31 research outputs found

    Self-adaptive and sensitivity-aware QoS modeling for the cloud

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    Given the elasticity, dynamicity and on-demand nature of the cloud, cloud-based applications require dynamic models for Quality of Service (QoS), especially when the sensitivity of QoS tends to fluctuate at runtime. These models can be autonomically used by the cloud-based application to correctly self-adapt its QoS provision. We present a novel dynamic and self-adaptive sensitivity-aware QoS modeling approach, which is fine-grained and grounded on sound machine learning techniques. In particular, we combine symmetric uncertainty with two training techniques: Auto-Regressive Moving Average with eXogenous inputs model (ARMAX) and Artificial Neural Network (ANN) to reach two formulations of the model. We describe a middleware for implementing the approach. We experimentally evaluate the effectiveness of our models using the RUBiS benchmark and the FIFA 1998 workload trends. The results show that our modeling approach is effective and the resulting models produce better accuracy when compared with conventional models

    A survey on elasticity management in PaaS systems

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    [EN] Elasticity is a goal of cloud computing. An elastic system should manage in an autonomic way its resources, being adaptive to dynamic workloads, allocating additional resources when workload is increased and deallocating resources when workload decreases. PaaS providers should manage resources of customer applications with the aim of converting those applications into elastic services. This survey identifies the requirements that such management imposes on a PaaS provider: autonomy, scalability, adaptivity, SLA awareness, composability and upgradeability. This document delves into the variety of mechanisms that have been proposed to deal with all those requirements. Although there are multiple approaches to address those concerns, providers main goal is maximisation of profits. This compels providers to look for balancing two opposed goals: maximising quality of service and minimising costs. Because of this, there are still several aspects that deserve additional research for finding optimal adaptability strategies. Those open issues are also discussed.This work has been partially supported by EU FEDER and Spanish MINECO under research Grant TIN2012-37719-C03-01.Muñoz-Escoí, FD.; Bernabeu Aubán, JM. (2017). A survey on elasticity management in PaaS systems. Computing. 99(7):617-656. https://doi.org/10.1007/s00607-016-0507-8S617656997Ajmani S (2004) Automatic software upgrades for distributed systems. PhD thesis, Department of Electrical and Computer Science, Massachusetts Institute of Technology, USAAjmani S, Liskov B, Shrira L (2006) Modular software upgrades for distributed systems. In: 20th European Conference on Object-Oriented Programming (ECOOP), Nantes, France, pp 452–476Alhamad M, Dillon TS, Chang E (2010) Conceptual SLA framework for cloud computing. 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    Computing on the Edge of the Network

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    Um Systeme der fünften Generation zellularer Kommunikationsnetze (5G) zu ermöglichen, sind Energie effiziente Architekturen erforderlich, die eine zuverlässige Serviceplattform für die Bereitstellung von 5G-Diensten und darüber hinaus bieten können. Device Enhanced Edge Computing ist eine Ableitung des Multi-Access Edge Computing (MEC), das Rechen- und Speicherressourcen direkt auf den Endgeräten bereitstellt. Die Bedeutung dieses Konzepts wird durch die steigenden Anforderungen von rechenintensiven Anwendungen mit extrem niedriger Latenzzeit belegt, die den MEC-Server allein und den drahtlosen Kanal überfordern. Diese Dissertation stellt ein Berechnungs-Auslagerungsframework mit Berücksichtigung von Energie, Mobilität und Anreizen in einem gerätegestützten MEC-System mit mehreren Benutzern und mehreren Aufgaben vor, das die gegenseitige Abhängigkeit der Aufgaben sowie die Latenzanforderungen der Anwendungen berücksichtigt.To enable fifth generation cellular communication network (5G) systems, energy efficient architectures are required that can provide a reliable service platform for the delivery of 5G services and beyond. Device Enhanced Edge Computing is a derivative of Multi-Access Edge Computing (MEC), which provides computing and storage resources directly on the end devices. The importance of this concept is evidenced by the increasing demands of ultra-low latency computationally intensive applications that overwhelm the MEC server alone and the wireless channel. This dissertation presents a computational offloading framework considering energy, mobility and incentives in a multi-user, multi-task device-based MEC system that takes into account task interdependence and application latency requirements

