Performance et qualité de service de l'ordonnanceur dans un environnement virtualisé

Confrontées à l'augmentation des coûts de mise en place et de maintenance des systèmes informatiques, les entreprises se tournent vers des solutions d'externalisation telles que le Cloud Computing. Le Cloud se basent sur la virtualisation comme principale technologie permettant la mutualisation. L'utilisation de la virtualisation apporte de nombreux défis donc les principaux portent sur les performances des applications dans les machines virtuelles (VM) et la prévisibilité de ces performances. Dans un système virtualisé, les ressources matérielles sont partagées entre toutes les VMs du système. Dans le cas du CPU, c'est l'ordonnanceur de l'hyperviseur qui se charge de le partager entre tous les processeurs virtuels (vCPU) des VMs. L'hyperviseur réalise une allocation à temps partagé du CPU entre tous les vCPUs des VMs. Chaque vCPU a accès au CPU périodiquement. Ainsi, les vCPUs des VMs n'ont pas accès de façon continue au CPU, mais plutôt discontinue. Cette discontinuité est à l'origine de nombreux problèmes sur des mécanismes tels que la gestion d'interruption et les mécanismes de synchronisation de bas niveau dans les OS invités. Dans cette thèse, nous proposons deux contributions pour répondre à ces problèmes dans la virtualisation. La première est un nouvel ordonnanceur de l'hyperviseur qui adapte dynamiquement la valeur du quantum dans l'hyperviseur en fonction du type des applications dans les VMs sur une plate-forme multi-coeurs. La seconde contribution est une nouvelle primitive de synchronisation (nommée I-Spinlock) dans l'OS invité. Dans un Cloud fournissant un service du type IaaS, la VM est l'unité d'allocation. Le fournisseur établit un catalogue des types de VMs présentant les différentes quantités de ressources qui sont allouées à la VM vis-à-vis des différents périphériques. Ces ressources allouées à la VM correspondent à un contrat sur une qualité de service négocié par le client auprès du fournisseur. L'imprévisibilité des performances est la conséquence de l'incapacité du fournisseur à garantir cette qualité de service. Deux principales causes sont à l'origine de ce problème dans le Cloud: (i) un mauvais partage des ressources entre les différentes VMs et (ii) l'hétérogénéité des infrastructures dans les centres d'hébergement. Dans cette thèse, nous proposons deux contributions pour répondre au problème d'imprévisibilité des performances. La première contribution s'intéresse au partage de la ressource logicielle responsable de la gestion des pilotes, et propose une approche de facturation du temps CPU utilisé par cette couche logiciel aux VMs. La deuxième contribution s'intéresse à l'allocation du CPU dans les Clouds hétérogènes. Dans cette contribution, nous proposons une approche d'allocation permettant de garantir la capacité de calcul allouée à une VM quelle que soit l'hétérogénéité des CPUs dans l'infrastructure

(No)Compromis: Paging Virtualization Is Not a Fatality

International audienceNested/Extended Page Table (EPT) is the current hardware solution for virtualizing memory in virtualized systems. It induces a significant performance overhead due to the 2D page walk it requires, thus 24 memory accesses on a TLB miss (instead of 4 memory accesses in a native system). This 2D page walk constraint comes from the utilization of paging for managing virtual machine (VM) memory. This paper shows that paging is not necessary in the hypervisor. Our solution Compromis, a novel Memory Management Unit, uses direct segments for VM memory management combined with paging for VM's processes. This is the first time that a direct segment based solution is shown to be applicable to the entire VM memory while keeping applications unchanged. Relying on the 310 studied datacenter traces, the paper shows that it is possible to provision up to 99.99% of the VMs using a single memory segment. The paper presents a systematic methodology for implementing Compromis in the hardware, the hypervisor and the datacenter scheduler. Evaluation results show that Compromis outperforms the two popular memory virtualization solutions: shadow paging and EPT by up to 30% and 370% respectively

