An empirical study of memory sharing in virtual machines

Content-based page sharing is a technique often used in virtualized environments to reduce server memory requirements. Many systems have been proposed to capture the benefits of page sharing. However, there have been few analyses of page sharing in general, both considering its real-world utility and typical sources of sharing potential. We provide insight into this issue through an exploration and analysis of memory traces captured from real user machines and controlled virtual machines. First, we observe that absolute sharing levels (excluding zero pages) generally remain under 15%, contrasting with prior work that has often reported savings of 30% or more. Second, we find that sharing within individual machines often accounts for nearly all (\u3e90%) of the sharing potential within a set of machines, with inter-machine sharing contributing only a small amount. Moreover, even small differences between machines significantly reduce what little inter-machine sharing might otherwise be possible. Third, we find that OS features like address space layout randomization can further diminish sharing potential. These findings both temper expectations of real-world sharing gains and suggest that sharing efforts may be equally effective if employed within the operating system of a single machine, rather than exclusively targeting groups of virtual machines

Cooperative Resource Management in a IaaS

International audienceVirtualized IaaS generally rely on a server consolidation system to pack virtual machines (VMs) on as few servers as possible, for energy saving. However, two situations are not taken into account, and could enhance consolidation. First, since the managed VMs can be of various sizes (small, medium, large, etc.), VMs packing can be obstructed when sizes don't fit available spaces on servers. Therefore, we would need to "split" such VMs. Second, two VMs which host replicas of the same application server (for scalability) could be "fusion Ned" when they are located on the same physical server, in order to reduce virtualization overhead and VMs memory footprint. Split and fusion operations lead to the management of elastic VMs and requires cooperation between the application level and the provider level, as they impact management at both levels. In this paper, we propose a IaaS resource management system which implements elastic VMs based on split/fusion operations and cooperative management. We show its benefit with a set of experiments

Deduplication potential of HPC applications' checkpoints

© 2016 IEEE. HPC systems contain an increasing number of components, decreasing the mean time between failures. Checkpoint mechanisms help to overcome such failures for long-running applications. A viable solution to remove the resulting pressure from the I/O backends is to deduplicate the checkpoints. However, there is little knowledge about the potential to save I/Os for HPC applications by using deduplication within the checkpointing process. In this paper, we perform a broad study about the deduplication behavior of HPC application checkpointing and its impact on system design

XLH: more effective memory deduplication scanners through cross-layer hints

Limited main memory size is the primary bottleneck for consolidating virtual machines (VMs) on hosting servers. Memory deduplication scanners reduce the memory footprint of VMs by eliminating redundancy. Our approach extends main memory deduplication scanners through Cross Layer I/O-based Hints (XLH) to find and exploit sharing opportunities earlier without raising the deduplication overhead. Prior work on memory scanners has shown great opportunity for memory deduplication. In our analyses, we have confirmed these results; however, we have found memory scanners to work well only for deduplicating fairly static memory pages. Current scanners need a considerable amount of time to detect new sharing opportunities (e.g., 5 min) and therefore do not exploit the full sharing potential. XLH’s early detection of sharing opportunities saves more memory by deduplicating otherwise missed short-lived pages and by increasing the time long-lived duplicates remain shared. Compared to I/O-agnostic scanners such as KSM, our benchmarks show that XLH can merge equal pages that stem from the virtual disk image earlier by minutes and is capable of saving up to four times as much memory; e.g., XLH saves 290 MiB vs. 75 MiB of main memory for two VMs with 512 MiB assigned memory each

Welcome to Zombieland: Practical and Energy-efficient Memory Disaggregation in a Datacenter

In this paper, we propose an effortless way for disaggregating the CPU-memory couple, two of the most important resources in cloud computing. Instead of redesigning each resource board, the disaggregation is done at the power supply domain level. In other words, CPU and memory still share the same board, but their power supply domains are separated. Besides this disaggregation, we make the two following contributions: (1) the prototyping of a new ACPI sleep state (called zombie and noted Sz) which allows to suspend a server (thus save energy) while making its memory remotely accessible; and (2) the prototyping of a rack-level system software which allows the transparent utilization of the entire rack resources (avoiding resource waste). We experimentally evaluate the effectiveness of our solution and show that it can improve the energy efficiency of state-of-the-art consolidation techniques by up to 86%, with minimal additional complexity

