An interface to implement NUMA policies in the Xen hypervisor

International audienceWhile virtualization only introduces a small overhead on machines with few cores, this is not the case on larger ones. Most of the overhead on the latter machines is caused by the Non-Uniform Memory Access (NUMA) architecture they are using. In order to reduce this overhead, this paper shows how NUMA placement heuristics can be implemented inside Xen. With an evaluation of 29 applications on a 48-core machine, we show that the NUMA placement heuristics can multiply the performance of 9 applications by more than 2

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vMCA: Memory Capacity Aggregation and Management in Cloud Environments

In cloud environments, the VMs within the computing nodes generate varying memory demand profiles. When memory utilization reaches its limits due to this, costly (virtual) disk accesses and/or VM migrations can occur. Since some nodes might have idle memory, some costly operations could be avoided by making the idle memory available to the nodes that need it. In view of this, new architectures have been introduced that provide hardware support for a shared global address space that, together with fast interconnects, can share resources across nodes. Thus, memory becomes a global resource. This paper presents a memory capacity aggregation mechanism for cloud environments called vMCA (Virtualized Memory Capacity Aggregation) based on Xen's Transcendent Memory (Tmem). vMCA distributes the system's total memory within a single node and globally across multiple nodes using a user-space process with high-level memory management policies. We evaluate vMCA using CloudSuite 3.0 on Linux and Xen. Our results demonstrate a peak running time improvement of 76.8% when aggregating memory, and of 37.5% when aggregating memory and implementing our policies.This research has received funding from the European Union’s Seventh Framework Programme for research, technological development and demonstration under grant agreement number 610456 (Euroserver). The research was also supported by the Ministry of Economy and Competitiveness of Spain (TIN2012-34557 and TIN2015-65316), HiPEAC Network of Excellence (ICT-287759 and ICT-687698), the FI-DGR Grant Program (2016FI-B-00947) of the Government of Catalonia and the Severo Ochoa Program (SEV-2011-00067) of the Spanish Government.Peer ReviewedPostprint (author's final draft

Aggregating and managing memory capacity across computing nodes in cloud environments

Managing memory capacity in cloud environments is a challenging issue, mainly due to the temporal variability in virtual machine (VM) memory demand. The Virtual Machine Manager or the hypervisor allocates a portion of the physical memory to the VMs, and it can change their allocation dynamically, depending on their needs. In a cloud-coimputing infrastructure, every computing node has an instance of a hypervisor. In many cases, the VMs demand for memory creates too much pressure on the memory resources of a node, prompting the need to make more memory resources available to the computing node. In our research, we have addressed and provided solutions for the two following problems: 1) how to efficiently manage the memory capacity available to a hypervisor? 2) how to aggregate memory capacity across multiple nodes

Efficient shared memory message passing for inter-VM communications

Thanks to recent advances in virtualization technologies, it is now possible to benefit from the flexibility brought by virtual machines at little cost in terms of CPU performance. However on HPC clusters some overheads remain which prevent widespread usage of virtualization. In this article, we tackle the issue of inter-VM MPI communications when VMs are located on the same physical machine. To achieve this we introduce a virtual device which provides a simple message passing API to the guest OS. This interface can then be used to implement an efficient MPI library for virtual machines. The use of a virtual device makes our solution easily portable across multiple guest operating systems since it only requires a small driver to be written for this device. We present an implementation based on Linux, the KVM hypervisor and Qemu as its userspace device emulator. Our implementation achieves near native performance in terms of MPI latency and bandwidth

Virtualization of Micro-architectural Components Using Software Solutions

Cloud computing has become a dominant computing paradigm in the information technology industry due to its flexibility and efficiency in resource sharing and management. The key technology that enables cloud computing is virtualization. Essential requirements in a virtualized system where several virtual machines (VMs) run on a same physical machine include performance isolation and predictability. To enforce these properties, the virtualization software (called the hypervisor) must find a way to divide physical resources (e.g., physical memory, processor time) of the system and allocate them to VMs with respect to the amount of virtual resources defined for each VM. However, modern hardware have complex architectures and some microarchitectural-level resources such as processor caches, memory controllers, interconnects cannot be divided and allocated to VMs. They are globally shared among all VMs which compete for their use, leading to contention. Therefore, performance isolation and predictability are compromised. In this thesis, we propose software solutions for preventing unpredictability in performance due to micro-architectural components. The first contribution is called Kyoto, a solution to the cache contention issue, inspired by the polluters pay principle. A VM is said to pollute the cache if it provokes significant cache replacements which impact the performance of other VMs. Henceforth, using the Kyoto system, the provider can encourage cloud users to book pollution permits for their VMs. The second contribution addresses the problem of efficiently virtualizing NUMA machines. The major challenge comes from the fact that the hypervisor regularly reconfigures the placement of a VM over the NUMA topology. However, neither guest operating systems (OSs) nor system runtime libraries (e.g., HotSpot) are designed to consider NUMA topology changes at runtime, leading end user applications to unpredictable performance. We presents eXtended Para-Virtualization (XPV), a new principle to efficiently virtualize a NUMA architecture. XPV consists in revisiting the interface between the hypervisor and the guest OS, and between the guest OS and system runtime libraries so that they can dynamically take into account NUMA topology changes

Adaptive runtime techniques for power and resource management on multi-core systems

