192 research outputs found

    Summary of the 11th international workshop on [email protected]

    Get PDF
    After last years anniversary, this year the 11th edition of the workshop [email protected] was held at the 19th International Conference on Model Driven Engineering Languages and Systems. The workshop took place in the city of Saint Malo, France, on the 4th of October 2016. The workshop was organized by Sebastian Götz, Nelly Bencomo, Kirstie Bellman and Gordon Blair. Here, we present a summary of the discussions at the workshop and a synopsis of the topics discussed and highlighted during the workshop

    Towards Practical Runtime Verification and Validation of Self-Adaptive Software Systems

    Get PDF
    International audienceSoftware validation and verification (V&V) ensures that software products satisfy user requirements and meet their expected quality attributes throughout their lifecycle. While high levels of adaptation and autonomy provide new ways for software systems to operate in highly dynamic environments, developing certifiable V&V methods for guaranteeing the achievement of self-adaptive software goals is one of the major challenges facing the entire research field. In this chapter we (i) analyze fundamental challenges and concerns for the development of V&V methods and techniques that provide certifiable trust in self-adaptive and self-managing systems; and (ii) present a proposal for including V&V operations explicitly in feedback loops for ensuring the achievement of software self-adaptation goals. Both of these contributions provide valuable starting points for V&V researchers to help advance this field

    A Monitoring Infrastructure for the Quality Assessment of Cloud Services

    Get PDF
    Service Level Agreements (SLAs) specify the strict terms under which cloud services must be provided. The assessment of the quality of services being provided is critical for both clients and service providers. In this context, stakeholders must be capable of monitoring services delivered as Software as a Service (SaaS) at runtime and of reporting any eventual non-compliance with SLAs in a comprehensive and flexible manner. In this paper, we present the definition of an SLA compliance monitoring infrastructure, which is based on the use of [email protected], its main components and artifacts, and the interactions among them. We place emphasis on the configuration of the artifacts that will enable the monitoring, and we present a prototype that can be used to perform this monitoring. The feasibility of our proposal is illustrated by means of a case study, which shows the use of the components and artifacts in the infrastructure and the configuration of a specific plan with which to monitor the services deployed on the Microsoft Azure© platform

    Towards a monitoring middleware for cloud services

    Full text link
    © 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Cloud Computing represents a new trend in the development and use of software. Many organizations are currently adopting the use of services that are hosted in the cloud by employing the Software as a Service (SaaS) model. Services are typically accompanied by a Service Level Agreement (SLA), which defines the quality terms that a provider offers to its customers. Many monitoring tools have been proposed to report compliance with the SLA. However, they have some limitations when changes to monitoring requirements must be made and because of the complexity involved in capturing low-level raw data from services at runtime. In this paper, we propose the design of a platform-independent monitoring middleware for cloud services, which supports the monitoring of SLA compliance and provides a report containing SLA violations that may help stakeholders to make decisions regarding how to improve the quality of cloud services. Moreover, our middleware definition is based on the use of [email protected], which allows the dynamic change of quality requirements and/or the dynamic selection of different metric operationalizations (i.e., calculation formulas) with which to measure the quality of services. In order to demonstrate the feasibility of our approach, we show the instantiation of the proposed middleware that can be used to monitor services when deployed on the Microsoft Azure© platform.This research is supported by the Value@Cloud project (TIN2013-46300-R); the Scholarship Program Senescyt - Ecuador; University of Cuenca – Ecuador; and the Microsoft Azure for Research Award ProgramCedillo Orellana, IP.; JimĂ©nez GĂłmez, J.; Abrahao Gonzales, SM.; Insfrán Pelozo, CE. (2015). Towards a monitoring middleware for cloud services. IEEE. https://doi.org/10.1109/SCC.2015.68

    Continuous Deployment of Trustworthy Smart IoT Systems.

    Get PDF
    While the next generation of IoT systems need to perform distributed processing and coordinated behaviour across IoT, Edge and Cloud infrastructures, their development and operation are still challenging. A major challenge is the high heterogeneity of their infrastructure, which broadens the surface for security attacks and increases the complexity of maintaining and evolving such complex systems. In this paper, we present our approach for Generation and Deployment of Smart IoT Systems (GeneSIS) to tame this complexity. GeneSIS leverages model-driven engineering to support the DevSecOps of Smart IoT Systems (SIS). More precisely, GeneSIS includes: (i) a domain specific modelling language to specify the deployment of SIS over IoT, Edge and Cloud infrastructure with the necessary concepts for security and privacy; and (ii) a [email protected] engine to enact the orchestration, deployment, and adaptation of these SIS. The results from our smart building case study have shown that GeneSIS can support security by design from the development (via deployment) to the operation of IoT systems and back again in a DevSecOps loop. In other words, GeneSIS enables IoT systems to keep up security and adapt to evolving conditions and threats while maintaining their trustworthiness.The research leading to these results has received funding from the European Commission’s H2020 Programme under grant agreement numbers 780351 (ENACT)
    • …
    corecore