The objective of this thesis is to study methods for the flexible and secure storage of sensitive data in an unaltered cloud. While current cloud storage providers make guarantees on the availability and security of data once it enters their domain, clients are not given any options for customization. All availability and security measures, along with any resulting performance hits, are applied to all requests, regardless of the data's sensitivity or client's wishes. In addition, once a client's data enters the cloud, it becomes vulnerable to different types of attacks. Other cloud users may access or disrupt the availability of their peers' data, and cloud providers cannot protect from themselves in the event of a malicious administrator or government directive. Current solutions use combinations of known encoding schemes and encryption techniques to provide confidentiality from peers and sometimes the cloud service provider, but its an all-or-nothing model. A client either uses the security methods of their system, or does not, regardless of whether the client's data needs more or less protection and availability. Our approach, referred to as the Data Storage Facilitating Service (DSFS), involves providing a basic set of proven protection schemes with configurable parameters that encode input data into a number of fragments and intelligently scatters them across the target cloud. A client may choose the encoding scheme most appropriate for the sensitivity of their data. If none of the supported schemes are sufficient for the client's needs or the client has their own custom encoding, DSFS can accept already encoded fragments and perform secure placement. Evaluation of our prototype service demonstrates clear trade-offs in performance between the different levels of security encoding provides, allowing clients to choose how much the importance of their data is worth. This amount of flexibility is unique to DSFS and turns it into more of a secure storage facilitator that can help clients as much or as little as required. We also see a significant effect on overhead from the service's location relative to its cloud when we compare performances of our own setup with a commercial cloud service.

Cloud computing enables data centres to provide resource sharing across multiple tenants. This sharing, however, usually comes at a cost in the form of reduced isolation between tenants, which can lead to inconsistent and unpredictable performance. This variability in performance becomes an impediment for clients whose services rely on consistent, responsive performance in cloud environments. The problem is exacerbated for applications that rely on cloud storage systems as performance in these systems is a ffected by disk access times, which often dominate overall request service times for these types of data services. In this thesis we introduce MicroFuge, a new distributed caching and scheduling middleware that provides performance isolation for cloud storage systems. To provide performance isolation, MicroFuge's cache eviction policy is tenant and deadline-aware, which enables the provision of isolation to tenants and ensures that data for queries with more urgent deadlines, which are most likely to be a ffected by competing requests, are less likely to be evicted than data for other queries. MicroFuge also provides simplifi ed, intelligent scheduling in addition to request admission control whose performance model of the underlying storage system will reject requests with deadlines that are unlikely to be satisfi ed. The middleware approach of MicroFuge makes it unique among other systems which provide performance isolation in cloud storage systems. Rather than providing performance isolation for some particular cloud storage system, MicroFuge can be deployed on top of any already deployed storage system without modifying it. Keeping in mind the wide spectrum of cloud storage systems available today, such an approach make MicroFuge very adoptable. In this thesis, we show that MicroFuge can provide signifi cantly better performance isolation between tenants with di fferent latency requirements than Memcached, and with admission control enabled, can ensure that more than certain percentage of requests meet their deadlines.

Cloud computing enables data centres to provide resource sharing across multiple tenants. This sharing, however, usually comes at a cost in the form of reduced isolation between tenants, which can lead to inconsistent and unpredictable performance. This variability in performance becomes an impediment for clients whose services rely on consistent, responsive performance in cloud environments. The problem is exacerbated for applications that rely on cloud storage systems as performance in these systems is a ffected by disk access times, which often dominate overall request service times for these types of data services. In this thesis we introduce MicroFuge, a new distributed caching and scheduling middleware that provides performance isolation for cloud storage systems. To provide performance isolation, MicroFuge's cache eviction policy is tenant and deadline-aware, which enables the provision of isolation to tenants and ensures that data for queries with more urgent deadlines, which are most likely to be a ffected by competing requests, are less likely to be evicted than data for other queries. MicroFuge also provides simplifi ed, intelligent scheduling in addition to request admission control whose performance model of the underlying storage system will reject requests with deadlines that are unlikely to be satisfi ed. The middleware approach of MicroFuge makes it unique among other systems which provide performance isolation in cloud storage systems. Rather than providing performance isolation for some particular cloud storage system, MicroFuge can be deployed on top of any already deployed storage system without modifying it. Keeping in mind the wide spectrum of cloud storage systems available today, such an approach make MicroFuge very adoptable. In this thesis, we show that MicroFuge can provide signifi cantly better performance isolation between tenants with di fferent latency requirements than Memcached, and with admission control enabled, can ensure that more than certain percentage of requests meet their deadlines.