Energy-related costs are among the major contributors to the total cost of ownership of data centers and high-performance computing (HPC) clusters. As a result, future data centers must be energy-efficient to meet the continuously increasing computational demand. Constraining the power consumption of the servers is a widely used approach for managing energy costs and complying with power delivery limitations. In tandem, virtualization has become a common practice, as virtualization reduces hardware and power requirements by enabling consolidation of multiple applications on to a smaller set of physical resources. However, administration and management of data center resources have become more complex due to the growing number of virtualized servers installed in data centers. Therefore, designing autonomous and adaptive energy efficiency approaches is crucial to achieve sustainable and cost-efficient operation in data centers. Many modern data centers running enterprise workloads successfully implement energy efficiency approaches today. However, the nature of multi-threaded applications, which are becoming more common in all computing domains, brings additional design and management challenges. Tackling these challenges requires a deeper understanding of the interactions between the applications and the underlying hardware nodes. Although cluster-level management techniques bring significant benefits, node-level techniques provide more visibility into application characteristics, which can then be used to further improve the overall energy efficiency of the data centers. This thesis proposes adaptive runtime power and resource management techniques on multi-core systems. It demonstrates that taking the multi-threaded workload characteristics into account during management significantly improves the energy efficiency of the server nodes, which are the basic building blocks of data centers. The key distinguishing features of this work are as follows: We implement the proposed runtime techniques on state-of-the-art commodity multi-core servers and show that their energy efficiency can be significantly improved by (1) taking multi-threaded application specific characteristics into account while making resource allocation decisions, (2) accurately tracking dynamically changing power constraints by using low-overhead application-aware runtime techniques, and (3) coordinating dynamic adaptive decisions at various layers of the computing stack, specifically at system and application levels. Our results show that efficient resource distribution under power constraints yields energy savings of up to 24% compared to existing approaches, along with the ability to meet power constraints 98% of the time for a diverse set of multi-threaded applications

An innovative approach to performance metrics calculus in cloud computing environments: a guest-to-host oriented perspective

In virtualized systems, the task of profiling and resource monitoring is not straight-forward. Many datacenters perform CPU overcommittment using hypervisors, running multiple virtual machines on a single computer where the total number of virtual CPUs exceeds the total number of physical CPUs available. From a customer point of view, it could be indeed interesting to know if the purchased service levels are effectively respected by the cloud provider. The innovative approach to performance profiling described in this work is based on the use of virtual performance counters, only recently made available by some hypervisors to their virtual machines, to implement guest-wide profiling. Although it isn't possible for the virtual machine to access Virtual Machine Monitor, with this method it is able to gather interesting informations to deduce the state of resource overcommittment of the virtualization host where it is executed. Tests have been carried out inside the compute nodes of FIWARE Genoa Node, an instance of a widely distributed federated community cloud, based on OpenStack and KVM. AgiLab-DITEN, the laboratory I belonged to and where I conducted my studies, together with TnT-Lab\u2013DITEN and CNIT-GE-Unit designed, installed and configured the whole Genoa Node, that was hosted on DITEN-UniGE equipment rooms. All the software measuring instruments, operating systems and programs used in this research are publicly available and free, and can be easily installed in a micro instance of virtual machine, rapidly deployable also in public clouds

Challenges in real-time virtualization and predictable cloud computing

Cloud computing and virtualization technology have revolutionized general-purpose computing applications in the past decade. The cloud paradigm offers advantages through reduction of operation costs, server consolidation, flexible system configuration and elastic resource provisioning. However, despite the success of cloud computing for general-purpose computing, existing cloud computing and virtualization technology face tremendous challenges in supporting emerging soft real-time applications such as online video streaming, cloud-based gaming, and telecommunication management. These applications demand real-time performance in open, shared and virtualized computing environments. This paper identifies the technical challenges in supporting real-time applications in the cloud, surveys recent advancement in real-time virtualization and cloud computing technology, and offers research directions to enable cloud-based real-time applications in the future

LibrettOS: A Dynamically Adaptable Multiserver-Library OS

We present LibrettOS, an OS design that fuses two paradigms to simultaneously address issues of isolation, performance, compatibility, failure recoverability, and run-time upgrades. LibrettOS acts as a microkernel OS that runs servers in an isolated manner. LibrettOS can also act as a library OS when, for better performance, selected applications are granted exclusive access to virtual hardware resources such as storage and networking. Furthermore, applications can switch between the two OS modes with no interruption at run-time. LibrettOS has a uniquely distinguishing advantage in that, the two paradigms seamlessly coexist in the same OS, enabling users to simultaneously exploit their respective strengths (i.e., greater isolation, high performance). Systems code, such as device drivers, network stacks, and file systems remain identical in the two modes, enabling dynamic mode switching and reducing development and maintenance costs. To illustrate these design principles, we implemented a prototype of LibrettOS using rump kernels, allowing us to reuse existent, hardened NetBSD device drivers and a large ecosystem of POSIX/BSD-compatible applications. We use hardware (VM) virtualization to strongly isolate different rump kernel instances from each other. Because the original rumprun unikernel targeted a much simpler model for uniprocessor systems, we redesigned it to support multicore systems. Unlike kernel-bypass libraries such as DPDK, applications need not be modified to benefit from direct hardware access. LibrettOS also supports indirect access through a network server that we have developed. Applications remain uninterrupted even when network components fail or need to be upgraded. Finally, to efficiently use hardware resources, applications can dynamically switch between the indirect and direct modes based on their I/O load at run-time. [full abstract is in the paper]Comment: 16th ACM SIGPLAN/SIGOPS International Conference on Virtual Execution Environments (VEE '20), March 17, 2020, Lausanne, Switzerlan