Performance characterization of black boxes with self-controlled load injection for simulation-based sizing

International audienceSizing and capacity planning are key issues that must be addressed by anyone wanting to ensure a distributed system will sustain an expected workload. Solutions typically consist in either benchmarking,or modeling and simulating the target system. However, full-scale benchmarking may be too costly and almost impossible, while the granularity of modeling is often limited by the huge complexity and the lack of information about the system. To extract a model for this kind of system, we propose a methodology that combines both solutions by first identifying a middle-grain model made of interconnected black boxes, and then to separately characterize the performance and resource consumption of these black boxes. Then, we present two important issues : saturation and stability, that are key to system capacity characterization. To experiment our methodology, we propose a component-based supporting architecture, introducing control theory issues in a general approach to autonomic computing infrastructures

Model-Based Performance Anticipation in Multi-tier Autonomic Systems: Methodology and Experiments

http://www.thinkmind.org/download.php?articleid=netser_v3_n34_2010_3International audienceThis paper advocates for the introduction of perfor- mance awareness in autonomic systems. Our goal is to introduce performance prediction of a possible target configuration when a self-* feature is planning a system reconfiguration. We propose a global and partially automated process based on queues and queuing networks modelling. This process includes decomposing a distributed application into black boxes, identifying the queue model for each black box and assembling these models into a queuing network according to the candidate target configuration. Finally, performance prediction is performed either through simulation or analysis. This paper sketches the global process and focuses on the black box model identification step. This step is automated thanks to a load testing platform enhanced with a workload control loop. Model identification is based on statistical tests. The identified models are then used in performance prediction of autonomic system configurations. This paper describes the whole process through a practical experiment with a multi-tier application

Model-Based Performance Anticipation in Multi-tier Autonomic Systems: Methodology and Experiments

http://www.thinkmind.org/download.php?articleid=netser_v3_n34_2010_3International audienceThis paper advocates for the introduction of perfor- mance awareness in autonomic systems. Our goal is to introduce performance prediction of a possible target configuration when a self-* feature is planning a system reconfiguration. We propose a global and partially automated process based on queues and queuing networks modelling. This process includes decomposing a distributed application into black boxes, identifying the queue model for each black box and assembling these models into a queuing network according to the candidate target configuration. Finally, performance prediction is performed either through simulation or analysis. This paper sketches the global process and focuses on the black box model identification step. This step is automated thanks to a load testing platform enhanced with a workload control loop. Model identification is based on statistical tests. The identified models are then used in performance prediction of autonomic system configurations. This paper describes the whole process through a practical experiment with a multi-tier application

Automatic performance modelling of black boxes targetting self-sizing

Modern distributed systems are characterized by a growing complexity of their architecture, functionalities and workload. This complexity, and in particular significant workloads, often lead to quality of service loss, saturation and sometimes unavailability of on-line services. To avoid troubles caused by important workloads and fulfill a given level of quality of service (such as response time), systems need to \emph{self-manage}, for instance by tuning or strengthening one tier through replication. This autonomic feature requires performance modelling of systems. In this objective, we developed an automatic identification process providing a queuing model for a part of distributed system considered as black box. This process is a part of a general approach targetting self-sizing for distributed systems and is based on a theoretical and experimental approach. In this report, we show how to derive automatically the performance model of one black box considered as a constituent of a distributed system, starting from load injection experiments. This model is determined progressively, using self-regulated test injections, from statistical analysis of measured metrics, namely response time. This process is illustrated through experimental results

Privacy Preserving Face Recognition in Cloud Robotics : A Comparative Study

Abstract: Real-time robotic applications encounter the robot on board resources’ limitations. The speed of robot face recognition can be improved by incorporating cloud technology. However, the transmission of data to the cloud servers exposes the data to security and privacy attacks. Therefore, encryption algorithms need to be set up. This paper aims to study the security and performance of potential encryption algorithms and their impact on the deep-learning-based face recognition task’s accuracy. To this end, experiments are conducted for robot face recognition through various deep learning algorithms after encrypting the images of the ORL database using cryptography and image-processing based algorithms

Self-scalable Benchmarking as a Service with Automatic Saturation Detection

Part 4: ServicesInternational audienceSoftware applications providers have always been required to perform load testing prior to launching new applications. This crucial test phase is expensive in human and hardware terms, and the solutions generally used would benefit from further development. In particular, designing an appropriate load profile to stress an application is difficult and must be done carefully to avoid skewed testing. In addition, static testing platforms are exceedingly complex to set up. New opportunities to ease load testing solutions are becoming available thanks to cloud computing. This paper describes a Benchmark-as-a-Service platform based on: (i) intelligent generation of traffic to the benched application without inducing thrashing (avoiding predefined load profiles), (ii) a virtualized and self-scalable load injection system. This platform was found to reduce the cost of testing by 50% compared to more commonly used solutions. It was experimented on the reference JEE benchmark RUBiS. This involved detecting bottleneck tiers

