A goal-oriented approach for adaptive SLA monitoring: A cloud provider case study

Toueir, Antoine; Broisin, Julien; Sibilla, Michelle

doi:10.1109/latincloud.2013.6842223

Cited by 6 publications

(4 citation statements)

References 17 publications

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“…To facilitate this task, we investigated the monitoring aspects that are subject to adaptation. As a result, we have identified various leaf goals belonging to four dimensions (i.e., Spatial, Metric, Temporal, Exchange) [16]. In Section 5, we pursuit in proposing monitoring adaptation patterns falling into those dimensions, in order to assist human administrators in refining goals.…”

Section: A Goal-oriented Methodology For Adaptive Quality-oriented Momentioning

confidence: 99%

“…During the first time-slot, we use the temporal pattern to relax polling & exporting by updating their periods (Update Polling & Exporting Period in Figure 3) with respect to the highest freshness range (6 seconds). If delivering freshness violates the highest freshness, that would be a result of overloading manager [16], thus we apply the spatial pattern as a second alternative, and consequently, a new autonomic manager will be deployed to assist the overloaded one (Launch Delegated Manager, Expand Perimeter & Shrink Perimeter ). As a third alternative, and in case that the overloaded autonomic manager monitors non-SLAs metrics (i.e., physical servers healthiness), the metric pattern could be applied to transfer them to other manager, in order to relax the first one (Add & Remove Aspects).…”

Section: Case-studymentioning

confidence: 99%

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Goal-Oriented Monitoring Adaptation: Methodology and Patterns

Toueir

Broisin

Sibilla

2014

Monitoring and Securing Virtualized Networks and Services

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Abstract. This paper argues that autonomic systems need to make their distributed monitoring adaptive in order to improve their "comprehensive" resulting quality; that means both the Quality of Service (QoS), and the Quality of Information (QoI). Thus, we propose a methodology to design monitoring adaptation based on high level objectives (goals) related to the management of quality requirements. One of the advantages of adopting a methodological approach, is that monitoring reconfiguration will be conducted through a consistent adaptation logic. Starting from a model-guided monitoring framework, we introduce our methodology to assist human administrators in eliciting the appropriate quality goals piloting the monitoring. Moreover, some monitoring adaptation patterns falling into reconfiguration dimensions are suggested and exploited in a cloud provider case-study illustrating the adaptation of Quality-Oriented monitoring.

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Section: A Goal-oriented Methodology For Adaptive Quality-oriented Momentioning

confidence: 99%

Section: Case-studymentioning

confidence: 99%

Goal-Oriented Monitoring Adaptation: Methodology and Patterns

Toueir

Broisin

Sibilla

2014

Monitoring and Securing Virtualized Networks and Services

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“…However, it is often expensive and intrusive. Thus, the design of a monitoring system (i.e., the software system that implements monitoring capabilities) usually involves tradeoffs between the impact caused by the action of monitoring and its expected quality of results, such as data accuracy, freshness and coverage, among others [6,7]. In addition, a monitoring system is exposed to a diversity of runtime events, e.g., structural or operational changes on the System under Monitoring (SuM), faults on the monitoring system's elements or the emergence of new monitoring requirements.…”

Section: Introductionmentioning

confidence: 99%

Adaptive monitoring: A systematic mapping

Zavala¹,

Franch²,

Marco³

2019

Information and Software Technology

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Context: Adaptive monitoring is a method used in a variety of domains for responding to changing conditions. It has been applied in different ways, from monitoring systems' customization to re-composition, in different application domains. However, to the best of our knowledge, there are no studies analyzing how adaptive monitoring differs or resembles among the existing approaches.Objective: To characterize the current state of the art on adaptive monitoring, specifically to: a) identify the main concepts in the adaptive monitoring topic; b) determine the demographic characteristics of the studies published in this topic; c) identify how adaptive monitoring is conducted and evaluated by the different approaches; d) identify patterns in the approaches supporting adaptive monitoring. Method:We have conducted a systematic mapping study of adaptive monitoring approaches following recommended practices. We have applied automatic search and snowballing sampling on different sources and used rigorous selection criteria to retrieve the final set of papers. Moreover, we have used an existing qualitative analysis method for extracting relevant data from studies. Finally, we have applied data mining techniques for identifying patterns in the solutions. Results:We have evaluated 110 studies organized in 81 approaches that support adaptive monitoring. By analyzing them, we have: (1) surveyed related terms and definitions of adaptive monitoring and proposed a generic one; (2) visualized studies' demographic data and arranged the studies into approaches; (3) characterized the main approaches' contributions; (4) determined how approaches conduct the adaptation process and evaluate their solutions. Conclusions:This cross-domain overview of the current state of the art on adaptive monitoring may be a solid and comprehensive baseline for researchers and practitioners in the field. Especially, it may help in identifying opportunities of research; for instance, the need of proposing generic and flexible software engineering solutions for supporting adaptive monitoring in a variety of systems.

