A survey and classification of storage deduplication systems

The automatic elimination of duplicate data in a storage system commonly known as deduplication is increasingly accepted as an effective technique to reduce storage costs. Thus, it has been applied to different storage types, including archives and backups, primary storage, within solid state disks, and even to random access memory. Although the general approach to deduplication is shared by all storage types, each poses specific challenges and leads to different trade-offs and solutions. This diversity is often misunderstood, thus underestimating the relevance of new research and development. The first contribution of this paper is a classification of deduplication systems according to six criteria that correspond to key design decisions: granularity, locality, timing, indexing, technique, and scope. This classification identifies and describes the different approaches used for each of them. As a second contribution, we describe which combinations of these design decisions have been proposed and found more useful for challenges in each storage type. Finally, outstanding research challenges and unexplored design points are identified and discussed.This work is funded by the European Regional Development Fund (EDRF) through the COMPETE Programme (operational programme for competitiveness) and by National Funds through the Fundacao para a Ciencia e a Tecnologia (FCT; Portuguese Foundation for Science and Technology) within project RED FCOMP-01-0124-FEDER-010156 and the FCT by PhD scholarship SFRH-BD-71372-2010

Cloud-Scale Entity Resolution: Current State and Open Challenges

Entity resolution (ER) is a process to identify records in information systems, which refer to the same real-world entity. Because in the two recent decades the data volume has grown so large, parallel techniques are called upon to satisfy the ER requirements of high performance and scalability. The development of parallel ER has reached a relatively prosperous stage, and has found its way into several applications. In this work, we first comprehensively survey the state of the art of parallel ER approaches. From the comprehensive overview, we then extract the classification criteria of parallel ER, classify and compare these approaches based on these criteria. Finally, we identify open research questions and challenges and discuss potential solutions and further research potentials in this field

Hardware accelerated redundancy elimination in network system

With the tremendous growth in the amount of information stored on remote locations and cloud systems, many service providers are seeking ways to reduce the amount of redundant information sent across networks by using data de-duplication techniques. Data de-duplication can reduce network traffic without the loss of information, and consequently increase available network bandwidth by reducing redundant traffic. However, due to the heavy computation required for detecting and reducing redundant data transmission, de-duplication itself can become a bottleneck in high capacity links. We completed two parts of work in this research study, Hardware Accelerated Redundancy Elimination in Network Systems (HARENS) and Distributed Redundancy Elimination System Simulation (DRESS). HARENS can significantly improve the performance of redundancy elimination algorithm in a network system by leveraging General Purpose Graphic Processing Unit (GPGPU) techniques as well as other big data optimizations such as the use of a hierarchical multi-threaded pipeline, single machine Map-Reduce, and memory efficiency techniques. Our results indicate that throughput can be increased by a factor of 9 times compared to a naive implementation of the data de-duplication algorithm, providing a net transmission increase of up to 3.0 Gigabits per second (Gbps). DRESS provides further acceleration to the redundancy elimination in network system by deploying HARENS as the server\u27s side redundancy elimination module, and four cooperative distributed byte caches on the clients\u27 side. A client\u27s side distributed byte cache broadcast its cached chunks by sending hash values to other byte caches, so that they can keep a record of all the chunks in the cooperative distributed cache system. When duplications are detected, a client\u27s side byte cache can fetch a chunk directly from either its own cache or peer byte caches rather than server\u27s side redundancy elimination module. Our results indicate that bandwidth savings of the redundancy elimination system with cooperative distributed byte cache can be increased by 12% compared to the one without distributed byte cache, when transferring about 48 Gigabits of data

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

Effectiveness of Similarity Digest Algorithms for Binary Code Similarity in Memory Forensic Analysis

