The “HPI Future SOC Lab” is a cooperation of the Hasso-Plattner-Institut (HPI) and industrial partners. Its mission is to enable and promote exchange and interaction between the research community and the industrial partners. The HPI Future SOC Lab provides researchers with free of charge access to a complete infrastructure of state of the art hard- and software. This infrastructure includes components, which might be too expensive for an ordinary research environment, such as servers with up to 64 cores. The offerings address researchers particularly from but not limited to the areas of computer science and business information systems. Main areas of research include cloud computing, parallelization, and In-Memory technologies. This technical report presents results of research projects executed in 2013. Selected projects have presented their results on April 10th and September 24th 2013 at the Future SOC Lab Day events. / Das Future SOC Lab am HPI ist eine Kooperation des Hasso-Plattner-Instituts mit verschiedenen Industriepartnern. Seine Aufgabe ist die Ermöglichung und Förderung des Austausches zwischen Forschungsgemeinschaft und Industrie. Am Lab wird interessierten Wissenschaftlern eine Infrastruktur von neuester Hard- und Software kostenfrei für Forschungszwecke zur Verfügung gestellt. Dazu zählen teilweise noch nicht am Markt verfügbare Technologien, die im normalen Hochschulbereich in der Regel nicht zu finanzieren wären, bspw. Server mit bis zu 64 Cores und 2 TB Hauptspeicher. Diese Angebote richten sich insbesondere an Wissenschaftler in den Gebieten Informatik und Wirtschaftsinformatik. Einige der Schwerpunkte sind Cloud Computing, Parallelisierung und In-Memory Technologien. In diesem Technischen Bericht werden die Ergebnisse der Forschungsprojekte des Jahres 2013 vorgestellt. Ausgewählte Projekte stellten ihre Ergebnisse am 10. April 2013 und 24. September 2013 im Rahmen der Future SOC Lab Tag Veranstaltungen vor.

The “HPI Future SOC Lab” is a cooperation of the Hasso-Plattner-Institut (HPI) and industrial partners. Its mission is to enable and promote exchange and interaction between the research community and the industrial partners. The HPI Future SOC Lab provides researchers with free of charge access to a complete infrastructure of state of the art hard- and software. This infrastructure includes components, which might be too expensive for an ordinary research environment, such as servers with up to 64 cores. The offerings address researchers particularly from but not limited to the areas of computer science and business information systems. Main areas of research include cloud computing, parallelization, and In-Memory technologies. This technical report presents results of research projects executed in 2012. Selected projects have presented their results on June 18th and November 26th 2012 at the Future SOC Lab Day events. / Das Future SOC Lab am HPI ist eine Kooperation des Hasso-Plattner-Instituts mit verschiedenen Industriepartnern. Seine Aufgabe ist die Ermöglichung und Förderung des Austausches zwischen Forschungsgemeinschaft und Industrie. Am Lab wird interessierten Wissenschaftlern eine Infrastruktur von neuester Hard- und Software kostenfrei für Forschungszwecke zur Verfügung gestellt. Dazu zählen teilweise noch nicht am Markt verfügbare Technologien, die im normalen Hochschulbereich in der Regel nicht zu finanzieren wären, bspw. Server mit bis zu 64 Cores und 2 TB Hauptspeicher. Diese Angebote richten sich insbesondere an Wissenschaftler in den Gebieten Informatik und Wirtschaftsinformatik. Einige der Schwerpunkte sind Cloud Computing, Parallelisierung und In-Memory Technologien. In diesem Technischen Bericht werden die Ergebnisse der Forschungsprojekte des Jahres 2012 vorgestellt. Ausgewählte Projekte stellten ihre Ergebnisse am 18. April 2012 und 14. November 2012 im Rahmen der Future SOC Lab Tag Veranstaltungen vor.

