Evolving multi-tenant SaaS cloud applications using model-driven engineering

Cloud computing promotes multi-tenancy for efficient resource utilization by sharing hardware and software infrastructure among multiple clients. Multi-tenant applications running on a cloud infrastructure are provided to clients as Software-as-a-Service (SaaS) over the network. Despite its benefits, multi-tenancy introduces additional challenges, such as p artitioning, extensibility, and customizability during the application development. Over time, after the application deployment, new requirements of clients and changes in business environment result application evolution. As the application evolves, its complexity also increases. In multi-tenancy, evolution demanded by individual clients should not affect availability , security , and performance of the application for other clients. Thus, the multi- tenancy concerns add more complexity by causing variability in design decisions. Managing this complexity requires adequate approaches and tools. In this paper, we propose modeling techniques from software product lines (SPL) and model-driven engineering (MDE) to manage variability and support evolution of multi-tenant applications and their requirements. Specifically, SPL was ap p lied to define technological and concep tual variabilities during the application design, where MDE was suggested to manage these variabilities. We also present a process of how MDE can address evolution of multi-tenant applications using variability models

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

Using DSML for Handling Multi-tenant Evolution in Cloud Applications

Multi-tenancy is sharing a single application's resources to serve more than a single group of users (i.e. tenant). Cloud application providers are encouraged to adopt multi-tenancy as it facilitates increased resource utilization and ease of maintenance, translating into lower operational and energy costs. However, introducing multi-tenancy to a single-tenant application requires significant changes in its structure to ensure tenant isolation, configurability and extensibility. In this paper, we analyse and address the different challenges associated with evolving an application's architecture to a multi-tenant cloud deployment. We focus specifically on multi-tenant data architectures, commonly the prime candidate for consolidation and multi-tenancy. We present a Domain-Specific Modeling language (DSML) to model a multi-tenant data architecture, and automatically generate source code that handles the evolution of the application's data layer. We apply the DSML on a representative case study of a single-tenant application evolving to become a multi-tenant cloud application under two resource sharing scenarios. We evaluate the costs associated with using this DSML against the state of the art and against manual evolution, reporting specifically on the gained benefits in terms of development effort and reliability

SOA: Trends and Directions

While many organizations have adopted SOA there are recent indications that not all organizations are willing to make substantial investments in new skills and technologies required for the transition to SOA in the current economic climate. The recent emergence of Cloud Computing is continuing the trend of delivering enterprise applications and IT infrastructure in the form of externally sourced services, providing an alternative to on-premise solutions. The convergence of Cloud Computing and Web 2.0 is redefining the very basis on which the computer industry has operated for decades, challenging some of the basic SOA assumptions and principles. In this paper we discuss the synergies between the above technology trends and consider the likely impact of these trends on enterprise computing

Architecting Enterprise Applications for the Cloud: The Unicorn Universe Cloud Framework

© Springer International Publishing AG, part of Springer Nature 2018. Recent IT advances that include extensive use of mobile and IoT devices and wide adoption of cloud computing are creating a situation where existing architectures and software development frameworks no longer fully support the requirements of modern enterprise application. Furthermore, the separation of software development and operations is no longer practicable in this environment characterized by fast delivery and automated release and deployment of applications. This rapidly evolving situation requires new frameworks that support the DevOps approach and facilitate continuous delivery of cloud-based applications using micro-services and container-based technologies allowing rapid incremental deployment of application components. It is also becoming clear that the management of large-scale container-based environments has its own challenges. In this paper, we first discuss the challenges that developers of enterprise applications face today and then describe the Unicorn cloud framework (uuCloud) designed to support the development and deployment of cloud-based applications that incorporate mobile and IoT devices. We use a doctor surgery reservation application “Lekar” case study to illustrate how uuCloud is used to implement a large-scale cloud-based application

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 modeling language for multi-tenant data architecture evolution in cloud applications

Multi-tenancy enables efficient resource utilization by sharing application resources across multiple customers (i.e., tenants). Hence, applications built using this pat- tern can be offered at a lower price and reduce maintenance effort as less application instances and supporting cloud resources must be maintained. These properties en- courage cloud application providers to adopt multi-tenancy to their existing applications, yet introducing this pattern requires significant changes in the application structure to address multi-tenancy requirements such as isolation of tenants, extensibility of the application, and scalability of the solution. In cloud applications, the data layer is often the prime candidate for multi-tenancy, and it usually comprises a combination of different cloud storage solutions such as blob storage, relational and non-relational databases. These storage types are conceptually and tangibly divergent, each requiring its own partitioning schemes to meet multi-tenancy requirements. Currently, multi-tenant data architectures are implemented using manual coding methods, at times following guidance and patterns offered by cloud providers. However, such manual implementation approach tends to be time consuming and error prone. Several modeling methods based on Model-Driven Engineer- ing (MDE) and Software Product Line Engineering (SPLE) have been proposed to capture multi-tenancy in cloud applications. These methods mainly generate cloud deployment configurations from an application model, though they do not automate implementation or evolution of applications. This thesis aims to facilitate development of multi-tenant cloud data architectures using model-driven engineering techniques. This is achieved by designing and implementing a novel modeling language, CadaML, that provides concepts and notations to model multi-tenant cloud data architectures in an abstract way. CadaML also provides a set of tools to validate the data architecture and automatically produce corresponding data access layer code. The thesis demonstrates the feasibility of the modeling language in a practical setting and adequacy of multi-tenancy implementation by the generated code on an industrial business process analyzing application. Moreover, the modeling language is empirically compared against manual implementation methods to inspect its effect on developer productivity, development effort, reliability of the application code, and usability of the language. These outcomes provide a strong argument that the CadaML modeling language effectively mitigates the high overhead of manual implementation of multi-tenant cloud data layers, significantly reducing the required development complexity and time