Privacy in cloud computing

Tese de mestrado em Segurança Informática, apresentada à Universidade de Lisboa, através da Faculdade de Ciências, 2010O paradigma cloud computing está progressivamente a integrar-se nas tecnologias de informação e é também visto por muitos como a próxima grande viragem na indústria da computação. A sua integração significa grandes alterações no modo como olhamos para a segurança dos dados de empresas que decidem confiar informação confidencial aos fornecedores de serviços cloud. Esta alteração implica um nível muito elevado de confiança no fornecedor do serviço. Ao mudar para a cloud, uma empresa relega para o fornecedor do serviço controlo sobre os seus dados, porque estes vão executar em hardware que é propriedade do fornecedor e sobre o qual a empresa não tem qualquer controlo. Este facto irá pesar muito na decisão, de mudar para a cloud, de empresas que tratam informação delicada (p.ex., informação médica ou financeira). Neste trabalho propomos demonstrar de que forma um administrador malicioso, com acesso ao hardware do fornecedor, consegue violar a privacidade dos dados que o utilizador da cloud confiou ao prestador desses serviços. Definimos como objectivo uma análise detalhada de estratégias de ataque que poderão ajudar um administrador malicioso a quebrar a privacidade de clientes da cloud, bem como a eficácia demonstrada contra esses mesmos ataques por mecanismos de protecção já propostos para a cloud. Pretendemos que este trabalho seja capaz de alertar a comunidade científica para a gravidade dos problemas de segurança que actualmente existem na cloud e, que ao mesmo tempo, sirva como motivação para uma acção célere desta, de forma a encontrar soluções para esses problemas.The paradigm of cloud computing is progressively integrating itself in the Information Technology industry and it is also seen by many experts as the next big shift in this industry. This integration implies considerable alterations in the security schemes used to ensure that the privacy of confidential information, companies entrust to the cloud provider, is kept. It also means that the level of trust in the cloud provider must be considerably high. When moving to the cloud, a company relinquishes control over its data to the cloud provider. This happens because, when operating in the cloud, the data is going to execute on top of the hardware owned by the cloud provider and, in this scenario, the client has no control over that hardware. Companies that deal with sensitive data (e.g., medical or financial records) have to weigh the importance of this problem when considering moving their data to the cloud. In this work, we provide a demonstration of how a malicious administrator, with access to the hardware of the cloud provider, is capable of violating the privacy of the data entrusted to the cloud provider by his clients. Our objective is to offer a detailed analysis of attack strategies that can be used by a malicious administrator to break the privacy of cloud clients, as well as the level of efficacy demonstrated by some protection mechanism that have already been proposed for the cloud. We also hope that this work is capable of capturing the attention of the research community to the security problems existent in the cloud and, that at the same time, it works as a motivation factor for a prompt action in order to find solutions for these problems

BlobCR: Efficient Checkpoint-Restart for HPC Applications on IaaS Clouds using Virtual Disk Image Snapshots

International audienceInfrastructure-as-a-Service (IaaS) cloud computing is gaining significant interest in industry and academia as an alternative platform for running scientific applications. Given the dynamic nature of IaaS clouds and the long runtime and resource utilization of such applications, an efficient checkpoint-restart mechanism becomes paramount in this context. This paper proposes a solution to the aforementioned challenge that aims at minimizing the storage space performance overhead of checkpoint-restart. We introduce a framework that combines checkpoint-restart protocols at guest level with virtual machine (VM) disk-image multi-snapshotting and multi-deployment at host level in order to efficiently capture and potentially roll back the complete state of the application, including file system modifications. Experiments on the G5K testbed show substantial improvement for MPI applications over existing approaches, both for the case when customized checkpointing is available at application level and the case when it needs to be handled at process level

