Relevant bibliographies by topics / Database Scalability

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Database Scalability'

Author: Grafiati
Published: 7 June 2025
Last updated: 11 July 2025

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Journal articles on the topic "Database Scalability"

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Sai Venkata Kondapalli. "Cloud Database Scalability: Meeting Modern Enterprise Demands." World Journal of Advanced Engineering Technology and Sciences 15, no. 1 (2025): 2278–90. https://doi.org/10.30574/wjaets.2025.15.1.0469.

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Cloud database technologies have emerged as a critical solution for enterprises grappling with explosive data growth and unpredictable workload patterns. This comprehensive article examines how modern cloud database systems address enterprise scalability challenges through dynamic resource allocation, distributed architectures, and automated management capabilities. Further, we deep dive into the core scalability technologies, including horizontal and vertical scaling approaches, automatic scaling mechanisms, and distributed database architectures that enable organizations to handle exponentially growing datasets. The article further analyzes various database service models (DBaaS, cloud-native distributed databases, self-managed deployments), resource optimization strategies (connection pooling, query optimization, workload management), and crucial implementation considerations for successful cloud database migrations. Through real-world examples across industries, this article demonstrates how properly implementing these technologies allows enterprises to balance performance requirements with cost optimization while maintaining the business agility required in today's data-driven landscape.
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Pokorny, Jaroslav. "NoSQL databases: a step to database scalability in web environment." International Journal of Web Information Systems 9, no. 1 (2013): 69–82. http://dx.doi.org/10.1108/17440081311316398.

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Gupta Lakkimsetty, N. V. Rama Sai Chalapathi. "Database Optimization Strategies: Enhancing Performance and Scalability." International Journal of Computer Science and Mobile Computing 12, no. 11 (2023): 69–89. https://doi.org/10.47760/ijcsmc.2023.v12i11.006.

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Database optimization is critical for ensuring efficient data retrieval and storage, enabling high performance and scalability in modern applications. This paper explores comprehensive strategies for optimizing databases, focusing on query performance, schema design, caching, scalability, and cloud-based databases. Through an analysis of best practices and advanced techniques, this paper highlights methods to improve performance metrics and reduce bottlenecks, with a focus on practical implementation for real-world scenarios.
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Priyanka, Gowda Ashwath Narayana Gowda. "SQL vs. NoSQL Databases: Choosing the Right Option for FinTech." European Journal of Advances in Engineering and Technology 7, no. 8 (2020): 100–104. https://doi.org/10.5281/zenodo.13950855.

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The paper discusses the critical decision-making in choosing between SQL and NoSQL databases for FinTech applications. FinTech, founded on large-scale data processing, transactional integrity, and real-time analytics, warrants robust and highly scalable database solutions. SQL databases are very suitable for applications such as payment processing, customer relationship management, and core banking systems because of their strong consistency, reliability, and mature ecosystem. On the other hand, NoSQL databases offer flexibility in handling unstructured data, horizontal scalability, and high availability for big data analytics, real-time fraud detection, and personalized finance services. The paper contrasts SQL and NoSQL databases concerning data structure, scalability, consistency, and availability statements of strengths and limitations in FinTech. We provide insights into which database type would be more applicable for specific FinTech applications through several practical use cases and performance evaluations. The analysis describes that SQL databases are very relevant in cases with high transactional integrity within the application or system and structured data management. In contrast, a NoSQL database would find an application in scenarios requiring flexibility and scalability with diverse data types. FinTech companies, thereby, have to think very carefully about individual needs and options to choose the right database technology, ensuring it aligns with operational requirements and strategies for future growth.
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Naqvi, Kaynat Zehra. "Difficulties Associated with Replicated Data in Distributed Real-Time Database Systems." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 04 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem30824.

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In both Distributed and Real Time Databases Systems replication are interesting areas for the new researchers. In this paper, we provide an overview to compare replication techniques available for these database systems. Data consistency and scalability are the issues that are considered in this paper. Those issues are maintaining consistency between the actual state of the real-time object of the external environment and its images as reflected by all its replicas distributed over multiple nodes. We discuss a frame to create a replicated real- time database and preserve all timing constrains. In order to enlarge the idea for modelling a large scale database, we present a general outline that consider improving the Data consistency and scalability by using and accessible algorithm applied on the both database, with the goal to lower the degree of replication enables segments to have individual degrees of replication with the purpose of avoiding extreme resource usage, which all together contribute in solving the scalability problem for Distributed Real Time Database Systems. Keywords— Replicated database, Replicated Database Design, Replicated database protocols, Transactional replication, Data consistency and Scalability, Active and Passive replication.
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Guleria, Pratiyush. "Data Access Layer: A Programming Paradigm on Cloud." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 11, no. 3 (2013): 2341–45. http://dx.doi.org/10.24297/ijct.v11i3.1164.

