Using Microservices to Customize Multi-Tenant SaaS: From Intrusive to Non-Intrusive

Customization is a widely adopted practice on enterprise software applications such as Enterprise resource planning (ERP) or Customer relation management (CRM). Software vendors deploy their enterprise software product on the premises of a customer, which is then often customized for different specific needs of the customer. When enterprise applications are moving to the cloud as mutli-tenant Software-as-a-Service (SaaS), the traditional way of on-premises customization faces new challenges because a customer no longer has an exclusive control to the application. To empower businesses with specific requirements on top of the shared standard SaaS, vendors need a novel approach to support the customization on the multi-tenant SaaS. In this paper, we summarize our two approaches for customizing multi-tenant SaaS using microservices: intrusive and non-intrusive. The paper clarifies the key concepts related to the problem of multi-tenant customization, and describes a design with a reference architecture and high-level principles. We also discuss the key technical challenges and the feasible solutions to implement this architecture. Our microservice-based customization solution is promising to meet the general customization requirements, and achieves a balance between isolation, assimilation and economy of scale

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

Toward Customizable Multi-tenant SaaS Applications

abstract: Nowadays, Computing is so pervasive that it has become indeed the 5th utility (after water, electricity, gas, telephony) as Leonard Kleinrock once envisioned. Evolved from utility computing, cloud computing has emerged as a computing infrastructure that enables rapid delivery of computing resources as a utility in a dynamically scalable, virtualized manner. However, the current industrial cloud computing implementations promote segregation among different cloud providers, which leads to user lockdown because of prohibitive migration cost. On the other hand, Service-Orented Computing (SOC) including service-oriented architecture (SOA) and Web Services (WS) promote standardization and openness with its enabling standards and communication protocols. This thesis proposes a Service-Oriented Cloud Computing Architecture by combining the best attributes of the two paradigms to promote an open, interoperable environment for cloud computing development. Mutil-tenancy SaaS applicantions built on top of SOCCA have more flexibility and are not locked down by a certain platform. Tenants residing on a multi-tenant application appear to be the sole owner of the application and not aware of the existence of others. A multi-tenant SaaS application accommodates each tenant’s unique requirements by allowing tenant-level customization. A complex SaaS application that supports hundreds, even thousands of tenants could have hundreds of customization points with each of them providing multiple options, and this could result in a huge number of ways to customize the application. This dissertation also proposes innovative customization approaches, which studies similar tenants’ customization choices and each individual users behaviors, then provides guided semi-automated customization process for the future tenants. A semi-automated customization process could enable tenants to quickly implement the customization that best suits their business needs.Dissertation/ThesisDoctoral Dissertation Computer Science 201

Creating a Relational Distributed Object Store

In and of itself, data storage has apparent business utility. But when we can convert data to information, the utility of stored data increases dramatically. It is the layering of relation atop the data mass that is the engine for such conversion. Frank relation amongst discrete objects sporadically ingested is rare, making the process of synthesizing such relation all the more challenging, but the challenge must be met if we are ever to see an equivalent business value for unstructured data as we already have with structured data. This paper describes a novel construct, referred to as a relational distributed object store (RDOS), that seeks to solve the twin problems of how to persistently and reliably store petabytes of unstructured data while simultaneously creating and persisting relations amongst billions of objects.Comment: 12 pages, 5 figure

An efficient resource sharing technique for multi-tenant databases

Multi-tenancy is one of the key components of cloud computing environment. Multi-tenant database system in SaaS (Software as a Service) has gained a lot of attention in academics, research and business arena. These database systems provide scalability and economic benefits for both cloud service providers and customers(organizations/companies referred as tenants) by sharing same resources and infrastructure in isolation of shared databases, network and computing resources with Service level agreement (SLA) compliances. In a multitenant scenario, active tenants compete for resources in order to access the database. If one tenant blocks up the resources, the performance of all the other tenants may be restricted and a fair sharing of the resources may be compromised. The performance of tenants must not be affected by resource-intensive activities and volatile workloads of other tenants. Moreover, the prime goal of providers is to accomplish low cost of operation, satisfying specific schemas/SLAs of each tenant. Consequently, there is a need to design and develop effective and dynamic resource sharing algorithms which can handle above mentioned issues. This work presents a model embracing a query classification and worker sorting technique to efficiently share I/O, CPU and Memory thus enhancing dynamic resource sharing and improvising the utilization of idle instances proficiently. The model is referred as Multi-Tenant Dynamic Resource Scheduling Model (MTDRSM) .The MTDRSM support workload execution of different benchmark such as TPC-C(Transaction Processing Performance Council), YCSB(The Yahoo! Cloud Serving Benchmark)etc. and on different database such as MySQL, Oracle, H2 database etc. Experiments are conducted for different benchmarks with and without SLA compliances to evaluate the performance of MTDRSM in terms of latency and throughput achieved. The experiments show significant performance improvement over existing Mute Bench model in terms of latency and throughput