A Review Of Multi-Tenant Database And Factors That Influence Its Adoption.

A Multi-tenant database (MTD) is a way of deploying a Database as a Service (DaaS). This is gaining momentum with significant increase in the number of organizations ready to take advantage of the technology. A multi-tenant database refers to a principle where a single instance of a Database Management System (DBMS) runs on a server, serving multiple clients organizations (tenants). This is a database which provides database support to a number of separate and distinct groups of users or tenants. This concept spreads the cost of hardware, software and other services to a large number of tenants, therefore significantly reducing per tenant cost. Three different approaches of implementing multi-tenant database have been identified. These methods have been shown to be increasingly better at pooling resources and also processing administrative operations in bulk. This paper reports the requirement of multi-tenant databases, challenges of implementing MTD, database migration for elasticity in MTD and factors influencing the choice of models in MTD. An insightful discussion is presented in this paper by grouping these factors into four categories. This shows that the degree of tenancy is an influence to the approach to be adopted and the capital and operational expenditure are greatly reduced in comparison with an on-premises solutio

A native enhanced elastic extension tables multi-tenant database

A fundamental factor of digital image compression is the conversion processes. The intention of this process is to understand the shape of an image and to modify the digital image to a grayscale configuration where the encoding of the compression technique is operational. This article focuses on an investigation of compression algorithms for images with artistic effects. A key component in image compression is how to effectively preserve the original quality of images. Image compression is to condense by lessening the redundant data of images in order that they are transformed cost-effectively. The common techniques include discrete cosine transform (DCT), fast Fourier transform (FFT), and shifted FFT (SFFT). Experimental results point out compression ratio between original RGB images and grayscale images, as well as comparison. The superior algorithm improving a shape comprehension for images with grahic effect is SFFT technique

CLOUDIO: a cloud computing-oriented multi-tenant architecture for business information systems.

Proceedings of: 2010 IEEE 3rd International Conference on Cloud Computing, CLOUD 2010, 5-10 July, Miami, Florida, USACloud Computing is evolving from a mere "storage" technology to a new vehicle for Business Information Systems (BIS) to manage, organize and provide added-value strategies to current business models. However, the underlying infrastructure for Software-as-a-Service (SaaS) to become a new platform for trading partners and transactions must rely on intelligent, flexible, context-aware Multi-Tenant Architectures. In this paper, we present Cloudio, a Cloud Computing-based metadata-powered Multi-Tenant Architecture, backed with a proof-of-concept J2EE implementation.This work is supported by the Spanish Ministry of Industry, Tourism, and Commerce under the project GODO2 (TSI- 020100-2008-564), SONAR2 (TSI-020100-2008-665), and SITIO (TSI-0204000-2009-148), under the PIBES (TEC2006-12365-C02-01) and MID-CBR (TIN2006-15140- C03-02) projects of the Spanish Committee of Education & Science.Publicad

Cloud BI: Future of business intelligence in the Cloud

In self-hosted environments it was feared that business intelligence (BI) will eventually face a resource crunch situation due to the never ending expansion of data warehouses and the online analytical processing (OLAP) demands on the underlying networking. Cloud computing has instigated a new hope for future prospects of BI. However, how will BI be implemented on Cloud and how will the traffic and demand profile look like? This research attempts to answer these key questions in regards to taking BI to the Cloud. The Cloud hosting of BI has been demonstrated with the help of a simulation on OPNET comprising a Cloud model with multiple OLAP application servers applying parallel query loads on an array of servers hosting relational databases. The simulation results reflected that extensible parallel processing of database servers on the Cloud can efficiently process OLAP application demands on Cloud computing

MapperMania: A Framework for Native Multi-Tenancy Business Object Mapping to a Persistent Data Source

The Software-as-a-Service delivery model bears new challenges for application developers. Especially in the context of enterprise resource planning software targeting the SME market, new problems arise. Most of them agglomerate around the occurrence of multi-tenancy. This paper describes the framework MapperMania which aims to establish an abstraction layer between the persistence layer and the domain model. Leveraging MapperMania, the domain model is able to abstract from multi-tenancy and changes in the underlying infrastructure

Storage Solutions for Big Data Systems: A Qualitative Study and Comparison

Big data systems development is full of challenges in view of the variety of application areas and domains that this technology promises to serve. Typically, fundamental design decisions involved in big data systems design include choosing appropriate storage and computing infrastructures. In this age of heterogeneous systems that integrate different technologies for optimized solution to a specific real world problem, big data system are not an exception to any such rule. As far as the storage aspect of any big data system is concerned, the primary facet in this regard is a storage infrastructure and NoSQL seems to be the right technology that fulfills its requirements. However, every big data application has variable data characteristics and thus, the corresponding data fits into a different data model. This paper presents feature and use case analysis and comparison of the four main data models namely document oriented, key value, graph and wide column. Moreover, a feature analysis of 80 NoSQL solutions has been provided, elaborating on the criteria and points that a developer must consider while making a possible choice. Typically, big data storage needs to communicate with the execution engine and other processing and visualization technologies to create a comprehensive solution. This brings forth second facet of big data storage, big data file formats, into picture. The second half of the research paper compares the advantages, shortcomings and possible use cases of available big data file formats for Hadoop, which is the foundation for most big data computing technologies. Decentralized storage and blockchain are seen as the next generation of big data storage and its challenges and future prospects have also been discussed

A Query, a Minute: Evaluating Performance Isolation in Cloud Databases

Several cloud providers offer reltional databases as part of their portfolio. It is however not obvious how resource virtualization and sharing, which is inherent to cloud computing, influence performance and predictability of these cloud databases. Cloud providers give little to no guarantees for consistent execution or isolation from other users. To evaluate the performance isolation capabilities of two commercial cloud databases, we ran a series of experiments over the course of a week (a query, a minute) and report variations in query response times. As a baseline, we ran the same experiments on a dedicated server in our data center. The results show that in the cloud single outliers are up to 31 times slower than the average. Additionally, one can see a point in time after which the average performance of all executed queries improves by 38 %

A combined computing framework for load balancing in multi-tenant cloud eco-system

Since the world is getting digitalized, cloud computing has become a core part of it. Massive data on a daily basis is processed, stored, and transferred over the internet. Cloud computing has become quite popular because of its superlative quality and enhanced capability to improvise data management, offering better computing resources and data to its user bases (UBs). However, there are many issues in the existing cloud traffic management approaches and how to manage data during service execution. The study introduces two distinct research models: data center virtualization framework under multi-tenant cloud-ecosystem (DCVF-MT) and collaborative workflow of multi-tenant load balancing (CW-MTLB) with analytical research modeling. The sequence of execution flow considers a set of algorithms for both models that address the core problem of load balancing and resource allocation in the cloud computing (CC) ecosystem. The research outcome illustrates that DCVF-MT, outperforms the one-to-one approach by approximately 24.778% performance improvement in traffic scheduling. It also yields a 40.33% performance improvement in managing cloudlet handling time. Moreover, it attains an overall 8.5133% performance improvement in resource cost optimization, which is significant to ensure the adaptability of the frameworks into futuristic cloud applications where adequate virtualization and resource mapping will be required