Practical Implementation of the Virtual Organization Cluster Model

Virtualization has great potential in the realm of scientific computing because of its inherent advantages with regard to environment customization and isolation. Virtualization technology is not without it\u27s downsides, most notably, increased computational overhead. This thesis introduces the operating mechanisms of grid technologies in general, and the Open Science Grid in particular, including a discussion of general organization and specific software implementation. A model for utilization of virtualization resources with separate administrative domains for the virtual machines (VMs) and the physical resources is then presented. Two well-known virtual machine monitors, Xen and the Kernel-based Virtual Machine (KVM), are introduced and a performance analysis conducted. The High-Performance Computing Challenge (HPCC) benchmark suite is used in conjunction with independent High-Performance Linpack (HPL) trials in order to analyze specific performance issues. Xen was found to introduce much lower performance overhead than KVM, however, KVM retains advantages with regard to ease of deployment, both of the VMM itself and of the VM images. KVM\u27s snapshot mode is of special interest, as it allows multiple VMs to be instantiated from a single image located on a network store. With virtualization overhead shown to be acceptable for high-throughput computing tasks, the Virtual Organization Cluster (VOC) Model was implemented as a prototype. Dynamic scaling and multi-site scheduling extensions were also successfully implemented using this prototype. It is also shown that traditional overlay networks have scaling issues and that a new approach to wide-area scheduling is needed. The use of XMPP messaging and the Google App Engine service to implement a virtual machine monitoring system is presented. Detailed discussions of the relevant sections of the XMPP protocol and libraries are presented. XMPP is found to be a good choice for sending status information due to its inherent advantages in a bandwidth-limited NAT environment. Thus, it is concluded that the VOC Model is a practical way to implement virtualization of high-throughput computing tasks. Smaller VOCs may take advantage of traditional overlay networks whereas larger VOCs need an alternative approach to scheduling

Virtual Organization Clusters: Self-Provisioned Clouds on the Grid

Virtual Organization Clusters (VOCs) provide a novel architecture for overlaying dedicated cluster systems on existing grid infrastructures. VOCs provide customized, homogeneous execution environments on a per-Virtual Organization basis, without the cost of physical cluster construction or the overhead of per-job containers. Administrative access and overlay network capabilities are granted to Virtual Organizations (VOs) that choose to implement VOC technology, while the system remains completely transparent to end users and non-participating VOs. Unlike alternative systems that require explicit leases, VOCs are autonomically self-provisioned according to configurable usage policies. As a grid computing architecture, VOCs are designed to be technology agnostic and are implementable by any combination of software and services that follows the Virtual Organization Cluster Model. As demonstrated through simulation testing and evaluation of an implemented prototype, VOCs are a viable mechanism for increasing end-user job compatibility on grid sites. On existing production grids, where jobs are frequently submitted to a small subset of sites and thus experience high queuing delays relative to average job length, the grid-wide addition of VOCs does not adversely affect mean job sojourn time. By load-balancing jobs among grid sites, VOCs can reduce the total amount of queuing on a grid to a level sufficient to counteract the performance overhead introduced by virtualization