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Database is important for any application and critical part of private and public cloud platforms. For compatibility with cloud computing we can follow architectures like three tier architecture in .Net Technologies such that database layer should be separate from user and business logic layers. There are some other issues like following ACID properties in databases, providing dynamic scalability by using Shared-disk Architecture and efficient multi-tenancy, elastic scalability, and database privacy.
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Swapnil, Raj, and Kumar Raghav Anuj. "Elasticity in the cloud related to database autonomies and scalability." i-manager’s Journal on Cloud Computing 9, no. 1 (2022): 26. http://dx.doi.org/10.26634/jcc.9.1.18719.

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Cloud computing has been a very popular paradigm for implementing online applications. Scalability, elasticity, cost of use, and large-scale economies are the main reasons for the effective and widespread acceptance of cloud computing. In this paper, we outline our work to inject the aforementioned "cloud capabilities" into a database system designed to support various applications deployed in the cloud: designing scalable databases using autonomies database and elasticity that enables lightweight resiliency using low-cost live database migrations and an intelligent and autonomous controller designed for system management without human intervention.
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Helal, Abdelsalam, and Judson Fortner. "Achieving scalability in highly contentious database systems." Information Sciences 89, no. 1-2 (1996): 39–61. http://dx.doi.org/10.1016/0020-0255(95)00223-5.

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Shekhar Mishra. "Building Scalable Cloud Databases with Database Reliability Engineering." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 11, no. 1 (2025): 1322–33. https://doi.org/10.32628/cseit251112125.

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This comprehensive article explores the evolution and implementation of Database Reliability Engineering (DBRE) in cloud environments, focusing on the transformation from traditional database management to modern cloud-based solutions. The article examines key aspects of scalable database architectures, including elastic scalability, serverless solutions, and advanced scaling techniques. The article investigates various strategies for ensuring database reliability, performance optimization, and cost management while addressing challenges in data distribution and consistency maintenance. Through analysis of multiple cloud platforms and implementation approaches, the article demonstrates how organizations can effectively leverage automation, monitoring, and best practices to build robust, scalable database solutions that meet contemporary demands for performance and reliability.
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Y. Aldailamy, Ali, Abdullah Muhammed, Waidah Ismail, and Abduljalil Radman. "Comparative Study for Load Management of HBase and Cassandra Distributed Databases in Big Data." International Journal of Engineering & Technology 7, no. 4.31 (2018): 375–80. http://dx.doi.org/10.14419/ijet.v7i4.31.23715.

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The advancement in cloud computing, the increasing size of databases and the emergence of big data have made traditional data management system to be insufficient solution to store and manage such large-scale data. Therefore, there has been an emergence of new mechanisms for data storage that can handle large-scale data. NoSQL databases are used to store and manage large amount of data. They are intended to be open source, distributed and horizontally scalable in order to provide high performance. Scalability is one of the important features of such systems, it means that by increasing the number of nodes, more requests can be served per unit of time. Distribution and scalability are always companied with load management, which provides load balancing of work among multiple nodes. Load management efficiency varies from system to another according to the used load balancing technique. In this study, HBase and Cassandra load management with scalability will be evaluated as they are the most popular NoSQL databases modeled based on BigTable. In particular, this paper will compare and analyze the load management for the distributed performance of HBase and Cassandra using standard benchmark tool named Yahoo! Cloud Serving Benchmark (YCSB). The experiments will measure the performance of database operations with a different number of connections using different numbers of operations, database size, and processing nodes. The experimental results showed that HBase can provide better performance as the number of connections increase in the presence of horizontal scalability. Â
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Dissertations / Theses on the topic "Database Scalability"

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Yee, Wai Gen. "Improving the performance and scalability of intermittently synchronized database systems." Diss., Georgia Institute of Technology, 2003. http://hdl.handle.net/1853/8311.

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Håkansson, Kristina, and Andreas Rosenqvist. "Evaluation of CockroachDB in a cloud-native environment." Thesis, Blekinge Tekniska Högskola, Institutionen för programvaruteknik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-21671.

