server from a single system into a sophisticated orchestration of multiple self-managed processing units.However, the failure rate of these systems has significantly increased, as errors from either the DPU or the Host can crash the entire machine.All these changes necessitate a comprehensive revamp of current VM fault tolerance and isolation mechanisms.This paper explores a novel approach to VM fault tolerance by treating the split hypervisors on the DPU SoC and the Host as redundant peers.We propose a fault-escaping scheme that enables VMs to escape from a failing SoC or Host.With hybrid synchronization, the entire VM state becomes fully accessible on either the SoC or the Host with minimum synchronization overhead (in kilobytes).Instead of recovering the failed hypervisor, this scheme migrates the verified VM state, allowing the VM to escape from the failure

Apsara vSwitch (AVS) is a per-host deployed forwarding component for instance network connectivity in the Alibaba Cloud. To meet the growing performance demands, we accelerated AVS by adopting the most widely used "Sep-path" offloading architecture, which introduces a separate hardware data path to speed up popular traffic. However, the deployment results prove that it is difficult to bridge the gap in performance and programming flexibility of the software and hardware data paths, resulting in unpredictable performance and low iteration velocity.

Device pass-through has been widely adopted by cloud service providers to achieve near bare-metal I/O performance in virtual machines (VMs). However, this approach requires static pinning of VM memory, making on-demand paging unavailable. The hardware device I/O page fault (IOPF) capability offers an optimal solution to this limitation. Current IOPF approaches, using either standard IOMMU capabilities (ATS+PRI) or devices with independent IOMMU implementations, have not gained widespread adoption in public Infrastructure-as-a-Service clouds. This is due to high costs, platform dependency, and significant impacts on performance and service level objectives (SLOs). We present the Virtualized Page Request Interface (VPRI), a novel IOPF system developed through software-hardware collaboration. VPRI is not only platform-independent, free from address translation complexities, but also cost-effective, and designed to minimize SLO impact. Our work enables large-scale deployment of IOPF capability in Alibaba Cloud with negligible impact on SLOs. When integrated with memory management software, it significantly enhances memory utilization in public IaaS clouds, effectively overcoming the static memory pinning restriction associated with pass-through devices.

The algorithm proposed in this paper integrates the concepts of variable frame rate and discriminative analysis based on Tanimoto ratio to modify the conventional Viterbi algorithm, in such a way that the steady or stationary signal is compressed, while transitional or non-stationary signal is emphasized through the frame-by-frame searching process. The usefulness of each frame is decided entirely within the Viterbi process and needs not to be the same for different models. To evaluate this algorithm, we tested a speech database of 9 highly confusable E-set English letters. With 5 state and 6 mixture components, the conventional HMM baseline system only delivered a recognition accuracy of 73.9%. In the preliminary experiment using the algorithm proposed, the recognition accuracy was increased to 82.5%.

As an application of robot vision to automatic recognition of industriel parts, we present a method which is capable of recognizing isolated or partially occluded two-dimensional objects. Starting from contour following, global features are measured and compared to stored data in order to make a preselection of the models. In the case of isolated object, the preselection leads to an immediate identification of objects. Otherwise, overlapping boundaries are detected and used to accomplish the recognition of occluded objects. By the combination of overlapping boundaries and the object's contour, a sequential segmentation algorithm is applied to find the complete contour of no hidden objects. As soon as we have obtained the complete contour of an object, we can use once again the global features to perform identification processing. This method has the advantage of being simple,rapide and efficient. Il works well even when the number of objects to be recognized is large.