Energy-Aware Data Management on NUMA Architectures

The ever-increasing need for more computing and data processing power demands for a continuous and rapid growth of power-hungry data center capacities all over the world. As a first study in 2008 revealed, energy consumption of such data centers is becoming a critical problem, since their power consumption is about to double every 5 years. However, a recently (2016) released follow-up study points out that this threatening trend was dramatically throttled within the past years, due to the increased energy efficiency actions taken by data center operators. Furthermore, the authors of the study emphasize that making and keeping data centers energy-efficient is a continuous task, because more and more computing power is demanded from the same or an even lower energy budget, and that this threatening energy consumption trend will resume as soon as energy efficiency research efforts and its market adoption are reduced. An important class of applications running in data centers are data management systems, which are a fundamental component of nearly every application stack. While those systems were traditionally designed as disk-based databases that are optimized for keeping disk accesses as low a possible, modern state-of-the-art database systems are main memory-centric and store the entire data pool in the main memory, which replaces the disk as main bottleneck. To scale up such in-memory database systems, non-uniform memory access (NUMA) hardware architectures are employed that face a decreased bandwidth and an increased latency when accessing remote memory compared to the local memory. In this thesis, we investigate energy awareness aspects of large scale-up NUMA systems in the context of in-memory data management systems. To do so, we pick up the idea of a fine-grained data-oriented architecture and improve the concept in a way that it keeps pace with increased absolute performance numbers of a pure in-memory DBMS and scales up on NUMA systems in the large scale. To achieve this goal, we design and build ERIS, the first scale-up in-memory data management system that is designed from scratch to implement a data-oriented architecture. With the help of the ERIS platform, we explore our novel core concept for energy awareness, which is Energy Awareness by Adaptivity. The concept describes that software and especially database systems have to quickly respond to environmental changes (i.e., workload changes) by adapting themselves to enter a state of low energy consumption. We present the hierarchically organized Energy-Control Loop (ECL), which is a reactive control loop and provides two concrete implementations of our Energy Awareness by Adaptivity concept, namely the hardware-centric Resource Adaptivity and the software-centric Storage Adaptivity. Finally, we will give an exhaustive evaluation regarding the scalability of ERIS as well as our adaptivity facilities

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

Mining a Small Medical Data Set by Integrating the Decision Tree and t-test

[[abstract]]Although several researchers have used statistical methods to prove that aspiration followed by the injection of 95% ethanol left in situ (retention) is an effective treatment for ovarian endometriomas, very few discuss the different conditions that could generate different recovery rates for the patients. Therefore, this study adopts the statistical method and decision tree techniques together to analyze the postoperative status of ovarian endometriosis patients under different conditions. Since our collected data set is small, containing only 212 records, we use all of these data as the training data. Therefore, instead of using a resultant tree to generate rules directly, we use the value of each node as a cut point to generate all possible rules from the tree first. Then, using t-test, we verify the rules to discover some useful description rules after all possible rules from the tree have been generated. Experimental results show that our approach can find some new interesting knowledge about recurrent ovarian endometriomas under different conditions.[[journaltype]]國外[[incitationindex]]EI[[booktype]]紙本[[countrycodes]]FI

Data systems elements technology assessment and system specifications, issue no. 2

The ability to satisfy the objectives of future NASA Office of Applications programs is dependent on technology advances in a number of areas of data systems. The hardware and software technology of end-to-end systems (data processing elements through ground processing, dissemination, and presentation) are examined in terms of state of the art, trends, and projected developments in the 1980 to 1985 timeframe. Capability is considered in terms of elements that are either commercially available or that can be implemented from commercially available components with minimal development

Fourth Annual Workshop on Space Operations Applications and Research (SOAR 90)

The proceedings of the SOAR workshop are presented. The technical areas included are as follows: Automation and Robotics; Environmental Interactions; Human Factors; Intelligent Systems; and Life Sciences. NASA and Air Force programmatic overviews and panel sessions were also held in each technical area

First Annual Workshop on Space Operations Automation and Robotics (SOAR 87)

Several topics relative to automation and robotics technology are discussed. Automation of checkout, ground support, and logistics; automated software development; man-machine interfaces; neural networks; systems engineering and distributed/parallel processing architectures; and artificial intelligence/expert systems are among the topics covered