Variability modeling to support customization and deployment of multi-tenant-aware Software as a Service applications

Mietzner, Ralph; Metzger, Andreas; Leymann, Frank; Pohl, Klaus

doi:10.1109/pesos.2009.5068815

Cited by 142 publications

(96 citation statements)

References 9 publications

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“…(Bezemer & Zaidman) defined the multi-tenant application by its main feature which is "the application that allows customers (tenants) to share the same hardware resources, by offering one shared application and database instance" [15]. In other words, in multitenant environment different organizations requests are served by one application instance resides on a shared software and hardware infrastructure which is isolated from other shares with regard to performance and data privacy [16]- [18]. Nevertheless, SaaS application with multi-tenant emphasizes on the idea of the (pay as you use).…”

Section: B Saas Definitionmentioning

confidence: 99%

Configuration as a Service in Multi-Tenant Enterprise Resource Planning System

Al-Shardan¹,

Ziani²

2015

LNSE

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Abstract-Enterprise resource planning (ERP) systems are the organizations tickets to the global market. Thus, organizations can manage and coordinate all functions, processes, resources and data from different departments by a single software. However, many organizations consider the cost of traditional ERP is expensive and look for alternative affordable solutions within their budget. One of these alternative solutions is providing ERP over a software as a service (SaaS) model. A key feature of any SaaS system is the multi-tenancy architecture where multiple customers (tenants) share the system software. However, the SaaS developers accommodate each tenant's unique requirements by allowing tenant-level customization or configuration. While customization requires source code changes and in most cases a programming experience, the configuration process allows user to change many features within a pre-defined scope in an easy and controlled manner. Although literature provides many techniques to accomplish the configuration process in different SaaS systems, the nature and complexity of SaaS ERP are merely described in previous researches. Thus, this research is built on strong knowledge regarding the configuration in SaaS to define specifically the configuration borders in SaaS ERP and to design a configuration service with the consideration of the different configuration aspects.Index Terms-Configuration, software as a service, multi-tenancy, ERP.

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Section: B Saas Definitionmentioning

confidence: 99%

Configuration as a Service in Multi-Tenant Enterprise Resource Planning System

Al-Shardan¹,

Ziani²

2015

LNSE

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“…This makes it possible to characterize runtime variability and reconfigure applications at runtime. SPLE has been used in cloud environments [8,9,10], but the approaches tend to focus mostly on development, deployment and configuration. We however focus specifically on runtime resource allocation for customizable SPLE applications by adding awareness of application variability to the cloud management algorithms.…”

Section: Related Workmentioning

confidence: 99%

“…Furthermore, our algorithm also adds explicit support for software variability. This enables the management system to dynamically fill in undecided variability, referred to as open variation points [10], at runtime.…”

Section: Related Workmentioning

confidence: 99%

“…be cheaper to add encryption for a client, even if he has not selected the feature, if only instances with encryption exist than to create a new instance specifically for this client. These undecided features are referred to as open variation points [10]. If, for the model in Figure 3, {a, c, d} are selected and {b} is excluded, e and f remain as open variation points.…”

Section: Feature Placementmentioning

confidence: 99%

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Allocating resources for customizable multi-tenant applications in clouds using dynamic feature placement

Moens

Dhoedt

Turck

2015

Future Generation Computer Systems

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Multi-tenancy, where multiple end users make use of the same application instance, is often used in clouds to reduce hosting costs. A disadvantage of multi-tenancy is however that it makes it difficult to create customizable applications, as all end users use the same application instance. In this article, we describe an approach for the development and management of highly customizable multi-tenant cloud applications. We apply software product line engineering techniques to cloud applications, and use an approach where applications are composed of multiple interacting components, referred to as application features. Using this approach, multiple features can be shared between different applications. Allocating resources for these feature-based applications is complex, as relations between components must be taken into account, and is referred to as the feature placement problem.In this article, we describe dynamic feature placement algorithms that minimize migrations between subsequent invocations, and evaluate them in dynamic scenarios where applications are added and removed throughout the evaluation scenario. We find that the developed algorithm achieves a low cost, while resulting in few resource migrations. In our evaluations, we observe that adding migration-awareness to the management algorithms reduces the number of instance migrations by more than 77% and reduces the load moved between instances by more than 96% when compared to a static management approach. Despite this reduction in number of migrations, a cost that is on average less than 3% more than the optimal cost is achieved.

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“…To ensure that constraints in the FD are incorporated in the efficient sampling of t-wise tests, the scalable solver proposed by Perrouin et al [18] is used. In [14], variability in software as a service applications are modeled using the orthogonal variability model to study the customization choices in such workflows.…”

Section: Related Workmentioning

confidence: 99%

Pairwise testing of dynamic composite services

Kattepur

Sen

Baudry

et al. 2011

Proceedings of the 6th International Symposium on Software Engineering for Adaptive and Self-Managing Systems

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Online services encapsulate enterprises, people, software systems and often operate in poorly understood environments. Using such services in tandem to predictably orchestrate a complex task is one of the principal challenges of serviceoriented computing. A composite service orchestration soliciting multiple atomic services is plagued by a number of sources of variation. For instance, availability of an atomic service and its response time are two important sources of variation. Moreover, the number of possible variations in a composite service increases exponentially with increase in the number of atomic services. Testing such a composite service presents a crucial challenge as its often very expensive to exhaustively examine the variation space. Can we effectively test the dynamic behavior of a composite service using only a subset of these variations? This is the question that intrigues us. In this paper, we first model composite service variability as a feature diagram (FD) that captures all valid configurations of its orchestration. Second, we apply pairwise testing to sample the set of all possible configurations to obtain a concise subset. Finally, we test the composite service for selected pairwise configurations for a variety of QoS metrics such as response time, data quality, and availability. Using two case studies, Car crash crisis management and eHealth management, we demonstrate that pairwise generation effectively samples the full range of QoS variations in a dynamic orchestration. The pairwise sampling technique eliminates over 99% redundancy in configurations, while still calling all atomic services at least once. We rigorously evaluate pairwise testing for the criteria such as: a) ability to sample the extreme QoS metrics of the service b) stable behavior of the extracted configurations c) compact set of configurations that can help evaluate QoS tradeoffs and d) comparison with random sampling.

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Variability modeling to support customization and deployment of multi-tenant-aware Software as a Service applications

Cited by 142 publications

References 9 publications

Configuration as a Service in Multi-Tenant Enterprise Resource Planning System

Configuration as a Service in Multi-Tenant Enterprise Resource Planning System

Allocating resources for customizable multi-tenant applications in clouds using dynamic feature placement

Pairwise testing of dynamic composite services

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