BORG: Block-reORGanization and Self-optimization in Storage Systems

This paper presents the design, implementation, and evaluation of BORG, a self-optimizing storage system that performs automatic block reorganization based on the observed I/O workload. BORG is motivated by three characteristics of I/O workloads: non-uniform access frequency distribution, temporal locality, and partial determinism in non-sequential accesses. To achieve its objective, BORG manages a small, dedicated partition on the disk drive, with the goal of servicing a majority of the I/O requests from within this partition with significantly reduced seek and rotational delays. BORG is transparent to the rest of the storage stack, including applications, file system(s), and I/O schedulers, thereby requiring no or minimal modification to storage stack implementations. We evaluated a Linux implementation of BORG using several real-world workloads, including individual user desktop environments, a web-server, a virtual machine monitor, and an SVN server. These experiments comprehensively demonstrate BORG’s effectiveness in improving I/O performance and its incurred resource overhead

The Design of a High-Integrity Disk Management Subsystem

This dissertation describes and experimentally evaluates the design of the Logical Disk, a disk management subsystem that guarantees the integrity of data stored on disk even after system failures, while still providing performance competitive to other storage systems. Current storage systems that use the hard disk as storage medium, such as file systems, often do not provide sufficient protection against loss of data after a system failure. The designers of such systems are afraid that the amount of effort necessary for data protection would also result in too much loss of performance. The Logical Disk uses many different techniques to guarantee data integrity, including the support to execute multiple commands as one atomic action and avoiding `in-place updates' at all times. The techniques used to provide competitive performance include the technique of combining many, small write commands into one large, sequential, and thus efficient, write to disk, and clustering the data on disk continuously and automatically.Tanenbaum, A.S. [Promotor]Jonge, W. de [Copromotor

Transactions Chasing Scalability and Instruction Locality on Multicores

For several decades, online transaction processing (OLTP) has been one of the main server applications that drives innovations in the data management ecosystem, and in turn the database and computer architecture communities. Recent hardware trends oblige software to overcome two major challenges against systems scalability on modern multicore processors: (1) exploiting the abundant thread-level parallelism across cores and (2) taking advantage of the implicit parallelism within a core. The traditional design of the OLTP systems, however, faces inherent scalability problems due to its tightly coupled components. In addition, OLTP cannot exploit the full capability of the micro-architectural resources of modern processors because of the conventional scheduling decisions that ignore the cache locality for transactions. As a result, today’s commonly used server hardware remains largely underutilized leading to a huge waste of hardware resources and energy. .... In this thesis, we first identify the unbounded critical sections of traditional OLTP systems as the main enemy of thread-level parallelism. We design an alternative shared-everything system based on physiological partitioning (PLP) to eliminate the unbounded critical sections while providing an infrastructure for low-cost dynamic repartitioning and without introducing high-cost distributed transactions. Then, we demonstrate that L1 instruction cache stalls are the dominant factor leading to underutilization in the commodity servers. However, we also observe that independently of their high-level functionality, transactions running in parallel on a multicore system share significant amount of common instructions. By adaptively spreading the execution of a transaction over multiple cores through thread migration or multiplexing transactions on one core, we enable both an ample L1 instruction cache capacity for a transaction and reuse of common instructions across concurrent transactions. .... As the hardware demands more from the software to exploit the complexity and parallelism it offers in the multicore era, this work would change the way we traditionally schedule transactions. Instead of viewing a transaction as a single big task, we split it into smaller parts that can exploit data and instruction locality through careful dynamic scheduling decisions. The methods this thesis presents are not only specific to OLTP systems, but they can also benefit other types of applications that have concurrent requests executing a series of actions from a predefined set and face similar scalability problems on emerging hardware