Impact of single parameter changes on Ceph cloud storage performance

In a general purpose cloud system efficiencies are yet to be had from supporting diverse applications and their requirements within a storage system used for a private cloud. Supporting such diverse requirements poses a significant challenge in a storage system that supports fine grained configuration on a variety of parameters. This paper uses the Ceph distributed file system, and in particular its global parameters, to show how a single changed parameter can effect the performance for a range of access patterns when tested with an OpenStack cloud system

Service-oriented models for audiovisual content storage

What are the important topics to understand if involved with storage services to hold digital audiovisual content? This report takes a look at how content is created and moves into and out of storage; the storage service value networks and architectures found now and expected in the future; what sort of data transfer is expected to and from an audiovisual archive; what transfer protocols to use; and a summary of security and interface issues

An Analysis of Storage Virtualization

Investigating technologies and writing expansive documentation on their capabilities is like hitting a moving target. Technology is evolving, growing, and expanding what it can do each and every day. This makes it very difficult when trying to snap a line and investigate competing technologies. Storage virtualization is one of those moving targets. Large corporations develop software and hardware solutions that try to one up the competition by releasing firmware and patch updates to include their latest developments. Some of their latest innovations include differing RAID levels, virtualized storage, data compression, data deduplication, file deduplication, thin provisioning, new file system types, tiered storage, solid state disk, and software updates to coincide these technologies with their applicable hardware. Even data center environmental considerations like reusable energies, data center environmental characteristics, and geographic locations are being used by companies both small and large to reduce operating costs and limit environmental impacts. Companies are even moving to an entire cloud based setup to limit their environmental impact as it could be cost prohibited to maintain your own corporate infrastructure. The trifecta of integrating smart storage architectures to include storage virtualization technologies, reducing footprint to promote energy savings, and migrating to cloud based services will ensure a long-term sustainable storage subsystem

PROPOSED MIDDLEWARE SOLUTION FOR RESOURCE-CONSTRAINED DISTRIBUTED EMBEDDED NETWORKS

The explosion in processing power of embedded systems has enabled distributed embedded networks to perform more complicated tasks. Middleware are sets of encapsulations of common and network/operating system-specific functionality into generic, reusable frameworks to manage such distributed networks. This thesis will survey and categorize popular middleware implementations into three adapted layers: host-infrastructure, distribution, and common services. This thesis will then apply a quantitative approach to grading and proposing a single middleware solution from all layers for two target platforms: CubeSats and autonomous unmanned aerial vehicles (UAVs). CubeSats are 10x10x10cm nanosatellites that are popular university-level space missions, and impose power and volume constraints. Autonomous UAVs are similarly-popular hobbyist-level vehicles that exhibit similar power and volume constraints. The MAVLink middleware from the host-infrastructure layer is proposed as the middleware to manage the distributed embedded networks powering these platforms in future projects. Finally, this thesis presents a performance analysis on MAVLink managing the ARM Cortex-M 32-bit processors that power the target platforms

Matching distributed file systems with application workloads

Modern storage systems have a large number of configurable parameters, distributed over many layers of abstraction. The number of combinations of these parameters, that can be altered to create an instance of such a system, is enormous. In practise, many of these parameters are never altered; instead default values, intended to support generic workloads and access patterns, are used. As systems become larger and evolve to support different workloads, the appropriateness of using default parameters in this way comes into question. This thesis examines the implications of changing some of these parameters and explores the effects these changes have on performance. As part of that work multiple contributions have been made, including the creation of a structured method to create and evaluate different storage configurations, choosing appropriate access sizes for the evaluation, picking representative cloud workloads and capturing storage traces for further analysis, extraction of the workload storage characteristics, creating logical partitions of the distributed file system used for the optimization, the creation of heterogeneous storage pools within the homogeneous system and the mapping and evaluation of the chosen workloads to the examined configurations

Data-intensive Systems on Modern Hardware : Leveraging Near-Data Processing to Counter the Growth of Data