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The increased demand for using large databases that scale easily and stay consistent requires service providers to find new solutions for storing data in databases. One solution that has emerged is cloud-native databases. Service providers who effectively can transit to cloud-native databases will benefit from new enterprise applications, industrial automation, Internet of Things (IoT) as well as consumer services, such as gaming and AR/VR. This consequently changes the requirements on a database's architecture and infrastructure in terms of being compatible with the services deployed in a cloud-native environment - this is where CockroachDB comes into the picture. CockroachDB is relatively new and is built from the ground up to run in a cloud-native environment. It is built up with nodes that work as individual machines, and these nodes form a cluster. The authors of this report aim to evaluate the characteristics of the Cockroach database to get an understanding of what it offers to companies that are in a cloud-infrastructure transition phase. For the scope of characteristics, this report is focusing on performance, throughput, stress-test, version hot-swapping, horizontal-/vertical scaling, and node disruptions. To do this, a CockroachDB database was deployed on a Kubernetes cluster, in which simulated traffic was conducted. For the throughput measurement, the TPC-C transaction processing benchmark was used. For scaling, version hot-swapping, and node disruptions, an experimental method was performed. The result of the study confirms the expected outcome. CockroachDB does in fact scale easily, both horizontally and vertically, with minimal effort. It also shows that the throughput remains the same when the cluster is scaled up and out since CockroachDB does not have a master write-node, which is the case with some other databases. CockroachDB also has built-in functionality to handle configuration changes like version hot-swapping and node disruptions. This study concluded that CockroachDB lives up to its promises regarding the subjects handled in the report, and can be seen as a robust, easily scalable database that can be deployed in acloud-native environment.
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Mathiason, Gunnar. "Virtual Full Replication for Scalable Distributed Real-Time Databases." Doctoral thesis, Linköpings universitet, Institutionen för datavetenskap, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-20661.

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A fully replicated distributed real-time database provides high availability and predictable access times, independent of user location, since all the data is available at each node. However, full replication requires that all updates are replicated to every node, resulting in exponential growth of bandwidth and processing demands with the number of nodes and objects added. To eliminate this scalability problem, while retaining the advantages of full replication, this thesis explores Virtual Full Replication (ViFuR); a technique that gives database users a perception of using a fully replicated database while only replicating a subset of the data. We use ViFuR in a distributed main memory real-time database where timely transaction execution is required. ViFuR enables scalability by replicating only data used at the local nodes. Also, ViFuR enables flexibility by adaptively replicating the currently used data, effectively providing logical availability of all data objects. Hence, ViFuR substantially reduces the problem of non-scalable resource usage of full replication, while allowing timely execution and access to arbitrary data objects. In the thesis we pursue ViFuR by exploring the use of database segmentation. We give a scheme (ViFuR-S) for static segmentation of the database prior to execution, where access patterns are known a priori. We also give an adaptive scheme (ViFuR-A) that changes segmentation during execution to meet the evolving needs of database users. Further, we apply an extended approach of adaptive segmentation (ViFuR-ASN) in a wireless sensor network - a typical dynamic large-scale and resource-constrained environment. We use up to several hundreds of nodes and thousands of objects per node, and apply a typical periodic transaction workload with operation modes where the used data set changes dynamically. We show that when replacing full replication with ViFuR, resource usage scales linearly with the required number of concurrent replicas, rather than exponentially with the system size.
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Umair, Muhammad. "Performance Evaluation and Elastic Scaling of an IP Multimedia Subsystem Implemented in a Cloud." Thesis, KTH, Radio Systems Laboratory (RS Lab), 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-124578.

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Network (NGN) technology which enables telecommunication operators to provide multimedia services over fixed and mobile networks. All of the IMS infrastructure protocols work over IP which makes IMS easy to deploy on a cloud platform. The purpose of this thesis is to analysis a novel technique of “cloudifying” the OpenIMS core infrastructure. The primary goal of running OpenIMS in the cloud is to enable a highly available and horizontally scalable Home Subscriber Server (HSS). The resulting database should offer high availability, and high scalability. The prototype developed in this thesis project demonstrates a virtualized OpenIMS core with an integrated horizontal scalable HSS. Functional and performance measurements of the system under test (i.e. the virtualized OpenIMS core with horizontally scalable HSS) were conducted. The results of this testing include an analysis of benchmarking scenarios, the CPU utilization, and the available memory of the virtual machines. Based on these results we conclude that it is both feasible and desirable to deploy the OpenIMS core in a cloud.<br>IP Multimedia Subsystem (IMS) ramverk är ett Next Generation Network (NGN) teknik som möjliggör teleoperatörer att erbjuda multimediatjänster via fasta och mobila nät. Alla IMS infrastruktur protokollen fungera över IP som gör IMS lätt att distribuera på ett moln plattform. Syftet med denna uppsats är att analysera en ny teknik för “cloudifying” den OpenIMS kärninfrastrukturen.  Det primära målet med att köra OpenIMS i molnet är att möjliggöra en hög tillgänglighet och horisontellt skalbara Server Home Subscriber (HSS). Den resulterande databasen bör erbjuda hög tillgänglighet och hög skalbarhet. Prototypen utvecklas i detta examensarbete visar en virtualiserad OpenIMS kärna med en integrerad horisontell skalbar HSS. Funktionella och prestanda mätningar av systemet under test (dvs. virtualiserade OpenIMS kärnan med horisontellt skalbara HSS) genomfördes. Resultaten av detta test inkluderar en analys av benchmarking scenarier, CPU-användning, och tillgängligt minne för de virtuella maskinerna. Baserat på dessa resultat drar vi slutsatsen att det är både möjligt och önskvärt att distribuera OpenIMS kärnan i ett moln.
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Mukhammadov, Ruslan. "A scalable database for a remote patient monitoring system." Thesis, KTH, Radio Systems Laboratory (RS Lab), 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-124603.