As a result of evergrowing data and recent interest in storing and analyzing it, distributed storage systems (DSS), which is also known as cloud storage, have become one of the most important research areas in the literature. Not only such networks are being used as backbone systems for companies like Google, Microsoft and Facebook but also they have accelerated the growth of cloud computing, which is an essential business line for institutions such as IBM, Amazon and Salesforce. In this dissertation, the focus is on the storage side of cloud in order to address the important questions in designing such systems. First, coding theoretic approach is taken to handle correlated failures of multiple storage nodes. In particular, this dissertation studies distributed storage systems that can provide resilience against correlated failure patterns that affect the availability of multiple storage nodes, i.e., power loss that may affect multiple disks. Maximum file size that can be stored in such DSS is studied and then optimal code constructions are provided. This dissertation also studies cloud storage systems that prevent data loss from mixed failure patterns of disks and sectors in disk drives. Specifically, a general code construction is proposed to overcome such failures for any given parameter set. Due to its large field size requirement of proposed construction, a relaxation on the efficiency of storage system is considered to provide codes with smaller field sizes. Maintenance of cloud storage systems is also studied. To that end, this dissertation first studies the maintenance of DSS that include a backup node, which is called hierarchical DSS. Hierarchical DSS can model cellular networks such as femtocell as well as caching in wireless networks. In particular, we present an upper bound on the file size that can be stored over hierarchical DSS and propose optimal code constructions. Then, maintenance cost and data access cost for users of such DSS are studied. Lastly, mobility effects of cloud storage over wireless devices are studied. Specifically, an analysis on the mobile cloud storage system that initiates the maintenance process after certain number of devices remains in the network is performed and different maintenance strategies are proposed that are optimal with respect to average cost in certain mobility regimes.

We present the results of a survey and interviews focused on user perceptions and behaviors with respect to cloud storage services. In particular, we study behaviors such as which services are used, what types of data are stored, and how collaboration and sharing are performed. We also investigate user attitudes toward cloud storage on topics such as payment, privacy, security, and robustness. We find that users are drawn to cloud storage because it enables robust, ubiquitous access to their files, as well as enabling sharing and collaborative efforts. However, users' preferred medium for file sharing continues to be email, due to its ubiquity and role as "lowest common denominator." Privacy and security are of great concern to users, and though users vocally describe feeling "safe" on the cloud, this is because they actively filter the content they store in cloud services. Payment is a sensitive issue, with users exhibiting a strong aversion to any form of direct payment, preferring even disliked alternative funding mechanisms such as targeted advertising. Finally, the cloud serves as an important backup location for users, although space limitations prevent them from using it as a full backup solution.

Submitted by Irene Nascimento (irene.kessia@ufpe.br) on 2016-10-18T19:25:38Z No. of bitstreams: 2 license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) mestrado_thiago_jamir.pdf: 1864217 bytes, checksum: 4e432bf2e6196fc2e2fb739007114e2e (MD5) / Made available in DSpace on 2016-10-18T19:25:38Z (GMT). No. of bitstreams: 2 license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) mestrado_thiago_jamir.pdf: 1864217 bytes, checksum: 4e432bf2e6196fc2e2fb739007114e2e (MD5) Previous issue date: 2014-04-05 / Nos últimos anos, o volume de dados gerados por indivíduos e organizações tem crescido exponencialmente. Estima-se que globalmente existia 2.7 zetabytes em 2012 e esse número tem dobrado a cada dois anos. Além disso, com a popularização de dispositivos móveis conectados, cresceu-se a necessidade de que usuários tenham acesso a arquivos de forma ubíqua. As soluções tradicionais de backup e armazenamento de arquivos online já não conseguem suprir as necessidades atuais dos usuários. A utilização de Cloud Storage para backup e sincronização de arquivos vem a ser uma ferramenta de grande valia para esse tipo de problema. Porém, implementar um sistema deste tipo vem a ser um desafio tecnológico relevante. Nesse sentido, este trabalho se propõe a resolver o problema de armazenamento de arquivos, propondo uma arquitetura de Cloud Storage para armazenamento de arquivos. Ao longo trabalho, é feita uma análise dos principais direcionadores de negócio para Cloud Storage e armazenamento de arquivos, levantando insumos para se projetar uma arquitetura. Tal arquitetura é descrita em nível de detalhe para que se possa ser implementada. Finalmente, o trabalho é validado através de uma avaliação de arquitetura cuja metodologia foi adaptada de acordo com as características da equipe de avaliação. / In the last years, the amount of data generated by individuals and organizations has grown exponentially. It is estimated that there were 2.7 zettabytes of global data in 2012, and this number has doubled each two years. In addition to this, with the popularization of mobile connected devices, the user’s need to have ubiquous access has grown. Traditional solutions for backup and online file storage can no longer meet the current needs. The use of cloud storage for backup and file synchronization becomes a tool of great value to this kind of problem. However, implementing such a system becomes a significant technological challenge. Thus, this works proposes to solve the problem of storing files, designing a Cloud Storage architecture for storing archives. Throughout work, an analysis of the key business drivers for Cloud Storage and File storage is done by lifting inputs for designing an architecture. This architecture is described in detail for level that can be implemented. Finally, the work is validated through an evaluation of architecture whose methodology was adapted according to the characteristics of the evaluation team.