Serverless Computing Strategies on Cloud Platforms

[ES] Con el desarrollo de la Computación en la Nube, la entrega de recursos virtualizados a través de Internet ha crecido enormemente en los últimos años. Las Funciones como servicio (FaaS), uno de los modelos de servicio más nuevos dentro de la Computación en la Nube, permite el desarrollo e implementación de aplicaciones basadas en eventos que cubren servicios administrados en Nubes públicas y locales. Los proveedores públicos de Computación en la Nube adoptan el modelo FaaS dentro de su catálogo para proporcionar computación basada en eventos altamente escalable para las aplicaciones. Por un lado, los desarrolladores especializados en esta tecnología se centran en crear marcos de código abierto serverless para evitar el bloqueo con los proveedores de la Nube pública. A pesar del desarrollo logrado por la informática serverless, actualmente hay campos relacionados con el procesamiento de datos y la optimización del rendimiento en la ejecución en los que no se ha explorado todo el potencial. En esta tesis doctoral se definen tres estrategias de computación serverless que permiten evidenciar los beneficios de esta tecnología para el procesamiento de datos. Las estrategias implementadas permiten el análisis de datos con la integración de dispositivos de aceleración para la ejecución eficiente de aplicaciones científicas en plataformas cloud públicas y locales. En primer lugar, se desarrolló la plataforma CloudTrail-Tracker. CloudTrail-Tracker es una plataforma serverless de código abierto basada en eventos para el procesamiento de datos que puede escalar automáticamente hacia arriba y hacia abajo, con la capacidad de escalar a cero para minimizar los costos operativos. Seguidamente, se plantea la integración de GPUs en una plataforma serverless local impulsada por eventos para el procesamiento de datos escalables. La plataforma admite la ejecución de aplicaciones como funciones severless en respuesta a la carga de un archivo en un sistema de almacenamiento de ficheros, lo que permite la ejecución en paralelo de las aplicaciones según los recursos disponibles. Este procesamiento es administrado por un cluster Kubernetes elástico que crece y decrece automáticamente según las necesidades de procesamiento. Ciertos enfoques basados en tecnologías de virtualización de GPU como rCUDA y NVIDIA-Docker se evalúan para acelerar el tiempo de ejecución de las funciones. Finalmente, se implementa otra solución basada en el modelo serverless para ejecutar la fase de inferencia de modelos de aprendizaje automático previamente entrenados, en la plataforma de Amazon Web Services y en una plataforma privada con el framework OSCAR. El sistema crece elásticamente de acuerdo con la demanda y presenta una escalado a cero para minimizar los costes. Por otra parte, el front-end proporciona al usuario una experiencia simplificada en la obtención de la predicción de modelos de aprendizaje automático. Para demostrar las funcionalidades y ventajas de las soluciones propuestas durante esta tesis se recogen varios casos de estudio que abarcan diferentes campos del conocimiento como la analítica de aprendizaje y la Inteligencia Artificial. Esto demuestra que la gama de aplicaciones donde la computación serverless puede aportar grandes beneficios es muy amplia. Los resultados obtenidos avalan el uso del modelo serverless en la simplificación del diseño de arquitecturas para el uso intensivo de datos en aplicaciones complejas.[CA] Amb el desenvolupament de la Computació en el Núvol, el lliurament de recursos virtualitzats a través d'Internet ha crescut granment en els últims anys. Les Funcions com a Servei (FaaS), un dels models de servei més nous dins de la Computació en el Núvol, permet el desenvolupament i implementació d'aplicacions basades en esdeveniments que cobreixen serveis administrats en Núvols públics i locals. Els proveïdors de computació en el Núvol públic adopten el model FaaS dins del seu catàleg per a proporcionar a les aplicacions computació altament escalable basada en esdeveniments. D'una banda, els desenvolupadors especialitzats en aquesta tecnologia se centren en crear marcs de codi obert serverless per a evitar el bloqueig amb els proveïdors del Núvol públic. Malgrat el desenvolupament alcançat per la informàtica serverless, actualment hi ha camps relacionats amb el processament de dades i l'optimització del rendiment d'execució en els quals no s'ha explorat tot el potencial. En aquesta tesi doctoral es defineixen tres estratègies informàtiques serverless que permeten demostrar els beneficis d'aquesta tecnologia per al processament de dades. Les estratègies implementades permeten l'anàlisi de dades amb a integració de dispositius accelerats per a l'execució eficient d'aplicacion scientífiques en plataformes de Núvol públiques i locals. En primer lloc, es va desenvolupar la plataforma CloudTrail-Tracker. CloudTrail-Tracker és una plataforma de codi obert basada en esdeveniments per al processament de dades serverless que pot escalar automáticament cap amunt i cap avall, amb la capacitat d'escalar a zero per a minimitzar els costos operatius. A continuació es planteja la integració de GPUs en una plataforma serverless local impulsada per esdeveniments per al processament de dades escalables. La plataforma admet l'execució d'aplicacions com funcions severless en resposta a la càrrega d'un arxiu en un sistema d'emmagatzemaments de fitxers, la qual cosa permet l'execució en paral·lel de les aplicacions segon sels recursos disponibles. Este processament és administrat per un cluster Kubernetes elàstic que creix i decreix automàticament segons les necessitats de processament. Certs enfocaments basats en tecnologies de virtualització de GPU com rCUDA i NVIDIA-Docker s'avaluen per a accelerar el temps d'execució de les funcions. Finalment s'implementa una altra solució basada en el model serverless per a executar la fase d'inferència de models d'aprenentatge automàtic prèviament entrenats en la plataforma de Amazon Web Services i en una plataforma privada amb el framework OSCAR. El sistema creix elàsticament d'acord amb la demanda i presenta una escalada a zero per a minimitzar els costos. D'altra banda el front-end proporciona a l'usuari una experiència simplificada en l'obtenció de la predicció de models d'aprenentatge automàtic. Per a demostrar les funcionalitats i avantatges de les solucions proposades durant esta tesi s'arrepleguen diversos casos d'estudi que comprenen diferents camps del coneixement com l'analítica d'aprenentatge i la Intel·ligència Artificial. Això demostra que la gamma d'aplicacions on la computació serverless pot aportar grans beneficis és molt àmplia. Els resultats obtinguts avalen l'ús del model serverless en la simplificació del disseny d'arquitectures per a l'ús intensiu de dades en aplicacions complexes.[EN] With the development of Cloud Computing, the delivery of virtualized resources over the Internet has greatly grown in recent years. Functions as a Service (FaaS), one of the newest service models within Cloud Computing, allows the development and implementation of event-based applications that cover managed services in public and on-premises Clouds. Public Cloud Computing providers adopt the FaaS model within their catalog to provide event-driven highly-scalable computing for applications. On the one hand, developers specialized in this technology focus on creating open-source serverless frameworks to avoid the lock-in with public Cloud providers. Despite the development achieved by serverless computing, there are currently fields related to data processing and execution performance optimization where the full potential has not been explored. In this doctoral thesis three serverless computing strategies are defined that allow to demonstrate the benefits of this technology for data processing. The implemented strategies allow the analysis of data with the integration of accelerated devices for the efficient execution of scientific applications on public and on-premises Cloud platforms. Firstly, the CloudTrail-Tracker platform was developed to extract and process learning analytics in the Cloud. CloudTrail-Tracker is an event-driven open-source platform for serverless data processing that can automatically scale up and down, featuring the ability to scale to zero for minimizing the operational costs. Next, the integration of GPUs in an event-driven on-premises serverless platform for scalable data processing is discussed. The platform supports the execution of applications as serverless functions in response to the loading of a file in a file storage system, which allows the parallel execution of applications according to available resources. This processing is managed by an elastic Kubernetes cluster that automatically grows and shrinks according to the processing needs. Certain approaches based on GPU virtualization technologies such as rCUDA and NVIDIA-Docker are evaluated to speed up the execution time of the functions. Finally, another solution based on the serverless model is implemented to run the inference phase of previously trained machine learning models on theAmazon Web Services platform and in a private platform with the OSCAR framework. The system grows elastically according to demand and is scaled to zero to minimize costs. On the other hand, the front-end provides the user with a simplified experience in obtaining the prediction of machine learning models. To demonstrate the functionalities and advantages of the solutions proposed during this thesis, several case studies are collected covering different fields of knowledge such as learning analytics and Artificial Intelligence. This shows the wide range of applications where serverless computing can bring great benefits. The results obtained endorse the use of the serverless model in simplifying the design of architectures for the intensive data processing in complex applications.Naranjo Delgado, DM. (2021). Serverless Computing Strategies on Cloud Platforms [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/160916TESI

