Database As A Service Research Articles

A Performance Analysis of Hybrid and Columnar Cloud Databases for Efficient Schema Design in Distributed Data Warehouse as a Service

A Data Warehouse (DW) is a centralized database that stores large volumes of historical data for analysis and reporting. In a world where enterprise data grows exponentially, new architectures are being investigated to overcome the deficiencies of traditional Database Management Systems (DBMSs), driving a shift towards more modern, cloud-based solutions that provide resources such as distributed processing, columnar storage, and horizontal scalability without the overhead of physical hardware management, i.e., a Database as a Service (DBaaS). Choosing the appropriate class of DBMS is a critical decision for organizations, and there are important differences that impact data volume and query performance (e.g., architecture, data models, and storage) to support analytics in a distributed cloud environment efficiently. In this sense, we carry out an experimental evaluation to analyze the performance of several DBaaS and the impact of data modeling, specifically the usage of a partially normalized Star Schema and a fully denormalized Flat Table Schema, to further comprehend their behavior in different configurations and designs in terms of data schema, storage form, memory availability, and cluster size. The analysis is done in two volumes of data generated by a well-established benchmark, comparing the performance of the DW in terms of average execution time, memory usage, data volume, and loading time. Our results provide guidelines for efficient DW design, showing, for example, that the denormalization of the schema does not guarantee improved performance, as solutions performed differently depending on its architecture. We also show that a Hybrid Processing (HTAP) NewSQL solution can outperform solutions that support only Online Analytical Processing (OLAP) in terms of overall execution time, but that the performance of each query is deeply influenced by its selectivity and by the number of join functions.

Solver-In-The-Loop Cluster Resource Management for Database-as-a-Service

In Database-as-a-Service (DBaaS) clusters, resource management is a complex optimization problem that assigns tenants to nodes, subject to various constraints and objectives. Tenants share resources within a node, however, their resource demands can change over time and exhibit high variance. As tenants may accumulate large state, moving them to a different node becomes disruptive, making intelligent placement decisions crucial to avoid service disruption. Placement decisions need to account for dynamic changes in tenant resource demands, different causes of service disruption, and various placement constraints, giving rise to a complex search space. In this paper, we show how to bring combinatorial solvers to bear on this problem, formulating the objective of minimizing service disruption as an optimization problem amenable to fast solutions. We implemented our approach in the Service Fabric cluster manager codebase. Experiments show significant reductions in constraint violations and tenant moves, compared to the previous state-of-the-art, including the unmodified Service Fabric cluster manager, as well as recent research on DBaaS tenant placement.

Insider threat prevention in distributed database as a service cloud environment

This paper addresses the problem of increasing chances of insider threats in cloud computing. In Database as a Service (DBaaS), where multiple dependencies exist among data. Insiders tries to infer knowledge on the bases of dependencies. There is a requirement of Insider threat prevention mechanism for DBaaS due to its dynamic nature. Various researchers have proposed a Single PDP and Multiple PEPs to solve this problem with the limitations on authentication, scalability, tracking of insiders. This research aims to fill the gap by providing an effective framework on access control mechanism designed to prevent insider threats using a Blockchain-based authentication mechanism, a distributed architecture-based, and insider activity tracking mechanism. The framework enables us to handle authentication mechanism, distributed DBaaS insider activity tracking. Experimental results show that this framework is effective, dynamic and scalable. The validation of this framework has been done with the help of synthetic data due to lack of standardized dataset and its applicability has been demonstrated with the help of a case study involving the use of real cloud data.

