Data Access Performance Research Articles

PROPEL Discharge: An Interdisciplinary Throughput Initiative

BackgroundIncreased care demands at a healthcare institution led to strained resources, emergency department (ED) congestion, safety events, and patient and employee dissatisfaction. Moreover, high volumes of afternoon discharges contributed to limited early morning bed availability and admission bottlenecks. MethodsA 29-month pre-post design quality improvement project on 19 acute care, adult medicine units across two campuses at a large academic medical center was implemented to improve discharge timeliness, length of stay (LOS), and ED throughput by increasing pre-11:00 AM discharges. Based on Lean Six Sigma methodology, interventions included standardized interdisciplinary discharge processes and roles, processes to ensure performance data transparency and access, a recognition program, and a barrier tracking and mitigation process for continued improvements. ResultsDuring the intervention period, pre-11:00 AM discharges increased from 5.1% to 21.8% (p < 0.001), discharge orders were entered 42 minutes earlier (p < 0.001), patients were discharged 56 minutes earlier (p < 0.001), the percentage of discharges completed within 90 minutes from discharge order improved from 26.2% to 38.1% (p < 0.001), the percentage of discharges by 3:00 PM improved from 44.7% to 55.9% (p < 0.001), ED admissions arrived to units 44 minutes earlier (p < 0.001), median LOS decreased by 0.46 days (p < 0.001), median O:E LOS decreased by 0.05 (p < 0.001), and opportunity day reductions contributed to increased bed capacity of 18.84 beds per day. ConclusionEarly morning discharges are associated with improved patient throughput and are safe, achievable, and sustainable via interventions focused on frontline engagement, interdisciplinary collaboration, standardization, barrier mitigation, data accessibility, and accountability.

Design of software system architecture for reading and publishing amateur literary works

The article discusses the functionality and key requirements for a web application for reading and publishing amateur literary works. Against the backdrop of current changes in the book industry caused by the development of technology and the emergence of self-publishing, the need to create platforms that provide authors with the opportunity to publish content independently is emphasized. One of the main problems that authors face when publishing is the loss of the original formatting of their works when transferring them to text editors on web platforms. This paper proposes a solution to this problem by developing a web application with the function of importing the content of text files while preserving their original formatting. The article also contains a comparative analysis of popular web-based platforms for publishing works, such as Archive of Our Own, Wattpad, and Arkush, which helped to highlight the advantages and disadvantages of each of them. In particular, the difficulties associated with storing and processing unstructured data, such as images and text files, were investigated. To solve the problem of storing such data, a comparative analysis of three approaches was conducted: storing data in relational databases, local file systems, and cloud storage. Taking into account the results of this analysis, the web application under development was chosen to integrate with the Amazon Web Services cloud provider and use Amazon S3 cloud storage, which provides scalability, reliability, and convenience in managing large amounts of unstructured data. It was also decided to use the Amazon CloudFront CDN service to improve the speed of data access and increase overall system performance. In addition, the article describes the choice of a technology stack for implementing the web application, which includes Java, Spring Framework, MySQL, ORM Hibernate for the server side and HTML, CSS, React.js for the client side. The article highlights important aspects of designing a web application architecture aimed at scalability and resilience to large amounts of data and an increase in the number of active users, and provides a basis for further system improvement.

Data-access performance anti-patterns in data-intensive systems

Data-access performance anti-patterns in data-intensive systems

Blockchain-based data sharing algorithm in distributed network data storage

This paper proposes a sharing algorithm based on blockchain principles to address the issues of data sharing, low efficiency, and performance in traditional systems. The algorithm is integrated with the domain name system to develop a data storage system based on blockchain. The performance of the sharing algorithm is evaluated, and the data storage system is tested. This demonstrates that the sharing algorithm’s average latency is 436 ms and average throughput is 5439 tps. Furthermore, it outperforms the other comparison algorithms. Additionally, the study conducts performance experiments to compare the data storage system. The data storage system proposed in this study demonstrates a higher average throughput of 6.42*108 tps and a faster data access time of 0.15 s than the other comparison systems. The comprehensive results show that the proposed sharing algorithm and data storage system outperform the comparison algorithm and system in terms of latency, throughput, and data access performance. The constructed model exhibits good centralized and distributed storage crawling performance, which can achieve more secure, efficient, and trustworthy data sharing in distributed network data storage.