    Learning by doing on the EGEE GRID and first performance analysis of CODESA-3D multirun submission

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    The project TEMA (Training on Environmental Modelling and Applications) is a CRS4 training initiative in the field of computational hydrology and grid computing (Jan-Sept, 2006). The personnel involved were Fabrizio Murgia (trainee) and Giuditta Lecca (tutor). The objectives of the project were: " To aquire specialized skills about grid computing with special emphasis on computational sub-surface hydrology; " To develop and test software procedures to run Monte Carlo simulations on the EGEE production grid; " To produce a technical report and some seminars about grid computing. The aquired competences and skills will be used in the ongoing projects GRIDA3, CyberSAR and DEGREE

    A Study of Very Short Intermittent DDoS Attacks on the Performance of Web Services in Clouds

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    Distributed Denial-of-Service (DDoS) attacks for web applications such as e-commerce are increasing in size, scale, and frequency. The emerging elastic cloud computing cannot defend against ever-evolving new types of DDoS attacks, since they exploit various newly discovered network or system vulnerabilities even in the cloud platform, bypassing not only the state-of-the-art defense mechanisms but also the elasticity mechanisms of cloud computing. In this dissertation, we focus on a new type of low-volume DDoS attack, Very Short Intermittent DDoS Attacks, which can hurt the performance of web applications deployed in the cloud via transiently saturating the critical bottleneck resource of the target systems by means of external attack HTTP requests outside the cloud or internal resource contention inside the cloud. We have explored external attacks by modeling the n-tier web applications with queuing network theory and implementing the attacking framework based-on feedback control theory. We have explored internal attacks by investigating and exploiting resource contention and performance interference to locate a target VM (virtual machine) and degrade its performance

    On the role of performance interference in consolidated environments

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    Cotutela Universitat Politècnica de Catalunya i KTH Royal Institute of TechnologyWith the advent of resource shared environments such as the Cloud, virtualization has become the de facto standard for server consolidation. While consolidation improves utilization, it causes performance-interference between Virtual Machines (VMs) from contention in shared resources such as CPU, Last Level Cache (LLC) and memory bandwidth. Over-provisioning resources for performance sensitive applications can guarantee Quality of Service (QoS), however, it results in low machine utilization. Thus, assuring QoS for performance sensitive applications while allowing co-location has been a challenging problem. In this thesis, we identify ways to mitigate performance interference without undue over-provisioning and also point out the need to model and account for performance interference to improve the reliability and accuracy of elastic scaling. The end goal of this research is to leverage on the observations to provide efficient resource management that is both performance and cost aware. Our main contributions are threefold; first, we improve the overall machine utilization by executing best-e↵ort applications along side latency critical applications without violating its performance requirements. Our solution is able to dynamically adapt and leverage on the changing workload/phase behaviour to execute best-e↵ort applications without causing excessive interference on performance; second, we identify that certain performance metrics used for elastic scaling decisions may become unreliable if performance interference is unaccounted. By modelling performance interference, we show that these performance metrics become reliable in a multi-tenant environment; and third, we identify and demonstrate the impact of interference on the accuracy of elastic scaling and propose a solution to significantly minimise performance violations at a reduced cost.Con la aparición de entornos con recurso compartidos tales como la nube, la virtualización se ha convertido en el estándar de facto para la consolidación de servidores. Mientras que la consolidación mejora la utilización, también causa interferencia en el rendimiento de las máquinas virtuales (VM) debido a la contención en recursos compartidos, tales como CPU, el último nivel de cache (LLC) y el ancho de banda de memoria. El exceso de aprovisionamiento de recursos para aplicaciones sensibles al rendimiento puede garantizar la calidad de servicio (QoS), sin embargo, resulta en una baja utilización de la maquina. Por lo tanto, asegurar QoS en aplicaciones sensibles al rendimiento, al tiempo que permitir la co-localización ha sido un problema difícil. En esta tesis, se identifican las formas de mitigar la interferencia sin necesidad de sobre-aprovisionamiento y también se señala la necesidad de modelar y contabilizar la interferencia en el desempeño para mejorar la fiabilidad y la precisión del escalado elástico. El objetivo final de esta investigación consiste en aprovechar las observaciones para proporcionar una gestión eficiente de los recursos considerando tanto el rendimiento como el coste. Nuestras contribuciones principales son tres; primero, mejoramos la utilización total de la maquina mediante la ejecución de aplicaciones best-effort junto con aplicaciones críticas en latencia sin vulnerar sus requisitos de rendimiento. Nuestra solución es capaz de adaptarse de forma dinámica y sacar provecho del comportamiento cambiante de la carga de trabajo y sus cambios de fase para ejecutar aplicaciones best-effort, sin causar interferencia excesiva en el rendimiento; segundo, identificamos que ciertos parámetros de rendimiento utilizados para las decisiones de escalado elástico pueden no ser fiables si no se tiene en cuenta la interferencia en el rendimiento. Al modelar la interferencia en el rendimiento, se muestra que estas métricas de rendimiento resultan fiables en un entorno multi-proveedor; y tercero, se identifica y muestra el impacto de la interferencia en la precisión del escalado elástico y se propone una solución para minimizar significativamente vulneraciones de rendimiento con un coste reducido.Postprint (published version

