Referential Integrity in Cloud NoSQL Databases

<p>Cloud computing delivers on-demand access to essential computing services providing benefits such as reduced maintenance, lower costs, global access, and others. One of its important and prominent services is Database as a Service (DaaS) which includes cloud Database Management Systems (DBMSs). Cloud DBMSs commonly adopt the key-value data model and are called Not only SQL (NoSQL) DBMSs. These provide cloud suitable features like scalability, flexibility and robustness, but in order to provide these, features such as referential integrity are often sacrificed. In such cases, referential integrity is left to be dealt with by the applications instead of being handled by the cloud DBMSs. Thus, applications are required to either deal with inconsistency in the data (e.g. dangling references) or to incorporate the necessary logic to ensure that referential integrity is maintained. This thesis presents an Application Programming Interface (API) that serves as a middle layer between the applications and the cloud DBMS in order to maintain referential integrity. The API provides the necessary Create, Read, Update and Delete (CRUD) operations to be performed on the DBMS while ensuring that the referential integrity constraints are satisfied. These constraints are represented as metadata and four different approaches are provided to store it. Furthermore, the performance of these approaches is measured with different referential integrity constraints and evaluated upon a set of experiments in Apache Cassandra, a prominent cloud NoSQL DBMS. The results showed significant differences between the approaches in terms of performance. However, the final word on which one is better depends on the application demands as each approach presents different trade-offs.</p>

Referential Integrity in Cloud NoSQL Databases

<p>Cloud computing delivers on-demand access to essential computing services providing benefits such as reduced maintenance, lower costs, global access, and others. One of its important and prominent services is Database as a Service (DaaS) which includes cloud Database Management Systems (DBMSs). Cloud DBMSs commonly adopt the key-value data model and are called Not only SQL (NoSQL) DBMSs. These provide cloud suitable features like scalability, flexibility and robustness, but in order to provide these, features such as referential integrity are often sacrificed. In such cases, referential integrity is left to be dealt with by the applications instead of being handled by the cloud DBMSs. Thus, applications are required to either deal with inconsistency in the data (e.g. dangling references) or to incorporate the necessary logic to ensure that referential integrity is maintained. This thesis presents an Application Programming Interface (API) that serves as a middle layer between the applications and the cloud DBMS in order to maintain referential integrity. The API provides the necessary Create, Read, Update and Delete (CRUD) operations to be performed on the DBMS while ensuring that the referential integrity constraints are satisfied. These constraints are represented as metadata and four different approaches are provided to store it. Furthermore, the performance of these approaches is measured with different referential integrity constraints and evaluated upon a set of experiments in Apache Cassandra, a prominent cloud NoSQL DBMS. The results showed significant differences between the approaches in terms of performance. However, the final word on which one is better depends on the application demands as each approach presents different trade-offs.</p>

Revisiting the design of LSM-tree Based OLTP storage engine with persistent memory

The recent byte-addressable and large-capacity commercialized persistent memory (PM) is promising to drive database as a service (DBaaS) into unchartered territories. This paper investigates how to leverage PMs to revisit the conventional LSM-tree based OLTP storage engines designed for DRAM-SSD hierarchy for DBaaS instances. Specifically we (1) propose a light-weight PM allocator named Hal-loc customized for LSM-tree, (2) build a high-performance Semi-persistent Memtable utilizing the persistent in-memory writes of PM, (3) design a concurrent commit algorithm named Reorder Ring to aschieve log-free transaction processing for OLTP workloads and (4) present a Global Index as the new globally sorted persistent level with non-blocking in-memory compaction. The design of Reorder Ring and Semi-persistent Memtable achieves fast writes without synchronized logging overheads and achieves near instant recovery time. Moreover, the design of Semi-persistent Memtable and Global Index with in-memory compaction enables the byte-addressable persistent levels in PM, which significantly reduces the read and write amplification as well as the background compaction overheads. The overall evaluation shows that the performance of our proposal over PM-SSD hierarchy outperforms the baseline by up to 3.8x in YCSB benchmark and by 2x in TPC-C benchmark.

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.

Data integrity issues and challenges in next generation non-relational document-oriented database outsourced in public cloud

Database as a Service provides high availability and scalability in cloud but users do not physically control over their data, therefore, data integrity is serious concerned. Authenticity of data and its verification is one of major risk in next generation document-oriented databases. It is possible that malicious insider and outsider can change and compromise the data. A system is proposed in which cloud environment secure storage and access semi-structured data for non-relational document-oriented database. The proposed system effectively provides data integrity for sensitive and confidential fields and verification of data whether it has been altered or not in outsource database in the public domain.

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.

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.

On the Performance of Cloud Services and Databases for Industrial IoT Scalable Applications

In the Industry 4.0 the communication infrastructure is derived from the Internet of Things (IoT), and it is called Industrial IoT or IIoT. Smart objects deployed on the field collect a large amount of data which is stored and processed in the Cloud to create innovative services. However, differently from most of the consumer applications, the industrial scenario is generally constrained by time-related requirements and its needs for real-time behavior (i.e., bounded and possibly short delays). Unfortunately, timeliness is generally ignored by traditional service provider, and the Cloud is treated as a black box. For instance, Cloud databases (generally seen as “Database as a service”—DBaaS) have unknown or hard-to-compare impact on applications. The novelty of this work is to provide an experimental measurement methodology based on an abstract view of IIoT applications, in order to define some easy-to-evaluate metrics focused on DBaaS latency (no matter the actual implementation details are). In particular, the focus is on the impact of DBaaS on the overall communication delays in a typical IIoT scalable context (i.e., from the field to the Cloud and the way back). In order to show the effectiveness of the proposed approach, a real use case is discussed (it is a predictive maintenance application with a Siemens S7 industrial controller transmitting system health status information to a Cloudant DB inside the IBM Bluemix platform). Experiments carried on in this use case provide useful insights about the DBaaS performance: evaluation of delays, effects of involved number of devices (scalability and complexity), constraints of the architecture, and clear information for comparing with other implementations and for optimizing configuration. In other words, the proposed evaluation strategy helps in finding out the peculiarities of Cloud Database service implementations.

Fast protein database as a service with kAAmer

Identification of proteins is one of the most computationally intensive steps in genomics studies. It usually relies on aligners that don’t accommodate rich information on proteins and require additional pipelining steps for protein identification. We introduce kAAmer, a protein database engine based on amino-acid k-mers, that supports fast identification of proteins with complementary annotations. Moreover, the databases can be hosted and queried remotely.