Mapeamento de redes virtuais ciente do consumo de energia

Orientador: Nelson Luis Saldanha da FonsecaDissertação (mestrado) - Universidade Estadual de Campinas, Instituto de ComputaçãoResumo: A virtualização de redes é uma tecnologia promissora para a Internet do futuro, já que facilita a implementação de novos protocolos e aplicações sem a necessidade de alterar o núcleo da rede. Um passo chave para instanciar redes virtuais é a alocação de recursos físicos para elementos virtuais (roteadores e enlaces). A fim de contribuir para o esforço global de poupança de energia, a escolha de recursos físicos para instanciar uma rede virtual deveria minimizar o consumo de energia rede. No entanto, esta não é uma tarefa trivial, já que requerimentos de QoS devem ser atingidos. A busca da solução ótima deste problema é NP-difícil. O mapeamento de redes virtuais em substratos de rede físicos em cenários de alocaç?o e desalocaç?o de redes virtuais pode não levar a um consumo mínimo de energia devido à dinâmica das atribuições dos elementos virtuais previamente alocados. Tal dinâmica pode levar à subutilização da rede substrato. Para reduzir os efeitos negativos desta dinâmica, técnicas tais como a migração de redes virtuais em tempo real podem ser empregadas para rearranjar as redes virtuais previamente mapeadas para poupar energia. Esta dissertação apresenta um conjunto de novos algoritmos para o mapeamento de redes virtuais em substratos de rede com o objetivo de reduzir o consumo de energia. Além disso, dois novos algoritmos são propostos para a migração dos roteadores e enlaces virtuais para reduzir o número de roteadores e amplificadores ópticos requeridos. Os resultados obtidos por simulação mostram a eficácia dos algoritmos propostosAbstract: Network virtualization is a promising technology for the Internet of the Future since it facilitates the deployment of new protocols and applications without the need of changing the core of the network. A key step to instantiate virtual networks is the allocation of physical resources to virtual elements (routers and links). In order to contribute to the global effort of saving energy, choice of physical resources to instantiate a virtual network needs to minimize the network energy consumption. However, this is not a trivial task, since the QoS of the application requirements has to be supported. Indeed, the search for the optimal solution of this problem is NP-hard. The mapping of virtual networks on network substrates at the arrival time of requests to the establishment of virtual networks may not lead to a global minimum energy consumption of energy due to the dynamic allocations and deallocations of virtual networks. Actually, such dynamics can lead to the underutilization of the network substrate. To mitigate the negative effect of this dynamics, techniques such as live migration can be employed to rearrange already mapped virtual networks to achieve energy savings. This dissertation presents a set of new algorithms for the mapping of virtual networks on network substrates aiming to reduce energy consumption. Additionally, two new algorithms are proposed for the migration of virtual routers and links to reduce the number of powered routers and optical amplifiers. Results derived by simulation show the efficacy of the proposed algorithmsMestradoCiência da ComputaçãoMestre em Ciência da Computaçã

Energy Efficiency through Virtual Machine Redistribution in Telecommunication Infrastructure Nodes

Energy efficiency is one of the key factors impacting the green behavior and operational expenses of telecommunication core network operations. This thesis study is aimed for finding out possible technique to reduce energy consumption in telecommunication infrastructure nodes. The study concentrates on traffic management operation (e.g. media stream control, ATM adaptation) within network processors [LeJ03], categorized as control plane. The control plane of the telecommunication infrastructure node is a custom built high performance cluster which consists of multiple GPPs (General Purpose Processor) interconnected by high-speed and low-latency network. Due to application configurations in particular GPP unit and redundancy issues, energy usage is not optimal. In this thesis, our approach is to gain elastic capacity within the control plane cluster to reduce power consumption. This scales down and wakes up certain GPP units depending on traffic load situations. For elasticity, our study moves toward the virtual machine (VM) migration technique in the control plane cluster through system virtualization. The traffic load situation triggers VM migration on demand. Virtual machine live migration brings the benefit of enhanced performance and resiliency of the control plane cluster. We compare the state-of-the-art power aware computing resource scheduling in cluster-based nodes with VM migration technique. Our research does not propose any change in data plane architecture as we are mainly concentrating on the control plane. This study shows, VM migration can be an efficient approach to significantly reduce energy consumption in control plane of cluster-based telecommunication infrastructure nodes without interrupting performance/throughput, while guaranteeing full connectivity and maximum link utilization