Durch die immer stärker werdende Flut an digitalen Informationen basieren immer mehr Anwendungen auf der Nutzung von kostengünstigen Cloud Storage Diensten. Die Anzahl der Anbieter, die diese Dienste zur Verfügung stellen, hat sich in den letzten Jahren deutlich erhöht. Um den passenden Anbieter für eine Anwendung zu finden, müssen verschiedene Kriterien individuell berücksichtigt werden. In der vorliegenden Studie wird eine Auswahl an Anbietern etablierter Basic Storage Diensten vorgestellt und miteinander verglichen. Für die Gegenüberstellung werden Kriterien extrahiert, welche bei jedem der untersuchten Anbieter anwendbar sind und somit eine möglichst objektive Beurteilung erlauben. Hierzu gehören unter anderem Kosten, Recht, Sicherheit, Leistungsfähigkeit sowie bereitgestellte Schnittstellen. Die vorgestellten Kriterien können genutzt werden, um Cloud Storage Anbieter bezüglich eines konkreten Anwendungsfalles zu bewerten. / Due to the ever-increasing flood of digital information, more and more applications make use of cost-effective cloud storage services. The number of vendors that provide these services has increased significantly in recent years. The identification of an appropriate service provider requires an individual consideration of several criteria. This survey presents a comparison of some established basic storage providers. For this comparison, several criteria are extracted that are applicable to any of the selected providers and thus allow for an assessment that is as objective as possible. The criteria include factors like costs, legal information, security, performance, and supported interfaces. The presented criteria can be used to evaluate cloud storage providers in a specific use case in order to identify the most suitable service based on individual requirements.

Les centres de données verts de dernière génération ont été conçus pour une consommation optimisée et une meilleure qualité du niveau de service SLA. Cependant,ces dernières années, le marché des centres de données augmente rapidement,et la concentration de la puissance de calcul est de plus en plus importante, ce qui fait augmenter les besoins en puissance électrique et refroidissement. Un centre de données est constitué de ressources informatiques, de systèmes de refroidissement et de distribution électrique. De nombreux travaux de recherche se sont intéressés à la réduction de la consommation des centres de données afin d'améliorer le PUE, tout en garantissant le même niveau de service. Certains travaux visent le dimensionnement dynamique des ressources en fonction de la charge afin de réduire le nombre de serveurs démarrés, d'autres cherchent à optimiser le système de refroidissement qui représente un part important de la consommation globale.Dans cette thèse, afin de réduire le PUE, nous étudions la mise en place d'un système autonome d'optimisation globale du refroidissement, qui se base sur des sources de données externes tel que la température extérieure et les prévisions météorologiques, couplé à un module de prédiction de charge informatique globale pour absorber les pics d'activité, pour optimiser les ressources utilisés à un moindre coût, tout en préservant la qualité de service. Afin de garantir un meilleur SLA, nous proposons une architecture distribuée pour déceler les anomalies de fonctionnements complexes en temps réel, en analysant de gros volumes de données provenant des milliers de capteurs du centre de données. Détecter les comportements anormaux au plus tôt, permet de réagir plus vite face aux menaces qui peuvent impacter la qualité de service, avec des boucles de contrôle autonomes qui automatisent l'administration. Nous évaluons les performances de nos contributions sur des données provenant d'un centre de donnée en exploitation hébergeant des applications réelles. / Next-generation green datacenters were designed for optimized consumption and improved quality of service level Service Level Agreement (SLA). However, in recent years, the datacenter market is growing rapidly, and the concentration of the computing power is increasingly important, thereby increasing the electrical power and cooling consumptions. A datacenter consists of computing resources, cooling systems, and power distribution. Many research studies have focused on reducing the consumption of datacenters to improve the PUE, while guaranteeing the same level of service. Some works aims the dynamic sizing of resources according to the load, to reduce the number of started servers, others seek to optimize the cooling system which represents an important part of total consumption. In this thesis, in order to reduce the PUE, we study the design of an autonomous system for global cooling optimization, which is based on external data sources such as the outside temperature and weather forecasting, coupled with an overall IT load prediction module to absorb the peaks of activity, to optimize activere sources at a lower cost while preserving service level quality. To ensure a better SLA, we propose a distributed architecture to detect the complex operation anomalies in real time, by analyzing large data volumes from thousands of sensors deployed in the datacenter. Early identification of abnormal behaviors, allows a better reactivity to deal with threats that may impact the quality of service, with autonomous control loops that automate the administration. We evaluate the performance of our contributions on data collected from an operating datacenter hosting real applications.