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Today one of the fast growing social services is the ability for doctors to monitor patients in their residences. The proposed highly scalable database system is designed to support a Remote Patient Monitoring system (RPMS). In an RPMS, a wide range of applications are enabled by collecting health related measurement results from a number of medical devices in the patient’s home, parsing and formatting these results, and transmitting them from the patient’s home to specific data stores. Subsequently, another set of applications will communicate with these data stores to provide clinicians with the ability to observe, examine, and analyze these health related measurements in (near) real-time. Because of the rapid expansion in the number of patients utilizing RPMS, it is becoming a challenge to store, manage, and process the very large number of health related measurements that are being collected. The primary reason for this problem is that most RPMSs are built on top of traditional relational databases, which are inefficient when dealing with this very large amount of data (often called “big data”). This thesis project analyzes scalable data management to support RPMSs, introduces a new set of open-source technologies that efficiently store and manage any amount of data which might be used in conjunction with such a scalable RPMS based upon HBase, implements these technologies, and as a proof of concept, compares the prototype data management system with the performance of a traditional relational database (specifically MySQL). This comparison considers both a single node and a multi node cluster. The comparison evaluates several critical parameters, including performance, scalability, and load balancing (in the case of multiple nodes). The amount of data used for testing input/output (read/write) and data statistics performance is 1, 10, 50, 100, and 250 GB. The thesis presents several ways of dealing with large amounts of data and develops &amp; evaluates a highly scalable database that could be used with a RPMS. Several software suites were used to compare both relational and non-relational systems and these results are used to evaluate the performance of the prototype of the proposed RPMS. The results of benchmarking show that MySQL is better than HBase in terms of read performance, while HBase is better in terms of write performance. Which of these types of databases should be used to implement a RPMS is a function of the expected ratio of reads and writes. Learning this ratio should be the subject of a future thesis project.<br>En av de snabbast växande sociala tjänsterna idag är möjligheten för läkare att övervaka patienter i sina bostäder. Det beskrivna, mycket skalbara databassystemet är utformat för att stödja ett sådant Remote Patient Monitoring-system (RPMS). I ett RPMS kan flertalet applikationer användas med hälsorelaterade mätresultat från medicintekniska produkter i patientens hem, för att analysera och formatera resultat, samt överföra dem från patientens hem till specifika datalager. Därefter kommer ytterligare en uppsättning program kommunicera med dessa datalager för att ge kliniker möjlighet att observera, undersöka och analysera dessa hälsorelaterade mått i (nära) realtid. På grund av den snabba expansionen av antalet patienter som använder RPMS, är det en utmaning att hantera och bearbeta den stora mängd hälsorelaterade mätningar som samlas in. Den främsta anledningen till detta problem är att de flesta RPMS är inbyggda i traditionella relationsdatabaser, som är ineffektiva när det handlar om väldigt stora mängder data (ofta kallat "big data"). Detta examensarbete analyserar skalbar datahantering för RPMS, och inför en ny uppsättning av teknologier baserade på öppen källkod som effektivt lagrar och hanterar godtyckligt stora datamängder. Dessa tekniker används i en prototypversion (proof of concept) av ett skalbart RPMS baserat på HBase. Implementationen av det designade systemet jämförs mot ett RPMS baserat på en traditionell relationsdatabas (i detta fall MySQL). Denna jämförelse ges för både en ensam nod och flera noder. Jämförelsen utvärderar flera kritiska parametrar, inklusive prestanda, skalbarhet, och lastbalansering (i fallet med flera noder). Datamängderna som används för att testa läsning/skrivning och statistisk prestanda är 1, 10, 50, 100 respektive 250 GB. Avhandlingen presenterar flera sätt att hantera stora mängder data och utvecklar samt utvärderar en mycket skalbar databas, som är lämplig för användning i RPMS. Flera mjukvaror för att jämföra relationella och icke-relationella system används för att utvärdera prototypen av de föreslagna RPMS och dess resultat. Resultaten av dessa jämförelser visar att MySQL presterar bättre än HBase när det gäller läsprestanda, medan HBase har bättre prestanda vid skrivning. Vilken typ av databas som bör väljas vid en RMPS-implementation beror därför på den förväntade kvoten mellan läsningar och skrivningar. Detta förhållande är ett lämpligt ämne för ett framtida examensarbete.
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Vrbík, Tomáš. "Srovnání distribuovaných "NoSQL" databází s důrazem na výkon a škálovatelnost." Master's thesis, Vysoká škola ekonomická v Praze, 2011. http://www.nusl.cz/ntk/nusl-124673.

