Integrating performance analysis in the model driven development of software product lines

The paper proposes to integrate performance analysis in the early phases of the model-driven development process for Software Product Lines (SPL). We start by adding generic performance annotations to the UML model representing the set of core reusable SPL assets. The annotations are generic and use the MARTE Profile recently adopted by OMG. A first model transformation realized in the Atlas Transformation Language (ATL), which is the focus of this paper, derives the UML model of a specific product with concrete MARTE performance annotations from the SPL model. A second transformation generates a Layered Queueing Network performance model for the given product by applying an existing transformation approach named PUMA, developed in previous work. The proposed technique is illustrated with an e-commerce case study that models the commonality and variability in both structural and behavioural SPL views. A product is derived and the performance of two design alternatives is compared

A Proactive Approach to Application Performance Analysis, Forecast and Fine-Tuning

A major challenge currently faced by the IT industry is the cost, time and resource associated with repetitive performance testing when existing applications undergo evolution. IT organizations are under pressure to reduce the cost of testing, especially given its high percentage of the overall costs of application portfolio management. Previously, to analyse application performance, researchers have proposed techniques requiring complex performance models, non-standard modelling formalisms, use of process algebras or complex mathematical analysis. In Continuous Performance Management (CPM), automated load testing is invoked during the Continuous Integration (CI) process after a build. CPM is reactive and raises alarms when performance metrics are violated. The CI process is repeated until performance is acceptable. Previous and current work is yet to address the need of an approach to allow software developers proactively target a specified performance level while modifying existing applications instead of reacting to the performance test results after code modification and build. There is thus a strong need for an approach which does not require repetitive performance testing, resource intensive application profilers, complex software performance models or additional quality assurance experts. We propose to fill this gap with an innovative relational model associating the operation‟s Performance with two novel concepts – the operation‟s Admittance and Load Potential. To address changes to a single type or multiple types of processing activities of an application operation, we present two bi-directional methods, both of which in turn use the relational model. From annotations of Delay Points within the code, the methods allow software developers to either fine-tune the operation‟s algorithm “targeting” a specified performance level in a bottom-up way or to predict the operation‟s performance due to code changes in a top-down way under a given workload. The methods do not need complex performance models or expensive performance testing of the whole application. We validate our model on a realistic experimentation framework. Our results indicate that it is possible to characterize an application Performance as a function of its Admittance and Load Potential and that the application Admittance can be characterized as a function of the latency of its Delay Points. Applying this method to complex large-scale systems has the potential to significantly reduce the cost of performance testing during system maintenance and evolution

WS-Pro: a Petri net based performance-driven service composition framework

As an emerging area gaining prevalence in the industry, Web Services was established to satisfy the needs for better flexibility and higher reliability in web applications. However, due to the lack of reliable frameworks and difficulties in constructing versatile service composition platform, web developers encountered major obstacles in large-scale deployment of web services. Meanwhile, performance has been one of the major concerns and a largely unexplored area in Web Services research. There is high demand for researchers to conceive and develop feasible solutions to design, monitor, and deploy web service systems that can adapt to failures, especially performance failures. Though many techniques have been proposed to solve this problem, none of them offers a comprehensive solution to overcome the difficulties that challenge practitioners. Central to the performance-engineering studies, performance analysis and performance adaptation are of paramount importance to the success of a software project. The industry learned through many hard lessons the significance of well-founded and well-executed performance engineering plans. An important fact is that it is too expensive to tackle performance evaluation, mostly through performance testing, after the software is developed. This is especially true in recent decades when software complexity has risen sharply. After the system is deployed, performance adaptation is essential to maintaining and improving software system reliability. Performance adaptation provides techniques to mitigate the consequence of performance failures and therefore is an important research issue. Performance adaptation is particularly meaningful for mission-critical software systems and software systems with inevitable frequent performance failures, such as Web Services. This dissertation focuses on Web Services framework and proposes a performance-driven service composition scheme, called WS-Pro, to support both performance analysis and performance adaptation. A formalism of transformation from WS-BPEL to Petri net is first defined to enable the analysis of system properties and facilitate quality prediction. A state-transition based proof is presented to show that the transformed Petri net model correctly simulates the behavior of the WS-BPEL process. The generated Petri net model was augmented using performance data supplied by both historical data and runtime data. Results of executing the Petri nets suggest that optimal composition plans can be achieved based on the proposed method. The performance of service composition procedure is an important research issue which has not been sufficiently treated by researchers. However, such an issue is critical for dynamic service composition, where re-planning must be done in a timely manner. In order to improve the performance of service composition procedure and enhance performance adaptation, this dissertation presents an algorithm to remove loops in the reachability graphs so that a large portion of the computation time of service composition can be moved to a pre-processing unit; hence the response time is shortened during runtime. We also extended the WS-Pro to the ubiquitous computing area to improve fault-tolerance

