No description available.

Caching is a classical but effective way to improve system performance. To improve system performance, servers, such as database servers and storage servers, contain significant amounts of memory that act as a fast cache. Meanwhile, as new storage devices such as flash-based solid state drives (SSDs) are added to storage systems over time, using the memory cache is not the only way to improve system performance. In this thesis, we address the problems of how to manage the cache of a storage server and how to utilize the SSD in a hybrid storage system. Traditional caching policies are known to perform poorly for storage server caches. One promising approach to solving this problem is to use hints from the storage clients to manage the storage server cache. Previous hinting approaches are ad hoc, in that a predefined reaction to specific types of hints is hard-coded into the caching policy. With ad hoc approaches, it is difficult to ensure that the best hints are being used, and it is difficult to accommodate multiple types of hints and multiple client applications. In this thesis, we propose CLient-Informed Caching (CLIC), a generic hint-based technique for managing storage server caches. CLIC automatically interprets hints generated by storage clients and translates them into a server caching policy. It does this without explicit knowledge of the application-specific hint semantics. We demonstrate using trace-based simulation of database workloads that CLIC outperforms hint-oblivious and state-of-the-art hint-aware caching policies. We also demonstrate that the space required to track and interpret hints is small. SSDs are becoming a part of the storage system. Adding SSD to a storage system not only raises the question of how to manage the SSD, but also raises the question of whether current buffer pool algorithms will still work effectively. We are interested in the use of hybrid storage systems, consisting of SSDs and hard disk drives (HDD), for database management. We present cost-aware replacement algorithms for both the DBMS buffer pool and the SSD. These algorithms are aware of the different I/O performance of HDD and SSD. In such a hybrid storage system, the physical access pattern to the SSD depends on the management of the DBMS buffer pool. We studied the impact of the buffer pool caching policies on the access patterns of the SSD. Based on these studies, we designed a caching policy to effectively manage the SSD. We implemented these algorithms in MySQL's InnoDB storage engine and used the TPC-C workload to demonstrate that these cost-aware algorithms outperform previous algorithms.

Time Series Forecasting has become vital in various industries ranging from weather forecasting to business forecasting. There is a need to research database storage solutions for companies in order to optimize resource allocation, enhance decision making process and enable predictive data storage maintenance. With the introduction of Artificial Intelligence and a branch of AI, Machine Learning, Time Series Forecasting has become more powerful and efficient. This project attempts to validate the possibility of using time series forecasting on database storage data to make business predictions. Currently, predicting capabilities of database storage is an area which is not fully explored, despite the growing necessity of databases. Currently, most of the optimization of databases is left to human touch which is ultimately slower and more error prone. As such, this research will investigate the possibilities of time series forecasting in database storage. This project will use Machine Learning and Time-series Forecasting to predict the future trend of database storage to give information on how the trend of the data will change. Examining the pattern of database storage fluctuations will allow the respective owners an overview of their storage and in turn, make decisions on optimizing the database to prevent critical problems ahead of time. Three distinct approaches - employing a traditional linear model fore forecasting, utilizing a Convolutional Neural Network (CNN) to detect local changes in time series data, and leveraging a Recurrent Neural Network (RNN) to capture long term temporal dependencies - are implemented to assess which of these techniques is better suited for the provided dataset. Furthermore, two settings (single step and multi step) have been tested in order to test the changes in accuracy from a small prediction step to a major. The research indicates that currently the models do not have the possibility to be used. This is due to the mean absolute error being very big. The main purpose of the project was to establish which of the three different techniques is the best for the particular dataset provided by the company. In general, across all approaches (Linear, CNN, RNN), their performance was superior in the single step method. In the multi step aspect, The linear model suffered the greatest in the accuracy drop with CNN and RNN performing slightly better. The findings also indicated that the model with local change detection (CNN) performs better for the provided dataset in both single and multi step settings, as evidenced by its minimal Mean Absolute Error (MAE). This is because the dataset is comprised of local data and the models are only trained to check for normal changes. If the research had also checked for seasonality or sequential patterns, then it is possible that LSTM may have had a better outcome due to its capability of capturing those dependencies. The accuracy of single step forecasting using CNN is good (MAE = 0.25) but must be further explored and improved.