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This paper focuses on NoSQL database systems. These systems currently serve rather as supplement than replacement of relational database systems. The aim of this paper is to compare 4 selected NoSQL database systems (MongoDB, Apache Cassandra, Apache HBase and Redis) with a main focus on performance and scalability. Performance comparison is done using simulated workload in a 4 nodes cluster environment. One relational SQL database is also benchmarked to provide comparison between classic and modern way of maintaining structured data. As the result of comparison I found out that none of these database systems can be labeled as "the best" as each of the compared systems is suitable for different production deployment.
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RODRIGUES, JUNIOR Paulo Lins. "Upper: uma ferramenta para escolha de servidor e estimação de gatilhos de escalabilidade de banco de dados relacionais na plataforma Amazon AWS." Universidade Federal de Pernambuco, 2013. https://repositorio.ufpe.br/handle/123456789/17509.

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Submitted by Irene Nascimento (irene.kessia@ufpe.br) on 2016-07-21T16:43:15Z No. of bitstreams: 2 license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) Upper.pdf: 1291176 bytes, checksum: 335e26f2c99d96f05a40fca5acb1fed1 (MD5)<br>Made available in DSpace on 2016-07-21T16:43:15Z (GMT). No. of bitstreams: 2 license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) Upper.pdf: 1291176 bytes, checksum: 335e26f2c99d96f05a40fca5acb1fed1 (MD5) Previous issue date: 2013-12-09<br>A escalabilidade de uma aplicação é de vital importância para o sucesso de um negócio, sendo considerado um dos atributos mais importantes das aplicações atualmente. Diversas aplicações atualmente são voltadas diretamente a dados, o que torna o banco de dados uma camada crítica em toda estrutura do sistema. Entre os tipos de bancos de dados existentes, destacam-se os bancos de dados relacionais por fornecerem sobretudo um nível de consistência adequado a maioria destas aplicações. A projeção de infraestrutura e de gatilhos de escalabilidade são tarefas complexas até mesmo para profissionais experientes, e erros nestas tarefas podem representar perdas significativas de negócio. A plataforma de computação em nuvem, em particular o modelo de infraestrutura como serviço se torna vantajosa por proporcionar um baixo investimento inicial e modelos de escala conforme demanda. Para se usufruir das vantagens oferecidas pela plataforma, os administradores de sistema ainda tem a difícil tarefa de definir o servidor adequado assim como estimar o momento certo de escalar atendendo as necessidades da aplicação e garantindo eficiência na alocação de recursos. Este trabalho propõe um ambiente de simulação para auxílio na definição do servidor adequado e dos gatilhos de escalabilidade do servidor de banco de dados na Amazon Web Services, plataforma líder de serviços de computação em nuvem. A principal contribuição desta ferramenta, chamada Upper, é facilitar o trabalho do administrador de sistema, possibilitando-o executar a tarefa de estimativa de forma mais rápida e precisa.<br>The scalability of an application is of vital importance to the success of a business, being considered one of the most important attributes of current applications. Many applications are now directly targeting to data, which makes the database a critical layer throughout the system structure. Among the types of existing databases, highlight the relational databases primarily for providing an appropriate level of consistency needed for most of these applications. The projection of infrastructure and scalability triggers is complex even for senior professionals, and errors in these tasks can result in significant business losses. The platform of cloud computing, in particular the model of infrastructure as a service becomes advantageous for providing a low initial investment and models of scale on demand. To benefit from the advantages offered by the platform, system administrators still have the difficult task of defining the appropriate server as well as estimating the right time to scale ensuring the performance needs of the application and efficiency in resource allocation. This paper proposes a simulation environment to aid in defining the appropriate server and scalability triggers of the database server on Amazon Web Services, a leading platform for cloud computing services. The main contribution of this tool, called Upper, is to facilitate the work of system administrator, providing him means to perform the task of estimation faster and more accurately.
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Xiong, Fanfan. "Resource Efficient Parallel VLDB with Customizable Degree of Redundancy." Diss., Temple University Libraries, 2009. http://cdm16002.contentdm.oclc.org/cdm/ref/collection/p245801coll10/id/43445.

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Computer and Information Science<br>Ph.D.<br>This thesis focuses on the practical use of very large scale relational databases. It leverages two recent breakthroughs in parallel and distributed computing: a) synchronous transaction replication technologies by Justin Y. Shi and Suntain Song; and b) Stateless Parallel Processing principle pioneered by Justin Y. Shi. These breakthroughs enable scalable performance and reliability of database service using multiple redundant shared-nothing database servers. This thesis presents a Functional Horizontal Partitioning method with customizable degree of redundancy to address practical very large scale database applications problems. The prototype VLDB implementation is designed for transparent non-intrusive deployments. The prototype system supports Microsoft SQL Servers databases. Computational experiments are conducted using industry-standard benchmark (TPC-E).<br>Temple University--Theses
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Gottemukkala, Vibby. "Scalability issues in distributed and parallel databases." Diss., Georgia Institute of Technology, 1996. http://hdl.handle.net/1853/8176.