Cooperative Resource Management in a IaaS

Virtualized IaaS generally rely on a server consolidation system to pack virtual machines (VMs) on as few servers as possible, for energy saving. However, two situations are not taken into account, and could enhance consolidation. First, since the managed VMs can be of various sizes (small, medium, large, etc.), VMs packing can be obstructed when sizes don't fit available spaces on servers. Therefore, we would need to "split" such VMs. Second, two VMs which host replicas of the same application server (for scalability) could be "fusion Ned" when they are located on the same physical server, in order to reduce virtualization overhead and VMs memory footprint. Split and fusion operations lead to the management of elastic VMs and requires cooperation between the application level and the provider level, as they impact management at both levels. In this paper, we propose a IaaS resource management system which implements elastic VMs based on split/fusion operations and cooperative management. We show its benefit with a set of experiments

Prebaked µVMs: Scalable, Instant VM Startup for IaaS Clouds

Abstract-IaaS clouds promise instantaneously available resources to elastic applications. In practice, however, virtual machine (VM) startup times are in the order of several minutes, or at best, several tens of seconds, negatively impacting the elasticity of applications like Web servers that need to scale out to handle dynamically increasing load. VM startup time is strongly influenced by booting the VM's operating system. In this work, we propose using so-called prebaked µVMs to speed up VM startup. µVMs are snapshots of minimal VMs that can be quickly resumed and then configured to application needs by hot-plugging resources. To serve µVMs, we extend our VM boot cache service, Squirrel, allowing to store µVMs for large numbers of VM images on the hosts of a data center. Our experiments show that µVMs can start up in less than one second on a standard file system. Using 1000+ VM images from a production cloud, we show that the respective µVMs can be stored in a compressed and deduplicated file system within 50 GB storage per host, while starting up within 2-3 seconds on average

High performance cloud computing on multicore computers

The cloud has become a major computing platform, with virtualization being a key to allow applications to run and share the resources in the cloud. A wide spectrum of applications need to process large amounts of data at high speeds in the cloud, e.g., analyzing customer data to find out purchase behavior, processing location data to determine geographical trends, or mining social media data to assess brand sentiment. To achieve high performance, these applications create and use multiple threads running on multicore processors. However, existing virtualization technology cannot support the efficient execution of such applications on virtual machines, making them suffer poor and unstable performance in the cloud. Targeting multi-threaded applications, the dissertation analyzes and diagnoses their performance issues on virtual machines, and designs practical solutions to improve their performance. The dissertation makes the following contributions. First, the dissertation conducts extensive experiments with standard multicore applications, in order to evaluate the performance overhead on virtualization systems and diagnose the causing factors. Second, focusing on one main source of the performance overhead, excessive spinning, the dissertation designs and evaluates a holistic solution to make effective utilization of the hardware virtualization support in processors to reduce excessive spinning with low cost. Third, focusing on application scalability, which is the most important performance feature for multi-threaded applications, the dissertation models application scalability in virtual machines and analyzes how application scalability changes with virtualization and resource sharing. Based on the modeling and analysis, the dissertation identifies key application features and system factors that have impacts on application scalability, and reveals possible approaches for improving scalability. Forth, the dissertation explores one approach to improving application scalability by making fully utilization of virtual resources of each virtual machine. The general idea is to match the workload distribution among the virtual CPUs in a virtual machine and the virtual CPU resource of the virtual machine manager

Memory Page Stability and its Application to Memory Deduplication

In virtualized environments, typically cloud computing environments, multiple virtual machines run on the same physical host. These virtual machines usually run the same operating systems and applications. This results in a lot of duplicate data blocks in memory. Memory deduplication is a memory optimization technique that attempts to remove this redundancy by storing one copy of these duplicate blocks in the machine memory which in turn results in a better utilization of the available memory capacity.In this dissertation, we characterize the nature of memory pages that contribute to memory deduplication techniques. We show how such characterization can give useful insights towards better design and implementation of software and hardware-assisted memory deduplication systems. In addition, we also quantify the performance impact of different memory deduplication techniques and show that even though memory deduplication allows for a better cache hierarchy performance, there is a performance overhead associated with copy-on-write exceptions that is associated with diverging pages.We propose a generic prediction framework that is capable of predicting the stability of memory pages based on the page flags available through the Linux kernel. We evaluate the proposed prediction framework and then discuss various applications that can benefit from it, specifically memory deduplication and live migration