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

Exploring heterogeneity of unreliable machines for p2p backup

P2P architecture is a viable option for enterprise backup. In contrast to dedicated backup servers, nowadays a standard solution, making backups directly on organization's workstations should be cheaper (as existing hardware is used), more efficient (as there is no single bottleneck server) and more reliable (as the machines are geographically dispersed). We present the architecture of a p2p backup system that uses pairwise replication contracts between a data owner and a replicator. In contrast to standard p2p storage systems using directly a DHT, the contracts allow our system to optimize replicas' placement depending on a specific optimization strategy, and so to take advantage of the heterogeneity of the machines and the network. Such optimization is particularly appealing in the context of backup: replicas can be geographically dispersed, the load sent over the network can be minimized, or the optimization goal can be to minimize the backup/restore time. However, managing the contracts, keeping them consistent and adjusting them in response to dynamically changing environment is challenging. We built a scientific prototype and ran the experiments on 150 workstations in the university's computer laboratories and, separately, on 50 PlanetLab nodes. We found out that the main factor affecting the quality of the system is the availability of the machines. Yet, our main conclusion is that it is possible to build an efficient and reliable backup system on highly unreliable machines (our computers had just 13% average availability)

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

Computing at massive scale: Scalability and dependability challenges

Large-scale Cloud systems and big data analytics frameworks are now widely used for practical services and applications. However, with the increase of data volume, together with the heterogeneity of workloads and resources, and the dynamic nature of massive user requests, the uncertainties and complexity of resource management and service provisioning increase dramatically, often resulting in poor resource utilization, vulnerable system dependability, and user-perceived performance degradations. In this paper we report our latest understanding of the current and future challenges in this particular area, and discuss both existing and potential solutions to the problems, especially those concerned with system efficiency, scalability and dependability. We first introduce a data-driven analysis methodology for characterizing the resource and workload patterns and tracing performance bottlenecks in a massive-scale distributed computing environment. We then examine and analyze several fundamental challenges and the solutions we are developing to tackle them, including for example incremental but decentralized resource scheduling, incremental messaging communication, rapid system failover, and request handling parallelism. We integrate these solutions with our data analysis methodology in order to establish an engineering approach that facilitates the optimization, tuning and verification of massive-scale distributed systems. We aim to develop and offer innovative methods and mechanisms for future computing platforms that will provide strong support for new big data and IoE (Internet of Everything) applications

Big data reduction framework for value creation in sustainable enterprises

Value creation is a major sustainability factor for enterprises, in addition to profit maximization and revenue generation. Modern enterprises collect big data from various inbound and outbound data sources. The inbound data sources handle data generated from the results of business operations, such as manufacturing, supply chain management, marketing, and human resource management, among others. Outbound data sources handle customer-generated data which are acquired directly or indirectly from customers, market analysis, surveys, product reviews, and transactional histories. However, cloud service utilization costs increase because of big data analytics and value creation activities for enterprises and customers. This article presents a novel concept of big data reduction at the customer end in which early data reduction operations are performed to achieve multiple objectives, such as a) lowering the service utilization cost, b) enhancing the trust between customers and enterprises, c) preserving privacy of customers, d) enabling secure data sharing, and e) delegating data sharing control to customers. We also propose a framework for early data reduction at customer end and present a business model for end-to-end data reduction in enterprise applications. The article further presents a business model canvas and maps the future application areas with its nine components. Finally, the article discusses the technology adoption challenges for value creation through big data reduction in enterprise applications

ON OPTIMIZATIONS OF VIRTUAL MACHINE LIVE STORAGE MIGRATION FOR THE CLOUD

Virtual Machine (VM) live storage migration is widely performed in the data cen- ters of the Cloud, for the purposes of load balance, reliability, availability, hardware maintenance and system upgrade. It entails moving all the state information of the VM being migrated, including memory state, network state and storage state, from one physical server to another within the same data center or across different data centers. To minimize its performance impact, this migration process is required to be transparent to applications running within the migrating VM, meaning that ap- plications will keep running inside the VM as if there were no migration operations at all. In this dissertation, a thorough literature review is conducted to provide a big picture of the VM live storage migration process, its problems and existing solutions. After an in-depth examination, we observe that a severe IO interference between the VM IO threads and migration IO threads exists and causes both types of the IO threads to suffer from performance degradation. This interference stems from the fact that both types of IO threads share the same critical IO path by reading from and writing to the same shared storage system. Owing to IO resource contention and requests interference between the two different types of IO requests, not only will the IO request queue lengthens in the storage system, but the time-consuming disk seek operations will also become more frequent. Based on this fundamental observation, this dissertation research presents three related but orthogonal solutions that tackle the IO interference problem in order to improve the VM live storage migration performance. First, we introduce the Workload-Aware IO Outsourcing scheme, called WAIO, to improve the VM live storage migration efficiency. Second, we address this problem by proposing a novel scheme, called SnapMig, to improve the VM live storage migration efficiency and eliminate its performance impact on user applications at the source server by effectively leveraging the existing VM snapshots in the backup servers. Third, we propose the IOFollow scheme to improve both the VM performance and migration performance simultaneously. Finally, we outline the direction for the future research work. Advisor: Hong Jian

THE USE OF ROUGH CLASSIFICATION AND TWO THRESHOLD TWO DIVISORS FOR DEDUPLICATION

The data deduplication technique efficiently reduces and removes redundant data in big data storage systems. The main issue is that the data deduplication requires expensive computational effort to remove duplicate data due to the vast size of big data. The paper attempts to reduce the time and computation required for data deduplication stages. The chunking and hashing stage often requires a lot of calculations and time. This paper initially proposes an efficient new method to exploit the parallel processing of deduplication systems with the best performance. The proposed system is designed to use multicore computing efficiently. First, The proposed method removes redundant data by making a rough classification for the input into several classes using the histogram similarity and k-mean algorithm. Next, a new method for calculating the divisor list for each class was introduced to improve the chunking method and increase the data deduplication ratio. Finally, the performance of the proposed method was evaluated using three datasets as test examples. The proposed method proves that data deduplication based on classes and a multicore processor is much faster than a single-core processor. Moreover, the experimental results showed that the proposed method significantly improved the performance of Two Threshold Two Divisors (TTTD) and Basic Sliding Window BSW algorithms