Cette thèse porte sur le paradigme « Mobile Edge cloud» qui rapproche le cloud des utilisateurs mobiles et qui déploie une architecture de clouds locaux dans les terminaisons du réseau. Les utilisateurs mobiles peuvent désormais décharger leurs tâches de calcul pour qu’elles soient exécutées par les femto-cellules (FCs) dotées de capacités de calcul et de stockage. Nous proposons ainsi un concept de regroupement de FCs dans des clusters de calculs qui participeront aux calculs des tâches déchargées. A cet effet, nous proposons, dans un premier temps, un algorithme de décision de déportation de tâches vers le cloud, nommé SM-POD. Cet algorithme prend en compte les caractéristiques des tâches de calculs, des ressources de l’équipement mobile, et de la qualité des liens de transmission. SM-POD consiste en une série de classifications successives aboutissant à une décision de calcul local, ou de déportation de l’exécution dans le cloud.Dans un deuxième temps, nous abordons le problème de formation de clusters de calcul à mono-utilisateur et à utilisateurs multiples. Nous formulons le problème d’optimisation relatif qui considère l’allocation conjointe des ressources de calculs et de communication, et la distribution de la charge de calcul sur les FCs participant au cluster. Nous proposons également une stratégie d’éparpillement, dans laquelle l’efficacité énergétique du système est améliorée au prix de la latence de calcul. Dans le cas d’utilisateurs multiples, le problème d’optimisation d’allocation conjointe de ressources n’est pas convexe. Afin de le résoudre, nous proposons une reformulation convexe du problème équivalente à la première puis nous proposons deux algorithmes heuristiques dans le but d’avoir un algorithme de formation de cluster à complexité réduite. L’idée principale du premier est l’ordonnancement des tâches de calculs sur les FCs qui les reçoivent. Les ressources de calculs sont ainsi allouées localement au niveau de la FC. Les tâches ne pouvant pas être exécutées sont, quant à elles, envoyées à une unité de contrôle (SCM) responsable de la formation des clusters de calculs et de leur exécution. Le second algorithme proposé est itératif et consiste en une formation de cluster au niveau des FCs ne tenant pas compte de la présence d’autres demandes de calculs dans le réseau. Les propositions de cluster sont envoyées au SCM qui évalue la distribution des charges sur les différentes FCs. Le SCM signale tout abus de charges pour que les FCs redistribuent leur excès dans des cellules moins chargées.Dans la dernière partie de la thèse, nous proposons un nouveau concept de mise en cache des calculs dans l’Edge cloud. Afin de réduire la latence et la consommation énergétique des clusters de calculs, nous proposons la mise en cache de calculs populaires pour empêcher leur réexécution. Ici, notre contribution est double : d’abord, nous proposons un algorithme de mise en cache basé, non seulement sur la popularité des tâches de calculs, mais aussi sur les tailles et les capacités de calculs demandés, et la connectivité des FCs dans le réseau. L’algorithme proposé identifie les tâches aboutissant à des économies d’énergie et de temps plus importantes lorsqu’elles sont téléchargées d’un cache au lieu d’être recalculées. Nous proposons ensuite d’exploiter la relation entre la popularité des tâches et la probabilité de leur mise en cache, pour localiser les emplacements potentiels de leurs copies. La méthode proposée est basée sur ces emplacements, et permet de former des clusters de recherche de taille réduite tout en garantissant de retrouver une copie en cache. / Mobile Edge Cloud brings the cloud closer to mobile users by moving the cloud computational efforts from the internet to the mobile edge. We adopt a local mobile edge cloud computing architecture, where small cells are empowered with computational and storage capacities. Mobile users’ offloaded computational tasks are executed at the cloud-enabled small cells. We propose the concept of small cells clustering for mobile edge computing, where small cells cooperate in order to execute offloaded computational tasks. A first contribution of this thesis is the design of a multi-parameter computation offloading decision algorithm, SM-POD. The proposed algorithm consists of a series of low complexity successive and nested classifications of computational tasks at the mobile side, leading to local computation, or offloading to the cloud. To reach the offloading decision, SM-POD jointly considers computational tasks, handsets, and communication channel parameters. In the second