Giovanni Toffetti

An architecture for self-managing microservices

Running applications in the cloud efficiently requires much more than deploying software in virtual machines. Cloud applications have to be continuously managed: 1) to adjust their resources to the incoming load and 2) to face transient failures replicating and restarting components to provide resiliency on unreliable infrastructure. Continuous management monitors application and infrastructural metrics to provide automated and responsive reactions to failures (health management) and changing environmental conditions (auto-scaling) minimizing human intervention. In the current practice, management functionalities are provided as infrastructural or third party services. In both cases they are external to the application deployment. We claim that this approach has intrinsic limits, namely that separating management functionalities from the application prevents them from naturally scaling with the application and requires additional management code and human intervention. Moreover, using infrastructure provider services for management functionalities results in vendor lock-in effectively preventing cloud applications to adapt and run on the most effective cloud for the job. In this position paper we propose a novel architecture that enables scalable and resilient self-management of microservices applications on cloud.

Modeling cloud performance with kriging

Cloud infrastructures allow service providers to implement elastic applications. These can be scaled at runtime to dynamically adjust their resources allocation to maintain consistent quality of service in response to changing working conditions, like flash crowds or periodic peaks. Providers need models to predict the system performances of different resource allocations to fully exploit dynamic application scaling. Traditional performance models such as linear models and queueing networks might be simplistic for real Cloud applications; moreover, they are not robust to change. We propose a performance modeling approach that is practical for highly variable elastic applications in the Cloud and automatically adapts to changing working conditions. We show the effectiveness of the proposed approach for the synthesis of a self-adaptive controller.

Self-managing cloud-native applications : design, implementation and experience

Abstract Running applications in the cloud efficiently requires much more than deploying software in virtual machines. Cloud applications have to be continuously managed: (1) to adjust their resources to the incoming load and (2) to face transient failures replicating and restarting components to provide resiliency on unreliable infrastructure. Continuous management monitors application and infrastructural metrics to provide automated and responsive reactions to failures (health management) and changing environmental conditions (auto-scaling) minimizing human intervention. In the current practice, management functionalities are provided as infrastructural or third party services. In both cases they are external to the application deployment. We claim that this approach has intrinsic limits, namely that separating management functionalities from the application prevents them from naturally scaling with the application and requires additional management code and human intervention. Moreover, using infrastructure provider services for management functionalities results in vendor lock-in effectively preventing cloud applications to adapt and run on the most effective cloud for the job. In this paper we discuss the main characteristics of cloud native applications, propose a novel architecture that enables scalable and resilient self-managing applications in the cloud, and relate on our experience in porting a legacy application to the cloud applying cloud-native principles.

Assurance of Self-adaptive Controllers for the Cloud

In this paper we discuss the assurance of self-adaptive controllers for the Cloud, and we propose a taxonomy of controllers based on the supported assurance level. Self-adaptive systems for the Cloud are commonly built by means of controllers that aim to guarantee the required quality of service while containing costs, through a careful allocation of resources. Controllers determine the allocation of resources at runtime, based on the inputs and the status of the system, and referring to some knowledge, usually represented as adaptation rules or models. Assuring the reliability of self-adaptive controllers account to assuring that the adaptation rules or models represent well the system evolution. In this paper, we identify different categories of control models based on the assurance approaches. We introduce two main dimensions that characterize control models. The dimensions refer to the flexibility and scope of the system adaptability, and to the accuracy of the assurance results. We group control models in three main classes that depend on the kind of supported assurance that may be checked either at design or runtime. Controllers that support assurance of the control models at design time privilege reliability over adaptability. They usually represent the system at a high granularity level and come with high costs. Controllers that support assurance of the control models at runtime privilege adaptability over reliability. They represent the system at a finer granularity level and come with reduced costs. Controllers that combine different models may balance verification at design and runtime and find a good trade off between reliability, adaptability, granularity and costs.