Optimizing the Performance of Data Warehouse by Query Cache Mechanism in Big Data

In today's world of Business Intelligence (BI), fast and efficient access to data from Data Warehouses (DW) is crucial. With the increasing amount of Big Data, caching has become one of the most effective techniques for improving data access performance. DWs are widely used by organizations for managing and using data in Decision Support Systems (DSS). To optimize the performance of fetching data from DWs, various methods have been employed, and one of them is the Query Cache method. Our proposed work focuses on a cache-based mechanism that improves the performance of DWs in two ways. First, it reduces the execution time by directly accessing records from cache memory, and second, it saves cache memory space by eliminating non-frequently used data. Our goal is to fill the cache memory with the most frequently used data. To achieve this objective, we utilize an aging-based Least Frequently Used (LFU) algorithm that considers the size and frequency of data simultaneously. This algorithm manages the priority and expiry age of the data in cache memory by taking into account both the size and frequency of data. LFU assigns priorities and counts the age of data placed in the cache memory. The cache block entry with the lowest age count and priority is eliminated first. Ultimately, our proposed cache mechanism efficiently utilizes cache memory and significantly improves the performance of data access between the main DW and the business user query

Granularity-Driven Management for Reliable and Efficient Skyrmion Racetrack Memories

Skyrmion racetrack memory is a rising star of nonvolatile memories due to its outstanding access performance and storage density. However, the working principle of skyrmion racetrack memory introduces unique issues, such as the position error and data representation problem, which considerably impact the data reliability. Although brilliant error correction codes have been proposed to detect and correct bit errors, however, their time-consuming encoding and decoding processes cannot fully match the nanosecond-level access latency of skyrmion racetrack memory. Observing the dilemma among data reliability, access performance, and space utilization, we propose a granularity-driven management scheme for skyrmion racetrack memory. While eliminating the errors incurred due to the position error and data representation problem, the proposed management scheme aims to jointly optimize access performance and space utilization. To achieve this goal, the proposed scheme adaptively selects different combinations of data encoding, layout, and indexing schemes for the data of different granularities. Moreover, we investigate the port selection problem under our proposed data layouts to minimize shift overheads on data accesses. Through analytical and experimental studies, the proposed management scheme is evaluated, and the obtained results are quite encouraging.

Optimization Design and Performance Analysis of Improved IEEE802.11p MAC Mechanism Based on High Mobility of Vehicle

The high-speed movement of vehicles is the core factor affecting the rapid development of vehicular ad hoc network (VANET), especially the impact of vehicle mobility on data access and transmission performance. However, the classic IEEE802.11p data access and transmission protocol are still used in the multi-hop VANET under the highway system, which is adopted in the low-speed environment. Therefore, the impact of vehicle movement on the performance of the existing network is analyzed theoretically, and it is found that the network performance will be greatly reduced with the increase of vehicle speed. In order to improve the network performance in high-speed environment, an improved network access transmission protocol is proposed. Through Markov modeling, the analytic expressions of system delay, throughput, and packet loss rate are obtained. Finally, the effectiveness of the improved mechanism is verified by comparing the simulation results of two mechanisms.

Dimensional Modeling Method Discussion for the Profits from Mineral Rights Transfer Management

In the informatization process of the profits from mineral rights transfer management, it is of great practical significance to establish a data warehouse with easy to understand, efficient query performance and strong scalability, so as to provide good decision support services for managers. As an important method of data warehouse modeling, dimensional modeling method has significant advantages over others in data understandability, scalability and data access performance compared with other data warehouse modeling methods. Combined with the business needs of mineral rights transfer income (mineral rights price) assessment and collection decision analysis, the key design ideas of dimensional modeling are expounded from the perspectives of dimension content change processing design, dimension role-playing, shared dimension design and audit dimension design. Taking the actual engineering project as an example, dimension modeling results can be queried easily in order to help domestic peers in the construction of DW/BI projects.