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“…However, it is often expensive and intrusive. Thus, the design of a monitoring system (i.e., the software system that implements monitoring capabilities) usually involves tradeoffs between the impact caused by the action of monitoring and its expected quality of results, such as data accuracy, freshness and coverage, among others [8], [9]. In addition, a monitoring system is exposed to a diversity of runtime events, e.g., structural or operational changes on the System under Monitoring (SuM), faults on the monitoring system's elements or the emergence of new monitoring requirements.…”

Section: Study On Adaptive Monitoringmentioning

confidence: 99%

Towards adaptative monitoring for self-adaptative systems

Zavala Rodríguez

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Nowadays, most of the approaches supporting self-adaptive systems (SASs) rely on static feedback control loops, for managing their adaptation process. One of the most popular feedback loops is the MAPE-K loop. In this loop, the Monitor element plays a crucial role since the quality of the monitoring data (e.g., timeliness, freshness, accuracy, availability, etc.) affects directly the performance of the rest of the elements of the loop, and in consequence the quality of the resulting adaptation decisions. Assuming static feedback loops implies that the structure and behavior of the elements of the loop should be determined at design-time and cannot change at runtime, i.e., in the case of the Monitor, systems’ owners should know everything to be monitored at design time. If that the case, current self-adaptive systems would not be able to react to unpredictable runtime events such as faults or changing requirements. Motivated by this fact, in this thesis, we address the automatic runtime adaptation of SASs’ feedback control loops, particularly the Monitor element, in order to respond to changes in the systems, the environment and the elements of the loop themselves. Concretely, we have presented HAFLoop, an architectural proposal for supporting the adaptation of the MAPE-K loop at runtime. We have identified open research challenges affecting SASs’ and feedback loops’ adaptation and analyzed whether and how existing approaches address those challenges. We have also studied how state-of-the-art approaches support adaptive monitoring in current monitoring systems. Although great efforts have been done for supporting the adaptation of feedback loops in SASs’, none of the state-of-the-art solutions satisfactorily addresses all the open research challenges. HAFLoop, in conjunction with its implementation in the form of a framework named HAFLoop4J, is a generic and reusable solution that easies the design and development of adaptive feedback loops, from higher to lower levels. Our solution enables loops to support different types of adaptation in a variety of settings. HAFLoop has been evaluated in different scenarios and in both simulation and real environments. The evaluations of HAFLoop have been conducted in the domain of smart vehicles with very promising results. Actualmente, la mayoría de las soluciones que soportan sistemas de software autoadaptativos (SASs) se basan en bucles estáticos de control para administrar el proceso de adaptación. Uno de los bucles más populares es el bucle MAPE-K. En este bucle, el elemento Monitor desempeña un papel crucial ya que la calidad de los datos monitoreados (por ejemplo, su precisión, su disponibilidad, etc.) afecta directamente el rendimiento del resto de los elementos del bucle, y en consecuencia el de la calidad de las decisiones de adaptación resultantes. Asumir que los bucles de los SASs son estáticos, implica que la estructura y comportamiento de sus elementos deben determinarse al momento de su diseño y no pueden cambiarse en tiempo de ejecución. Por ejemplo, en el caso del Monitor, todo lo que se ha de monitorear en tiempo de ejecución y como se ha de obtener, debe conocerse al momento de diseñar el sistema. En este escenario, los sistemas autoadaptativos actuales no serían capaces de reaccionar ante eventos impredecibles en tiempo de ejecución, tales como fallas de sensores o cambios en los requisitos. Motivados por este hecho, en esta tesis, abordamos la adaptación automática en tiempo de ejecución de los bucles de control para los SASs, en particular la adaptación del elemento Monitor, con el fin de responder a cambios en los sistemas, su entorno y cambios en los propios elementos del bucle. Concretamente, presentamos HAFLoop, una propuesta arquitectónica para la adaptación del bucle MAPE-K en tiempo de ejecución. Hemos identificado desafíos de investigación que afectan actualmente la adaptación de los SASs así como de los bucles que conducen su adaptación, también hemos analizado si las propuestas existentes los abordan y como lo hacen. Hemos estudiado también cómo las propuestas actuales abordan el monitoreo adaptativo en los sistemas de monitorización existentes. Si bien se han realizado grandes esfuerzos para habilitar la adaptación de bucles SASs, ninguna de las propuestas que evaluamos aborda satisfactoriamente todos los desafíos que aún afectan este campo de estudio. HAFLoop, junto con su implementación en forma de un marco denominado HAFLoop4J, es una solución genérica y reutilizable que facilita el diseño y el desarrollo de bucles adaptativos para SASs, desde los niveles técnicos más altos, como el diseño de componentes genéricos, a los más bajos, como la implementación de componentes específicos para una aplicación en particular. Nuestra solución permite que los bucles se adapten de diferentes maneras y en una variedad de configuraciones distintas. HAFLoop se ha evaluado en diferentes escenarios y en entornos de simulación así como reales. Las evaluaciones de HAFLoop se han realizado en el dominio de vehículos inteligentes con resultados muy prometedores.

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A goal-oriented approach for adaptive SLA monitoring: A cloud provider case study

Cited by 6 publications

References 17 publications

Goal-Oriented Monitoring Adaptation: Methodology and Patterns

Goal-Oriented Monitoring Adaptation: Methodology and Patterns

Adaptive monitoring: A systematic mapping

Towards adaptative monitoring for self-adaptative systems

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