Integration Model between Heterogeneous Data Services in a Cloud

With the growth of cloud services, many companies have begun to persist and make their data available through services such as Data as a Service (DaaS) and Database as a Service (DBaaS). The DaaS model provides on-demand data through an Application Programming Inter- face (API), while DBaaS model provides on-demand database management systems. Different data sources require efforts to integrate data from different models. These model types include unstructured, semi-structured, and structured data. Heterogeneity from DaaS and DBaaS makes it challenging to integrate data from different services. In response to this problem, we developed the Data Join (DJ) method to integrate heterogeneous DaaS and DBaaS sources. DJ was described through canonical models and incorporated into a middleware as a proof-of-concept. A test case and three experiments were performed to validate our DJ method: the first experiment tackles data from DaaS and DBaaS in isolation; the second experiment associates data from different DaaS and DBaaS through one join clause; and the third experiment integrates data from three sources (one DaaS and two DBaaS) based on different data type (relational, NoSQL, and NewSQL) through two join clauses. Our experiments evaluated the viability, functionality, integration, and performance of the DJ method. Results demonstrate that DJ method outperforms most of the related work on selecting and integrating data in a cloud environment.

Efficient Oblivious Data Structures for Database Services on the Cloud

Database-as-a-service (DBaaS) allows the client to store and manage structured data on the cloud remotely. Despite its merits, DBaaS also brings significant privacy issues. Existing encryption techniques (e.g., SQL-aware encryption) can mitigate privacy concerns, but they still leak information through access patterns, which are vulnerable to statistical inference attacks. Oblivious Random Access Machine (ORAM) can seal such leakages; however, the recent studies showed significant challenges on the integration of ORAM into databases. That is, the direct usage of ORAM on databases is not only costly but also permits very limited query functionalities. In this paper, we propose new oblivious data structures called Oblivious Matrix Structure (OMAT), which allow tree-based ORAM to be integrated into database systems in a more efficient manner with diverse query functionalities supported. OMAT provides special ORAM packaging strategies for table structures, which not only offers a significantly better performance but also enables a broad range of query types that may not be efficient in existing frameworks. On the other hand, OTREE allows oblivious conditional queries to be performed on tree-indexed databases more efficiently than existing techniques. We implemented our proposed techniques and evaluated their performance on a real cloud database with various metrics, compared with state-of-the-art counterparts.

Biomedical Cyber-Physical Systems in the Light of Database as a Service (DBaaS) Paradigm

Background: A database (DB) to store indexed information about drug delivery, test, and their temporal behavior is paramount in new Biomedical Cyber-Physical Systems (BCPSs). The term Database as a Service (DBaaS) means that a corporation delivers the hardware, software, and other infrastructure required by companies to operate their databases according to their demands instead of keeping an internal data warehouse.
 Methods: BCPSs attributes are presented and discussed. One needs to retrieve detailed knowledge reliably to make adequate healthcare treatment decisions. Furthermore, these DBs store, organize, manipulate, and retrieve the necessary data from an ocean of Big Data (BD) associated processes. There are Search Query Language (SQL), and NoSQL DBs. 
 Results: This work investigates how to retrieve biomedical-related knowledge reliably to make adequate healthcare treatment decisions. Furthermore, Biomedical DBaaSs store, organize, manipulate, and retrieve the necessary data from an ocean of Big Data (BD) associated processes.
 Conclusion: A NoSQL DB allows more flexibility with changes while the BCPSs are running, which allows for queries and data handling according to the context and situation. A DBaaS must be adaptive and permit the DB management within an extensive variety of distinctive sources, modalities, dimensionalities, and data handling according to conventional ways.

A systematic review of scheduling approaches on multi-tenancy cloud platforms

Scheduling in cloud is complicated as a result of multi-tenancy. Diverse tenants have different requirements, including service functions, response time, QoS and throughput. Diverse tenants require different scheduling capabilities, resource consumption and competition. Multi-tenancy scheduling approaches have been developed for different service models, such as Software as a Service (SaaS), Platform as a service (PaaS), Infrastructure as a Service (IaaS), and Database as a Service (DBaaS).