Partage efficace des ressources de calcul dans le nuage informatique

L'informatique en nuage ou l'infonuagique est apparue comme un nouveau paradigm capable de gérer une infrastructure informatique à grande échelle. Toutefois, la plupart des infrastructures infonuagiques existantes ne sont pas exploitées efficacement, et la surprovision de ressources est un problème émergent. En raison des exigences variables au cours du temps des ressources virtuelles, les plateformes physiques pourraient être utilisées de manière inadéquate, ce qui entraîne des coûts opérationnels supplémentaires. Des techniques de migration ont été proposées pour améliorer l'utilisation des ressources physiques, par exemple la consolidation des ressources virtuelles sur les ressources physiques. Les travaux antérieurs motivés par des objectifs énergétiques et d'équilibrage de charge sont souvent limités à une seule technique de migration. Ce mémoire présente un modèle de migration optimisé pour les machines virtuelles basé sur l’état courant et futur de l’utilisation des ressources physiques tout en considérant les différentes techniques de migration. Cette future utilisation s’appuie sur la prédiction de la charge de travail. Notre modèle a pour fin de minimiser le coût opérationnel lors du partage de l'infrastructure sousjacente. L'expérimentation menée dans le cadre de ce mémoire montre la capacité de notre modèle pour réaliser un meilleur partage des ressources physique et ce, en réduisant le coût opérationnel de 16% en comparaison à une solution existante. L’expérimentation montre également que l’intégration de différentes techniques de migration dans le même modèle implique une optimisation globale par rapport à l’intégration d’une seule technique

Bandwidth management in live virtual machine migration

In this thesis I investigated the bandwidth management problem on live migration of virtual machine in different environment. First part of the thesis is dedicated to intra-data-center bandwidth optimization problem, while in the second part of the document I present the solution for wireless live migration in 5G and edge computing emerging technologies. Live virtual machine migration aims at enabling the dynamic balanced use of the networking/computing physical resources of virtualized data centers, so to lead to reduced energy consumption and improve data centers’ flexibility. However, the bandwidth consumption and latency of current state-of-the-art live VM migration techniques still reduce the experienced benefits to much less than their potential. Motivated by this consideration I analytically characterize and test the optimal bandwidth manager for intra-data-center live migration of VMs. The goal is to min- imize the migration-induced communication energy consumption under service level agreement (SLA)-induced hard constraints on the total migration time, downtime, slowdown of the migrating applications and overall available bandwidth

Network bandwidth aware dynamic automated framework for Virtual Machine Live Migration in cloud environments

Live migration is a very important feature of virtualisation, a running VM can be seamlessly moved between different physical hosts. The source VM’s CPU state, storage, memory and network resources can be completely moved to a target host without disrupting the users or running applications. Live VM migration is an extremely powerful tool in many key scenarios such as load balancing, online maintenance, proactive fault tolerance and power management. There are four steps involved in the live VM migration, the setup stage, memory transfer stage, VM storage transfer stage and the network clean up stage. The most important part of live VM migration is transferring the main memory state of the VM from the source to the destination host which can consume a significant amount of network bandwidth in a short period of time. Modern cloud based data centres generate a significant amount of network traffic apart from VM live migration traffic. If VM migration occurs during a peak time, VM migration and user traffic will compete for network bandwidth, then the data centre’s network may not have enough resources to support both VM migration and demands of application users, which would create a bottleneck in the network. Therefore, this research presents a centralised, bandwidth aware, dynamic, and automated framework for live VM migration in Cloud environments. The proposed framework adopted a heuristic approach, and it provides guaranteed bandwidth for VM live migration by controlling user traffic on the network while scheduling live VM migration in an efficient manner. The framework consists with two main components, The Central Controller and the Local Controller. The Local Controller is responsible for collecting resources usage data from VMs and PMs however the Central Controller makes global management decisions. The Central Controller is based on four algorithms which are called a migration policy. The migration policy contains the following algorithms: the host overloaded detection, host underloaded detection, VM selection and VM placement algorithms which are proposed in this research. The proposed migration policy has been implemented in CloudSim and evaluated against two benchmark migration policies in CloudSim. Five evaluation metrics have been used in the simulation to evaluate the performance of the proposed migration policy. The results reveal that the proposed migration policy outperformed the two benchmark policies

Carbon-profit-aware job scheduling and load balancing in geographically distributed cloud for HPC and web applications

This thesis introduces two carbon-profit-aware control mechanisms that can be used to improve performance of job scheduling and load balancing in an interconnected system of geographically distributed data centers for HPC and web applications. These control mechanisms consist of three primary components that perform: 1) measurement and modeling, 2) job planning, and 3) plan execution. The measurement and modeling component provide information on energy consumption and carbon footprint as well as utilization, weather, and pricing information. The job planning component uses this information to suggest the best arrangement of applications as a possible configuration to the plan execution component to perform it on the system. For reporting and decision making purposes, some metrics need to be modeled based on directly measured inputs. There are two challenges in accurately modeling of these necessary metrics: 1) feature selection and 2) curve fitting (regression). First, to improve the accuracy of power consumption models of the underutilized servers, advanced fitting methodologies were used on the selected server features. The resulting model is then evaluated on real servers and is used as part of load balancing mechanism for web applications. We also provide an inclusive model for cooling system in data centers to optimize the power consumption of cooling system, which in turn is used by the planning component. Furthermore, we introduce another model to calculate the profit of the system based on the price of electricity, carbon tax, operational costs, sales tax, and corporation taxes. This model is used for optimized scheduling of HPC jobs. For position allocation of web applications, a new heuristic algorithm is introduced for load balancing of virtual machines in a geographically distributed system in order to improve its carbon awareness. This new heuristic algorithm is based on genetic algorithm and is specifically tailored for optimization problems of interconnected system of distributed data centers. A simple version of this heuristic algorithm has been implemented in the GSN project, as a carbon-aware controller. Similarly, for scheduling of HPC jobs on servers, two new metrics are introduced: 1) profitper-core-hour-GHz and 2) virtual carbon tax. In the HPC job scheduler, these new metrics are used to maximize profit and minimize the carbon footprint of the system, respectively. Once the application execution plan is determined, plan execution component will attempt to implement it on the system. Plan execution component immediately uses the hypervisors on physical servers to create, remove, and migrate virtual machines. It also executes and controls the HPC jobs or web applications on the virtual machines. For validating systems designed using the proposed modeling and planning components, a simulation platform using real system data was developed, and new methodologies were compared with the state-of-the-art methods considering various scenarios. The experimental results show improvement in power modeling of servers, significant carbon reduction in load balancing of web applications, and significant profit-carbon improvement in HPC job scheduling