    Proceedings of the Second International Workshop on Sustainable Ultrascale Computing Systems (NESUS 2015) Krakow, Poland

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    Proceedings of: Second International Workshop on Sustainable Ultrascale Computing Systems (NESUS 2015). Krakow (Poland), September 10-11, 2015

    Software Defined Application Delivery Networking

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    In this thesis we present the architecture, design, and prototype implementation details of AppFabric. AppFabric is a next generation application delivery platform for easily creating, managing and controlling massively distributed and very dynamic application deployments that may span multiple datacenters. Over the last few years, the need for more flexibility, finer control, and automatic management of large (and messy) datacenters has stimulated technologies for virtualizing the infrastructure components and placing them under software-based management and control; generically called Software-defined Infrastructure (SDI). However, current applications are not designed to leverage this dynamism and flexibility offered by SDI and they mostly depend on a mix of different techniques including manual configuration, specialized appliances (middleboxes), and (mostly) proprietary middleware solutions together with a team of extremely conscientious and talented system engineers to get their applications deployed and running. AppFabric, 1) automates the whole control and management stack of application deployment and delivery, 2) allows application architects to define logical workflows consisting of application servers, message-level middleboxes, packet-level middleboxes and network services (both, local and wide-area) composed over application-level routing policies, and 3) provides the abstraction of an application cloud that allows the application to dynamically (and automatically) expand and shrink its distributed footprint across multiple geographically distributed datacenters operated by different cloud providers. The architecture consists of a hierarchical control plane system called Lighthouse and a fully distributed data plane design (with no special hardware components such as service orchestrators, load balancers, message brokers, etc.) called OpenADN . The current implementation (under active development) consists of ~10000 lines of python and C code. AppFabric will allow applications to fully leverage the opportunities provided by modern virtualized Software-Defined Infrastructures. It will serve as the platform for deploying massively distributed, and extremely dynamic next generation application use-cases, including: Internet-of-Things/Cyber-Physical Systems: Through support for managing distributed gather-aggregate topologies common to most Internet-of-Things(IoT) and Cyber-Physical Systems(CPS) use-cases. By their very nature, IoT and CPS use cases are massively distributed and have different levels of computation and storage requirements at different locations. Also, they have variable latency requirements for their different distributed sites. Some services, such as device controllers, in an Iot/CPS application workflow may need to gather, process and forward data under near-real time constraints and hence need to be as close to the device as possible. Other services may need more computation to process aggregated data to drive long term business intelligence functions. AppFabric has been designed to provide support for such very dynamic, highly diversified and massively distributed application use-cases. Network Function Virtualization: Through support for heterogeneous workflows, application-aware networking, and network-aware application deployments, AppFabric will enable new partnerships between Application Service Providers (ASPs) and Network Service Providers (NSPs). An application workflow in AppFabric may comprise of application services, packet and message-level middleboxes, and network transport services chained together over an application-level routing substrate. The Application-level routing substrate allows policy-based service chaining where the application may specify policies for routing their application traffic over different services based on application-level content or context. Virtual worlds/multiplayer games: Through support for creating, managing and controlling dynamic and distributed application clouds needed by these applications. AppFabric allows the application to easily specify policies to dynamically grow and shrink the application\u27s footprint over different geographical sites, on-demand. Mobile Apps: Through support for extremely diversified and very dynamic application contexts typical of such applications. Also, AppFabric provides support for automatically managing massively distributed service deployment and controlling application traffic based on application-level policies. This allows mobile applications to provide the best Quality-of-Experience to its users without This thesis is the first to handle and provide a complete solution for such a complex and relevant architectural problem that is expected to touch each of our lives by enabling exciting new application use-cases that are not possible today. Also, AppFabric is a non-proprietary platform that is expected to spawn lots of innovations both in the design of the platform itself and the features it provides to applications. AppFabric still needs many iterations, both in terms of design and implementation maturity. This thesis is not the end of journey for AppFabric but rather just the beginning

