Comparison Performance Analysis of HDD, SSD and NVME for OLTP Database Server

Improving the efficiency of business process management is a complex and extremely important task, the solution of which is unthinkable without the use of advanced information systems and management decision technologies. The paper presents the results of research on data mining technologies using On-Line Transaction Processing and On-Line Analytical Processing methods. The study proposed a generalized hierarchical representation of data processing methodologies with integration of heterogeneous sources to management decision making. Data mining base methods are systematized: classification, regression, prediction, clustering, interdependence, visualization, deviation (anomaly) detection, estimation, and feature selection (engineering). We described the possibilities of using data mining in the field of information technology, marketing, trade, financial and insurance activities. The study examined data mining technologies in sport products business using the Microsoft® Excel® application and a special add-in of the Microsoft® SQL Server® relational database management system, capable of identifying implicit (hidden) factors that affect or, equally important, do not affect sales volumes.

Running an online transaction processing (OLTP) system is one of the most daunting tasks required of database administrators (DBAs). As businesses rely on OLTP databases to support their mission-critical and real-time applications, poor database performance directly impacts their revenue and user experience. As a result, DBAs constantly monitor, diagnose, and rectify any performance decays. Unfortunately, the manual process of debugging and diagnosing OLTP performance problems is extremely tedious and non-trivial. Rather than being caused by a single slow query, performance problems in OLTP databases are often due to a large number of concurrent and competing transactions adding up to compounded, non-linear effects that are difficult to isolate. Sudden changes in request volume, transactional patterns, network traffic, or data distribution can cause previously abundant resources to become scarce, and the performance to plummet.

SSD is a revolutionary new storage technologies. Enterprise storage system using full SSD is still very expensive, while HDD is still widely used. This study discusses hybrid configuration storage in virtualized server database with benchmark against four hybrid storage configuration for four databases, ORACLE, SQL Server, MySQL and PostgreSQL on Windows Server virtualization. Benchmark using TPC-C and TPC-H to get the best performance of four configurations were tested. The results of this study indicate HDD storage configurations as visual disk drive OS and SSD as visual disk drives database get better performance as OLTP and OLAP database server compared with SSD as visual disk drive OS and HDD as a visual disk drive database. Based on the data research TPC-C, OLTP get best performance at HDD storage configurations as visual disk drive OS and SSD as a visual disk drives database and temporary files.

chapter 5 - Oracle dimension objects

Hybrid storage systems are widely used in big data fields to balance system performance and cost. However, due to a poor understanding of the characteristics of database block requests, past studies in this area cannot fully utilize the performance gain from emerging storage devices. This study presents a hybrid storage database system, called HeuristicDB, which uses an emerging non-volatile memory (NVM) block device as an extension of the database buffer pool. To consider the unique performance behaviors of NVM block devices and the block-level characteristics of database requests, a set of heuristic rules that associate database (block) requests with the appropriate quality of service for the purpose of caching priority are proposed. Using online analytical processing (OLAP) and online transactional processing (OLTP) benchmarks, both trace-based examination and system implementation on MySQL are carried out to evaluate the effectiveness of the proposed design. The experimental results indicate that HeuristicDB provides up to 75% higher performance and migrates 18X fewer data between storage and the NVM block device than existing systems.

Many storage systems are undergoing a significant shift from dedicated appliance-based model to software-defined storage (SDS) because the latter is flexible, scalable and cost-effective for modern workloads. However, it is challenging to provide a reliable guarantee of end-to-end performance in SDS due to complex software stack, time-varying workload and performance interference among tenants. Therefore, modeling and monitoring the performance of storage systems is critical for ensuring reliable QoS guarantees. Existing approaches such as performance benchmarking and analytical modeling are inadequate because they are not efficient in exploring large configuration space, and cannot support elastic operations and diverse storage services in SDS. This paper presents Inside-Out, an automatic model building tool that creates accurate performance models for distributed storage services. Inside-Out is a black-box approach. It builds high-level performance models by applying machine learning techniques to low-level system performance metrics collected from individual components of the distributed SDS system. Inside-Out uses a two-level learning method that combines two machine learning models to automatically filter irrelevant features, boost prediction accuracy and yield consistent prediction. Our in-depth evaluation shows that Inside-Out is a robust solution that enables SDS to predict end-to-end performance even in challenging conditions, e.g., changes in workload, storage configuration, available cloud resources, size of the distributed storage service, and amount of interference due to multi-tenants. Our experiments show that Inside-Out can predict end-to-end performance with 91.1% accuracy on average. Its prediction accuracy is consistent across diverse storage environments.