Hoy en dı́a, cualquier organización que esté conectada a Internet es susceptible de sufrir incidentes de ciberseguridad y por tanto, debe contar con un plan de respuesta a incidentes. Este plan ayuda a prevenir, detectar, priorizar y gestionar los incidentes de ciberseguridad. Uno de los pasos para gestionar estos incidentes es la fase de eliminación, que se encarga de neutralizar la persistencia de los ataques, evaluar el alcance de los mismos e identificar el grado de compromiso. Uno de los puntos clave de esta fase es la identicación mediante triaje de la información que es relevante en el incidente. Esto suele hacerse comparando los elementos disponibles con información conocida, centrándose ası́ en aquellos elementos que tienen relevancia para la investigación (llamados evidencias).Este objetivo puede alcanzarse estudiando dos fuentes de información. Por un lado, mediante el análisis de los datos persistentes, como los datos de los discos duros o los dispositivos USB. Por otro lado, mediante el análisis de los datos volátiles, como los datos de la memoria RAM. A diferencia del análisis de datos persistentes, el análisis de datos volátiles permite determinar el alcance de algunos tipos de ataque que no guardan su código en dispositivos de persistencia o cuando los archivos ejecutables almacenados en el disco están cifrados; cuyo código sólo se muestra cuando está en la memoria y se está ejecutado.Existe una limitación en el uso de hashes criptográficos, comúnmente utilizados en el caso de identificación de evidencias en datos persistentes, para identificar evidencias de memoria. Esta limitación se debe a que las evidencias nunca serán idénticas porque la ejecución modifica el contenido de la memoria constantemente. Además, es imposible adquirir la memoria más de una vez con todos los programas en el mismo punto de ejecución. Por lo tanto, los hashes son un método de identificación inválido para el triaje de memoria. Como solución a este problema, en esta tesis se propone el uso de algoritmos de similitud de digest, que miden la similitud entre dos entradas de manera aproximada.Las principales aportaciones de esta tesis son tres. En primer lugar, se realiza un estudio del dominio del problema en el que se evalúa la gestión de la memoria y la modificación de la misma en ejecución. A continuación, se estudian los algoritmos de similitud de digest, desarrollando una clasificación de sus fases y de los ataques contra estos algoritmos, correlacionando las caracterı́sticas de la primera clasificación con los ataques identificados. Por último, se proponen dos métodos de preprocesamiento del contenido de volcados de memoria para mejorar la identificación de los elementos de interés para el análisis.Como conclusión, en esta tesis se muestra que la modificación de bytes dispersos afecta negativamente a los cálculos de similitud entre evidencias de memoria. Esta modificación se produce principalmente por el gestor de memoria del sistema operativo. Además, se muestra que las técnicas propuestas para preprocesar el contenido de volcados de memoria permiten mejorar el proceso de identificación de evidencias en memoria.<br /

Memory Deduplication: An Effective Approach to Improve the Memory System

Programs now have more aggressive demands of memory to hold their data than before. This paper analyzes the characteristics of memory data by using seven real memory traces. It observes that there are a large volume of memory pages with identical contents contained in the traces. Furthermore, the unique memory content accessed are much less than the unique memory address accessed. This is incurred by the traditional address-based cache replacement algorithms that replace memory pages by checking the addresses rather than the contents of those pages, thus resulting in many identical memory contents with different addresses stored in the memory. For example, in the same file system, opening two identical files stored in different directories, or opening two similar files that share a certain amount of contents in the same directory, will result in identical data blocks stored in the cache due to the traditional address-based cache replacement algorithms. Based on the observations, this paper evaluates memory compression and memory deduplication. As expected, memory deduplication greatly outperforms memory compression. For example, the best deduplication ratio is 4.6 times higher than the best compression ratio. The deduplication time and restore time are 121 times and 427 times faster than the compression time and decompression time, respectively. The experimental results in this paper should be able to offer useful insights for designing systems that require abundant memory to improve the system performance

Memory Deduplication: An Effective Approach to Improve the Memory System

Programs now have more aggressive demands of memory to hold their data than before. This paper analyzes the characteristics of memory data by using seven real memory traces. It observes that there are a large volume of memory pages with identical contents contained in the traces. Furthermore, the unique memory content accessed are much less than the unique memory address accessed. This is incurred by the traditional address-based cache replacement algorithms that replace memory pages by checking the addresses rather than the contents of those pages, thus resulting in many identical memory contents with different addresses stored in the memory. For example, in the same file system, opening two identical files stored in different directories, or opening two similar files that share a certain amount of contents in the same directory, will result in identical data blocks stored in the cache due to the traditional address-based cache replacement algorithms. Based on the observations, this paper evaluates memory compression and memory deduplication. As expected, memory deduplication greatly outperforms memory compression. For example, the best deduplication ratio is 4.6 times higher than the best compression ratio. The deduplication time and restore time are 121 times and 427 times faster than the compression time and decompression time, respectively. The experimental results in this paper should be able to offer useful insights for designing systems that require abundant memory to improve the system performance