SimuBoost: Scalable Parallelization of Functional System Simulation

Für das Sammeln detaillierter Laufzeitinformationen, wie Speicherzugriffsmustern, wird in der Betriebssystem- und Sicherheitsforschung häufig auf die funktionale Systemsimulation zurückgegriffen. Der Simulator führt dabei die zu untersuchende Arbeitslast in einer virtuellen Maschine (VM) aus, indem er schrittweise Instruktionen interpretiert oder derart übersetzt, sodass diese auf dem Zustand der VM arbeiten. Dieser Prozess ermöglicht es, eine umfangreiche Instrumentierung durchzuführen und so an Informationen zum Laufzeitverhalten zu gelangen, die auf einer physischen Maschine nicht zugänglich sind. Obwohl die funktionale Systemsimulation als mächtiges Werkzeug gilt, stellt die durch die Interpretation oder Übersetzung resultierende immense Ausführungsverlangsamung eine substanzielle Einschränkung des Verfahrens dar. Im Vergleich zu einer nativen Ausführung messen wir für QEMU eine 30-fache Verlangsamung, wobei die Aufzeichnung von Speicherzugriffen diesen Faktor verdoppelt. Mit Simulatoren, die umfangreichere Instrumentierungsmöglichkeiten mitbringen als QEMU, kann die Verlangsamung um eine Größenordnung höher ausfallen. Dies macht die funktionale Simulation für lang laufende, vernetzte oder interaktive Arbeitslasten uninteressant. Darüber hinaus erzeugt die Verlangsamung ein unrealistisches Zeitverhalten, sobald Aktivitäten außerhalb der VM (z. B. Ein-/Ausgabe) involviert sind. In dieser Arbeit stellen wir SimuBoost vor, eine Methode zur drastischen Beschleunigung funktionaler Systemsimulation. SimuBoost führt die zu untersuchende Arbeitslast zunächst in einer schnellen hardwaregestützten virtuellen Maschine aus. Dies ermöglicht volle Interaktivität mit Benutzern und Netzwerkgeräten. Während der Ausführung erstellt SimuBoost periodisch Abbilder der VM (engl. Checkpoints). Diese dienen als Ausgangspunkt für eine parallele Simulation, bei der jedes Intervall unabhängig simuliert und analysiert wird. Eine heterogene deterministische Wiederholung (engl. heterogeneous deterministic Replay) garantiert, dass in dieser Phase die vorherige hardwaregestützte Ausführung jedes Intervalls exakt reproduziert wird, einschließlich Interaktionen und realistischem Zeitverhalten. Unser Prototyp ist in der Lage, die Laufzeit einer funktionalen Systemsimulation deutlich zu reduzieren. Während mit herkömmlichen Verfahren für die Simulation des Bauprozesses eines modernen Linux über 5 Stunden benötigt werden, schließt SimuBoost die Simulation in nur 15 Minuten ab. Dies sind lediglich 16% mehr Zeit, als der Bau in einer schnellen hardwaregestützten VM in Anspruch nimmt. SimuBoost ist imstande, diese Geschwindigkeit auch bei voller Instrumentierung zur Aufzeichnung von Speicherzugriffen beizubehalten. Die vorliegende Arbeit ist das erste Projekt, welches das Konzept der Partitionierung und Parallelisierung der Ausführungszeit auf die interaktive Systemvirtualisierung in einer Weise anwendet, die eine sofortige parallele funktionale Simulation gestattet. Wir ergänzen die praktische Umsetzung mit einem mathematischen Modell zur formalen Beschreibung der Beschleunigungseigenschaften. Dies erlaubt es, für ein gegebenes Szenario die voraussichtliche parallele Simulationszeit zu prognostizieren und gibt eine Orientierung zur Wahl der optimalen Intervalllänge. Im Gegensatz zu bisherigen Arbeiten legt SimuBoost einen starken Fokus auf die Skalierbarkeit über die Grenzen eines einzelnen physischen Systems hinaus. Ein zentraler Schlüssel hierzu ist der Einsatz moderner Checkpointing-Technologien. Im Rahmen dieser Arbeit präsentieren wir zwei neuartige Methoden zur effizienten und effektiven Kompression von periodischen Systemabbildern