Improving storage performance through layout optimizations

Disk drives are the bottleneck in the processing of large amounts of data used in almost all common applications. File systems attempt to reduce this by storing data sequentially on the disk drives, thereby reducing the access latencies. Although this strategy is useful when data is retrieved sequentially, the access patterns in real world workloads is not necessarily sequential and this mismatch results in storage I/O performance degradation. This thesis demonstrates that one way to improve the storage performance is to reorganize data on disk drives in the same way in which it is mostly accessed. We identify two classes of accesses: static, where access patterns do not change over the lifetime of the data and dynamic, where access patterns frequently change over short durations of time, and propose, implement and evaluate layout strategies for each of these. Our strategies are implemented in a way that they can be seamlessly integrated or removed from the system as desired. We evaluate our layout strategies for static policies using tree-structured XML data where accesses to the storage device are mostly of two kinds—parent-to-child or child-to-sibling. Our results show that for a specific class of deep-focused queries, the existing file system layout policy performs better by 5–54X. For the non-deep-focused queries, our native layout mechanism shows an improvement of 3–127X. To improve performance of the dynamic access patterns, we implement a self-optimizing storage system that performs rearranges popular block accesses on a dedicated partition based on the observed workload characteristics. Our evaluation shows an improvement of over 80% in the disk busy times over a range of workloads. These results show that applying the knowledge of data access patterns for allocation decisions can substantially improve the I/O performance.

Dynamic modeling and analysis of single-stage boost inverters under normal and abnormal conditions

Inverters play key roles in connecting sustainable energy (SE) sources to the local loads and the ac grid. Although there has been a rapid expansion in the use of renewable sources in recent years, fundamental research, on the design of inverters that are specialized for use in these systems, is still needed. Recent advances in power electronics have led to proposing new topologies and switching patterns for single-stage power conversion, which are appropriate for SE sources and energy storage devices. The current source inverter (CSI) topology, along with a newly proposed switching pattern, is capable of converting the low dc voltage to the line ac in only one stage. Simple implementation and high reliability, together with the potential advantages of higher efficiency and lower cost, turns the so-called, single-stage boost inverter (SSBI), into a viable competitor to the existing SE-based power conversion technologies.^ The dynamic model is one of the most essential requirements for performance analysis and control design of any engineering system. Thus, in order to have satisfactory operation, it is necessary to derive a dynamic model for the SSBI system. However, because of the switching behavior and nonlinear elements involved, analysis of the SSBI is a complicated task.^ This research applies the state-space averaging technique to the SSBI to develop the state-space-averaged model of the SSBI under stand-alone and grid-connected modes of operation. Then, a small-signal model is derived by means of the perturbation and linearization method. An experimental hardware set-up, including a laboratory-scaled prototype SSBI, is built and the validity of the obtained models is verified through simulation and experiments. Finally, an eigenvalue sensitivity analysis is performed to investigate the stability and dynamic behavior of the SSBI system over a typical range of operation. ^

XML storage and retrieval using the semantic binary object -oriented database system (SEM-ODB)

Over the past five years, XML has been embraced by both the research and industrial community due to its promising prospects as a new data representation and exchange format on the Internet. The widespread popularity of XML creates an increasing need to store XML data in persistent storage systems and to enable sophisticated XML queries over the data. The currently available approaches to addressing the XML storage and retrieval issue have the limitations of either being not mature enough (e.g. native approaches) or causing inflexibility, a lot of fragmentation and excessive join operations (e.g. non-native approaches such as the relational database approach). ^ In this dissertation, I studied the issue of storing and retrieving XML data using the Semantic Binary Object-Oriented Database System (Sem-ODB) to leverage the advanced Sem-ODB technology with the emerging XML data model. First, a meta-schema based approach was implemented to address the data model mismatch issue that is inherent in the non-native approaches. The meta-schema based approach captures the meta-data of both Document Type Definitions (DTDs) and Sem-ODB Semantic Schemas, thus enables a dynamic and flexible mapping scheme. Second, a formal framework was presented to ensure precise and concise mappings. In this framework, both schemas and the conversions between them are formally defined and described. Third, after major features of an XML query language, XQuery, were analyzed, a high-level XQuery to Semantic SQL (Sem-SQL) query translation scheme was described. This translation scheme takes advantage of the navigation-oriented query paradigm of the Sem-SQL, thus avoids the excessive join problem of relational approaches. Finally, the modeling capability of the Semantic Binary Object-Oriented Data Model (Sem-ODM) was explored from the perspective of conceptually modeling an XML Schema using a Semantic Schema. ^ It was revealed that the advanced features of the Sem-ODB, such as multi-valued attributes, surrogates, the navigation-oriented query paradigm, among others, are indeed beneficial in coping with the XML storage and retrieval issue using a non-XML approach. Furthermore, extensions to the Sem-ODB to make it work more effectively with XML data were also proposed. ^

Improving Storage Performance Through Layout Optimizations

Disk drives are the bottleneck in the processing of large amounts of data used in almost all common applications. File systems attempt to reduce this by storing data sequentially on the disk drives, thereby reducing the access latencies. Although this strategy is useful when data is retrieved sequentially, the access patterns in real world workloads is not necessarily sequential and this mismatch results in storage I/O performance degradation. This thesis demonstrates that one way to improve the storage performance is to reorganize data on disk drives in the same way in which it is mostly accessed. We identify two classes of accesses: static, where access patterns do not change over the lifetime of the data and dynamic, where access patterns frequently change over short durations of time, and propose, implement and evaluate layout strategies for each of these. Our strategies are implemented in a way that they can be seamlessly integrated or removed from the system as desired. We evaluate our layout strategies for static policies using tree-structured XML data where accesses to the storage device are mostly of two kinds - parent-tochild or child-to-sibling. Our results show that for a specific class of deep-focused queries, the existing file system layout policy performs better by 5-54X. For the non-deep-focused queries, our native layout mechanism shows an improvement of 3-127X. To improve performance of the dynamic access patterns, we implement a self-optimizing storage system that performs rearranges popular block accesses on a dedicated partition based on the observed workload characteristics. Our evaluation shows an improvement of over 80% in the disk busy times over a range of workloads. These results show that applying the knowledge of data access patterns for allocation decisions can substantially improve the I/O performance.