An End to End Cloud Computing Privacy Framework Using Blind Processing

Database-as-a-service (DBaaS) is a trend allowing organizations to outsource their databases and computations to external parties. However, despite the many advantages provided by this service in terms of cost reduction and efficiency, DBaaS raises many security issues regarding data privacy and access control. The protection of privacy has been addressed by several research contributions proposing efficient solutions such as encrypted databases and blind queries over encrypted data, called blind processing. In this latter context, almost all proposed schemes consider an architecture of a single user (the data owner) that requests the database server for encrypted records while he is the only one capable of decrypting. From a practical perspective, a database system is set up to support not only a single user but multiple users initiating multiple queries. However, managing various accesses to an encrypted database introduces several challenges by itself, like key sharing, key revocation, and data re-encryption. In this article, we propose a simple and efficient blind processing protocol that allows multiple users to query the same encrypted data and decrypt the retrieved results without getting access to the secret key.

OLAP parallel query processing in clouds with C‐ParGRES

SummaryThe advent of big data technologies has changed the way many companies manage their data. Several companies moved their data to the cloud using the concept of database‐as‐a‐service (DBaaS). Moving databases to the cloud presents several challenges related to flexible and scalable management of data. Although some of these companies migrated to NoSQL databases, most still rely on relational databases in the cloud to manage data, especially data that is critical to the decision making process. Online analytical processing (OLAP) queries take a long time to be processed, thus demanding high‐performance capabilities from their associated database systems to get results in a feasible time. In this article, we propose a middleware solution that can be deployed in any cloud provider, named C‐ParGRES, which explores database replication and interquery and intraquery parallelism to efficiently support OLAP queries in the cloud. C‐ParGRES is an extension of ParGRES, an open‐source database cluster middleware for high‐performance OLAP query processing in clusters. C‐ParGRES exploits cloud capabilities such as on‐demand resource provisioning and elasticity. In addition, C‐ParGRES can create multiple and independent virtual clusters for different database and users. We evaluate C‐ParGRES with two real‐world OLAP applications, both from the Brazilian Institute of Geography and Statistics. Results show that C‐ParGRES is a cost‐effective solution for OLAP query processing in the cloud.

VRS-DB: preserve confidentiality of users’ data using encryption approach

We focus on security and privacy problems within a cloud database framework, exploiting the DataBase as a Service (DBaaS). In this framework, an information proprietor drives out its information to a cloud database professional company. The Data-Owner (DO) encrypts the delicate information before transmission at the cloud database professional company end to offer information security. Current encryption ideas, nonetheless, are just halfway homomorphic as all of them intend to enable an explicit kind of calculation, which is accomplished on scrambled information. These current plans can’t be coordinated to solve genuine functional queries that include activities of various types. We propose and evaluate a Verifiable Reliable Secure-DataBase (VRS-DB) framework on shared tables along with many primary operations on scrambled information, which enables information interoperability, and permits an extensive possibility of Structured Query Language (SQL) queries to be prepared by the service provider on the encoded data. We show that our security and privacy idea is protected from two forms of threats and are fundamentally proficient.

Integrity and Confidentiality in Cloud Outsourced Data

Abstract Cloud services have become an increasingly popular solution to provide different services to clients. One of the cloud services is database as a service (DBaaS), in which the service provider offers different resources such as software, hardware and network to the clients to be able to manage and administer the database. However, the data and the execution of database queries are under control of the distrustful service provider. This lack of trust opens up new security issues and serves as the chief motivation of our work. This study presents an overview of different cryptographic algorithms, based on different schemes in the outsourced database security and query authentication. We conclude the paper by proposing a new architecture that achieves the confidentiality and integrity of query results of the outsourced database.

SaaS level database integration in cloud environment through database as a service

Data retrieval from multiple heterogeneous cloud databases is a complex engineering task. Ensuring scalability, flexibility and data availability while integrating data from the multiple heterogeneous cloud databases are the major research issues in such task. A proper integration semantics for cloud databases and data retrieval through Database as a Service (DBaaS) can be helpful to solve those issues. In this paper, a graph semantic based model for SaaS level database integration (SLDI) has been proposed, which is useful towards realization of various cloud database integration approaches through services. Several important quality metrics like integration complexity, degree of consistency, degree of availability and degree of partitioning tolerance also have been defined over the proposed model. The proposed SLDI model is compatible with CAP (Consistence-Availability-Partitioning) theorem. Besides, the proposed approach is capable of handling data heterogeneity through service while ensuring scalability and flexibility in data retrieval from multiple cloud databases.