Performance and quality of service of the scheduler in a virtualized environment

Confrontées à l'augmentation des coûts de mise en place et de maintenance des systèmes informatiques, les entreprises se tournent vers des solutions d'externalisation telles que le Cloud Computing. Le Cloud se basent sur la virtualisation comme principale technologie permettant la mutualisation. L'utilisation de la virtualisation apporte de nombreux défis donc les principaux portent sur les performances des applications dans les machines virtuelles (VM) et la prévisibilité de ces performances. Dans un système virtualisé, les ressources matérielles sont partagées entre toutes les VMs du système. Dans le cas du CPU, c'est l'ordonnanceur de l'hyperviseur qui se charge de le partager entre tous les processeurs virtuels (vCPU) des VMs. L'hyperviseur réalise une allocation à temps partagé du CPU entre tous les vCPUs des VMs. Chaque vCPU a accès au CPU périodiquement. Ainsi, les vCPUs des VMs n'ont pas accès de façon continue au CPU, mais plutôt discontinue. Cette discontinuité est à l'origine de nombreux problèmes sur des mécanismes tels que la gestion d'interruption et les mécanismes de synchronisation de bas niveau dans les OS invités. Dans cette thèse, nous proposons deux contributions pour répondre à ces problèmes dans la virtualisation. La première est un nouvel ordonnanceur de l'hyperviseur qui adapte dynamiquement la valeur du quantum dans l'hyperviseur en fonction du type des applications dans les VMs sur une plate-forme multi-coeurs. La seconde contribution est une nouvelle primitive de synchronisation (nommée I-Spinlock) dans l'OS invité. Dans un Cloud fournissant un service du type IaaS, la VM est l'unité d'allocation. Le fournisseur établit un catalogue des types de VMs présentant les différentes quantités de ressources qui sont allouées à la VM vis-à-vis des différents périphériques. Ces ressources allouées à la VM correspondent à un contrat sur une qualité de service négocié par le client auprès du fournisseur. L'imprévisibilité des performances est la conséquence de l'incapacité du fournisseur à garantir cette qualité de service. Deux principales causes sont à l'origine de ce problème dans le Cloud: (i) un mauvais partage des ressources entre les différentes VMs et (ii) l'hétérogénéité des infrastructures dans les centres d'hébergement. Dans cette thèse, nous proposons deux contributions pour répondre au problème d'imprévisibilité des performances. La première contribution s'intéresse au partage de la ressource logicielle responsable de la gestion des pilotes, et propose une approche de facturation du temps CPU utilisé par cette couche logiciel aux VMs. La deuxième contribution s'intéresse à l'allocation du CPU dans les Clouds hétérogènes. Dans cette contribution, nous proposons une approche d'allocation permettant de garantir la capacité de calcul allouée à une VM quelle que soit l'hétérogénéité des CPUs dans l'infrastructure.As a reaction to the increasing costs of setting up and maintaining IT systems, companies are turning to solutions such as Cloud Computing. Cloud computing is based on virtualization as the main technology for mutualisation. The use of virtualization brings many challenges. The main ones concern the performance of the applications in the virtual machines (VM) and the predictability of these performances. In a virtualized system, hardware resources are shared among all VMs in the system. In the case of the CPU, it is the scheduler of the hypervisor that is in charge of sharing the CPU among all the virtual processors (vCPU) of the VMs. The hypervisor uses a time-sharing approach to allocate the CPU. Each vCPU has access to the CPU periodically. Thus, the vCPU of the VMs do not have continuous access to the CPU, but rather discontinuous. This discontinuity is causing many problems on mechanisms such as interuption handling and low-level synchronization mechanisms in guest OSs. In this thesis, we propose two contributions to address these problems in virtualization. The first is a new hypervisor scheduler that dynamically adapts the quantum value in the hypervisor according to the type of applications in the VMs on a multi-core platform. The second contribution is a new synchronization primitive (named I-Spinlock) in the guest OS. In a cloud providing a service of the IaaS type, the VM is the allocation unit. The provider establishes a catalogue presenting the different quantities of resources that are allocated to the VM regarding various devices. These resources allocated to the VM correspond to a contract on a quality of service negotiated by the customer with the provider. The unpredictability of performance is the consequence of the incapability of the provider to guarantee this quality of service. There are two main causes of this problem in the Cloud: (i) poor resource sharing between different VMs and (ii) heterogeneity of infrastructure in hosting centers. In this thesis, we propose two contributions to answer the problem of performance unpredictability. The first contribution focuses on the sharing of the software resource responsible for managing the drivers, and proposes to bill the CPU time used by this software layer to VMs. The second contribution focuses on the allocation of the CPU in heterogeneous clouds. In this contribution, we propose an allocation approach to guarantee the computing capacity allocated to a VM regardless of the heterogeneity of the CPUs in the infrastructure

Billing system CPU time on individual VM

International audienceIn virtualized cloud hosting centers, a virtual machine (VM) is generally allocated a fixed computing capacity. The virtualization system schedules the VMs and guarantees that each VM capacity is provided and respected. However, a significant amount of CPU time is consumed by the underlying virtualization system, which generally includes device drivers (mainly network and disk drivers). In today's virtualization systems, this CPU time consumed is difficult to monitor and it is not charged to VMs. Such a situation can have important consequences for both clients and provider: performance isolation and predictability for the former and resource management (and especially consolidation) for the latter. In this paper, we propose a virtualization system mechanism which allows estimating the CPU time used by the virtualization system on behalf of VMs. Subsequently, this CPU time is charged to VMs, thus removing the two previous side effects. This mechanism has been implemented in Xen. Its benefits have been evaluated using reference benchmarks