Virtual Machine Lifecycle Management in Grid and Cloud Computing

Virtualisierungstechnologie ist die Grundlage für zwei wichtige Konzepte: Virtualized Grid Computing und Cloud Computing. Ersteres ist eine Erweiterung des klassischen Grid Computing. Es hat zum Ziel, die Anforderungen kommerzieller Nutzer des Grid hinsichtlich der Isolation von gleichzeitig ausgeführten Batch-Jobs und der Sicherheit der zugehörigen Daten zu erfüllen. Dabei werden Anwendungen in virtuellen Maschinen ausgeführt, um sie voneinander zu isolieren und die von ihnen verarbeiteten Daten vor anderen Nutzern zu schützen. Darüber hinaus löst Virtualized Grid Computing das Problem der Softwarebereitstellung, eines der bestehenden Probleme des klassischen Grid Computing. Cloud Computing ist ein weiteres Konzept zur Verwendung von entfernten Ressourcen. Der Fokus dieser Dissertation bezüglich Cloud Computing liegt auf dem “Infrastructure as a Service Modell”, das Ideen des (Virtualized) Grid Computing mit einem neuartigen Geschäftsmodell kombiniert. Dieses besteht aus der Bereitstellung von virtuellen Maschinen auf Abruf und aus einem Tarifmodell, bei dem lediglich die tatsächliche Nutzung berechnet wird. Der Einsatz von Virtualisierungstechnologie erhöht die Auslastung der verwendeten (physischen) Rechnersysteme und vereinfacht deren Administration. So ist es beispielsweise möglich, eine virtuelle Maschine zu klonen oder einen Snapshot einer virtuellen Maschine zu erstellen, um zu einem definierten Zustand zurückkehren zu können. Jedoch sind noch nicht alle Probleme im Zusammenhang mit der Virtualisierungstechnologie gelöst. Insbesondere entstehen durch den Einsatz in den sehr dynamischen Umgebungen des Virtualized Grid Computing und des Cloud Computing neue Herausforderungen für die Virtualisierungstechnologie. Diese Dissertation befasst sich mit verschiedenen Aspekten des Einsatzes von Virtualisierungstechnologie in Virtualized Grid und Cloud Computing Umgebungen. Zunächst wird der Lebenszyklus von virtuellen Maschinen in diesen Umgebungen untersucht, und es werden Modelle dieses Lebenszyklus entwickelt. Anhand der entwickelten Modelle werden Probleme identifiziert und Lösungen für diese Probleme entwickelt. Der Fokus liegt dabei auf den Bereichen Speicherung, Bereitstellung und Ausführung von virtuellen Maschinen. Virtuelle Maschinen werden üblicherweise in so genannten Disk Images, also Abbildern von virtuellen Festplatten, gespeichert. Dieses Format hat nicht nur Einfluss auf die Speicherung von größeren Mengen virtueller Maschinen, sondern auch auf deren Bereitstellung. In den untersuchten Umgebungen hat es zwei konkrete Nachteile: es verschwendet Speicherplatz und es verhindert eine effiziente Bereitstellung von virtuellen Maschinen. Maßnahmen zur Steigerung der Sicherheit von virtuellen Maschinen haben auf alle drei genannten Bereiche Einfluss. Beispielsweise sollte vor der Bereitstellung einer virtuellen Maschine geprüft werden, ob die darin installierte Software noch aktuell ist. Weiterhin sollte die Ausführungsumgebung Möglichkeiten bereitstellen, um die virtuelle Infrastruktur wirksam zu überwachen. Die erste in dieser Dissertation vorgestellte Lösung ist das Konzept der Image Composition. Es beschreibt die Komposition eines kombinierten Disk Images aus mehreren Schichten. Dadurch können Teile der einzelnen Schichten, die von mehreren virtuellen Maschinen verwendet werden, zwischen diesen geteilt und somit der Speicherbedarf für die Gesamtheit der virtuellen Maschinen reduziert werden. Der Marvin Image Compositor ist die Umsetzung dieses Konzepts. Die zweite Lösung ist der Marvin Image Store, ein Speichersystem für virtuelle Maschinen, das nicht auf den traditionell genutzten Disk Images basiert, sondern die darin enthaltenen Daten und Metadaten auf eine effiziente Weise getrennt voneinander speichert. Weiterhin werden vier Lösungen vorgestellt, die die Sicherheit von virtuellen Maschine verbessern können: Der Update Checker ist eine Lösung, die es ermöglicht, veraltete Software in virtuellen Maschinen zu identifizieren. Dabei spielt es keine Rolle, ob die jeweilige virtuelle Maschine gerade ausgeführt wird oder nicht. Die zweite Sicherheitslösung ermöglicht es, mehrere virtuelle Maschinen, die auf dem Konzept der Image Composition basieren, zentral zu aktualisieren. Das bedeutet, dass die einmalige Installation einer neuen Softwareversion ausreichend ist, um mehrere virtuelle Maschinen auf den neuesten Stand zu bringen. Die dritte Sicherheitslösung namens Online Penetration Suite ermöglicht es, virtuelle Maschinen automatisiert nach Schwachstellen zu durchsuchen. Die Überwachung der virtuellen Infrastruktur auf allen Ebenen ist der Zweck der vierten Sicherheitslösung. Zusätzlich zur Überwachung ermöglicht diese Lösung auch eine automatische Reaktion auf sicherheitsrelevante Ereignisse. Schließlich wird ein Verfahren zur Migration von virtuellen Maschinen vorgestellt, welches auch ohne ein zentrales Speichersystem eine effiziente Migration ermöglicht