A Secure Multi-User Database-as-a-Service Approach for Cloud Computing Privacy

Database-as-a-Service (DBaaS) is a new trend that allows industries and organizations outsource their databases and computations to external parties. However, despite the many advantages provided by this service in terms of cost reduction and efficiency, DBaaS raises many security issues regarding data privacy. The protection of privacy has been addressed by several research contributions proposing efficient solutions such as encrypted databases and blind queries over encrypted records. However, access control techniques still suffer from a lack of efficiency especially when dealing with encrypted databases. In this latter context, almost all proposed schemes consider an architecture of a single user (the data owner) that queries his encrypted database that he is the only one capable of decrypting. From a practical perspective, a database system is set up to support not only a single user but multiple users initiating multiple queries. However, managing multiple accesses to an encrypted database introduces several challenges like key sharing, key revocation, and data re-encryption. In this paper, we propose a simple and efficient proved protocol that allows multiple users to query the same database and decrypt the retrieved results without getting access to the secret key. In this protocol, the data owner is not risking the data privacy since he does not need to share the secret key to enable a multi-party collaboration.

Authentication Scheme for Database as a Service (DBAAS) Solutions

Authentication Scheme for Database as a Service (DBAAS) Solutions

DBaaS Multitenancy, Auto-tuning and SLA Maintenance in Cloud Environments: a Brief Survey

Cloud computing is a paradigm that presents many advantages to both costumers and service providers, such as low upfront investment, pay-per-use and easiness of use, delivering/enabling scalable services using Internet technologies. Among many types of services we have today, Database as a Service (DBaaS) is the one where a database is provided in the cloud in all its aspects. Examples of aspects related to DBaaS utilization are data storage, resources management and SLA maintenance. In this context, an important feature, related to it, is resource management and performance, which can be done in many different ways for several reasons, such as saving money, time, and meeting the requirements agreed between client and provider, that are defined in the Service Level Agreement (SLA). A SLA usually tries to protect the costumer from not receiving the contracted service and to ensure that the provider reaches the profit intended. In this paper it is presented a classification based on three main parameters that aim to manage resources for enhancing the performance on DBaaS and guarantee that the SLA is respected for both user and provider sides benefit. The proposal is based upon a survey of existing research work efforts.

Towards efficiently supporting database as a service with QoS guarantees

Quality of Service (QoS) is at the core of the vision of Database as a Service (DBaaS), which provides guarantees to database users on the usability of database services, even when the underlying database infrastructure is shared by multiple users. When QoS guarantees are necessitated, traditional approaches in DBaaS often have to reserve computation resources (e.g. CPU and memory) for tenants according to their performance Service Level Objectives (SLOs), so that the database engine always possesses sufficient resources to accomplish the expected workloads under any circumstance. Such resource reservation schemes inevitably result in poor resource utilization, as the actual workloads of tenants are usually below their maximal workload expectation described in their SLOs.In this paper, we propose a novel scheme called FrugalDB to further improve resource utilization and thus reduce operational cost for DBaaS systems with QoS guarantees. FrugalDB accommodates two independent database engines, an in-memory engine for heavy workloads with tight SLOs, and a disk-based engine for light workloads with loose SLOs. The in-memory database is leveraged to migrate temporary heavy workloads from the disk-based database, when the latter itself does not suffice to handle all active tenants’ workloads, and thus it could be relieved of the tremendous data serving pressure yielded by those heavy workloads, and focus on processing massive numbers of light workloads. When the heavy workloads fade out, FrugalDB reversely migrates a tenant’s workload from the in-memory database into the disk-based database, so that the occupied memory resources could be recollected for subsequent workload migrations. By an effective workload estimation method and an efficient migration schedule algorithm, FrugalDB tries to minimize workload migration cost incurred in moving workloads between the two engines. By allocating each tenant’s workload to an appropriate engine via workload migration, this dual-engine scheme can substantially save computation resources, and thus consolidate more tenants on a single database server. We evaluate FrugalDB with extensive experiments, which show that it has a higher tenant consolidation rate with performance SLO guarantees and fewer performance SLO violations than the existing systems, as well as with acceptable response latency.