ATOM: model-driven autoscaling for microservices

Microservices based architectures are increasinglywidespread in the cloud software industry. Still, there is ashortage of auto-scaling methods designed to leverage the uniquefeatures of these architectures, such as the ability to indepen-dently scale a subset of microservices, as well as the ease ofmonitoring their state and reciprocal calls.We propose to address this shortage with ATOM, a model-driven autoscaling controller for microservices. ATOM instanti-ates and solves at run-time a layered queueing network model ofthe application. Computational optimization is used to dynami-cally control the number of replicas for each microservice and itsassociated container CPU share, overall achieving a fine-grainedcontrol of the application capacity at run-time.Experimental results indicate that for heavy workloads ATOMoffers around 30%-37% higher throughput than baseline model-agnostic controllers based on simple static rules. We also find thatmodel-driven reasoning reduces the number of actions needed toscale the system as it reduces the number of bottleneck shiftsthat we observe with model-agnostic controllers

An Integrated Modeling Framework for Managing the Deployment and Operation of Cloud Applications

Cloud computing can help Software as a Service (SaaS) providers to take advantage of the sheer number of cloud benefits such as, agility, continuity, cost reduction, autonomy, and easy management of resources. To reap the benefits, SaaS providers should create their applications to utilize the cloud platform capabilities. However, this is a daunting task. First, it requires a full understanding of the service offerings from different providers, and the meta-data artifacts required by each provider to configure the platform to efficiently deploy, run and manage the application. Second, it involves complex decisions that are specified by different stakeholders. Examples include, financial decisions (e.g., selecting a platform to reduces costs), architectural decisions (e.g., partition the application to maximize scalability), and operational decisions (e.g., distributing modules to insure availability and porting the application to other platforms). Finally, while each stakeholder may conduct a certain type of change to address a specific concern, the impact of a change may span multiple models and influence the decisions of several stakeholders. These factors motivate the need for: (i) a new architectural view model that focuses on service operation and reflects the cloud stakeholder perspectives, and (ii) a novel framework that facilitates providing holistic as well as partial architectural views, and generating the required platform artifacts by fragmenting the model into artifacts that can be easily modified separately. This PhD research devises a novel architecture framework, "The 5+1 Architectural View Model", for cloud applications, in which each view corresponds to a different perspective on cloud application deployment. The architectural framework is realized as a cloud modeling framework, called "StratusML", which consists of a modeling language that uses layers to specify the cloud configuration space, and a transformation engine to generate the configuration space artifacts. The usefulness and practical applicability of StratusML to model multi-cloud and multi-tenant applications have been demonstrated though a representative domain example. Moreover, to automate the framework evolution as new concerns and cloud platforms emerge, this research work introduces also a novel schema matching technique, called "Liberate". Liberate supports the process of domain model creation, evolution, and transformations. Liberate helps solve the vendor lock-in problem by reducing the manual efforts required to map complex correspondences between cloud schemas whose domain concepts do not share linguistic similarities. The evaluation of Liberate shows its superiority in the cloud domain over existing schema matching approaches