CSP technologies are essential to allow large shares of renewables into the grid due to their unique ability to cope with the large variability of the energy resource by means of technically and economically feasible thermal energy storage (TES) systems. However, there is still the need and sought to achieve technological breakthroughs towards cost reductions and increased efficiencies. For this, research on advanced power cycles, like the Decoupled Solar Combined Cycle (DSCC) is, are regarded as a key objective. The DSCC concept is, basically, a Combined Brayton-Rankine cycle in which the bottoming cycle is decoupled from the operation of the topping cycle by means of an intermediate storage system. According to this concept, one or several solar towers driving a solar air receiver and a Gas Turbine (Brayton cycle) feed through their exhaust gasses a single storage system and bottoming cycle. This general concept benefits from a large flexibility in its design. On the one hand, different possible schemes related to number and configuration of solar towers, storage systems media and configuration, bottoming cycles, etc. are possible. On the other, within a specific scheme a large number of design parameters can be optimized, including the solar field size, the operating temperatures and pressures of the receiver, the power of the Brayton and Rankine cycles, the storage capacity and others. Heretofore, DSCC plants have been analyzed by means of simple steady-state models with pre-stablished operating parameters in the power cycles. In this work, a detailed transient simulation model for DSCC plants has been developed and is used to analyze different DSCC plant schemes. For each of the analyzed plant schemes, a sensitivity analysis and selection of the main design parameters is carried out. Results show that an increase in annual solar to electric efficiency of 30% (from 12.91 to 16.78) can be achieved by using two bottoming Rankine cycles at two different temperatures, enabling low temperature heat recovery from the receiver and Gas Turbine exhaust gasses.

Technical and economic evaluation, comparison and optimization of a Carnot battery with two different layouts

Efficiently storing large data sets of human genomes is a long-term ambition from both the research and clinical life sciences communities. For instance, biobanks stock thousands to millions of biological physical samples and have been under pressure to store also their resulting digitized genomes. However, these and other life sciences institutions lack the infrastructure and expertise to efficiently store this data. Cloud computing is a natural economic alternative to private infrastructures, but it is not as good an alternative in terms of security and privacy. In this work, we present an end-to-end composite pipeline intended to enable the efficient, dependable cloud-based storage of human genomes by integrating three mechanisms we have recently proposed. These mechanisms encompass (1) a privacy-sensitivity detector for human genomes, (2) a similarity-based deduplication and delta-encoding algorithm for sequencing data, and (3) an auditability scheme to verify who has effectively read data in storage systems that use secure information dispersal. By integrating them with appropriate storage configurations, one can obtain reasonable privacy protection, security, and dependability guarantees at modest costs (e.g., less than $1/Genome/Year). Our preliminary analysis indicates that this pipeline costs only 3% more than non-replicated systems, 48% less than fully-replicating all data, and 31% less than secure information dispersal schemes.

Background: Remote mining operations in Western Australia are faced with the challenge of transitioning to renewable energy solutions to achieve net-zero carbon emissions by 2050. A promising strategy for these off-grid sites is to integrate wind and solar energy with battery storage systems, reducing their reliance on fossil fuel-based energy sources like natural gas turbines. Objectives: This study investigates the feasibility of using two types of battery energy storage system – Lithium-Ion (Li-ion) and Vanadium Flow Batteries (VFB) – in off-grid Western Australian mine sites to achieve the net-zero emissions reduction target. Li-ion batteries possess the advantages of higher energy density, higher efficiency and widespread commercial availability whilst VFBs offer scalability, long cycle life and inherent safety features. Methods: A microgrid model was developed using HOMER (Hybrid Optimization of Multiple Energy Resources) Pro version 3.17.1. Key economic parameters – including the capital, operations and maintenance costs of wind turbines, photovoltaic panels, natural gas turbines, converters and batteries – were integrated into the model. Results: For each storage configuration, the simulation results which yielded the lowest discounted cash flows relative to a configuration in which only natural gas turbines were used to meet 100% of the projected load may be seen in Figure 1. The 4-hr Li-ion batteries (80% DOD) had the lowest initial capital costs for both the 0% and 10% capacity shortage simulations of all cases which met the minimum renewable fraction criteria. This was because of the higher round trip efficiency of the Li-ion batteries when compared to VFBs and the high DOD when compared to the 60% DOD Li-ion simulations. These factors resulted in fewer MWh of 4-hr Li-ion packs being needed to meet the projected load. However, the 80% DOD also resulted in the Li-ion batteries needing to be replaced more frequently than the 60% DOD Li-ion batteries, as shown by the sudden drops in the Total Discounted Cash Flow plot. By the end of the 20-year simulation period, the configurations supported by 10-hour duration VFBs yielded the highest cash flow increase due to their longer lifespan.Figure 1. Cumulative cash flow over a 20-year period for a Western Australian mine site microgrid implementing Li-ion batteries and VFBs.All storage options dramatically reduced the levelised cost of energy (LCOE) when compared to the ‘No Storage’ configurations, as shown in Table 1. This was due to a greater amount of solar and wind power generation being required when no storage was present, as shown in the Total Power Capacity results in Figure 1. These results also revealed that when storage was present, the simulations which produced the lowest net present cost had no wind power generation. This was due to a significant overlap in the periods of peak wind and solar generation in the region and the relatively low projected cost of solar generation. The results also showed that allowing a capacity shortage of 10% dramatically reduced the LCOE and cumulative costs associated with each storage type; however, further analysis is needed to assess the impact of shutting down the mine site during these shortage periods.Table 1. Levelized cost of energy associated with each storage configuration over the twenty-year simulation period. System Levelized Cost of Energy (AUD/kWh) 0% Capacity Shortage 10% Capacity Shortage Natural Gas Turbines Only (0% Renewable Fraction) 0.095 0.089 Solar + Natural Gas Turbines + Wind + No Storage (50% Renewable Fraction) 0.180 0.143 Solar + Natural Gas Turbines + 4-hr Li-Ion Packs (60% DOD) (50% Renewable Fraction) 0.124 0.112 Solar + Natural Gas Turbines + 4-hr Li-Ion Packs (80% DOD) (50% Renewable Fraction) 0.119 0.109 Solar + Natural Gas Turbines + 4-hr VFB Packs (80% DOD) (50% Renewable Fraction) 0.108 0.101 Solar + Natural Gas Turbines + 10-hr VFB Packs (80% DOD) (50% Renewable Fraction) 0.109 0.099 Figure 1