Application-specific quantum for multi-core platform scheduler

International audienceScheduling has a significant influence on application performance. Deciding on a quantum length can be very tricky, especially when concurrent applications have various characteristics. This is actually the case in virtualized cloud computing environments where virtual machines from different users are colocated on the same physical machine. We claim that in a multi-core virtualized platform, different quantum lengths should be associated with different application types. We apply this principle in a new scheduler called AQL_Sched. We identified 5 main application types and experimentally found the best quantum length for each of them. Dynamically, AQL_Sched associates an application type with each virtual CPU (vCPU) and schedules vCPUs according to their type on physical CPU (pCPU) pools with the best quantum length. Therefore, each vCPU is scheduled on a pCPU with the best quantum length. We implemented a prototype of AQL_Sched in Xen and we evaluated it with various reference benchmarks (SPECweb2009, SPECmail2009, SPEC CPU2006, and PARSEC). The evaluation results show that AQL_Sched outperforms Xen's credit scheduler. For instance, up to 20%, 10% and 15% of performance improvements have been obtained with SPECweb2009, SPEC CPU2006 and PARSEC, respectively

Billing the CPU Time Used by System Components on Behalf of VMs

International audienceNowadays, virtualization is present in almost all cloud infrastructures. In virtualized cloud, virtual machines (VMs) are the basis for allocating resources. A VM is launched with a fixed allocated computing capacity that should be strictly provided by the hosting system scheduler. Unfortunately, this allocated capacity is not always respected, due to mechanisms provided by the virtual machine monitoring system (also known as hypervisor). For instance, we observe that a significant amount of CPU is consumed by the underlying system components. This consumed CPU time is not only difficult to monitor, but also is not charged to VM capacities. Consequently, we have VMs using more computing capacities than the allocated values. Such a situation can lead to performance unpredictability for cloud clients, and resources waste for the cloud provider. In this paper, we present the design and evaluation of a mechanism which solves this issue. The proposed mechanism consists of estimating the CPU time consumed by the system component on behalf of individual VM. Subsequently, this estimated CPU time is charged to VM. We have implemented a prototype of the mechanism in Xen system. The prototype has been validated with extensive evaluations using reference benchmarks

Respect du SLA dans un IaaS Hétérogène

Conférence invitéeInternational audienc

CASY: A CPU Cache Allocation System for FaaS Platform

International audienceFunction as a Service (FaaS) has become a key service in the cloud. It enables customers to conceive their application as a collection of minimal serverless functions interacting with each other. FaaS platforms abstract all the management complexity to the client. This emerging paradigm is also attractive because of its billing model. Clients are charged based on the execution time of functions, allowing finer-grained pricing. Therefore, executing functions as fast as possible is very important to lower the cost. Several research studies have investigated runtime optimization in FaaS environments, but none have explored CPU cache allocation. Indeed, CPU cache contention is a well-known issue in software and FaaS is not exempt from this issue. Various hardware improvements have been made to address the CPU cache partitioning problem. Among other things, Intel has implemented a new technology in their new processors that allows cache partitioning: Cache Allocation Technology (CAT). This technology allows allocating cache ways to processes, and the usage of the cache by each process will be limited to the allocated amount. In this paper, we propose CASY (CPU Cache Allocation SYstem), a system that performs CPU cache allocation for serverless functions using the Intel CAT technology. CASY uses machine learning to build a cache usage profile for functions and uses this profile to predict the cache requirements based on the function's input data. Because the CPU's cache size is small, CASY integrates an allocation algorithm which ensures that the cache loads are balanced on all cache ways. We implemented our system and integrated it into the OpenWhisk FaaS platform. Our evaluations show a 11% decrease in execution time for some serverless functions without degrading the performance of other functions

Mitigating Performance Unpredictability in Heterogeneous Clouds

International audienceThe speed of a device may vary since (i) IaaS hardware infrastructures are increasingly heterogeneous and (ii) devices often have a dynamically adjusted speed in order to adapt their energy consumption according to the load. This paper addresses SLA enforcement in a IaaS which includes devices whose speed vary. We show that resource management should rely on an absolute value SLA specification (i.e., a performance metric which is independent from the device speed) and a dynamic translation of this SLA into actual allocations according to the device speed. Surprisingly, while disk or network resource allocations already integrate such a scheme, CPU does not. We propose a CPU resource management system which implements absolute CPU allocation and dynamically translates it into actual CPU allocations according to CPU speed. We demonstrate and evaluate the benefits of this resource management system

Cacol: a zero overhead and non-intrusive double caching mitigation system

International audienceIn a virtualized server, a page cache (which caches I/O data) is managed in both the hypervisor and the guest Operating System (OS). This leads to a well-known issue called double caching. Double caching is the presence of the same data in both the hypervisor page cache and guest OS page caches. Some experiments we performed show that almost 64% of the hypervisor page cache content is also present in guest page caches.Therefore, double caching is a huge source of memory waste in virtualized servers, particularly when I/O disk-intensive workloads are executed (which is common in today’s data centers). Knowing that memory is the limiting resource for workload consolidation, double caching is a critical issue.This paper presents a novel solution (called Cacol) to avoid double caching. Unlike existing solutions, Cacol is not intrusive as it does not require guest OS modification and induces very little performance overhead for user applications. We implemented Cacol in KVM hypervisor, a very popular virtualization system. We evaluated Cacol and compared it with Linux default solutions. The results confirm the advantages (no guest modification and no overhead) of Cacol against existing solutions