Platform as a Service (PaaS) cloud brings great benefits of an elastic platform with many prefabricated services. At the same time, however, it challenges software architects who need to navigate a rich set of PaaS services, variability of PaaS cloud environment and quality conflicts in existing design tactics, which makes it very hard to foresee the impact of architectural design decisions on the overall application quality. To support the architects in the design of PaaS cloud applications, we propose a design-time quality evaluation approach for PaaS cloud applications based on automatically generated prototypes, which are deployed to the cloud and evaluated in the context of multiple quality attributes and environment configurations. In this paper, we outline the approach and its implementation in a prototype generation and evaluation tool, referred to as PaaSArch Cloud Prototyper1.
PaaS cloud has over time became a popular platform for hosting software applications, which
however need to be architected consciously to take advantage of the Paas cloud services [
To address this, we propose an approach and tool set, called PaaSArch Cloud Prototyper1, supporting automated prototyping of PaaS cloud applications, enabling quick assessment of various architectural options and integrating diferent PaaS cloud services. The tool receives a model of the application architecture (together with some details about the inner behaviour of the application and its usage of cloud services), and translates it via an automated process into a source code of a fully functional application prototype, which is together with automatically generated sample data deployed to the cloud and benchmarked.
Model-driven quality prediction approaches are a popular way to support quality-driven design
of software systems. To this end, the application is modeled with UML or other domain
specific modelling notation, e.g. Palladio Component Model (PCM) [
Another way to assess the quality of the PaaS applications is to develop a simplified version
of the application, deploy the application and run a set of benchmarks. The benefit of this option
is that the system resources are closer to the final execution environment [
Instead of synthetic workload, a better option is to generate prototypes of the application
from its model. This strategy is used by ProtoCom [
Our approach allows software architects to evaluate performance related quality metrics of a proposed PaaS cloud application’s architecture in early design stages by leveraging automatically generated prototypes of the application. In this way, the architects can evaluate diferent variants of the proposed architecture with significantly lower efort than manual implementation of prototypes. In efect, they have better continuous control over the quality of the application and can prevent later costly reimplementation. As the software architecture of the application evolves, the model can be updated and performance of newly generated prototype can be compared with previous versions of the architecture.
The individual steps of our approach (done by the software architect as well as the prototyping tool) are depicted in Figure 1.
1Project homepage: https://lasaris.fi.muni.cz/research/software-architecture-optimization-in-paas-cloudapplications/paasarch-cloud-prototyper-tool
1. Model creation Manual process done by SW architect
Output: Application Model
Fully automated process Output: Validated Application Model
Fully automated process Output: Allocated resources in the cloud and their connection strings
Fully automated process Output: Generated source code of modelled application in target language (C#, Java)
Fully automated process Output: Application deployed in the cloud ready for benchmark execution (optional) 6. Sample data generation
Fully automated process Output: Application storage filled with sample data required for benchmark execution
Manual process done by SW architect Output: Performance measurements of the prototype e p y t o t o r p e h ft o e c n a m r o f r e p n o d e s a b e r u ittc e h c r a s ‘ n o itc a il p p a e h ft o s t n e m t s u j d A 1. Model creation – The software architect creates a model of the designed application that describes all important aspects that significantly influence evaluated quality metrics.
Simple sample model is in Figure 2. 2. Model analysis and validation – The model is loaded by the tool and is validated against our meta-model to determine if the prototype can be correctly generated. 3. Resource allocation – One of the advantages of our tool is that it automatically takes care of resource allocation in the cloud environment. Resources need to be allocated prior to code generation as connection strings need to be incorporated into the prototype. 4. Prototype source-code generation – Based on the description of application’s behavior in the model, the source code of executable prototype is generated. 5. Prototype compilation and deployment – Generated source code is compiled and the executable application is deployed to the target cloud hosting environment. 6. Sample data generation – Another advantage of our tool is that it can automatically generates sample data based on the description of their volume and complexity in the model. 7. Prototype evaluation – When the prototype is ready for evaluation, our tool generates a list of URL endpoints that can be passed to current state-of-the-art web benchmark tools.
C:\Users\David\Documents\simple-sample-app.json
Steps 2–6 in our approach are fully automated using our tool PaaSArch Cloud Prototyper. The
the tool itself is implemented as a set of libraries in the .NET framework using C# packed
together with a console application, which displays the progress of prototype generation based
on input model. The tool currently contains code generators, which generate final prototypes
in .NET/C# and supports automated deployment and management of Microsoft Azure cloud
resources. Thanks to its extensibility, new code generators can be implemented to support
extensibility of the application model by adding new operations and cloud resources. Support for
additional cloud providers (Amazon, Google, etc.) can be added via resource manager plug-ins.
Detailed description of the tool’s architecture is available in [
Our approach and outcomes of the implemented prototype generator were evaluated on multiple
case studies as described in [
In our work, we take advantage of the cloud elasticity and introduce a design-time quality evaluation technique for PaaS applications based on automatically generated application prototypes, which can be deployed to the cloud and evaluated in the context of multiple quality attributes and environment configurations. As part of this work, we discuss the implementation of the approach in terms of the PaaSArch Cloud Prototyper, which automates the approach to assist software architects in the design of cloud applications that will be efectively utilizing the PaaS cloud platform and combining the available services in an optimal way.