Doctor of Philosophy

dissertationIn the past few years, we have seen a tremendous increase in digital data being generated. By 2011, storage vendors had shipped 905 PB of purpose-built backup appliances. By 2013, the number of objects stored in Amazon S3 had reached 2 trillion. Facebook had stored 20 PB of photos by 2010. All of these require an efficient storage solution. To improve space efficiency, compression and deduplication are being widely used. Compression works by identifying repeated strings and replacing them with more compact encodings while deduplication partitions data into fixed-size or variable-size chunks and removes duplicate blocks. While we have seen great improvements in space efficiency from these two approaches, there are still some limitations. First, traditional compressors are limited in their ability to detect redundancy across a large range since they search for redundant data in a fine-grain level (string level). For deduplication, metadata embedded in an input file changes more frequently, and this introduces more unnecessary unique chunks, leading to poor deduplication. Cloud storage systems suffer from unpredictable and inefficient performance because of interference among different types of workloads. This dissertation proposes techniques to improve the effectiveness of traditional compressors and deduplication in improving space efficiency, and a new IO scheduling algorithm to improve performance predictability and efficiency for cloud storage systems. The common idea is to utilize similarity. To improve the effectiveness of compression and deduplication, similarity in content is used to transform an input file into a compression- or deduplication-friendly format. We propose Migratory Compression, a generic data transformation that identifies similar data in a coarse-grain level (block level) and then groups similar blocks together. It can be used as a preprocessing stage for any traditional compressor. We find metadata have a huge impact in reducing the benefit of deduplication. To isolate the impact from metadata, we propose to separate metadata from data. Three approaches are presented for use cases with different constrains. For the commonly used tar format, we propose Migratory Tar: a data transformation and also a new tar format that deduplicates better. We also present a case study where we use deduplication to reduce storage consumption for storing disk images, while at the same time achieving high performance in image deployment. Finally, we apply the same principle of utilizing similarity in IO scheduling to prevent interference between random and sequential workloads, leading to efficient, consistent, and predictable performance for sequential workloads and a high disk utilization

Towards Data Optimization in Storages and Networks

Title from PDF of title page, viewed on August 7, 2015Dissertation advisors: Sejun Song and Baek-Young ChoiVitaIncludes bibliographic references (pages 132-140)Thesis (Ph.D.)--School of Computing and Engineering. University of Missouri--Kansas City, 2015We are encountering an explosion of data volume, as a study estimates that data will amount to 40 zeta bytes by the end of 2020. This data explosion poses significant burden not only on data storage space but also access latency, manageability, and processing and network bandwidth. However, large portions of the huge data volume contain massive redundancies that are created by users, applications, systems, and communication models. Deduplication is a technique to reduce data volume by removing redundancies. Reliability will be even improved when data is replicated after deduplication. Many deduplication studies such as storage data deduplication and network redundancy elimination have been proposed to reduce storage consumption and network bandwidth consumption. However, existing solutions are not efficient enough to optimize data delivery path from clients to servers through network. Hence we propose a holistic deduplication framework to optimize data in their path. Our deduplication framework consists of three components including data sources or clients, networks, and servers. The client component removes local redundancies in clients, the network component removes redundant transfers coming from different clients, and the server component removes redundancies coming from different networks. We designed and developed components for the proposed deduplication framework. For the server component, we developed the Hybrid Email Deduplication System that achieves a trade-off of space savings and overhead for email systems. For the client component, we developed the Structure Aware File and Email Deduplication for Cloudbased Storage Systems that is very fast as well as having good space savings by using structure-based granularity. For the network component, we developed a system called Software-defined Deduplication as a Network and Storage service that is in-network deduplication, and that chains storage data deduplication and network redundancy elimination functions by using Software Defined Network to achieve both storage space and network bandwidth savings with low processing time and memory size. We also discuss mobile deduplication for image and video files in mobile devices. Through system implementations and experiments, we show that the proposed framework effectively and efficiently optimizes data volume in a holistic manner encompassing the entire data path of clients, networks and storage servers.Introduction -- Deduplication technology -- Existing deduplication approaches -- HEDS: Hybrid Email Deduplication System -- SAFE: Structure-aware File and Email Deduplication for cloud-based storage systems -- SoftDance: Software-defined Deduplication as a Network and Storage Service -- Moblie de-duplication -- Conclusion

Optimal Data Deduplication In Cloud With Authorization

Cloud technology is widely used technology as it allows sharing and centralized storage of data, sharing of data processing and online access of computer services and resources on various types of devices. One of the critical challenges of cloud storage services is the management of the ever-increasing volume of data .To address these data deduplication is one of the novel technique. Deduplication helps to remove and prevent from having duplicate copies of same data. Though deduplication has several benefits it adds concerns related to privacy and security of users as it can lead to insider or outsider attacks. Achieving deduplication along with data security in cloud environment makes it more critical problem to solve. Objective of this paper on Optima Authorized Data Deduplication in Cloud is to mention the proposed system and analysis of deduplication techniques and optimal authorization measures for security along with deduplication technique in cloud environment DOI: 10.17762/ijritcc2321-8169.15073