The fast growing bandwidth has made the development of cloud storage. More and more resource has put in cloud storage. In this thesis, we proposed a new cloud storage that consists of a single main server and multiple data servers. The main server controls system-wide activities such as data server management. It also periodically communicates with each data server and collects its state. Data servers store data on local disks as Windows files. In order to response to the large number of data access, Selection of the server which is necessary to offer equalized performance. In this paper, we propose a server selection algorithm using different parameters to get the performance metrics which enables us to balance multi-resource from server-side. We design new cloud storage and implement the algorithm. According to upload experiment, the difference between the maximum and the minimum free space when using our algorithm is less than 5GB. But using the random mode, the free space difference is increased as time, and the maximum is 30GB. In the mixed experiment, we added the download mode, and our algorithm is fewer than 10GB. The result of the random mode approximated to the first experiment. Finally, our algorithm obtains 10% and 3% speedup in upload throughput by upload experiment and mixed experiment, 10% speedup in download throughput by mixed experiment.

It is widely accepted that OAuth is the most popular authorization scheme adopted and implemented by industrial and academic world, however, it is difficult to adapt OAuth to the situation in which online applications registered with one cloud party intends to access data residing in another cloud party. In this thesis, by leveraging Ciphertext-Policy Attribute Based Encryption technique and Elgamal-like mask over the protocol, we propose a reading authorization scheme among diverse clouds, which is called fuzzy authorization, to facilitate an application registered with one cloud party to access to data residing in another cloud party. More importantly, we enable the fuzziness of authorization thus to enhance the scalability and flexibility of file sharing by taking advantage of the innate connections of Linear Secret-Sharing Scheme and Generalized Reed Solomon code. Furthermore, by conducting error checking and error correction, we eliminate operation of satisfying a access tree. In addition, the automatic revocation is realized with update of TimeSlot attribute when data owner modifies the data. We prove the security of our schemes under the selective-attribute security model. The protocol flow of fuzzy authorization is implemented with OMNET++ 4.2.2 and the bi-linear pairing is realized with PBC library. Simulation results show that our scheme can achieve fuzzy authorization among heterogeneous clouds with security and efficiency.

The rapid growth of Cloud based services on the Internet invited many critical security attacks. Consumers and corporations who use the Cloud to store their data encounter a difficult trade-off of accepting and bearing the security, reliability, and privacy risks as well as costs in order to reap the benefits of Cloud storage. The primary goal of this thesis is to resolve this trade-off while minimizing total costs. This thesis presents a system framework that solves this problem by using erasure codes to add redundancy and security to users’ data, and by optimally choosing Cloud storage providers to minimize risks and total storage costs. Detailed comparative analysis of the security and algorithmic properties of 7 different erasure codes is presented, showing codes with better data security comes with a higher cost in computational time complexity. The codes which granted the highest configuration flexibility bested their peers, as the flexibility directly corresponded to the level of customizability for data security and storage costs. In-depth analysis of the risks, benefits, and costs of Cloud storage is presented, and analyzed to provide cost-based and security-based optimal selection criteria for choosing appropriate Cloud storage providers. A brief historical introduction to Cloud Computing and security principles is provided as well for those unfamiliar with the field. The analysis results show that the framework can resolve the trade-off problem by mitigating and eliminating the risks while preserving and enhancing the benefits of using Cloud storage. However, it requires higher total storage space due to the redundancy added by the erasure codes. The storage provider selection criteria will minimize the total storage costs even with the added redundancies, and minimize risks.

CloudMe is a cloud computing service used for business and home users. CloudMe facilitates the user to store their personal files like music, video, documents and images. The primary focus of this thesis is on music. The personal music files can be uploaded to CloudMe manually or by using CloudMe sync in any order just like in personal computer. CloudMe offers different services to access the cloud from other devices like smart phones, web browser and the home computer.   Sonos wireless HiFi system is a set of Sonos component interconnected with the mesh network with the primary function to play digital audio. The components include subwoofer, speakers and Bridges in order to connect to wireless speakers. Sonos system is connected to internet through Ethernet or via Wi-Fi. Sonos gives access to music libraries stored in computer, free Internet radio stations and additional music services. The controller for the complete system has various choices as iPhone, Android and other specific Sonos controllers.   However, with Sonos, a computer is considered necessary to be running all the time in order to access the personal music files from the personal computer. Combining CloudMe to Sonos allow the requirement of an always-on computer to be removed. Instead the selected personal music files can be stored with the user‟s private CloudMe account, and the music can be accessed from the cloud storage through the Internet at anytime.   The main objective of this thesis is to build the given APIs from the Sonos that are required in order to access CloudMe from Sonos. Each API handles specific task to present CloudMe through Sonos to the user. For example an API handles user authentication and another API handles the metadata accessing. All the APIs are implemented in the given server from CloudMe. This integration not only provides access roughly the way the music files are stored in the cloud, but also implemented in a way to accesses via categories like artist, albums, genre, composers and also the playlist stored in the cloud. In order to get this menu view of all the music, the metadata of the entire music library from CloudMe is accessed and programmed to differentiate music options in the menu.