part of this thesis, we tackle the problem of small cell clusters set up for mobile edge cloud computing for both single-user and multi-user cases. The clustering problem is formulated as an optimization that jointly optimizes the computational and communication resource allocation, and the computational load distribution on the small cells participating in the computation cluster. We propose a cluster sparsification strategy, where we trade cluster latency for higher system energy efficiency. In the multi-user case, the optimization problem is not convex. In order to compute a clustering solution, we propose a convex reformulation of the problem, and we prove that both problems are equivalent. With the goal of finding a lower complexity clustering solution, we propose two heuristic small cells clustering algorithms. The first algorithm is based on resource allocation on the serving small cells where tasks are received, as a first step. Then, in a second step, unserved tasks are sent to a small cell managing unit (SCM) that sets up computational clusters for the execution of these tasks. The main idea of this algorithm is task scheduling at both serving small cells, and SCM sides for higher resource allocation efficiency. The second proposed heuristic is an iterative approach in which serving small cells compute their desired clusters, without considering the presence of other users, and send their cluster parameters to the SCM. SCM then checks for excess of resource allocation at any of the network small cells. SCM reports any load excess to serving small cells that re-distribute this load on less loaded small cells. In the final part of this thesis, we propose the concept of computation caching for edge cloud computing. With the aim of reducing the edge cloud computing latency and energy consumption, we propose caching popular computational tasks for preventing their re-execution. Our contribution here is two-fold: first, we propose a caching algorithm that is based on requests popularity, computation size, required computational capacity, and small cells connectivity. This algorithm identifies requests that, if cached and downloaded instead of being re-computed, will increase the computation caching energy and latency savings. Second, we propose a method for setting up a search small cells cluster for finding a cached copy of the requests computation. The clustering policy exploits the relationship between tasks popularity and their probability of being cached, in order to identify possible locations of the cached copy. The proposed method reduces the search cluster size while guaranteeing a minimum cache hit probability.

Cette thèse présente deux contributions. La première contribution consiste en l’étude des métriques de performance permettant de superviser l’activité des serveurs physiques et des machines virtuelles s’exécutant sur les hyperviseurs VMware et KVM. Cette étude met en avant les compteurs clés et propose des analyses avancées dans l’objectif de détecter ou prévenir d’anomalies liées aux quatreres sources principales d’un centre de données : le processeur, la mémoire, le disque et le réseau. La seconde contribution porte sur un outil pour la détection de machines virtuelles à comportements pré-déterminés et/ou atypiques. La détection de ces machines virtuelles à plusieurs objectifs. Le premier, permettre d’optimiser l’utilisation des ressources matérielles en libérant des ressources par la suppression de machines virtuelles inutiles ou en les redimensionnant. Le second, optimiser le fonctionnement de l’infrastructure en détectant les machines sous-dimensionnées, surchargées ou ayant une activité différente des autres machines virtuelles de l’infrastructure. / This thesis presents two contributions. The first contribution is the study of key performance indicators to monitor physical and virtual machines activity running on VMware and KVM hypervisors. This study highlights performance metrics and provides advanced analysis with the aim to prevent or detect abnormalities related to the four main resources of a datacenter: CPU, memory, disk and network. Thesecond contribution relates to a tool for virtual machines with pre-determined and / or atypical behaviors detection. The detection of these virtual machines has several objectives. First, optimize the use of hardware resources by freeing up resources by removing unnecessary virtual machines or by resizing those oversized. Second, optimize infrastructure performance by detecting undersized or overworked virtual machines and those having an atypical activity.