WukaStore: Scalable, Configurable and Reliable Data Storage on Hybrid Volunteered Cloud and Desktop Systems

In this paper, we propose a hybrid storage framework WukaStore, which offers a scalable, configurable and reliable big data storage service by integrating stable storage (such as Cloud storage or durable storage utilities) and volatile storage (such as idle storage harnessed from desktop PCs over the Internet). By configuring different storage strategies, WukaStore delivers cost-effective storage service to satisfy users&#x2019; requirements. We present trace-driven simulations to study the impact of different strategies and how to ensure high availability and durability in WukaStore. We present the prototype implementation of WukaStore based on BitDew, an open source middleware for Big Data management. We conduct performance evaluations to validate the effectiveness of WukaStore, through the deployment of WukaStore in the French Grid&#x0027;5000 experimental platform. In particular, we also present a case study where WukaStore is deployed in a hybrid environment taking advantage of storage provided by Amazon S3, Dropbox and a local Desktop Grid at the same time. Our evaluation results show that WukaStore provides users with an alternative method to store their big data on hybrid volunteered cloud and desktop systems and to decrease the cost of data storage, while providing great data access performance, scalability as well as reliability.

DirectNVM: Hardware-accelerated NVMe SSDs for High-performance Embedded Computing

With data-intensive artificial intelligence (AI) and machine learning (ML) applications rapidly surging, modern high-performance embedded systems, with heterogeneous computing resources, critically demand low-latency and high-bandwidth data communication. As such, the newly emerging NVMe (Non-Volatile Memory Express) protocol, with parallel queuing, access prioritization, and optimized I/O arbitration, starts to be widely adopted as a de facto fast I/O communication interface. However, effectively leveraging the potential of modern NVMe storage proves to be nontrivial and demands fine-grained control, high processing concurrency, and application-specific optimization. Fortunately, modern FPGA devices, capable of efficient parallel processing and application-specific programmability, readily meet the underlying physical layer requirements of the NVMe protocol, therefore providing unprecedented opportunities to implementing a rich-featured NVMe middleware to benefit modern high-performance embedded computing. In this article, we present how to rethink existing accessing mechanisms of NVMe storage and devise innovative hardware-assisted solutions to accelerating NVMe data access performance for the high-performance embedded computing system. Our key idea is to exploit the massively parallel I/O queuing capability, provided by the NVMe storage system, through leveraging FPGAs’ reconfigurability and native hardware computing power to operate transparently to the main processor. Specifically, our DirectNVM system aims at providing effective hardware constructs for facilitating high-performance and scalable userspace storage applications through (1) hardening all the essential NVMe driver functionalities, therefore avoiding expensive OS syscalls and enabling zero-copy data access from the application, (2) relying on hardware for the I/O communication control instead of relying on OS-level interrupts that can significantly reduce both total I/O latency and its variance, and (3) exposing cutting-edge and application-specific weighted-round-robin I/O traffic scheduling to the userspace. To validate our design methodology, we developed a complete DirectNVM system utilizing the Xilinx Zynq MPSoC architecture that incorporates a high-performance application processor (APU) equipped with DDR4 system memory and a hardened configurable PCIe Gen3 block in its programmable logic part. We then measured the storage bandwidth and I/O latency of both our DirectNVM system and a conventional OS-based system when executing the standard FIO benchmark suite [ 2 ]. Specifically, compared against the PetaLinux built-in kernel driver code running on a Zynq MPSoC, our DirectNVM has shown to achieve up to 18.4× higher throughput and up to 4.5× lower latency. To ensure the fairness of our performance comparison, we also measured our DirectNVM system against the Intel SPDK [ 26 ], a highly optimized userspace asynchronous NVMe I/O framework running on a X86 PC system. Our experiment results have shown that our DirectNVM, even running on a considerably less powerful embedded ARM processor than a full-scale AMD processor, achieved up to 2.2× higher throughput and 1.3× lower latency. Furthermore, by experimenting with a multi-threading test case, we have demonstrated that our DirectNVM’s weighted-round-robin scheduling can significantly optimize the bandwidth allocation between latency-constraint frontend applications and other backend applications in real-time systems. Finally, we have developed a theoretical framework of performance modeling with classic queuing theory that can quantitatively define the relationship between a system’s I/O performance and its I/O implementation.