Environmental impacts of balancing offshore wind power with compressed air energy storage (CAES)

For a large company the speed of processing and distribution of data is very important. However, at Company XYZ, this has not yet been implemented in its entirety. This hinders the progress of the company. To be able to solve these problems, a storage system is needed that can access data more quickly and effectively by using a data warehouse system. This study discusses the design and construction of a data warehouse using the Kimball method. The data warehouse schema is created using SQL Server Management Studio and Microsoft SQL Server Integration Service. The analysis results from the data warehouse are displayed in the form of a dashboard using Microsoft PowerBI and a web-based application. Judging from the dashboard display that has been made, the production environment at PT Bio Farma is stable so it does not interfere with production quality. The data is entered into a web-based application, with the percentage of application feasibility of 84% after testing.

Online-transaction-processing (OLTP) applications require the underlying storage system to guarantee consistency and serializability for distributed transactions involving large numbers of servers, which tends to introduce high coordination cost and cause low system performance. In-network coordination is a promising approach to alleviate this problem, which leverages programmable switches to move a piece of coordination functionality into the network. This paper presents a fast and scalable transaction processing system called SwitchTx. At the core of SwitchTx is a decentralized multi-switch in-network coordination mechanism, which leverages modern switches' programmability to reduce coordination cost while avoiding the central-switch-caused problems in the state-of-the-art Eris transaction processing system. SwitchTx abstracts various coordination tasks (e.g., locking, validating, and replicating) as in-switch gather-and-scatter (GaS) operations, and offloads coordination to a tree of switches for each transaction (instead of to a central switch for all transactions) where the client and the participants connect to the leaves. Moreover, to control the transaction traffic intelligently, SwitchTx reorders the coordination messages according to their semantics and redesigns the congestion control combined with admission control. Evaluation shows that SwitchTx outperforms current transaction processing systems in various workloads by up to 2.16X in throughput, 40.4% in latency, and 41.5% in lock time.

The decreasing costs of both PV and energy storage technologies have raised interest in the creation of combined PV plus storage systems to provide dispatchable energy and reliable capacity. In this study, we examine the tradeoffs among various PV plus storage configurations and quantify the impact of configuration on system net value.

Renewable distributed power sources such as wind power and photovoltaic power generation are connected to the power grid, which will bring problems such as increased network loss and reduced power quality. In this paper, a two-layer optimization model for energy storage systems is proposed under large-scale new energy access, and the coupling effects of energy storage planning and operation are considered comprehensively. In the upper layer model, energy storage planning is considered, with the location and capacity of energy storage configuration being the decision variables and the planning cost of the energy storage system being the objective function. The lower layer considers the economy and stability of the grid when the energy storage system is operating, with the output of the energy storage system at each moment as the decision variable and the grid vulnerability, active network loss, and power purchase cost as the objective function. The improved IEEE33 node system has been selected for simulation verification. The results show that the proposed model calculates the optimal capacity configurations of wind power combined energy storage as 0.919 and 0.820 MWh, respectively, and the model can obtain a reasonable energy storage configuration scheme and, in comparing the economic indicators under different scenarios, can obtain the dual storage configuration as the best scheme for the simulation system.