Cloud computing has a significant impact on information and communication technology (ICT) and is one of the most important technological drivers of the digitalization of enterprises. However, due to the increasing dissemination of cloud services and the growing number of cloud service providers (CSPs), the uncertainty and risks for user companies in adopting cloud services have also increased. In this paper, we address those aspects from the perspective of the CSPs. We identified relevant literature and studies and conducted interviews with business experts from 16 German CSPs. In our results, we present current customer requirements and barriers to using cloud services from a provider’s viewpoint and identify the actions of and obstacles for CSPs in meeting the needs and constraints of the customers. Finally, we identify current and future challenges for CSPs in dealing with customer requirements and barriers by addressing their root causes. One of the main challenges from the CSPs’ perspective is addressing customers appropriately and building relationships of trust. This also “forces” changes in the sales processes. In this process, the essential challenges can be identified as an increase in complexity and a simultaneous simplification of specific sales activities. Therefore, the necessity arises for the continuous support of business relationships through value-adding and additional services. However, this results in another challenge for the CSPs – Namely, to find the right balance between standardization and meeting customer-specific requirements. In our paper, we show that the perspective of the CSPs is rarely discussed in the literature. Nevertheless, understanding the perceptions of the providers and their actions and measures is essential for future research activities in the field of cloud service selection. Comparing the customers’ perspectives and viewpoints with the CSPs’ actions will enhance the development of a holistic selection approach for future cloud projects. Therefore, our paper’s contribution to research is also the identification of this missing integration.

L’arrivée de la prochaine technologie 5G va permettre la connectivité des billions de terminaux mobiles et donc une énorme augmentation du trafic de données. A cet égard, les fournisseurs des services Cloud doivent posséder les infrastructures physiques capables de supporter cette explosion de trafic. Malheureusement, les architectures filaires conventionnelles des centres de données deviennent staturées et la congestion des équipements d’interconnexion est souvent atteinte. Dans cette thèse, nous explorons une approche récente qui consiste à augmenter le réseau filaire du centre de données avec l’infrastructure sans fil. En effet, nous exploitons une nouvelle technologie émergente, la technologie 60 GHz, qui assure un débit de l’ordre de 7 Gbits/s afin d’améliorer la QoS. Nous concevons une architecture hybride (filaire/sans fil) du réseau de centre de données basée sur : i) le modèle "Cisco’s Massively Scalable Data Center" (MSDC), et ii) le standard IEEE 802.11ad. Dans une telle architecture, les serveurs sont regroupés dans des racks, et sont interconnectés à travers un switch Ethernet, appelé top-of-rack (ToR) switch. Chaque ToR switch possède plusieurs antennes utilisées en parallèle sur différents canaux sans fil. L’objectif final consiste à minimiser la congestion du réseau filaire, en acheminant le maximum du trafic sur les canaux sans fil. Pour ce faire, cette thèse se focalise sur l’optimisation du routage et de l’allocation des canaux sans fil pour les communications inter-rack, au sein d’un centre de données hybride (HDCN). Ce problème étant NP-difficile, nous allons procéder en trois étapes. En premier lieu, on considère le cas des communications à un saut, où les racks sont placés dans le même rayon de transmission. Nous proposons un nouvel algorithme d’allocation des canaux sans fil dans les HDCN, qui permet d’acheminer le maximum des communications en sans-fil, tout en améliorant les performances réseau en termes de débit et délai. En second lieu, nous nous adressons au cas des communications à plusieurs sauts, où les racks ne sont pas dans le même rayon de transmission. Nous allons proposer une nouvelle approche optimale traitant conjointement le problème du routage et de l’allocation de canaux sans fils dans le HDCN, pour chaque communication, dans un mode online. En troisième étape, nous proposons un nouvel algorithme qui calcule conjointement le routage et l’allocation des canaux pour un ensemble des communications arrivant en mode batch (i.e., par lot). En utilisant le simulateur réseau QualNet, considérant toute la pile TCP/IP, les résultats obtenus montrent que nos propositions