Incremental parallel and distributed systems

Incremental computation strives for efficient successive runs of applications by re-executing only those parts of the computation that are affected by a given input change instead of recomputing everything from scratch. To realize the benefits of incremental computation, researchers and practitioners are developing new systems where the application programmer can provide an efficient update mechanism for changing application data. Unfortunately, most of the existing solutions are limiting because they not only depart from existing programming models, but also require programmers to devise an incremental update mechanism (or a dynamic algorithm) on a per-application basis. In this thesis, we present incremental parallel and distributed systems that enable existing real-world applications to automatically benefit from efficient incremental updates. Our approach neither requires departure from current models of programming, nor the design and implementation of dynamic algorithms. To achieve these goals, we have designed and built the following incremental systems: (i) Incoop — a system for incremental MapReduce computation; (ii) Shredder — a GPU-accelerated system for incremental storage; (iii) Slider — a stream processing platform for incremental sliding window analytics; and (iv) iThreads — a threading library for parallel incremental computation. Our experience with these systems shows that significant performance can be achieved for existing applications without requiring any additional effort from programmers.Inkrementelle Berechnungen ermöglichen die effizientere Ausführung aufeinanderfolgender Anwendungsaufrufe, indem nur die Teilbereiche der Anwendung erneut ausgefürt werden, die von den Änderungen der Eingabedaten betroffen sind. Dieses Berechnungsverfahren steht dem konventionellen und vollständig neu berechnenden Verfahren gegenüber. Um den Vorteil inkrementeller Berechnungen auszunutzen, entwickeln sowohl Wissenschaft als auch Industrie neue Systeme, bei denen der Anwendungsprogrammierer den effizienten Aktualisierungsmechanismus für die Änderung der Anwendungsdaten bereitstellt. Bedauerlicherweise lassen sich existierende Lösungen meist nur eingeschränkt anwenden, da sie das konventionelle Programmierungsmodel beibehalten und dadurch die erneute Entwicklung vom Programmierer des inkrementellen Aktualisierungsmechanismus (oder einen dynamischen Algorithmus) für jede Anwendung verlangen. Diese Doktorarbeit stellt inkrementelle Parallele- und Verteiltesysteme vor, die es existierenden Real-World-Anwendungen ermöglichen vom Vorteil der inkre- mentellen Berechnung automatisch zu profitieren. Unser Ansatz erfordert weder eine Abkehr von gegenwärtigen Programmiermodellen, noch Design und Implementierung von anwendungsspezifischen dynamischen Algorithmen. Um dieses Ziel zu erreichen, haben wir die folgenden Systeme zur inkrementellen parallelen und verteilten Berechnung entworfen und implementiert: (i) Incoop — ein System für inkrementelle Map-Reduce-Programme; (ii) Shredder — ein GPU- beschleunigtes System zur inkrementellen Speicherung; (iii) Slider — eine Plat- tform zur Batch-basierten Streamverarbeitung via inkrementeller Sliding-Window- Berechnung; und (iv) iThreads — eine Threading-Bibliothek zur parallelen inkre- mentellen Berechnung. Unsere Erfahrungen mit diesen Systemen zeigen, dass unsere Methoden sehr gute Performanz liefern können, und dies ohne weiteren Aufwand des Programmierers

Incremental parallel and distributed systems

Incremental computation strives for efficient successive runs of applications by re-executing only those parts of the computation that are affected by a given input change instead of recomputing everything from scratch. To realize the benefits of incremental computation, researchers and practitioners are developing new systems where the application programmer can provide an efficient update mechanism for changing application data. Unfortunately, most of the existing solutions are limiting because they not only depart from existing programming models, but also require programmers to devise an incremental update mechanism (or a dynamic algorithm) on a per-application basis. In this thesis, we present incremental parallel and distributed systems that enable existing real-world applications to automatically benefit from efficient incremental updates. Our approach neither requires departure from current models of programming, nor the design and implementation of dynamic algorithms. To achieve these goals, we have designed and built the following incremental systems: (i) Incoop — a system for incremental MapReduce computation; (ii) Shredder — a GPU-accelerated system for incremental storage; (iii) Slider — a stream processing platform for incremental sliding window analytics; and (iv) iThreads — a threading library for parallel incremental computation. Our experience with these systems shows that significant performance can be achieved for existing applications without requiring any additional effort from programmers.Inkrementelle Berechnungen ermöglichen die effizientere Ausführung aufeinanderfolgender Anwendungsaufrufe, indem nur die Teilbereiche der Anwendung erneut ausgefürt werden, die von den Änderungen der Eingabedaten betroffen sind. Dieses Berechnungsverfahren steht dem konventionellen und vollständig neu berechnenden Verfahren gegenüber. Um den Vorteil inkrementeller Berechnungen auszunutzen, entwickeln sowohl Wissenschaft als auch Industrie neue Systeme, bei denen der Anwendungsprogrammierer den effizienten Aktualisierungsmechanismus für die Änderung der Anwendungsdaten bereitstellt. Bedauerlicherweise lassen sich existierende Lösungen meist nur eingeschränkt anwenden, da sie das konventionelle Programmierungsmodel beibehalten und dadurch die erneute Entwicklung vom Programmierer des inkrementellen Aktualisierungsmechanismus (oder einen dynamischen Algorithmus) für jede Anwendung verlangen. Diese Doktorarbeit stellt inkrementelle Parallele- und Verteiltesysteme vor, die es existierenden Real-World-Anwendungen ermöglichen vom Vorteil der inkre- mentellen Berechnung automatisch zu profitieren. Unser Ansatz erfordert weder eine Abkehr von gegenwärtigen Programmiermodellen, noch Design und Implementierung von anwendungsspezifischen dynamischen Algorithmen. Um dieses Ziel zu erreichen, haben wir die folgenden Systeme zur inkrementellen parallelen und verteilten Berechnung entworfen und implementiert: (i) Incoop — ein System für inkrementelle Map-Reduce-Programme; (ii) Shredder — ein GPU- beschleunigtes System zur inkrementellen Speicherung; (iii) Slider — eine Plat- tform zur Batch-basierten Streamverarbeitung via inkrementeller Sliding-Window- Berechnung; und (iv) iThreads — eine Threading-Bibliothek zur parallelen inkre- mentellen Berechnung. Unsere Erfahrungen mit diesen Systemen zeigen, dass unsere Methoden sehr gute Performanz liefern können, und dies ohne weiteren Aufwand des Programmierers