Ssd Flash Drives Used to Improve Performance with Clarity Data Warehouse

Since the introduction of solid-state devices (SSD), both storage area network (SAN) administrators and database administrators (DBA) have imagined the performance gains promised by replacing hard disk drives (HDD). The initial testing in the laboratory did not promise those gains in the real world. The SSD vendors worked between 2007 and 2010 to improve performance, which in industry standard tests showed steady progress. Despite the gains in the laboratory, there were few examples of real world usage particularly in the field of data warehousing. The process of extracting, transforming and loading (ETL) places extreme loads on the ability of the storage device to update data. This paper studies the effect on one such data warehouse

Using Intelligent Prefetching to Reduce the Energy Consumption of a Large-scale Storage System

Many high performance large-scale storage systems will experience significant workload increases as their user base and content availability grow over time. The U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) center hosts one such system that has recently undergone a period of rapid growth as its user population grew nearly 400% in just about three years. When administrators of these massive storage systems face the challenge of meeting the demands of an ever increasing number of requests, the easiest solution is to integrate more advanced hardware to existing systems. However, additional investment in hardware may significantly increase the system cost as well as daily power consumption. In this paper, we present evidence that well-selected software level optimization is capable of achieving comparable levels of performance without the cost and power consumption overhead caused by physically expanding the system. Specifically, we develop intelligent prefetching algorithms that are suitable for the unique workloads and user behaviors of the world\u27s largest satellite images distribution system managed by USGS EROS. Our experimental results, derived from real-world traces with over five million requests sent by users around the globe, show that the EROS hybrid storage system could maintain the same performance with over 30% of energy savings by utilizing our proposed prefetching algorithms, compared to the alternative solution of doubling the size of the current FTP server farm

C3S2E-2008-2016-FinalPrograms

This document records the final programs for each of the 9 meetings of the C* Conference on Computer Science & Software Engineering, C 3S2E which were organized in various locations on three continents. The papers published during these years are accessible from the digital librariy of ACM(2008-2016

Energy Performance Analysis of Software Applications on Servers

The power cost of running a data center is a significant portion of its total annual operating budget. Although the hardware subsystems, namely, processors, memory, disk, and network interfaces of a server actually consume power, it is the software activities that drive the operations of the hardware subsystems leading to varying dynamic power cost. With the aim of reducing power bills of data centers, "Green Computing" has emerged with the primary goal of making software more energy efficient without compromising the performance. Developers play an important role in controlling the energy cost of data center software while writing code. Bearing green principles in mind during design and coding stages of the software life-cycle can have a great impact on the energy efficiency of the final software product. There are a number of ways to optimize application programs at their design stages but it is difficult for the developers to analyse their applications in terms of power cost on the real servers. Reading big data, moving large amount of data from one server to another, compressing data to gain storage space, and decompressing it back are some key operations that are performed extensively on large scale servers in data centers. In the first part of this thesis, we present the design of an automated test bench to measure the power cost of an application running on a server. We show how our test bench can be used by software developers to measure and improve the energy cost of two Java file access methods. Another benefit of our test bench has been demonstrated by comparing the energy footprint measurements of compression and decompression features provided by two popular Linux packages: 7z and rar. This information will be helpful in choosing a Green Software among others to perform a desired function. In the second part, we show how software developers can contribute to energy efficiency of servers by choosing energy efficient APIs (Application Programming Interface) with the optimal choice of parameters while implementing file reading, file copy, file compression and file decompression operations in Java. We performed extensive measurements of energy cost of those operations on a Dell Power Edge 2950 machine running Linux and Windows servers. Measurement results show that energy costs of various APIs for those operations are sensitive to the buffer size selection. The choice of a particular Java API for file reading with different buffer sizes has significant impact on the energy cost, giving an opportunity to save up to 76%. To save energy while copying files, it is important to use APIs with tunable buffer sizes, rather than APIs using fixed size buffers. In addition, there is a trade off between compression ratio and energy cost: because of higher compression ratio, xz compression API consumes more energy than zip and gzip compression APIs. The third part of the thesis presents a design of a framework in which one developer generates energy cost models for the common design options. Afterwords, other developers can make use of those models to find the energy costs for the same design options instead of direct measurements. Overall, this thesis makes a contribution to reduce the perception gap between high level programs and the concept of energy efficiency

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