Engineering autonomic controllers for virtualized web applications

Modern Web applications are often hosted in a virtualized cloud computing infrastructure, and can dynamically scale in response to unpredictable changes in the workload to guarantee a given service level agreement. In this paper we propose to use Kriging surrogate models to approximate the performance profile of virtualized, multi-tier Web applications. The model is first built through a set of automated and controlled experiments at staging time, and can be later updated and refined by monitoring the Web application deployed in production. We claim that surrogate modeling makes a very good candidate for a modeldriven approach to the engineering of an autonomic controller. Our experimental evaluation shows that the model predictions are faithful to the observed systems performance, they improve with an increasing amount of samples and they can be computed quickly. We also provide evidence that the model can be effectively used to synthetize an aggregated objective function, a critical component of the autonomic controller. The approach is evaluated in the context of a RESTful Web service composition case study deployed on the RESERVOIR cloud.

Kriging-Based Self-Adaptive Cloud Controllers

Cloud technology is rapidly substituting classic computing solutions, and challenges the community with new problems. In this paper we focus on controllers for cloud application elasticity, and propose a novel solution for self-adaptive cloud controllers based on Kriging models. Cloud controllers are application specific schedulers that allocate resources to applications running in the cloud, aiming to meet the quality of service requirements while optimizing the execution costs. General-purpose cloud resource schedulers provide sub-optimal solutions to the problem with respect to application-specific solutions that we call cloud controllers. In this paper we discuss a general way to design self-adaptive cloud controllers based on Kriging models. We present Kriging models, and show how they can be used for building efficient controllers thanks to their unique characteristics. We report experimental data that confirm the suitability of Kriging models to support efficient cloud control and open the way to the development of a new generation of cloud controllers.

Experimental evaluation of the cloud-native application design

Cloud-Native Applications (CNA) are designed to run on top of cloud computing infrastructure services with inherent support for self-management, scalability and resilience across clustered units of application logic. Their systematic design is promising especially for recent hybrid virtual machine and container environments for which no dominant application development model exists. In this paper, we present a case study on a business application running as CNA and demonstrate the advantages of the design experimentally. We also present Dynamite, an application auto-scaler designed for containerised CNA. Our experiments on a Vagrant host, on a private OpenStack installation and on a public Amazon EC2 testbed show that CNA require little additional engineering.

Web engineering for cloud computing (web engineering forecast: cloudy with a chance of opportunities)

Web Engineering has always been concerned with modelling the functional aspects of Web applications. Non-functional (e.g., performance, availability) properties of Web applications have traditionally been a minor concern in the Web engineering community and have been seen as technologyor system-related issues. The advent of Cloud computing, with substantial delegation of “system concerns” to infrastructure or platform providers, seems at a first sight to confirm the validity of this choice. But is this really true? We will argue that, in order to be able to actually profit from the Cloud computing paradigm, Web Engineering methodologies need several interventions transcending the platform-specific concerns of adapting to Cloud technologies. In this position paper, we call for a long-due revamp of Web engineering methodologies to become more sound engineering practices with respect to both functional and non-functional aspects of Web applications. To this end, we propose a methodological framework that preserves the advantages of model-driven development, but also takes into account performance and cost considerations for Cloud-based applications.

Graph transformations and software engineering: Success stories and lost chances☆

Abstract Textual as well as visual and diagrammatic notations are essential in software engineering, and are used in many different contexts. Chomsky grammars are the key tool to handle textual notations, and find many applications for textual languages. Visual and diagrammatic languages add spatial dimensions that reduce the applicability of textual grammars and call for new tools. Graph transformation systems have been studied for over 40 years and are a powerful tool to deal with syntax, semantics and transformation of diagrammatic notations. The enormous importance of visual and diagrammatic languages and the strong support that graph transformation provide to the manipulation of diagrammatic notations would suggest a big success of graph transformation in software engineering. Graph transformation systems find their application both as language generating devices and specification means for system evolution, and thus can have many applications in software engineering. In this paper we discuss the main features of graph transformation and how they can help software engineers. We look back to the many attempts to use graph transformations in software engineering in the last 15 years, identify some success stories, and discuss to what extent graph transformation succeeded, when they have not succeeded yet, what are the main causes of failures, and how they can help software engineering in the next 15 years.

Future Internet Monitoring Platform for Computing Clouds

This paper presents a monitoring platform and demonstration enabling effectively operation of service clouds. It is designed for monitoring resources and services in virtualized environments. It provides data on the actual usage and changes in resources of the cloud and of the services running in the cloud.