améliorent les performances comparées aux méthodes de l’état de l’art / The high proliferation of smart devices and online services allows billions of users to connect with network while deploying a vast range of applications. Particularly, with the advent of the future 5G technology, it is expected that a tremendous mobile and data traffic will be crossing Internet network. In this regard, Cloud service providers are urged to rethink their data center architectures in order to cope with this unprecedented traffic explosion. Unfortunately, the conventional wired infrastructures struggle to resist to such a traffic growth and become prone to serious congestion problems. Therefore, new innovative techniques are required. In this thesis, we investigate a recent promising approach that augments the wired Data Center Network (DCN) with wireless communications. Indeed, motivated by the feasibility of the new emerging 60 GHz technology, offering an impressive data rate (≈ 7 Gbps), we envision, a Hybrid (wireless/wired) DCN (HDCN) architecture. Our HDCN is based on i) Cisco’s Massively Scalable Data Center (MSDC) model and ii) IEEE 802.11ad standard. Servers in the HDCN are regrouped into racks, where each rack is equipped with a: i) Ethernet top-of-rack (ToR) switch and ii) set of wireless antennas. Our research aims to optimize the routing and the allocation of wireless resources for inter-rack communications in HDCN while enhancing network performance and minimizing congestion. The problem of routing and resource allocation in HDCN is NP-hard. To deal with this difficulty, we will tackle the problem into three stages. In the first stage, we consider only one-hop inter-rack communications in HDCN, where all communicating racks are in the same transmission range. We will propound a new wireless channel allocation approach in HDCN to hardness both wireless and wired interfaces for incoming flows while enhancing network throughput. In the second stage, we deal with the multi-hop communications in HDCN where communicating racks can not communicate in one single-hop wireless path. We propose a new approach to jointly route and allocate channels for each single communication flow, in an online way. Finally, in the third stage, we address the batched arrival of inter-rack communications to the HDCN so as to further optimize the usage of wireless and wired resources. For that end, we propose: i) a heuristic-based and ii) an approximate, solutions, to solve the joint batch routing and channel assignment. Based on extensive simulations conducted in QualNet simulator while considering the full protocol stack, the obtained results for both real workload and uniform traces, show that our proposals outperform the prominent related strategies

Esta tese de Doutorado tem como objetivo apresentar uma arquitetura para metaescalonamento verde com provisão de qualidade de serviço em uma nuvem privada denominada GreenMACC. Essa nova arquitetura oferece a automatização na escolha de políticas em quatro estágios de escalonamento de uma nuvem privada, permitindo cumprir a negociação que foi estabelecida com o usuário. Devido a essa função, é possível garantir que o GreenMACC se comporte seguindo os princípios da computação verde sem deixar de se preocupar com a qualidade do serviço. Nesta tese o GreenMACC é apresentado, detalhado, discutido, validado e avaliado. Com os resultados apresentados pode-se concluir que a arquitetura proposta mostrou-se consistente, permitindo a execução dos serviços requisitados com diversas políticas de escalonamento em todos os seus estágios. Além disso, demonstrou flexibilidade em receber novas políticas, com focos verde e de qualidade de serviço, e eficiência na escolha das políticas de escalonamento de acordo com a negociação feita com o usuário. / This PhD thesis aims to present an architecture for green metascheduling with provision of quality of service in a private cloud called GreenMACC. This new architecture offers the possibility of choosing automatically the four stage scheduling policies of a private cloud, allowing to reach the users negotiation. As a result of this function, it is possible to ensure that GreenMACCs behavior follows the green computing principles and also is worried about the quality of the service. In this thesis Green- MACC is presented, particularized, discussed, validated and evaluated. The results show that the proposed architecture is consistent, allowing the execution of the requested services considering various scheduling policies in the stages. Moreover, GreenMACC proves to be flexible as allows new policies, focusing on green and quality of service, and to be efficient as chooses the scheduling policies following the users negotiation.