Dynamic Scale-out Mechanisms for Partitioned Shared-Nothing Databases

For a database system used in pay-per-use cloud environments, elastic scaling becomes an essential feature, allowing for minimizing costs while accommodating fluctuations of load. One approach to scalability involves horizontal database partitioning and dynamic migration of partitions between servers. We define a scale-out operation as a combination of provisioning a new server followed by migration of one or more partitions to the newly-allocated server. In this thesis we study the efficiency of different implementations of the scale-out operation in the context of online transaction processing (OLTP) workloads. We designed and implemented three migration mechanisms featuring different strategies for data transfer. The first one is based on a modification of the Xen hypervisor, Snowflock, and uses on-demand block transfers for both server provisioning and partition migration. The second one is implemented in a database management system (DBMS) and uses bulk transfers for partition migration, optimized for higher bandwidth utilization. The third one is a conventional application, using SQL commands to copy partitions between servers. We perform an experimental comparison of those scale-out mechanisms for disk-bound and CPU-bound configurations. When comparing the mechanisms we analyze their impact on whole-system performance and on the experience of individual clients

Система безперервної програмної обробки з використанням хмарних технологій

Робота публікується згідно наказу ректора від 29.12.2020 р. №580/од "Про розміщення кваліфікаційних робіт вищої освіти в репозиторії НАУ". Керівник проекту: к.т.н., доцент Телешко Ігор ВасильовичIn the modern economy, the use of digital tools in business decisions plays a defining role. With the increasing complexity of high-tech platforms, the continuity of critical IT systems is becoming an important factor. This trend also affected software development. Nowadays, a very high interest in the tasks of optimization, saving time and money for large and small businesses is determined by the need to automate both software processing and continuous integration in real time in cloud systems. Therefore, now the search and implementation of effective methods and principles of continuous software processing using cloud computing systems. I believe that more promising directions for solving this problem is based on the use of cloud platforms and services, as the most advanced solution to the problems of ensuring uninterrupted integration and delivery to ensure processing.У сучасній економіці використання цифрових інструментів у прийнятті бізнес-рішень відіграє визначальну роль. Зі збільшенням складності високотехнологічних платформ безперервність критично важливих ІТ-систем стає важливим фактором. Ця тенденція також вплинула на розробку програмного забезпечення. У наш час дуже високий інтерес до завдань оптимізації, економії часу та грошей для великого та малого бізнесу визначається необхідністю автоматизації як обробки програмного забезпечення, так і постійної інтеграції в реальному часі в хмарні системи. Тому зараз здійснюється пошук та впровадження ефективних методів та принципів безперервної обробки програмного забезпечення з використанням систем хмарних обчислень. Я вважаю, що більш перспективні напрямки вирішення цієї проблеми засновані на використанні хмарних платформ та сервісів як найдосконалішого рішення проблем забезпечення безперебійної інтеграції та доставки для забезпечення обробки