A data grouping model based on cache transaction for unstructured data storage systems

Cache prefetching technology has become the mainstream data access optimization strategy in the data centers. However, the rapidly increasing of unstructured data generates massive pairwise access relationships, which can result in a heavy computational burden for the existing prefetching model and lead to severe degradation in the performance of data access. We propose cache-transaction-based data grouping model (CTDGM) to solve the problems described above by optimizing the feature representation method and grouping efficiency. First, we provide the definition of the cache transaction and propose the method for extracting the cache transaction feature (CTF). Second, we design a data chunking algorithm based on CTF and spatiotemporal locality to optimize the relationship calculation efficiency. Third, we propose CTDGM by constructing a relation graph that groups data into independent groups according to the strength of the data access relation. Based on the results of the experiment, compared with the state-of-the-art methods, our algorithm achieves an average increase in the cache hit rate of 12% on the MSR dataset with small cache size (0.001% of all the data), which in turn reduces the number of data I/O accesses by 50% when the cache size is less than 0.008% of all the data.

A machine learning assisted data placement mechanism for hybrid storage systems

Emerging applications produce massive files that show different properties in file size, lifetime, and read/write frequency. Existing hybrid storage systems place these files onto different storage mediums assuming that the access patterns of files are fixed. However, we find that the access patterns of files are changeable during their lifetime. The key to improve the file access performance is to adaptively place the files on the hybrid storage system using the run-time status and the properties of both files and the storage systems. In this paper, we propose a machine learning assisted data placement mechanism that adaptively places files onto the proper storage medium by predicting access patterns of files. We design a PMFS based tracer to collect file access features for prediction and show how this approach is adaptive to the changeable access pattern. Based on data access prediction results, we present a linear data placement algorithm to optimize the data access performance on the hybrid storage mediums. Extensive experimental results show that the proposed learning algorithm can achieve over 90% accuracy for predicting file access patterns. Meanwhile, this paper can achieve over 17% improvement of system performance for file accesses compared with the state-of-the-art linear-time data placement methods.

Robustness of the Storage in Cloud Data Centers Based on Simple Swarm Optimization Algorithm

Storing extensive data in cloud environments affects service quality, transmission speed, and access to information in systems, which is becoming a growing challenge. In storage improvement, reducing various costs and reducing the shortest path in the storage of distributed cloud data centers are among the important issues in the field of cloud computing. In this paper, particle swarm optimization (PSO) algorithm and learning automaton (LA) are used to minimize the cost of a data center, which includes communication, data transfer, and storage and optimization of communication between data centers. To improve storage in distributed data centers, a new model called LAPSO is proposed by combining LA and PSO, in which LA improves particle control by searching for particle speed and position. In this method, LA moves each particle in the direction where it has the best individual and group experiences. In multipeak problems, it does not fall into local optimums. Results of the experiments are shown on the dataset of spatial information and cadastre of country lands, which includes 13 data centers. The proposed method evaluates and improves the optimal position parameters, minimum route cost, distance, data transfer cost, storage cost, data communication cost, load balance, and access performance better than other methods.

Blackbox Testing On E-Commerce System Web-Based Evermos (Feature: Registration Experiment & Revamp)

Software Testing/System Testing is a critical element of assurance software quality that represents the main study of specifications, design, and coding. Increased visibility (capabilities) of the software as a system element and the costs that arise due to device failure lenient, motivates good planning through its testing thorough.Black-box testing techniques focus on the information domain of the software, by doing a test case by partitioning the input domain of a program in a way that provides in-depth testing coverage. The Black Box itself has several methods in testing. Namely, the test method Graph-based explores the relationship between objects and behavior program. The equivalent partition divides the input domain into data classes it is possible to perform certain software functions. Boundary value analysis check the program's ability to handle data to the extent it can received.Evermos is a platform, a platform, to sell products Indonesian Muslims. As “Everyday Need for Every Moslem”, Evermos was started from a dream, vision and goal to helping small businesses and people individuals to compete with large and existing companies advanced. Testing software using the Black Box method is expected to increase visibility and meet quality requirements.Evermos software itself. The results of testing the software is aimed at finding errors in the category of functions incorrect, interface errors, errors in data structures or access external database, performance errors, initialization and termination errors.Evermos Web software test results is to provide documentation of test results which informs the suitability of the software being tested with predetermined specifications and finds errors on Evermos.