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Mathew, Ajit. "Multicore Scalability Through Asynchronous Work." Thesis, Virginia Tech, 2020. http://hdl.handle.net/10919/104116.

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With the end of Moore's Law, computer architects have turned to multicore architecture to provide high performance. Unfortunately, to achieve higher performance, multicores require programs to be parallelized which is an untamed problem. Amdahl's law tells that the maximum theoretical speedup of a program is dictated by the size of the non-parallelizable section of a program. Hence to achieve higher performance, programmers need to reduce the size of sequential code in the program. This thesis explores asynchronous work as a means to reduce sequential portions of program. Using asynchronous work, a programmer can remove tasks which do not affect data consistency from the critical path and can be performed using background thread. Using this idea, the thesis introduces two systems. First, a synchronization mechanism, Multi-Version Read-Log-Update(MV-RLU), which extends Read-Log-Update (RLU) through multi-versioning. At the core of MV-RLU design is a concurrent garbage collection algorithm which reclaims obsolete versions asynchronously reducing blocking of threads. Second, a concurrent and highly scalable index-structure called Hydralist for multi-core. The key idea behind design of Hydralist is that an index-structure can be divided into two component (search layer and data layer) and updates to data layer can be done synchronously while updates to search layer can be propagated asynchronously using background threads.<br>Master of Science<br>Up until mid-2000s, Moore's law predicted that performance CPU doubled every two years. This is because improvement in transistor technology allowed smaller transistor which can switch at higher frequency leading to faster CPU clocks. But faster clock leads to higher heat dissipation and as chips reached their thermal limits, computer architects could no longer increase clock speeds. Hence they moved to multicore architecture, wherein a single die contains multiple CPUs, to allow higher performance. Now programmers are required to parallelize their code to take advangtage of all the CPUs in a chip which is a non trivial problem. The theoretical speedup achieved by a program on multicore architecture is dictated by Amdahl's law which describes the non parallelizable code in a program as the limiting factor for speedup. For example, a program with 99% parallelizable code can achieve speedup of 20 whereas a program with 50% parallelizable code can only achieve speedup of 2. Therefore to achieve high speedup, programmers need to reduce size of serial section in their program. One way to reduce sequential section in a program is to remove non-critical task from the sequential section and perform the tasks asynchronously using background thread. This thesis explores this technique in two systems. First, a synchronization mechanism which is used co-ordinate access to shared resource called Multi-Version Read-Log-Update (MV-RLU). MV-RLU achieves high performance by removing garbage collection from critical path and performing it asynchronously using background thread. Second, an index structure, Hydralist, which based on the insight that an index structure can be decomposed into two components, search layer and data layer, and decouples updates to both the layer which allows higher performance. Updates to search layer is done synchronously while updates to data layer is done asynchronously using background threads. Evaluation shows that both the systems perform better than state-of-the-art competitors in a variety of workloads.
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Books on the topic "Database Scalability"

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Liu, Henry H. Oracle database performance and scalability: A quantitative approach. Wiley, 2012.

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A, Steward Robert, ed. The data access handbook: Achieving optimal database application performance and scalability. Prentice Hall, 2009.

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Steward, Robert A. (Robert Allan), ed. The data access handbook: Achieving optimal database application performance and scalability. Prentice Hall, 2009.

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PhD, Mitra Pabitra, ed. Pattern recognition algorithms for data mining: Scalability, knowledge discovery and soft granular computing. Chapman & Hall/CRC, 2004.

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Vasiliev, Yuli. PHP Oracle web development: Data processing, security, caching, XML, web services and AJAX : a practical guide to combining the power, performance, scalability, and reliability of Oracle Database with the ease of use, short development time, and high performance of PHP. Packt Pub., 2007.

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Liu, Henry H. Oracle Database Performance and Scalability: A Quantitative Approach. IEEE Computer Society Press, 2011.

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Liu, Henry H. Oracle Database Performance and Scalability: A Quantitative Approach. IEEE Computer Society Press, 2011.

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Oracle Database Performance and Scalability: A Quantitative Approach. IEEE Computer Society Press, 2011.

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Data Access Handbook: Achieving Optimal Database Application Performance and Scalability. Pearson Education, Limited, 2009.

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Goodson, John, and Robert Steward. Data Access Handbook: Achieving Optimal Database Application Performance and Scalability. Pearson Education Canada, 2009.

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Book chapters on the topic "Database Scalability"

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Lake, Peter, and Paul Crowther. "Database Scalability." In Undergraduate Topics in Computer Science. Springer London, 2013. http://dx.doi.org/10.1007/978-1-4471-5601-7_9.