A Personal Virtual Computer Recorder

Continuing advances in hardware technology have enabled the proliferation of faster, cheaper, and more capable personal computers. Users of all backgrounds rely on their computers to handle ever-expanding information, communication, and computation needs. As users spend more time interacting with their computers, it is becoming increasingly important to archive and later search the knowledge, ideas and information that they have viewed through their computers. However, existing state-of-the-art web and desktop search tools fail to provide a suitable solution, as they focus on static, accessible documents in isolation. Thus, finding the information one has viewed among the ever-increasing and chaotic sea of data available from a computer remains a challenge. This dissertation introduces DejaView, a personal virtual computer recorder that enhances personal computers with the ability to process display-centric content to help users with all the information they see through their computers. DejaView continuously records a user's session to provide a complete WYSIWYS (What You Search Is What You've Seen) record of a desktop computing experience, enabling users to playback, browse, search, and revive records, making it easier to retrieve and interact with information they have seen before. DejaView records visual output, checkpoints corresponding application and file system states, and captures onscreen text with contextual information to index the record. A user can then browse and search the record for any visual information that has been previously displayed on the desktop, and revive and interact with the desktop computing state corresponding to any point in the record. DejaView introduces new, transparent operating system, display and file system virtualization techniques and novel semantic display-centric information recording, and combines them to provide its functionality without any modifications to applications, window systems, or operating system kernels. Our results demonstrate that DejaView can provide continuous low-overhead recording without any user-noticeable performance degradation, and allows users to playback, browse, search, and time-travel back to records fast enough for interactive use. This dissertation also demonstrates how DejaView's execution virtualization and recording extend beyond the desktop recorder context. We introduce a coordinated, parallel checkpoint-restart mechanism for distributed applications that minimizes synchronization overhead and uniquely supports complete checkpoint and restart of network state in a transport protocol independent manner, for both reliable and unreliable protocols. We introduce a scalable system that enables significant energy saving by migrating network state and applications off of idle hosts allowing the hosts to enter low-power suspend state, while preserving their network presence. Finally, we show how our techniques can be integrated into a commodity operating system, mainline Linux, thereby allowing the entire operating systems community to benefit from mature checkpoint-restart that is transparent, secure, reliable, efficient, and integral to the Linux kernel

Mobility models, mobile code offloading, and p2p networks of smartphones on the cloud

