The mobile virtualization technology provides a feasible way to improve the manageability and security for embedded systems. This paper presents an architecture named MobiXen to address these challenges. In the MobiXen, both Xen's physical memory space and virtual address space are shrunk as much as possible and thus Android owns more memory resource; optimizations are developed to reduce the virtualization overhead when Android is accessing system resources; new policies are implemented to achieve low suspend/resume latency. With these work adopted, MobiXen is customized as a high efficient mobile hypervisor. Detailed implementations shows that, most of the performance degradation brought by MobiXen is less than 3%, which is imperceptible by end users.

Virtual machine (VM) replication provides a software solution of for business continuity and disaster recovery through application-agnostic hardware fault tolerance by replicating the state of primary VM (PVM) to secondary VM (SVM) on a different physical node. Unfortunately, current VM replication approaches suffer from excessive overhead, which severely limit their applicability and suitability. In this paper, we leverage the practical effect of networked server-client system that PVM and SVM are considered as in the same state only if they can generate the same response from the clients' point of view, and this is exploited to optimize performance. To this end, we propose a generic and highly efficient non-stop service solution, named as "COLO" (COarse-grained LOck-stepping virtual machine) utilizing on-demand VM replication. COLO monitors the output responses of the PVM and SVM, and rules the SVM as a valid replica of the PVM according to the output similarity between PVM and SVM. If the responses do not match, the commit of network response is withheld until PVM's state has been synchronized to SVM. Hence, we ensure that the system is always capable of failover by SVM. Although non-determinism may mean a different internal state of SVM from that of the PVM, it is equally valid and remains consistent from external observations. Unlike earlier instruction level lock-stepping deterministic execution approaches, COLO can easily support Multi-Processors (MP) involving workloads with the satisfying performance. Results show that COLO significantly outperforms existing approaches, particularly on server-client workloads such as online databases and web server applications.

Virtualization is gaining popularity in cloud computing and has become the key enabling technology in cloud infrastructure. By replicating the virtual server state to multiple independent platforms, virtualization improves the reliability and availability of cloud systems. Unfortunately, existing Virtual Machine (VM) replication solutions were designed only for software virtualized I/O, which suffers from large performance and scalability overheads. Although hardware-assisted I/O virtualization (such as SR-IOV) can achieve close to native performance and very good scalability, they cannot be properly replicated across different physical machines due to architectural limitations (such as lack of efficient device state read/write, buffering outbound packets, etc.). In this paper, we address those architectural limitations, by proposing ReNIC, an architectural extension to SR-IOV I/O virtualization for efficient I/O replications. We have extended Xen hypervisor and the Remus rapid checkpoint solution to support this new architectural extension. We developed a system simulator on multi-core systems to extensively evaluate ReNIC. The experimental results demonstrate that ReNIC achieves up to 54% CPU usage reduction, compared to software based I/O virtualization at runtime, and up to 16.2% performance advantage over software based I/O virtualization in rapid checkpoint. During migration, ReNIC reduces service shutdown time by about 50%, compared to device emulation and paravirtualized I/O, and over 71% compared to teaming driver.

High-quality I/O virtualization (that is, complete device semantics, full-feature set, close-to-native performance and real-time response) is critical to both server and client virtualizations. Existing solutions for I/O virtualization (e.g., full device emulation, paravirtualization and direct I/O) cannot meet the requirements of high-quality I/O virtualization due to high overheads, lack of complete semantic or full-feature set support.