Deployment of Data Base as a Service and connecting it with the local server

Cloud Computing, is the on-demand delivery of computing resources-everything from applications to traditional IT infrastructure(Data centers)-over the internet on the pay-for-use basis. Cloud Computing is the current need of the IT industry. Cloud Computing offers on demand internet based IT services like IaaS(Infrastructure as a Service), PaaS(Platform as a Service), DBaaS(DataBase as a Service), etc. With the exponential increase of the IT industry it is very difficult to maintain all the resources physically and hence the Cloud computing comes into the picture. Every IT industry requires data to be maintained and updated constantly and datacenters in the traditional IT has proved to be inefficient. Datacenters have huge unused resource which too requires capital. With the increase of data, it becomes very difficult to manage the data in the physical resources and hence companies use DBaaS (DataBase as a Service), where the data is saved on the servers which are not physically present at that location. Pre-configured and pre-defined structure of the database is present and hence deploying and maintaining the database becomes easy. With the deployment of DBaaS, analyzing the performance and optimization of the database is much better than doing the same in physical database.

Efficient outsourcing of secure k-nearest neighbour query over encrypted database

Cloud computing allows a cloud user to outsource her data and the related computation to a cloud service provider to save storage and computational cost. This convenient service has brought a shift from the traditional client–server model to DataBase as a Service (DBaaS). Although DBaaS relieves the clients from the data management burdens, a significant concern about the data privacy remains. In this work, we focus on outsourcing secure k-nearest neighbour (k-NN) query, and provide the first sublinear solution (with preprocessing) with computational complexity O(klgn(lg2n+lg3k)). Our construction uses the data structure called kd-tree to achieve the sublinear query complexity. In order to protect data access patterns, garbled circuits are used to simulate Oblivious RAM (ORAM) for accessing data in the kd-tree. Compared with the existing solutions, our scheme imposes only constant overhead on both the data owner and the querying client.

Private Cloud Computing Services for an Interactive Multi-Campus University

<span>Cloud Computing is an environment where computing resources, such as processing power, storage, network, application, and other IT services, are provided to users as computing utility. This paper presents the university distributed databases consolidation and relocation to the cloud for the provision of Database as a Service (DBaaS), through their deployment into high availability, interactive, secure private cloud setup utilizing Al-Balqa Applied University (BAU) Private Cloud (BPC). This solution involves several transitions, and represents the main key step in BAU journey to the cloud to allow the university to achieve greater efficiency in terms of database services, and to improve performance, availability, interactivity, and security. The resulted service also reduces operating, management and capital costs.</span>

Not for the timid

To lower hosting costs and service prices, database-as-a-service (DBaaS) providers strive to maximize cluster utilization without negatively affecting their users' service experience. Some of the most effective approaches for increasing service efficiency result in the over-booking of the cluster with user databases. For instance, one approach is to reclaim cluster capacity from a database when it is idle, temporarily re-using the capacity for some other purpose, and over-booking the cluster's resources. Such approaches are largely driven by policies that determine when it is prudent to temporarily reclaim capacity from an idle database. In this paper, we examine policies that inherently tune the system's idle sensitivity. Increased sensitivity to idleness leads to aggressive over-booking while the converse leads to conservative reclamation and lower utilization levels. Aggressive over-booking also incurs a "reserve" capacity cost (for when we suddenly "owe" capacity to previously idle databases.) We answer these key questions in this paper: (1) how to find a "good" resource reclamation policy for a given DBaaS cluster of users; and (2) how to forecast the needed near-term reserve capacity. To help us answer these questions, we used production user activity traces from Azure SQL DB and built models of an over-booking mechanism. We show that choosing the right policy can substantially boost the efficiency of the service, facilitating lower service prices via lower amortized infrastructure costs.