It was just a few years ago when I bought my first smartphone. And now, (almost) all of my friends possess at least one of these powerful devices. International Data Corporation (IDC) reports that smartphone sales showed strong growth worldwide in 2011, with 491.4 million units sold – up to 61.3 percent from 2010. Furthermore, IDC predicts that 686 million smartphones will be sold in 2012, 38.4 percent of all handsets shipped. Silently, we are becoming part of a big mobile smartphone network, and it is amazing how the perception of the world is changing thanks to these small devices. If many years ago the birth of Internet enabled the possibility to be online, smartphones nowadays allow to be online all the time. Today we use smartphones to do many of the tasks we used to do on desktops, and many new ones. We browse the Internet, watch videos, upload data on social networks, use online banking, find our way by using GPS and online maps, and communicate in revolutionary ways. Along with these benefits, these fancy and exciting devices brought many challenges to the research area of mobile and distributed systems. One of the first problems that captured our attention was the study of the network that potentially could be created by interconnecting all the smartphones together. Typically, these devices are able to communicate with each other in short distances by using com- munication technologies such as Bluetooth or WiFi. The network paradigm that rises from this intermittent communication, also known as Pocket Switched Network (PSN) or Opportunistic Network ([10, 11]), is seen as a key technology to provide innovative services to the users without the need of any fixed infrastructure. In PSNs nodes are short range communicating devices carried by humans. Wireless communication links are created and dropped in time, depending on the physical distance of the device holders. From one side, social relations among humans yield recurrent movement patterns that help researchers design and build protocols that efficiently deliver messages to destinations ([12, 13, 14] among others). The complexity of these social relations, from the other side, makes it difficult to build simple mobility models, that in an efficient way, generate large synthetic mobility traces that look real. Traces that would be very valuable in protocol validation and that would replace the limited experimentally gathered data available so far. Traces that would serve as a common benchmark to researchers worldwide on which to validate existing and yet to be designed protocols. With this in mind we start our study with re-designing SWIM [15], an already exist- ing mobility model shown to generate traces with similar properties of that of existing real ones. We make SWIM able to easily generate large (small)-scale scenarios, starting from known small (large)-scale ones. To the best of our knowledge, this is the first such study. In addition, we study the social aspects of SWIM-generated traces. We show how to SWIM-generate a scenario in which a specific community structure of nodes is required. Finally, exploiting the scaling properties of SWIM, we present the first analysis of the scal- ing capabilities of several forwarding protocols such as Epidemic [16], Delegation [13], Spray&Wait [14], and BUBBLE [12]. The first results of these works appeared in [1], and, at the time of writing, [2] is accepted with minor revision. Next, we take into account the fact that in PSNs cannot be assumed full cooperation and fairness among nodes. Selfish behavior of individuals has to be considered, since it is an inherent aspect of humans, the device holders (see [17], [18]). We design a market-based mathematical framework that enables heterogeneous mobile users in an opportunistic mobile network to compromise optimally and efficiently on their QoS 3 demands. The goal of the framework is to satisfy each user with its achieved (lesser) QoS, and at the same time maximize the social welfare of users in the network. We base our study on the consideration that, in practice, users are generally tolerant on accepting lesser QoS guarantees than what they demand, with the degree of tolerance varying from user to user. This study is described in details in Chapter 2 of this dissertation, and is included in [3]. In general, QoS could be parameters such as response time, number of computations per unit time, allocated bandwidth, etc. Along the way toward our study of the smartphone-world, there was the big advent of mobile cloud computing—smartphones getting help from cloud-enabled services. Many researchers started believing that the cloud could help solving a crucial problem regarding smartphones: improve battery life. New generation apps are becoming very complex — gaming, navigation, video editing, augmented reality, speech recognition, etc., — which require considerable amount of power and energy, and as a result, smartphones suffer short battery lifetime. Unfortunately, as a consequence, mobile users have to continually upgrade their hardware to keep pace with increasing performance requirements but still experience battery problems. Many recent works have focused on building frameworks that enable mobile computation offloading to software clones of smartphones on the cloud (see [19, 20] among others), as well as to backup systems for data and applications stored in our devices [21, 22, 23]. However, none of these address dynamic and scalability features of execution on the cloud. These are very important problems, since users may request different computational power or backup space