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 multicore 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. 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 changes to ARM's hardware virtualization support that can potentially bridge the gap to bring its faster VM-to-hypervisor transition mechanism to modern Type 2 hypervisors running real applications. These changes have been incorporated into the latest ARM architecture.
Storage devices have been getting more and more diverse during the last decade. The advent of SSDs made it painfully clear that rotating devices, such as HDDs or magnetic tapes, were lacking in regards to response time. However, SSDs currently have a limited number of write cycles and a significantly larger price per capacity, which has prevented rotational technologies from begin abandoned. Additionally, Non-Volatile Memories (NVMs) have been lately gaining traction, offering devices that typically outperform NAND-based SSDs but exhibit a full new set of idiosyncrasies.Therefore, in order to appropriately support this diversity, intelligent mechanisms will be needed in the near-future to balance the benefits and drawbacks of each storage technology available to a system. In this paper, we present a first step towards such a mechanism called HetFS, an extension to the ZFS file system that is capable of choosing the storage device a file should be kept in according to preprogrammed filters. We introduce the prototype and show some preliminary results of the effects obtained when placing specific files into different devices.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.