    Improving Caches in Consolidated Environments

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    Memory (cache, DRAM, and disk) is in charge of providing data and instructions to a computer’s processor. In order to maximize performance, the speeds of the memory and the processor should be equal. However, using memory that always match the speed of the processor is prohibitively expensive. Computer hardware designers have managed to drastically lower the cost of the system with the use of memory caches by sacrificing some performance. A cache is a small piece of fast memory that stores popular data so it can be accessed faster. Modern computers have evolved into a hierarchy of caches, where a memory level is the cache for a larger and slower memory level immediately below it. Thus, by using caches, manufacturers are able to store terabytes of data at the cost of cheapest memory while achieving speeds close to the speed of the fastest one. The most important decision about managing a cache is what data to store in it. Failing to make good decisions can lead to performance overheads and over- provisioning. Surprisingly, caches choose data to store based on policies that have not changed in principle for decades. However, computing paradigms have changed radically leading to two noticeably different trends. First, caches are now consol- idated across hundreds to even thousands of processes. And second, caching is being employed at new levels of the storage hierarchy due to the availability of high-performance flash-based persistent media. This brings four problems. First, as the workloads sharing a cache increase, it is more likely that they contain dupli- cated data. Second, consolidation creates contention for caches, and if not managed carefully, it translates to wasted space and sub-optimal performance. Third, as contented caches are shared by more workloads, administrators need to carefully estimate specific per-workload requirements across the entire memory hierarchy in order to meet per-workload performance goals. And finally, current cache write poli- cies are unable to simultaneously provide performance and consistency guarantees for the new levels of the storage hierarchy. We addressed these problems by modeling their impact and by proposing solu- tions for each of them. First, we measured and modeled the amount of duplication at the buffer cache level and contention in real production systems. Second, we created a unified model of workload cache usage under contention to be used by administrators for provisioning, or by process schedulers to decide what processes to run together. Third, we proposed methods for removing cache duplication and to eliminate wasted space because of contention for space. And finally, we pro- posed a technique to improve the consistency guarantees of write-back caches while preserving their performance benefits
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