based on their workload and deadline for tasks. Considering this and advancing on previous works, we design, build, and implement the ThinkAir framework, which focuses on the elasticity and scalability of the server side and enhances the power of mobile cloud computing by parallelizing method execution using multiple Virtual Machine (VM) images. We evaluate the system using a range of benchmarks starting from simple micro-benchmarks to more complex applications. First, we show that the execution time and energy consumption decrease two orders of magnitude for the N-queens puzzle and one order of magnitude for a face detection and a virus scan application, using cloud offloading. We then show that a parallelizable application can invoke multiple VMs to execute in the cloud in a seamless and on-demand manner such as to achieve greater reduction on execution time and energy consumption. Finally, we use a memory-hungry image combiner tool to demonstrate that applications can dynamically request VMs with more computational power in order to meet their computational requirements. The details of the ThinkAir framework and its evaluation are described in Chapter 4, and are included in [6, 5]. Later on, we push the smartphone-cloud paradigm to a further level: We develop Clone2Clone (C2C), a distributed platform for cloud clones of smartphones. Along the way toward C2C, we study the performance of device-clones hosted in various virtualization environments in both private (local servers) and public (Amazon EC2) clouds. We build the first Amazon Customized Image (AMI) for Android-OS—a key tool to get reliable performance measures of mobile cloud systems—and show how it boosts up performance of Android images on the Amazon cloud service. We then design, build, and implement Clone2Clone, which associates a software clone on the cloud to every smartphone and in- terconnects the clones in a p2p fashion exploiting the networking service within the cloud. On top of C2C we build CloneDoc, a secure real-time collaboration system for smartphone users. We measure the performance of CloneDoc on a testbed of 16 Android smartphones and clones hosted on both private and public cloud services and show that C2C makes it possible to implement distributed execution of advanced p2p services in a network of mobile smartphones. The design and implementation of the Clone2Clone platform is included in [7], recently submitted to an international conference. We believe that Clone2Clone not only enables the execution of p2p applications in a network of smartphones, but it can also serve as a tool to solve critical security problems. In particular, we consider the problem of computing an efficient patching strategy to stop worm spreading between smartphones. We assume that the worm infects the devices and spreads by using bluetooth connections, emails, or any other form of communication used by the smartphones. The C2C network is used to compute the best strategy to patch the smartphones in such a way that the number of devices to patch is low (to reduce the load on the cellular infrastructure) and that the worm is stopped quickly. We consider two well defined worms, one spreading between the devices and one attacking the cloud before moving to the real smartphones. We describe CloudShield [8], a suite of protocols running on the peer-to-peer network of clones; and show by experiments with two different datasets (Facebook and LiveJournal) that CloudShield outperforms state-of-the-art worm-containment mechanisms for mobile wireless networks. This work is done in collaboration with Marco Valerio Barbera, PhD colleague in the same department, who contributed mainly in the implementation and testing of the malware spreading and patching strategies on the different datasets. The communication between the real devices and the cloud, necessary for mobile com- putation offloading and smartphone data backup, does certainly not come for free. To the best of our knowledge, none of the works related to mobile cloud computing explicitly studies the actual overhead in terms of bandwidth and energy to achieve full backup of both data/applications of a smartphone, as well as to keep, on the cloud, up-to-date clones of smartphones for mobile computation offload purposes. In the last work during my PhD—again, in collaboration with Marco Valerio Barbera—we studied the feasibility of both mobile computation offloading and mobile software/data backup in real-life scenarios. This joint work resulted in a recent publication [9] but is not included in this thesis. As in C2C, we assume an architecture where each real device is associated to a software clone on the cloud. We define two types of clones: The off-clone, whose purpose is to support computation offloading, and the back-clone, which comes to use when a restore of user’s data and apps is needed. We measure the bandwidth and energy consumption incurred in the real device as a result of the synchronization with the off-clone or the back-clone. The evaluation is performed through an experiment with 11 Android smartphones and an equal number of clones running on Amazon EC2. We study the data communication overhead that is necessary to achieve different levels of synchronization (once every 5min, 30min, 1h, etc.) between devices and clones in both the off-clone and back-clone case, and report on the costs in terms of energy incurred by each of these synchronization frequencies as well as by the respective communication overhead. My contribution in this work is focused mainly on the experimental setup, deployment, and data collection

