Sören Frey

Search-based genetic optimization for deployment and reconfiguration of software in the cloud

Migrating existing enterprise software to cloud platforms involves the comparison of competing cloud deployment options (CDOs). A CDO comprises a combination of a specific cloud environment, deployment architecture, and runtime reconfiguration rules for dynamic resource scaling. Our simulator CDOSim can evaluate CDOs, e.g., regarding response times and costs. However, the design space to be searched for well-suited solutions is extremely huge. In this paper, we approach this optimization problem with the novel genetic algorithm CDOXplorer. It uses techniques of the search-based software engineering field and CDOSim to assess the fitness of CDOs. An experimental evaluation that employs, among others, the cloud environments Amazon EC2 and Microsoft Windows Azure, shows that CDOXplorer can find solutions that surpass those of other state-of-the-art techniques by up to 60%. Our experiment code and data and an implementation of CDOXplorer are available as open source software.

CDOSim: Simulating cloud deployment options for software migration support

The evaluation of competing cloud deployment options (CDOs) forms a major challenge when migrating software systems to cloud environments. For example, there exists a plethora of potential cloud provider candidates, components must be mapped to suitable virtual machine instances, and, to exploit elasticity, appropriate runtime adaptation strategies for specific usage profiles have to be defined. But analyzing potential CDOs manually is intractable, costly, and time-consuming due to the heterogeneity of the cloud environments and the overall combinatorial design space complexity. We present the simulation tool CDOSim that can simulate cost and performance properties of those CDOs. It builds upon and significantly extends the cloud simulator CloudSim and integrates into our cloud migration framework CloudMIG. Additionally, we created a cloud benchmark to augment CloudMIGs cloud environment models with provider-specific performance characteristics. Along with this simulation input, CDOSim utilizes reverse-engineered architectural models and can employ actual monitored workload. We report on extensive experiments incorporating Eucalyptus and Amazon EC2 which show that CDOSim can sufficiently accurate predict the cost and performance properties of CDOs.

Automatic conformance checking for migrating software systems to cloud infrastructures and platforms

The migration of software systems to IaaS (infrastructure as a service)‐ or PaaS (platform as a service)‐based cloud environments enables SaaS providers to benefit from the clouds merits, such as smoothly scaling up and down existing applications. Our approach, CloudMIG, aims at supporting SaaS providers to perform those migrations. Here, validating the specific constraints that are imposed by a cloud environment constitutes an important early‐phase activity. For example, the access to the file system, number of files, or calls to specific methods may be restricted by cloud providers. Those constraints have to be considered when evaluating the suitability of competing cloud environment candidates. In this paper, we describe CloudMIGs corresponding parts: a generic cloud environment model that incorporates these constraints and appropriate violation detection mechanisms. A software systems conformance can be examined with the assistance of constraint validators. They operate on extracted Knowledge Discovery Meta‐Model‐based system models and can, among others, apply metrics formulated with the Software Metrics Meta‐Model through our metrics execution engine. Additional constraint validators can be plugged into the validation process as needed. In this context, we implemented a prototype and modeled the PaaS environment Google App Engine for Java. We report on a quantitative evaluation regarding the detected constraint violations of five open‐source systems. Copyright

Cloud user-centric enhancements of the simulator cloudsim to improve cloud deployment option analysis

Cloud environments can be simulated using the toolkit CloudSim. By employing concepts such as physical servers in datacenters, virtual machine allocation policies, or coarse-grained models of deployed software, it focuses on a cloud provider perspective. In contrast, a cloud user who wants to migrate complex systems to the cloud typically strives to find a cloud deployment option that is best suited for its sophisticated system architecture, is interested in determining the best trade-off between costs and performance, or wants to compare runtime reconfiguration plans, for instance. We present significant enhancements of CloudSim that allow to follow this cloud user perspective and enable the frictionless integration of fine-grained application models that, to a great extent, can be derived automatically from software systems. Our quantitative evaluation demonstrates the applicability and accuracy of our approach by comparing its simulation results with actual deployments that utilize the cloud environment Amazon EC2.

An Extensible Architecture for Detecting Violations of a Cloud Environment's Constraints during Legacy Software System Migration

By utilizing cloud infrastructures or platforms as services, SaaS providers can counter fluctuating loads through smoothly scaling up and down and therefore improve resource- and cost-efficiency, or transfer responsibility for the maintenance of complete underlying software stacks to a cloud provider, for instance. Our model-based approach CloudMIG aims at supporting SaaS providers to semi-automatically migrate legacy software systems to the cloud. Thereby, the analysis of conformance with the specific constraints imposed by a cloud environment candidate along with the detection of constraint violations constitutes an important early phase activity. We present an extensible architecture for describing cloud environments, their corresponding constraints, and appropriate violation detection mechanisms. There exist predefined constraint types with specified domain semantics as well as generic variants for modeling arbitrary constraints. A software systems compliance can be examined with the assistance of so called constraint validators. They operate on discovered KDM-based models of a legacy system. Additional constraint validators can be plugged into the validation process as needed. In this context, we implemented a prototype and modeled the PaaS environment Google App Engine for Java. We report on a quantitative evaluation regarding the detected constraint violations of five open source systems.

MAMBA: Model-Based Software Analysis Utilizing OMG's SMM

Most software system properties can be quantified through applying measurement processes. OMGs Structured Metrics Meta-Model (SMM) supports the meta-model agnostic definition of those measurement processes with an emphasis on architecture-driven modernization scenarios. We present the MAMBA framework that addresses major obstacles software engineers currently face when using SMM in practice. Among those are (1) the lack of appropriate tool support, (2) the cumbersome integration of precomputed measurement data, and (3) the complexity of specifying SMM models and queries.