Archer: A Community Distributed Computing Infrastructure for Computer Architecture Research and Education

This paper introduces Archer, a community-based computing resource for computer architecture research and education. The Archer infrastructure integrates virtualization and batch scheduling middleware to deliver high-throughput computing resources aggregated from resources distributed across wide-area networks and owned by different participating entities in a seamless manner. The paper discusses the motivations leading to the design of Archer, describes its core middleware components, and presents an analysis of the functionality and performance of a prototype wide-area deployment running a representative computer architecture simulation workload.Comment: 11 pages, 2 figures. Describes the Archer project, http://archer-project.or

An efficient use of virtualization in grid/cloud environments

Grid is a hardware and software infrastructure that provides dependable, consistent, pervasive, and inexpensive access to high-end computational resources. Grid enables access to the resources but it does not guarantee any quality of service. Moreover, Grid does not provide performance isolation; job of one user can influence the performance of other user's job. The other problem with Grid is that the users of Grid belong to scientific community and the jobs require specific and customized software environment. Providing the perfect environment to the user is very difficult in Grid for its dispersed and heterogeneous nature. Though, Cloud computing provide full customization and control, but there is no simple procedure available to submit user jobs as in Grid. The Grid computing can provide customized resources and performance to the user using virtualization. A virtual machine can join the Grid as an execution node. The virtual machine can also be submitted as a job with user jobs inside. Where the first method gives quality of service and performance isolation, the second method also provides customization and administration in addition. In this thesis, a solution is proposed to enable virtual machine reuse which will provide performance isolation with customization and administration. The same virtual machine can be used for several jobs. In the proposed solution customized virtual machines join the Grid pool on user request. Proposed solution describes two scenarios to achieve this goal. In first scenario, user submits their customized virtual machine as a job. The virtual machine joins the Grid pool when it is powered on. In the second scenario, user customized virtual machines are preconfigured in the execution system. These virtual machines join the Grid pool on user request. Condor and VMware server is used to deploy and test the scenarios. Condor supports virtual machine jobs. The scenario 1 is deployed using Condor VM universe. The second scenario uses VMware-VIX API for scripting powering on and powering off of the remote virtual machines. The experimental results shows that as scenario 2 does not need to transfer the virtual machine image, the virtual machine image becomes live on pool more faster. In scenario 1, the virtual machine runs as a condor job, so it easy to administrate the virtual machine. The only pitfall in scenario 1 is the network traffic