Automatic performance modelling of black boxes towards self-sizing

De nos jours, les systèmes distribués sont caractérisés par une complexité croissante de l'architecture, des fonctionnalités et de la charge soumise. Cette complexité induit souvent une perte de la qualité de service offerte, ou une saturation des ressources, voire même l'indisponibilité des services en ligne, en particulier lorsque la charge est importante. Afin d'éviter les désagrèments causés par d'importantes charges et remplir le niveau attendu de la qualité de service, les systèmes nécessitent une auto-gestion, en optimisant par exemple un tier ou en le renforçant à travers la réplication. Cette propriété autonome requiert une modélisation des performances de ces systèmes. Visant cet objectif, nous développons un framework basé sur une méthodologie théorique et expérimentale d'identification automatique de modèle et de dimensionnement, fournissant en premier un modèle de réseau de file d'attente pour un système distribué. Ensuite, ce Modèle est utilisé au sein de notre framwork pour dimensionner le système à travers une analyse ou une simulation du réseau de file d'attente.Modern distributed systems are characterized by a growing complexity of their architecture, functionalities and workload. This complexity, and in particular significant workloads, often lead to quality of service loss, saturation and sometimes unavailability of on-line services. To avoid troubles caused by important workloads and fulfill a given level of quality of service (such as response time), systems need to self-manage, for instance by tuning or strengthening one tier through replication. This autonomic feature requires performance modelling of systems. In this objective, we developed a framework based on a theoretical and experimental approach for automatic identification process and sizing . This framework provid a queuing model for a distributed system. Then, this model is used in our Framwork to size the system through an analysis or simulation

Vers une modélisation et un dimensionnement automatique des systèmes répartis

Modern distributed systems are characterized by a growing complexity of their architecture, functionalities and workload. This complexity, and in particular significant workloads, often lead to quality of service loss, saturation and sometimes unavailability of on-line services. To avoid troubles caused by important workloads and fulfill a given level of quality of service (such as response time), systems need to self-manage, for instance by tuning or strengthening one tier through replication. This autonomic feature requires performance modelling of systems. In this objective, we developed a framework based on a theoretical and experimental approach for automatic identification process and sizing . This framework provid a queuing model for a distributed system. Then, this model is used in our Framwork to size the system through an analysis or simulation.De nos jours, les systèmes distribués sont caractérisés par une complexité croissante de l'architecture, des fonctionnalités et de la charge soumise. Cette complexité induit souvent une perte de la qualité de service offerte, ou une saturation des ressources, voire même l'indisponibilité des services en ligne, en particulier lorsque la charge est importante. Afin d'éviter les désagrèments causés par d'importantes charges et remplir le niveau attendu de la qualité de service, les systèmes nécessitent une auto-gestion, en optimisant par exemple un tier ou en le renforçant à travers la réplication. Cette propriété autonome requiert une modélisation des performances de ces systèmes. Visant cet objectif, nous développons un framework basé sur une méthodologie théorique et expérimentale d'identification automatique de modèle et de dimensionnement, fournissant en premier un modèle de réseau de file d'attente pour un système distribué. Ensuite, ce Modèle est utilisé au sein de notre framwork pour dimensionner le système à travers une analyse ou une simulation du réseau de file d'attente

Vers une modélisation et un dimensionnement automatique des systèmes répartis

De nos jours, les systèmes distribués sont caractérisés par une complexité croissante de l'architecture, des fonctionnalités et de la charge soumise. Cette complexité induit souvent une perte de la qualité de service offerte, ou une saturation des ressources, voire même l'indisponibilité des services en ligne, en particulier lorsque la charge est importante. Afin d'éviter les désagrèments causés par d'importantes charges et remplir le niveau attendu de la qualité de service, les systèmes nécessitent une auto-gestion, en optimisant par exemple un tier ou en le renforçant à travers la réplication. Cette propriété autonome requiert une modélisation des performances de ces systèmes. Visant cet objectif, nous développons un framework basé sur une méthodologie théorique et expérimentale d'identification automatique de modèle et de dimensionnement, fournissant en premier un modèle de réseau de file d'attente pour un système distribué. Ensuite, ce Modèle est utilisé au sein de notre framwork pour dimensionner le système à travers une analyse ou une simulation du réseau de file d'attente.Modern distributed systems are characterized by a growing complexity of their architecture, functionalities and workload. This complexity, and in particular significant workloads, often lead to quality of service loss, saturation and sometimes unavailability of on-line services. To avoid troubles caused by important workloads and fulfill a given level of quality of service (such as response time), systems need to self-manage, for instance by tuning or strengthening one tier through replication. This autonomic feature requires performance modelling of systems. In this objective, we developed a framework based on a theoretical and experimental approach for automatic identification process and sizing . This framework provid a queuing model for a distributed system. Then, this model is used in our Framwork to size the system through an analysis or simulation.SAVOIE-SCD - Bib.électronique (730659901) / SudocGRENOBLE1/INP-Bib.électronique (384210012) / SudocGRENOBLE2/3-Bib.électronique (384219901) / SudocSudocFranceF