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1. AvA: Accelerated Virtualization of Accelerators

   https://doi.org/10.1145/3373376.3378466  

   Yu, Hangchen; Peters, Arthur Michener; Akshintala, Amogh; Rossbach, Christopher J. (January 2020, ASPLOS '20: Proceedings of the Twenty-Fifth International Conference on Architectural Support for Programming Languages and Operating Systems)

   Applications are migrating en masse to the cloud, while accelerators such as GPUs, TPUs, and FPGAs proliferate in the wake of Moore's Law. These trends are in conflict: cloud applications run on virtual platforms, but existing virtualization techniques have not provided production-ready solutions for accelerators. As a result, cloud providers expose accelerators by dedicating physical devices to individual guests. Multi-tenancy and consolidation are lost as a consequence. We present AvA, which addresses limitations of existing virtualization techniques with automated construction of hypervisor-managed virtual accelerator stacks. AvA combines a DSL for describing APIs and sharing policies, device-agnostic runtime components, and a compiler to generate accelerator-specific components such as guest libraries and API servers. AvA uses Hypervisor Interposed Remote Acceleration (HIRA), a new technique to enable hypervisor-enforcement of sharing policies from the specification. We use AvA to virtualize nine accelerators and eleven framework APIs, including six for which no virtualization support has been previously explored. AvA provides near-native performance and can enforce sharing policies that are not possible with current techniques, with orders of magnitude less developer effort than required for hand-built virtualization support. 

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2. AvA: Accelerated Virtualization of Accelerators

   Yu, Hangchen and (January 2020, Proceedings of the Twenty-Fifth International Conference on Architectural Support for Programming Languages and Operating Systems)

   Applications are migrating en masse to the cloud, while ac- celerators such as GPUs, TPUs, and FPGAs proliferate in the wake of Moore’s Law. These trends are in conflict: cloud ap- plicationsrunonvirtualplatforms,butexistingvirtualization techniques have not provided production-ready solutions for accelerators. As a result, cloud providers expose accel- erators by dedicating physical devices to individual guests. Multi-tenancy and consolidation are lost as a consequence. We present AvA, which addresses limitations of existing virtualization techniques with automated construction of hypervisor-managed virtual accelerator stacks. AvA com- bines a DSL for describing APIs and sharing policies, device- agnostic runtime components, and a compiler to generate accelerator-specific components such as guest libraries and API servers. AvA uses Hypervisor Interposed Remote Acceleration (HIRA),anewtechniquetoenablehypervisor- enforcement of sharing policies from the specification. We use AvA to virtualize nine accelerators and eleven framework APIs, including six for which no virtualization support has been previously explored. AvA provides near- native performance and can enforce sharing policies that are not possible with current techniques, with orders of magnitude less developer effort than required for hand-built virtualization support. 

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3. PVM: Efficient Shadow Paging for Deploying Secure Containers in Cloud-native Environment

   https://doi.org/10.1145/3600006.3613158  

   Huang, Hang; Lai, Jiangshan; Rao, Jia; Lu, Hui; Hou, Wenlong; Su, Hang; Xu, Quan; Zhong, Jiang; Zeng, Jiahao; Wang, Xu; et al (October 2023, ACM)

   In cloud-native environments, containers are often deployed within lightweight virtual machines (VMs) to ensure strong security isolation and privacy protection. With the growing demand for customized cloud services, third-party vendors are turning to infrastructure-as-a-service (IaaS) cloud providers to build their own cloud-native platforms, necessitating the need to run a VM or a guest that hosts containers inside another VM instance leased from an IaaS cloud. State-of-the-art nested virtualization in the x86 architecture relies heavily on the host hypervisor to expose hardware virtualization support to the guest hypervisor, not only complicating cloud management but also raising concerns about an increased attack surface at the host hypervisor. This paper presents the design and implementation of PVM, a high-performance guest hypervisor for KVM that is transparent to the host hypervisor and assumes no hardware virtualization support. PVM leverages two key designs: 1) a minimal shared memory region between the guest and guest hypervisor to facilitate state transition between different privilege levels and 2) an efficient shadow page table design to reduce the cost of memory virtualization. PVM has been adopted by a major IaaS cloud provider for hosting tens of thousands of secure containers on a daily basis. Our experiments demonstrate that PVM significantly outperforms current nested virtualization in KVM for memory virtualization, particularly for concurrent workloads, while maintaining comparable performance in CPU and I/O virtualization. 

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4. ARM Virtualization: Performance and Architectural Implications

   https://doi.org/10.1145/3273982.3273987  

   Dall, Christoffer; Li, Shih-Wei; Lim, Jin Tack; Nieh, Jason (July 2018, Operating systems review)

   ARM servers are becoming increasingly common, making server technologies such as virtualization for ARM of growing importance. We present the first study of ARM virtualization performance on server hardware, including multi-core measurements of two popular ARM and x86 hypervisors, KVM and Xen. We show how ARM hardware support for virtualization can enable much faster transitions between VMs and the hypervisor, a key hypervisor operation. However, current hypervisor designs, including both Type 1 hypervisors such as Xen and Type 2 hypervisors such as KVM, are not able to leverage this performance benefit for real application workloads on ARMv8.0. We discuss the reasons why and show that other factors related to hypervisor software design and implementation have a larger role in overall performance. Based on our measurements, we discuss software changes and new hardware features, the Virtualization Host Extensions (VHE), added in ARMv8.1 that bridge the gap and bring ARM's faster VM-to-hypervisor transition mechanism to modern Type 2 hypervisors running real applications. 

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5. Big Data Aware Virtual Machine Placement in Cloud Data Centers

   https://doi.org/10.1145/3148055.3148057  

   Hall, Logan; Harris, Bryan; Tomes, Erica; Altiparmak, Nihat (December 2017, Proceedings of the Fourth IEEE/ACM International Conference on Big Data Computing, Applications and Technologies)

   While society continues to be transformed by insights from processing big data, the increasing rate at which this data is gathered is making processing in private clusters obsolete. A vast amount of big data already resides in the cloud, and cloud infrastructures provide a scalable platform for both the computational and I/O needs of big data processing applications. Virtualization is used as a base technology in the cloud; however, existing virtual machine placement techniques do not consider data replication and I/O bottlenecks of the infrastructure, yielding sub-optimal data retrieval times. This paper targets efficient big data processing in the cloud and proposes novel virtual machine placement techniques, which minimize data retrieval time by considering data replication, storage performance, and network bandwidth. We first present an integer-programming based optimal virtual machine placement algorithm and then propose two low cost data- and energy-aware virtual machine placement heuristics. Our proposed heuristics are compared with optimal and existing algorithms through extensive evaluation. Experimental results provide strong indications for the superiority of our proposed solutions in both performance and energy, and clearly outline the importance of big data aware virtual machine placement for efficient processing of large datasets in the cloud. 

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