Mobility models, mobile code offloading, and p2p networks of smartphones on the cloud

It was just a few years ago when I bought my first smartphone. And now, (almost) all of my friends possess at least one of these powerful devices. International Data Corporation (IDC) reports that smartphone sales showed strong growth worldwide in 2011, with 491.4 million units sold – up to 61.3 percent from 2010. Furthermore, IDC predicts that 686 million smartphones will be sold in 2012, 38.4 percent of all handsets shipped. Silently, we are becoming part of a big mobile smartphone network, and it is amazing how the perception of the world is changing thanks to these small devices. If many years ago the birth of Internet enabled the possibility to be online, smartphones nowadays allow to be online all the time. Today we use smartphones to do many of the tasks we used to do on desktops, and many new ones. We browse the Internet, watch videos, upload data on social networks, use online banking, find our way by using GPS and online maps, and communicate in revolutionary ways. Along with these benefits, these fancy and exciting devices brought many challenges to the research area of mobile and distributed systems. One of the first problems that captured our attention was the study of the network that potentially could be created by interconnecting all the smartphones together. Typically, these devices are able to communicate with each other in short distances by using com- munication technologies such as Bluetooth or WiFi. The network paradigm that rises from this intermittent communication, also known as Pocket Switched Network (PSN) or Opportunistic Network ([10, 11]), is seen as a key technology to provide innovative services to the users without the need of any fixed infrastructure. In PSNs nodes are short range communicating devices carried by humans. Wireless communication links are created and dropped in time, depending on the physical distance of the device holders. From one side, social relations among humans yield recurrent movement patterns that help researchers design and build protocols that efficiently deliver messages to destinations ([12, 13, 14] among others). The complexity of these social relations, from the other side, makes it difficult to build simple mobility models, that in an efficient way, generate large synthetic mobility traces that look real. Traces that would be very valuable in protocol validation and that would replace the limited experimentally gathered data available so far. Traces that would serve as a common benchmark to researchers worldwide on which to validate existing and yet to be designed protocols. With this in mind we start our study with re-designing SWIM [15], an already exist- ing mobility model shown to generate traces with similar properties of that of existing real ones. We make SWIM able to easily generate large (small)-scale scenarios, starting from known small (large)-scale ones. To the best of our knowledge, this is the first such study. In addition, we study the social aspects of SWIM-generated traces. We show how to SWIM-generate a scenario in which a specific community structure of nodes is required. Finally, exploiting the scaling properties of SWIM, we present the first analysis of the scal- ing capabilities of several forwarding protocols such as Epidemic [16], Delegation [13], Spray&Wait [14], and BUBBLE [12]. The first results of these works appeared in [1], and, at the time of writing, [2] is accepted with minor revision. Next, we take into account the fact that in PSNs cannot be assumed full cooperation and fairness among nodes. Selfish behavior of individuals has to be considered, since it is an inherent aspect of humans, the device holders (see [17], [18]). We design a market-based mathematical framework that enables heterogeneous mobile users in an opportunistic mobile network to compromise optimally and efficiently on their QoS 3 demands. The goal of the framework is to satisfy each user with its achieved (lesser) QoS, and at the same time maximize the social welfare of users in the network. We base our study on the consideration that, in practice, users are generally tolerant on accepting lesser QoS guarantees than what they demand, with the degree of tolerance varying from user to user. This study is described in details in Chapter 2 of this dissertation, and is included in [3]. In general, QoS could be parameters such as response time, number of computations per unit time, allocated bandwidth, etc. Along the way toward our study of the smartphone-world, there was the big advent of mobile cloud computing—smartphones getting help from cloud-enabled services. Many researchers started believing that the cloud could help solving a crucial problem regarding smartphones: improve battery life. New generation apps are becoming very complex — gaming, navigation, video editing, augmented reality, speech recognition, etc., — which require considerable amount of power and energy, and as a result, smartphones suffer short battery lifetime. Unfortunately, as a consequence, mobile users have to continually upgrade their hardware to keep pace with increasing performance requirements but still experience battery problems. Many recent works have focused on building frameworks that enable mobile computation offloading to software clones of smartphones on the cloud (see [19, 20] among others), as well as to backup systems for data and applications stored in our devices [21, 22, 23]. However, none of these address dynamic and scalability features of execution on the cloud. These are very important problems, since users may request different computational power or backup space based on their workload and deadline for tasks. Considering this and advancing on previous works, we design, build, and implement the ThinkAir framework, which focuses on the elasticity and scalability of the server side and enhances the power of mobile cloud computing by parallelizing method execution using multiple Virtual Machine (VM) images. We evaluate the system using a range of benchmarks starting from simple micro-benchmarks to more complex applications. First, we show that the execution time and energy consumption decrease two orders of magnitude for the