Pro NDN : MCDM-Based Interest Forwarding and Cooperative Data Caching for Named Data Networking

Named data networking (NDN), as a specific architecture design of information-centric networking (ICN), has quickly became a promising candidate for future Internet architecture, where communications are driven by data names instead of IP addresses. To realize the NDN communication paradigm in the future Internet, two important features, stateful forwarding and in-network caching, have been proposed to cope with drawbacks of host-based communication protocols. The stateful forwarding is designed to maintain the state of pending Interest packets to guide Data packets back to requesting consumers, while the in-network caching is used to reduce both network traffic and data access delay to improve the overall performance of data access. However, the conventional stateful forwarding approach is not adaptive and responsive to diverse network conditions because it fails to consider multiple network metrics to make Interest forwarding decision. In addition, the default in-network caching strategy relies on storing each received Data packet regardless of various caching constraints and criteria, which causes the routers in the vicinity of data producers to suffer from excessive caching overhead. In this paper, we propose the Pro NDN , a novel stateful forwarding and in-network caching strategy for NDN networks. The Pro NDN consists of multicriteria decision-making (MCDM) based interest forwarding and cooperative data caching. The basic idea of the MCDM-based interest forwarding is to employ Technique for Order Performance by Similarity to Idea Solution (TOPSIS) to dynamically evaluate outgoing interface alternatives based on multiple network metrics and objectively select an optimal outgoing interface to forward the Interest packet. In addition, the cooperative data caching consists of two schemes: CacheData, which caches the data, and CacheFace, which caches the outgoing interface. We conduct extensive simulation experiments for performance evaluation and comparison with prior schemes. The simulation results show that the Pro NDN can improve Interest satisfaction ratio and Interest satisfaction latency as well as reduce hop count and Content Store utilization ratio.

Improving in-memory file system reading performance by fine-grained user-space cache mechanisms

Nowadays, as the memory capacity of servers become larger and larger, distributed in-memory file systems, which enable applications to interact with data at fast speed, have been widely used. However, the existing distributed in-memory file systems still face the problem of low data access performance in small data reading, which seriously reduce their usefulness in many important big data scenarios. In this paper, we analyze the factors that affect the performance of reading in-memory files and propose a two-layer user space cache management mechanism: in the first layer, we cache data packet references to reduce frequent page fault interruptions (packet-level cache); in the second layer, we cache and manage small file data units to avoid redundant inter-process communications (object-level cache). We further design a fine-grained caching model based on the submodular function optimization theory, for efficiently managing the variable-length cache units with partially overlapping fragments on the client side. Experimental results on synthetic and real-world workloads show that compared with the existing cutting-edge systems, the first level cache can double the reading performance on average, and the second level cache can improve random reading performance by more than 4 times. Our caching strategies also outperform the cutting-edge cache algorithms over 20% on hit ratio. Furthermore, the proposed client-side caching framework idea has been adopted by the Alluxio open source community, which shows the practical benefits of this work. • A packet-level cache policy for reducing page faults during sequential data reading. • An in-memory cache layer to cache the variable-length hot file fragments. • Proved that the variable-length cache problem is a submodular optimization problem theoretically. • Comprehensive performance evaluation of the client-side cache strategies.

RETRACTED: Cloud music teaching database based on opencl design and neural network

The cloud music teaching learning and development of students and the lack of demand for mobile learning resource platform, this article focuses on the design and implementation of a mobile classroom in the cloud computing platform, a detailed analysis from four aspects: the background significance, platform design, architecture and main cloud technology application, the proposed system Flexible expansion module option information during transport and through the expansion towards the outside Prime work Mongo Pitty incorporate mapping. And previous system music teaching database work not load to cloud so to implementation Description of management. Music sharing and social functioning, communication across cultural boundaries between the language and the People, and through the development of neural networks, it has been upgraded from the choice of music discovery mode music cloud database under traditional record player mode. In this study, the social needs of the user's choice of music based on emotion net facilitate cloud music reviews for the dataset. Application Test actual teaching shows that the platform can meet the individual learning needs of students, enhance the interaction between teachers and students to achieve the flow of music teaching resources, sharing and fragmented organization. Cloud storage offers storage space, so primarily storage of user-friendly and timely acquisition and data, based Web user cloud-based applications of all types. The requirements for how to optimize cloud storage, in-depth analysis to improve data access and storage performance of the aims.