Over the last decades, a tremendous change toward using information technology in almost every daily routine of our lives can be perceived in our society, entailing an incredible growth of data collected day-by-day on Web, IoT, and AI applications. At the same time, magneto-mechanical HDDs are being replaced by semiconductor storage such as SSDs, equipped with modern Non-Volatile Memories, like Flash, which yield significantly faster access latencies and higher levels of parallelism. Likewise, the execution speed of processing units increased considerably as nowadays server architectures comprise up to multiple hundreds of independently working CPU cores along with a variety of specialized computing co-processors such as GPUs or FPGAs. However, the burden of moving the continuously growing data to the best fitting processing unit is inherently linked to today’s computer architecture that is based on the data-to-code paradigm. In the light of Amdahl's Law, this leads to the conclusion that even with today's powerful processing units, the speedup of systems is limited since the fraction of parallel work is largely I/O-bound. Therefore, throughout this cumulative dissertation, we investigate the paradigm shift toward code-to-data, formally known as Near-Data Processing (NDP), which relieves the contention on the I/O bus by offloading processing to intelligent computational storage devices, where the data is originally located. Firstly, we identified Native Storage Management as the essential foundation for NDP due to its direct control of physical storage management within the database. Upon this, the interface is extended to propagate address mapping information and to invoke NDP functionality on the storage device. As the former can become very large, we introduce Physical Page Pointers as one novel NDP abstraction for self-contained immutable database objects. Secondly, the on-device navigation and interpretation of data are elaborated. Therefore, we introduce cross-layer Parsers and Accessors as another NDP abstraction that can be executed on the heterogeneous processing capabilities of modern computational storage devices. Thereby, the compute placement and resource configuration per NDP request is identified as a major performance criteria. Our experimental evaluation shows an improvement in the execution durations of 1.4x to 2.7x compared to traditional systems. Moreover, we propose a framework for the automatic generation of Parsers and Accessors on FPGAs to ease their application in NDP. Thirdly, we investigate the interplay of NDP and modern workload characteristics like HTAP. Therefore, we present different offloading models and focus on an intervention-free execution. By propagating the Shared State with the latest modifications of the database to the computational storage device, it is able to process data with transactional guarantees. Thus, we achieve to extend the design space of HTAP with NDP by providing a solution that optimizes for performance isolation, data freshness, and the reduction of data transfers. In contrast to traditional systems, we experience no significant drop in performance when an OLAP query is invoked but a steady and 30% faster throughput. Lastly, in-situ result-set management and consumption as well as NDP pipelines are proposed to achieve flexibility in processing data on heterogeneous hardware. As those produce final and intermediary results, we continue investigating their management and identified that an on-device materialization comes at a low cost but enables novel consumption modes and reuse semantics. Thereby, we achieve significant performance improvements of up to 400x by reusing once materialized results multiple times

Capacity planning and stochastic scheduling in grid computing environments

Imperial Users onl

"No Going Back?" The final report of the Effective Records Management Project

The overall objective of the Effective Records Management project was the provision of ‘protocols and tools for the effective management of information in the digital order, with particular attention to information held in a document-based form’ . The project developed a demonstrator system which addressed the full range of issues involved in the management of a ‘testbed’ of digital records in the form of documents, from their creation through distribution and use to their final disposal or permanent retention. The testbed selected was the records of a subset of the university’s committees and was used to develop ideas and to assess how well they worked in practice. This project report indicates that the ERM project provides an investment in the future, opportunities for better information use or re-use, identification of legal risks and identification of good practice whether you be an administrator, a computing scientist, an archivist, a librarian or an information services manager

"No Going Back?" The final report of the Effective Records Management Project

The overall objective of the Effective Records Management project was the provision of ‘protocols and tools for the effective management of information in the digital order, with particular attention to information held in a document-based form’ . The project developed a demonstrator system which addressed the full range of issues involved in the management of a ‘testbed’ of digital records in the form of documents, from their creation through distribution and use to their final disposal or permanent retention. The testbed selected was the records of a subset of the university’s committees and was used to develop ideas and to assess how well they worked in practice. This project report indicates that the ERM project provides an investment in the future, opportunities for better information use or re-use, identification of legal risks and identification of good practice whether you be an administrator, a computing scientist, an archivist, a librarian or an information services manager