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Domdouzis, Konstantinos, Peter Lake, and Paul Crowther. "Database Scalability." In Undergraduate Topics in Computer Science. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-42224-0_12.

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Kemme, Bettina, Ricardo Jiménez Peris, and Marta Patiño-Martínez. "The Scalability of Replication." In Database Replication. Springer International Publishing, 2010. http://dx.doi.org/10.1007/978-3-031-01839-8_5.

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Jiménez-Peris, Ricardo, and Marta Patiño-Martínez. "Replication for Scalability." In Encyclopedia of Database Systems. Springer New York, 2016. http://dx.doi.org/10.1007/978-1-4899-7993-3_314-2.

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Jiménez-Peris, Ricardo, and Marta Patiño-Martínez. "Replication for Scalability." In Encyclopedia of Database Systems. Springer US, 2009. http://dx.doi.org/10.1007/978-0-387-39940-9_314.

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Jiménez-Peris, Ricardo, and Marta Patiño-Martínez. "Replication for Scalability." In Encyclopedia of Database Systems. Springer New York, 2018. http://dx.doi.org/10.1007/978-1-4614-8265-9_314.

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Smith, Marc, Bill Alexander, Haran Boral, et al. "An experiment on response time scalability in Bubba." In Database Machines. Springer Berlin Heidelberg, 1989. http://dx.doi.org/10.1007/3-540-51324-8_27.

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Mauri, Davide, Silvano Coriani, Anna Hoffman, Sanjay Mishra, and Jovan Popovic. "Scalability, Consistency, and Performance." In Practical Azure SQL Database for Modern Developers. Apress, 2020. http://dx.doi.org/10.1007/978-1-4842-6370-9_7.

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Neat, Adam. "WebSphere Database Performance and Optimization." In Maximizing Performance and Scalability with IBM WebSphere. Apress, 2004. http://dx.doi.org/10.1007/978-1-4302-0801-3_11.

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Agrawal, Divyakant, Amr El Abbadi, Sudipto Das, and Aaron J. Elmore. "Database Scalability, Elasticity, and Autonomy in the Cloud." In Database Systems for Advanced Applications. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-20149-3_2.

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Conference papers on the topic "Database Scalability"

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Sila, Daniel Caesar, Alexander Agung Santoso Gunawan, Anton Wijaya Tjong, and Muhammad Edo Syahputra. "Database Migration to Cloud Computing for Increased Scalability and Efficiency: Systematic Literature Review." In 2024 International Conference on Informatics, Multimedia, Cyber and Information System (ICIMCIS). IEEE, 2024. https://doi.org/10.1109/icimcis63449.2024.10957413.

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Dumitru, Alex Mircea, Vlad Merticariu, and Peter Baumann. "Array Database Scalability." In SSDBM '16: Conference on Scientific and Statistical Database Management. ACM, 2016. http://dx.doi.org/10.1145/2949689.2949717.

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Elnikety, Sameh, Steven Dropsho, Emmanuel Cecchet, and Willy Zwaenepoel. "Predicting replicated database scalability from standalone database profiling." In the fourth ACM european conference. ACM Press, 2009. http://dx.doi.org/10.1145/1519065.1519098.

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Manjhi, Amit, Phillip B. Gibbons, Anastassia Ailamaki, et al. "Invalidation Clues for Database Scalability Services." In 2007 IEEE 23rd International Conference on Data Engineering. IEEE, 2007. http://dx.doi.org/10.1109/icde.2007.367877.

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Tao, Quan. "Research on scalability of database design." In 2011 IEEE 3rd International Conference on Communication Software and Networks (ICCSN). IEEE, 2011. http://dx.doi.org/10.1109/iccsn.2011.6014401.

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Kunitsyn, V., and V. Fedorov. "FIREBASE (REALTIME DATABASE) MOBILE AND WEB APPLICATION DEVELOPMENT PLATFORM." In CHALLENGING ISSUES IN SYSTEMS MODELING AND PROCESSES. FSBE Institution of Higher Education Voronezh State University of Forestry and Technologies named after G.F. Morozov, 2025. https://doi.org/10.58168/cismp2024_349-352.

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The article is devoted to the Firebase platform developed by Google and its capabilities for creating modern mobile and web applications. It hosts key Firebase components such as cloud databases (Real time Database), user authentication, cloud storage, hosting and analytics. Particular attention is paid to the continued use of Firebase, which includes ease of integration, support for real-time data synchronization and scalability.
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Abdelhafiz, Bahaa Mahmoud, and Mourad Elhadef. "Sharding Database for Fault Tolerance and Scalability of Data." In 2021 2nd International Conference on Computation, Automation and Knowledge Management (ICCAKM). IEEE, 2021. http://dx.doi.org/10.1109/iccakm50778.2021.9357711.