Submitted by Fabio Sobreira Campos da Costa (fabio.sobreira@ufpe.br) on 2016-03-15T13:05:35Z No. of bitstreams: 2 license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) Disserta__o_Igor_Costa__Copy_(1).pdf: 11507063 bytes, checksum: f631fc6dd87314e89ea560a118301875 (MD5) / Made available in DSpace on 2016-03-15T13:05:35Z (GMT). No. of bitstreams: 2 license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) Disserta__o_Igor_Costa__Copy_(1).pdf: 11507063 bytes, checksum: f631fc6dd87314e89ea560a118301875 (MD5) Previous issue date: 2015-08-31 / As limitações da computação móvel abrem caminhos para utilizar recursos de computação em nuvem voltadas à dispositivos móveis, sendo este o principal objetivo da Mobile Cloud Computing (MCC). Questões como armazenamento e processamento podem afetar a disponibilidade de um serviço no dispositivo móvel, assim, para minimizar esses problemas é possível o particionamento da aplicação em frontend e backend. Os serviços de nuvem auxiliam esse processo com a utilização de ambientes Mobile Backend-as-a-Service (MBaaS), que permitem os desenvolvedores conectar o backend de suas aplicações para o armazenamento em nuvem. Uma plataforma de MBaaS oferece um serviço de sincronização completa para aplicações móveis. Uma vez que os dados armazenados no dispositivo móvel estão sincronizados com os centros de dados distribuídos, a disponibilidade do sistema no lado servidor é um atributo fundamental que requer investigação, pois sistemas computacionais tendem a falhar. As falhas podem ocorrer em hardwares, softwares e meios de conexão, acarretando assim, em prejuízos financeiros e comprometendo a credibilidade das empresas provedoras do serviço. Os administradores necessitam de mecanismos para estimar a disponibilidade de seus sistemas, podendo definir Service Level Agreement (SLA) com mais propriedade. Assim, modelos analíticos podem ser utilizados para avaliar a disponibilidade destes tipos de ambientes, bem como auxiliar a mitigar o downtime, aumentando a disponibilidade do serviço. Este trabalho propõe modelos analíticos para avaliar a disponibilidade desses ambientes. Para tanto, foi adotada uma metodologia: primeiramente definiu-se a arquitetura básica do serviço; a qual foi modelada a partir de um modelo hierárquico, composto de diagramas de blocos de confiabilidade (RBD) e cadeias de Markov de tempo contínuo (CTMC) e validado através de um testbed de injeção de falhas e reparos em um ambiente real. Baseado no modelo de serviço proposto, foi efetuada a análise de sensibilidade, que identificou o sistema como componente crítico. A partir disto, foram sugeridos modelos hierárquicos que representem o ambiente de nuvem, e com base neste ambiente, através da técnica de análise de sensibilidade, foram propostas quatro arquiteturas, sendo estas avaliados em termos de disponibilidade e downtime anual. Os resultados demonstram que a implementação de um processo de recuperação automática sobre o componente de software, Java Virtual Machine, reduz o downtime anual na arquitetura básica em cerca de 10%, bem como é possível observar que no ambiente de nuvem utilizando o mecanismo de redundância warm-standby nos nós e no frontend apresenta efetiva melhora na disponibilidade. Desta forma, a presente pesquisa pode orientar os administradores de sistemas MBaaS no planejamento de suas políticas de manutenção. / The mobile computer restrictions propose new ways to use cloud computing resources aimed at mobile devices, this is the Mobile Cloud Computing (MCC) primary goal. Issues such as storage and processing can impact the service availability on the mobile device. With the reducing purpose, these questions are its possible divide the application into two pieces, frontend, and backend. The cloud services assist this process with the Mobile Backend-as-a- Service (MBaaS) use. This tool allows the developers connect yours application backend to cloud storage. The MBaaS OpenMobster platform offers complete synchronization service to mobile applications. Since the data stored on mobile was synchronized distributed data center, the server’s system availability is an essential attribute and request attention, because computer systems will sometimes fail. The failures can happen on components variety as hardware, software and connections, causing financial losses and reliability compromising of the companies, which offer this services. The administrators need tools to project the system availability, with this they can define the SLA with more assurance. Analytic models can be used to availability evaluate in this environment and mitigate the downtime risk, this improves the service availability. This work primary goal is proposed analytic models to availability evaluated in these environments. It was adopted a methodology as follow: First, define the base service architecture. It was modeled by use a hierarchical model, using a reliability block diagram (RBD) and continuous-time Markov chain (CTMC). The validation considers a fault injection testbed and repairs on real environment. Considering the model proposed, it was done sensitivity analysis, these results present the system as a critical component. This analysis was proposed hierarchical models to represents cloud environment. On these sensibility analysis, a background was offered four scenarios. The scenarios were evaluated to determine the availability and annual downtime. The results show that the an automatic recovery implementation process on the software component, Java Virtual Machine, decrease the annual downtime on base architecture to 10%. The results present the availability improvement when adopted redundancy strategy as warm standby on a cloud environment. This way, the work can guide the MBaaS system administrators in planning their maintenance policies.