A popularity-aware reconstruction technique in erasure-coded storage systems

In this study, we develop a novel data reconstruction technique for parallel storage systems housed in modern data centers. We advocate for erasure-coded data storage systems to archive warm data (a.k.a., unpopular data), which attract a limited number of accesses or updates. Different from hot or cold data, warm data have to be treated in a distinctive way to optimize system performance and storage-space utilization. We pay particular attention to efficient data reconstruction in which faulty data nodes are rebuilt while responding to I/O requests. To achieve this goal, we employ two machine-learning algorithms to offer online data reconstruction in erasure coded storage systems. Our data reconstruction technique is conducive to recovering faulty nodes while boosting read performance for requests accessing data residing on the faulty nodes. Our system is reliant on a clustering mechanism to group files into multiple clusters, in each of which files share similar features. Furthermore, we implement a prediction module where a list of future popular data is projected by keeping track of historical I/O accesses. This popular-data list, in turn, provides predictions on files that are likely to be accessed in the not-too-distant future. The prediction module is responsible for computing similarities among users, thereby setting up priority levels of data blocks to be reconstructed. We implement our data reconstruction scheme in an erasure-coded parallel storage system to recover files with a guidance from the popular-data list. Our experimental results confirm that our system speeds up the data recovery of parallel storage systems while maintaining a high data access performance for on-line users.

Integrating cellular automata and discrete global grid systems: a case study into wildfire modelling

Abstract. With new forms of digital spatial data driving new applications for monitoring and understanding environmental change, there are growing demands on traditional GIS tools for spatial data storage, management and processing. Discrete Global Grid System (DGGS) are methods to tessellate globe into multiresolution grids, which represent a global spatial fabric capable of storing heterogeneous spatial data, and improved performance in data access, retrieval, and analysis. While DGGS-based GIS may hold potential for next-generation big data GIS platforms, few of studies have tried to implement them as a framework for operational spatial analysis. Cellular Automata (CA) is a classic dynamic modeling framework which has been used with traditional raster data model for various environmental modeling such as wildfire modeling, urban expansion modeling and so on. The main objectives of this paper are to (i) investigate the possibility of using DGGS for running dynamic spatial analysis, (ii) evaluate CA as a generic data model for dynamic phenomena modeling within a DGGS data model and (iii) evaluate an in-database approach for CA modelling. To do so, a case study into wildfire spread modelling is developed. Results demonstrate that using a DGGS data model not only provides the ability to integrate different data sources, but also provides a framework to do spatial analysis without using geometry-based analysis. This results in a simplified architecture and common spatial fabric to support development of a wide array of spatial algorithms. While considerable work remains to be done, CA modelling within a DGGS-based GIS is a robust and flexible modelling framework for big-data GIS analysis in an environmental monitoring context.

Learning-based dynamic cache management in a cloud

Caches are an important component of modern computing systems given their significant impact on performance. In particular, caches play a key role in the cloud due to the nature of large-scale, data-intensive processing. One of the key challenges for the cloud providers is how to share the caching capacity among tenants, under the circumstance that each often requires a different degree of quality of service (QoS) with respect to data access performance. The invariant is that the individual tenants’ QoS requirements should be satisfied while the cache usage is optimized in a system-wide manner. In this paper, we introduce a learning-based approach for dynamic cache management in a cloud, which is based on the estimation of data access pattern of a tenant and the prediction of cache performance for the access pattern in question. We consider a variety of probability distributions to estimate the data access pattern, and examine a set of learning-based regression techniques to predict the cache hit rate for the access pattern. The predicted cache hit rate is then used to make a decision whether reallocating cache space is needed to meet the QoS requirement for the tenant. Our experimental results with an extensive set of synthetic traces and the YCSB benchmark show that the proposed method consistently optimizes the cache space while satisfying the QoS requirement.