Virtual Machine Image Management for Elastic Resource Usage in Grid Computing

Grid Computing has evolved from an academic concept to a powerful paradigm in the area of high performance computing (HPC). Over the last few years, powerful Grid computing solutions were developed that allow the execution of computational tasks on distributed computing resources. Grid computing has recently attracted many commercial customers. To enable commercial customers to be able to execute sensitive data in the Grid, strong security mechanisms must be put in place to secure the customers' data. In contrast, the development of Cloud Computing, which entered the scene in 2006, was driven by industry: it was designed with respect to security from the beginning. Virtualization technology is used to separate the users e.g., by putting the different users of a system inside a virtual machine, which prevents them from accessing other users' data. The use of virtualization in the context of Grid computing has been examined early and was found to be a promising approach to counter the security threats that have appeared with commercial customers. One main part of the work presented in this thesis is the Image Creation Station (ICS), a component which allows users to administer their virtual execution environments (virtual machines) themselves and which is responsible for managing and distributing the virtual machines in the entire system. In contrast to Cloud computing, which was designed to allow even inexperienced users to execute their computational tasks in the Cloud easily, Grid computing is much more complex to use. The ICS makes it easier to use the Grid by overcoming traditional limitations like installing needed software on the compute nodes that users use to execute the computational tasks. This allows users to bring commercial software to the Grid for the first time, without the need for local administrators to install the software to computing nodes that are accessible by all users. Moreover, the administrative burden is shifted from the local Grid site's administrator to the users or experienced software providers that allow the provision of individually tailored virtual machines to each user. But the ICS is not only responsible for enabling users to manage their virtual machines themselves, it also ensures that the virtual machines are available on every site that is part of the distributed Grid system. A second aspect of the presented solution focuses on the elasticity of the system by automatically acquiring free external resources depending on the system's current workload. In contrast to existing systems, the presented approach allows the system's administrator to add or remove resource sets during runtime without needing to restart the entire system. Moreover, the presented solution allows users to not only use existing Grid resources but allows them to scale out to Cloud resources and use these resources on-demand. By ensuring that unused resources are shut down as soon as possible, the computational costs of a given task are minimized. In addition, the presented solution allows each user to specify which resources can be used to execute a particular job. This is useful when a job processes sensitive data e.g., that is not allowed to leave the company. To obtain a comparable function in today's systems, a user must submit her computational task to a particular resource set, losing the ability to automatically schedule if more than one set of resources can be used. In addition, the proposed solution prioritizes each set of resources by taking different metrics into account (e.g. the level of trust or computational costs) and tries to schedule the job to resources with the highest priority first. It is notable that the priority often mimics the physical distance from the resources to the user: a locally available Cluster usually has a higher priority due to the high level of trust and the computational costs, that are usually lower than the costs of using Cloud resources. Therefore, this scheduling strategy minimizes the costs of job execution by improving security at the same time since data is not necessarily transferred to remote resources and the probability of attacks by malicious external users is minimized. Bringing both components together results in a system that adapts automatically to the current workload by using external (e.g., Cloud) resources together with existing locally available resources or Grid sites and provides individually tailored virtual execution environments to the system's users

Testen von verteilten Systemen in heterogenen Netzwerken mit virtuellen Maschinen am Beispiel von MPI

Verteilte Systeme sind in zunehmendem Maße mit komplexen Netzwerkkonfigurationen konfrontiert, beispielsweise mit mehreren Netzwerkschnittstellen pro Knoten, NAT und gemischter IPv4/IPv6-Adressierung. Um Unterstützung dafür korrekt zu implementieren, ist regelmäßiges und gründliches Testen nerlässlich. Allerdings ist der Aufwand, derart vielfältige Teststellungen in Hardware vorzuhalten, so hoch, dass Testen nur sehr selten erfolgen kann und oftmals ganz unterbleibt. Gegenstand dieser Diplomarbeit ist die Entwicklung eines Virtual Test Environment (VTE), das (Multi-)Cluster-Umgebungen mit Hilfe von virtuellen Maschinen nachbildet und somit den erforderlichen Aufwand so weit senkt, dass kontinuierliches, entwicklungsbegleitendes Testen praktikabel wird

Single system image: A survey

Single system image is a computing paradigm where a number of distributed computing resources are aggregated and presented via an interface that maintains the illusion of interaction with a single system. This approach encompasses decades of research using a broad variety of techniques at varying levels of abstraction, from custom hardware and distributed hypervisors to specialized operating system kernels and user-level tools. Existing classification schemes for SSI technologies are reviewed, and an updated classification scheme is proposed. A survey of implementation techniques is provided along with relevant examples. Notable deployments are examined and insights gained from hands-on experience are summarized. Issues affecting the adoption of kernel-level SSI are identified and discussed in the context of technology adoption literature