N-queens puzzle and one order of magnitude for a face detection and a virus scan application, using cloud offloading. We then show that a parallelizable application can invoke multiple VMs to execute in the cloud in a seamless and on-demand manner such as to achieve greater reduction on execution time and energy consumption. Finally, we use a memory-hungry image combiner tool to demonstrate that applications can dynamically request VMs with more computational power in order to meet their computational requirements. The details of the ThinkAir framework and its evaluation are described in Chapter 4, and are included in [6, 5]. Later on, we push the smartphone-cloud paradigm to a further level: We develop Clone2Clone (C2C), a distributed platform for cloud clones of smartphones. Along the way toward C2C, we study the performance of device-clones hosted in various virtualization environments in both private (local servers) and public (Amazon EC2) clouds. We build the first Amazon Customized Image (AMI) for Android-OS—a key tool to get reliable performance measures of mobile cloud systems—and show how it boosts up performance of Android images on the Amazon cloud service. We then design, build, and implement Clone2Clone, which associates a software clone on the cloud to every smartphone and in- terconnects the clones in a p2p fashion exploiting the networking service within the cloud. On top of C2C we build CloneDoc, a secure real-time collaboration system for smartphone users. We measure the performance of CloneDoc on a testbed of 16 Android smartphones and clones hosted on both private and public cloud services and show that C2C makes it possible to implement distributed execution of advanced p2p services in a network of mobile smartphones. The design and implementation of the Clone2Clone platform is included in [7], recently submitted to an international conference. We believe that Clone2Clone not only enables the execution of p2p applications in a network of smartphones, but it can also serve as a tool to solve critical security problems. In particular, we consider the problem of computing an efficient patching strategy to stop worm spreading between smartphones. We assume that the worm infects the devices and spreads by using bluetooth connections, emails, or any other form of communication used by the smartphones. The C2C network is used to compute the best strategy to patch the smartphones in such a way that the number of devices to patch is low (to reduce the load on the cellular infrastructure) and that the worm is stopped quickly. We consider two well defined worms, one spreading between the devices and one attacking the cloud before moving to the real smartphones. We describe CloudShield [8], a suite of protocols running on the peer-to-peer network of clones; and show by experiments with two different datasets (Facebook and LiveJournal) that CloudShield outperforms state-of-the-art worm-containment mechanisms for mobile wireless networks. This work is done in collaboration with Marco Valerio Barbera, PhD colleague in the same department, who contributed mainly in the implementation and testing of the malware spreading and patching strategies on the different datasets. The communication between the real devices and the cloud, necessary for mobile com- putation offloading and smartphone data backup, does certainly not come for free. To the best of our knowledge, none of the works related to mobile cloud computing explicitly studies the actual overhead in terms of bandwidth and energy to achieve full backup of both data/applications of a smartphone, as well as to keep, on the cloud, up-to-date clones of smartphones for mobile computation offload purposes. In the last work during my PhD—again, in collaboration with Marco Valerio Barbera—we studied the feasibility of both mobile computation offloading and mobile software/data backup in real-life scenarios. This joint work resulted in a recent publication [9] but is not included in this thesis. As in C2C, we assume an architecture where each real device is associated to a software clone on the cloud. We define two types of clones: The off-clone, whose purpose is to support computation offloading, and the back-clone, which comes to use when a restore of user’s data and apps is needed. We measure the bandwidth and energy consumption incurred in the real device as a result of the synchronization with the off-clone or the back-clone. The evaluation is performed through an experiment with 11 Android smartphones and an equal number of clones running on Amazon EC2. We study the data communication overhead that is necessary to achieve different levels of synchronization (once every 5min, 30min, 1h, etc.) between devices and clones in both the off-clone and back-clone case, and report on the costs in terms of energy incurred by each of these synchronization frequencies as well as by the respective communication overhead. My contribution in this work is focused mainly on the experimental setup, deployment, and data collection

Managing Smartphone Testbeds with SmartLab

The explosive number of smartphones with ever growing sensing and computing capabilities have brought a paradigm shift to many traditional domains of the computing field. Re-programming smartphones and instrumenting them for application testing and data gathering at scale is currently a tedious and time-consuming process that poses significant logistical challenges. In this paper, we make three major contributions: First, we propose a comprehensive architecture, coined SmartLab1, for managing a cluster of both real and virtual smartphones that are either wired to a private cloud or connected over a wireless link. Second, we propose and describe a number of Android management optimizations (e.g., command pipelining, screen-capturing, file management), which can be useful to the community for building similar functionality into their systems. Third, we conduct extensive experiments and microbenchmarks to support our design choices providing qualitative evidence on the expected performance of each module comprising our architecture. This paper also overviews experiences of using SmartLab in a research-oriented setting and also ongoing and future development efforts