Towards critical event monitoring, detection and prediction for self-adaptive future Internet applications

The Future Internet (FI) will be composed of a multitude of diverse types of services that offer flexible, remote access to software features, content, computing resources, and middleware solutions through different cloud delivery models, such as IaaS, PaaS and SaaS. Ultimately, this means that loosely coupled Internet services will form a comprehensive base for developing value added applications in an agile way. Unlike traditional application development, which uses computing resources and software components under local administrative control, FI applications will thus strongly depend on third-party services. To maintain their quality of service, those applications therefore need to dynamically and autonomously adapt to an unprecedented level of changes that may occur during runtime. In this paper, we present our recent experiences on monitoring, detection, and prediction of critical events for both software services and multimedia applications. Based on these findings we introduce potential directions for future research on self-adaptive FI applications, bringing together those research directions

Monitoring the service-based system lifecycle with SALMon

Los Sistemas Basados en Servicios (SBS) son sistemas software altamente dinámicos compuestos por un conjunto de servicios web provenientes de distintos, y posiblemente heterogéneos, proveedores. En contraste con otros sistemas software tradicionales, el comportamiento dinámico de los SBS requiere de información actualizada sobre la calidad de servicio (QoS) para poder actuar i administrar correctamente las actividades en las distintas fases del ciclo de vida de los SBS (p.e., selección de servicios, despliegue, evaluación de niveles de acuerdo de servicio –SLA–, y adaptación). [...] Para cubrir esta brecha de investigación, presentamos SALMon, una plataforma de monitorización de servicios versátil que provee información acerca de la QoS según la forma y enfoque adecuado para las distintas actividades del ciclo de vida.Peer ReviewedPostprint (published version

Evaluator services for optimised service placement in distributed heterogeneous cloud infrastructures

Optimal placement of demanding real-time interactive applications in a distributed heterogeneous cloud very quickly results in a complex tradeoff between the application constraints and resource capabilities. This requires very detailed information of the various requirements and capabilities of the applications and available resources. In this paper, we present a mathematical model for the service optimization problem and study the concept of evaluator services as a flexible and efficient solution for this complex problem. An evaluator service is a service probe that is deployed in particular runtime environments to assess the feasibility and cost-effectiveness of deploying a specific application in such environment. We discuss how this concept can be incorporated in a general framework such as the FUSION architecture and discuss the key benefits and tradeoffs for doing evaluator-based optimal service placement in widely distributed heterogeneous cloud environments

Mercury: using the QuPreSS reference model to evaluate predictive services

Nowadays, lots of service providers offer predictive services that show in advance a condition or occurrence about the future. As a consequence, it becomes necessary for service customers to select the predictive service that best satisfies their needs. The QuPreSS reference model provides a standard solution for the selection of predictive services based on the quality of their predictions. QuPreSS has been designed to be applicable in any predictive domain (e.g., weather forecasting, economics, and medicine). This paper presents Mercury, a tool based on the QuPreSS reference model and customized to the weather forecast domain. Mercury measures weather predictive services' quality, and automates the context-dependent selection of the most accurate predictive service to satisfy a customer query. To do so, candidate predictive services are monitored so that their predictions can be eventually compared to real observations obtained from a trusted source. Mercury is a proof-of-concept of QuPreSS that aims to show that the selection of predictive services can be driven by the quality of their predictions. Throughout the paper, we show how Mercury was built from the QuPreSS reference model and how it can be installed and used.Peer ReviewedPostprint (author's final draft

An Exploratory Study of Field Failures

Field failures, that is, failures caused by faults that escape the testing phase leading to failures in the field, are unavoidable. Improving verification and validation activities before deployment can identify and timely remove many but not all faults, and users may still experience a number of annoying problems while using their software systems. This paper investigates the nature of field failures, to understand to what extent further improving in-house verification and validation activities can reduce the number of failures in the field, and frames the need of new approaches that operate in the field. We report the results of the analysis of the bug reports of five applications belonging to three different ecosystems, propose a taxonomy of field failures, and discuss the reasons why failures belonging to the identified classes cannot be detected at design time but shall be addressed at runtime. We observe that many faults (70%) are intrinsically hard to detect at design-time

Assessing open source communities' health using Service Oriented Computing concepts

© 2014 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.The quality of Open Source Software products is directly related to its community's health. To date, health analysis is made accessing available data repositories or using software management tools that are often too static or ad hoc. To address this issue, we propose to adopt principles and methods from the Service Oriented Computing field. Particularly, we propose to adapt the concepts of quality service and service level agreement, and propose to reuse the existing body of knowledge and techniques from SOC monitoring. To demonstrate the feasibility of the approach, we use a service monitoring framework called SALMonOSS as a proof of concept to realize the implementation of the proposal.Peer ReviewedPostprint (author's final draft

An Exploratory Study of Field Failures

Field failures, that is, failures caused by faults that escape the testing phase leading to failures in the field, are unavoidable. Improving verification and validation activities before deployment can identify and timely remove many but not all faults, and users may still experience a number of annoying problems while using their software systems. This paper investigates the nature of field failures, to understand to what extent further improving in-house verification and validation activities can reduce the number of failures in the field, and frames the need of new approaches that operate in the field. We report the results of the analysis of the bug reports of five applications belonging to three different ecosystems, propose a taxonomy of field failures, and discuss the reasons why failures belonging to the identified classes cannot be detected at design time but shall be addressed at runtime. We observe that many faults (70%) are intrinsically hard to detect at design-time

Verifying predictive services'quality with Mercury

Due to the success of service technology, there are lots of services nowadays that make predictions about the future in domains such as weather forecast, stock market and bookmakers. The value delivered by these predictive services relies on the quality of their predictions. This paper presents Mercury, a tool that measures predictive service quality in the domain of weather forecast, and automates the context-dependent selection of the most accurate predictive service to satisfy a customer query. To do so, candidate predictive services are monitored so that their predictions can be eventually compared with real observations obtained from some trusted source. Mercury is a proof-of-concept to show that the selection of predictive services can be driven by the quality of their predictions. Its service-oriented architecture (SOA) aims to support the easy adaptation to other prediction domains and makes feasible its integration in self-adaptive SOA systems, as well as its direct use by end-users as a classical web application. Thoughout the paper, we show how Mercury was built.Preprin

A constraint-based approach to quality assurance in service choreographies.

Knowledge about the quality characteristics (QoS) of service com- positions is crucial for determining their usability and economic value. Ser- vice quality is usually regulated using Service Level Agreements (SLA). While end-to-end SLAs are well suited for request-reply interactions, more complex, decentralized, multiparticipant compositions (service choreographies) typ- ically involve multiple message exchanges between stateful parties and the corresponding SLAs thus encompass several cooperating parties with interde- pendent QoS. The usual approaches to determining QoS ranges structurally (which are by construction easily composable) are not applicable in this sce- nario. Additionally, the intervening SLAs may depend on the exchanged data. We present an approach to data-aware QoS assurance in choreographies through the automatic derivation of composable QoS models from partici- pant descriptions. Such models are based on a message typing system with size constraints and are derived using abstract interpretation. The models ob- tained have multiple uses including run-time prediction, adaptive participant selection, or design-time compliance checking. We also present an experimen- tal evaluation and discuss the benefits of the proposed approach