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Understanding the dependency between performance metrics (such as response time) and software configuration or usage parameters is crucial in improving software quality. However, the size of most modern systems makes it nearly impossible to provide a complete performance model. Hence, we focus on scenario-specific problems where software engineers require practical and efficient approaches to draw conclusions, and we propose an automated, measurement-based model inference method to derive goal-oriented performance prediction functions. For the practicability of the approach it is essential to derive functional dependencies with the least possible amount of data. In this paper, we present different strategies for automated improvement of the prediction model through an adaptive selection of new measurement points based on the accuracy of the prediction model. In order to derive the prediction models, we apply and compare different statistical methods. Finally, we evaluate the different combinations based on case studies using SAP and SPEC benchmarks.

Cloud environments reduce data center operating costs through resource sharing and economies of scale. Infrastructure-as-a-Service is one example that leverages virtualization to share infrastructure resources. However, virtualization is often insufficient to provide Software-as-a-Service applications due to the need to replicate the operating system, middleware and application components for each customer. To overcome this problem, multi-tenancy has emerged as an architectural style that allows to share a single Web application instance among multiple independent customers, thereby significantly improving the efficiency of Software-as-a-Service offerings. A number of platforms are available today that support the development and hosting of multi-tenant applications by encapsulating multi-tenancy specific functionality. Although a lack of performance guarantees is one of the major obstacles to the adoption of cloud computing, in general, and multi-tenant applications, in particular, these kinds of applications and platforms have so far not been in the focus of the performance and benchmarking community. In this paper, we present an extended version of an existing and widely accepted application benchmark adding support for multi-tenant platform features. The benchmark is focused on evaluating the maximum throughput and the amount of tenants that can be served by a platform. We present a case study comparing virtualization and multi-tenancy. The results demonstrate the practical usability of the proposed benchmark in evaluating multi-tenant platforms and gives insights that help to decide for one sharing approach.

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