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H. Costa, Caio, João Vianney B. M. Filho, Paulo Henrique M. Maia, and Francisco Carlos M. B. Oliveira. "Sharding by Hash Partitioning - A Database Scalability Pattern to Achieve Evenly Sharded Database Clusters." In 17th International Conference on Enterprise Information Systems. SCITEPRESS - Science and and Technology Publications, 2015. http://dx.doi.org/10.5220/0005376203130320.

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Tomsic, Alejandro Z. "Boosting Transactional Protocol Scalability through Efficient Consistent Snapshots." In 2016 27th International Workshop on Database and Expert Systems Applications (DEXA). IEEE, 2016. http://dx.doi.org/10.1109/dexa.2016.036.

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"Enforcing Scalability: An Efficient Approach for Distributed Database Partial Replication." In International Conference on Advances in Engineering and Technology. International Institute of Engineers, 2014. http://dx.doi.org/10.15242/iie.e0314184.

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Reports on the topic "Database Scalability"

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Wei, Wenbin, Nigel Blampied, and Raajmaathangi Sreevijay. Evaluation, Comparison, and Improvement Recommendations for Caltrans Financial Programming Processes and Tools. Mineta Transportation Institute, 2023. http://dx.doi.org/10.31979/mti.2023.2058.

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The California Transportation Improvement Program System (CTIPS) is the main tool used by Caltrans’ Division of Financial Programming to support the business of transportation programming. It is a multi-agency joint-use project programming database system applied to develop and manage various state and federal transportation programming documents. The goal of this project is to evaluate CTIPS and explore various new options that will maintain the current functionality of CTIPS, meet legislative guidelines for ADA compliance, ensure security of the system, and have sufficient scalability and capabilities for integration with other systems in the future. The research is based on the review of current and historical documents, interviews, and surveys of the customers of the Division of Financial Programming; the survey of programming systems used by the other 49 states and District of Columbia (DC) in the U.S.; an interview with the CTIPS service support provider; and interviews and surveys of the software companies that provide services and products similar to CTIPS. This research identifies risks associated with CTIPS and opportunities for improvements; compares the processes in California with currently recognized best practices and with those used in the other states in the U.S.; and makes recommendations for the improvement of CTIPS. Research results could help Caltrans better capture current data needs and future analytics requirements and make an informed decision about modernizing and upgrading an essential programming database.
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Wang, Zhen, Colin P. West, Brianna E. Vaa Stelling, et al. Measuring Documentation Burden in Healthcare. Agency for Healthcare Research and Quality (AHRQ), 2024. http://dx.doi.org/10.23970/ahrqepctb47.

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Background. The 2009 enactment of the Health Information Technology for Economic and Clinical Health (HITECH) Act and the wide adoption of electronic health record systems (EHR) have ushered an increasing documentation burden, frequently cited as a key factor affecting the work experience of healthcare professionals and a contributor to burnout. Purpose. This Technical Brief aims to identify: (1) measures of documentation burden, including evaluation of validity evidence, strengths, and weaknesses; (2) different perspectives on the appropriateness of different measures of documentation burden; and (3) perceptions of documentation burden from people in different clinical roles including patients/caregivers. The targeted audiences of this Technical Brief are clinicians, researchers, healthcare system leaders, policymakers, and electronic health record (EHR) vendors. Methods. We integrated discussions with Key Informants and synthesis of evidence from a comprehensive search of the literature, including Embase®, Epub Ahead of Print, In-Process &amp; Other Non-Indexed Citations, MEDLINE® Daily, MEDLINE®, Cochrane Central Registrar of Controlled Trials, Ovid® Cochrane Database of Systematic Reviews, Scopus®, and select gray literature from January 2010 to December 2023. Findings. We identified 135 articles about measuring documentation burden. We identified 11 categories of measures for documentation burden: overall time spent in EHR, activities related to clinical documentation, inbox management, time spent in clinical review, time spent in orders, work outside work/after hours, administrative tasks (billing and insurance related), fragmentation of workflow, measures of efficiency, EHR activity rate, and usability. The most common source of data for most measures was EHR usage logs. Direct tracking such as through time–motion analysis was fairly uncommon. We found that measures have been developed and applied across a diverse range of settings, populations, and uses, with physicians and nurses in the United States being the most frequently represented groups. Evidence of validity of these measures was limited and incomplete. Published information on the appropriateness of measures in terms of scalability, feasibility, or equity across various contexts was limited. Physician perspective on documentation burden was the most robustly captured in the literature than other stakeholders and focused on increased stress and burnout due to documentation burden, satisfaction with EHR and its usability, EHR-associated workload, and impact on teaching. Conclusion. The current literature on documentation burden measures offers a wide range of measures, yet with serious limitations that must be remedied to further inform practical solutions. Greater diversity of settings and perspectives is needed for future development of valid measures. Identifying measurement gaps of documentation burden should serve as the basis for developing interventions and solutions, and benchmarking progression of mitigating documentation burden.
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