Experience with model-based performance, reliability and adaptability assessment of a complex industrial architecture

In this paper, we report on our experience with the application of validated models to assess performance, reliability, and adaptability of a complex mission critical system that is being developed to dynamically monitor and control the position of an oil-drilling platform. We present real-time modeling results that show that all tasks are schedulable. We performed stochastic analysis of the distribution of task execution time as a function of the number of system interfaces. We report on the variability of task execution times for the expected system configurations. In addition, we have executed a system library for an important task inside the performance model simulator. We report on the measured algorithm convergence as a function of the number of vessel thrusters. We have also studied the system architecture adaptability by comparing the documented system architecture and the implemented source code. We report on the adaptability findings and the recommendations we were able to provide to the system’s architect. Finally, we have developed models of hardware and software reliability. We report on hardware and software reliability results based on the evaluation of the system architecture

Pollen-ovule relation in Adesmia tristis and reflections on the seed–ovule ratio by interaction with pollinators in two vertical strata

The vertical distribution of pollinators is an important component in the foraging pattern of plants strata, and it influences the reproductive system (pollen/ovule ratio) and seed/ovule ratio. Niches in two different strata from Adesmia tristis Vogel were evaluated in these aspects. This plant is an endemic shrub from the Campos de Cima da Serra in Southern Brazil. The studies were carried out from January 2010, to January 2011, at Pró-Mata/PUCRS (Catholic University of Rio Grande do Sul) (29°27'-29°35'S and 50°08'-50°15'W), São Francisco de Paula, sate of Rio Grande do Sul, Brazil. Breeding system of A. tristis is mandatory allogamy. The vertical profile in A. tristis has differentiated foraging niches among the most common pollinators. Bees of Megachile genus forage in the upper stratum, and representative bees of the Andrenidae family explore the lower stratum. The upper stratum of the vertical profile had more contribution to seed production. Adesmia tristis showed evidence of pollination deficitA distribuição vertical dos polinizadores é um importante componente no padrão de forrageamento nos estratos das plantas e influencia o sistema reprodutivo (relação pólen/ óvulo) e a razão semente/óvulo. Nichos em dois estratos diferentes de Adesmia tristis Vogel foram avaliados quanto a esses aspectos. Essa planta é um arbusto endêmico dos campos de Cima da Serra no Sul do Brasil. Os estudos ocorreram de janeiro de 2010 a janeiro de 2011, no Pró- Mata/PUCRS (Pontifícia Universidade Católica do Rio Grande do Sul) (29°27'-29°35'S e 50°08'-50°15'W), São Francisco de Paula, estado do Rio Grande do Sul, Brasil. O sistema reprodutivo de A. tristis é alogamia obrigatória. O perfil vertical em A. tristis possui diferentes nichos de forrageamento entre os polinizadores mais comuns. Abelhas do gênero Megachile forrageiam no estrato superior e as abelhas representantes da família Andrenidae exploram o estrato inferior. O estrato superior do perfil vertical contribui mais na produção de sementes. Adesmia tristis apresentou evidências de déficit de polinizaçã

Power management by load forecasting in web server clusters

The complexity and requirements of web applications are increasing in order to meet more sophisticated business models (web services and cloud computing, for instance). For this reason, characteristics such as performance, scalability and security are addressed in web server cluster design. Due to the rising energy costs and also to environmental concerns, energy consumption in this type of system has become a main issue. This paper shows energy consumption reduction techniques that use a load forecasting method, combined with DVFS (Dynamic Voltage and Frequency Scaling) and dynamic configuration techniques (turning servers on and off), in a soft real-time web server clustered environment. Our system promotes energy consumption reduction while maintaining user's satisfaction with respect to request deadlines being met. The results obtained show that prediction capabilities increase the QoS (Quality of Service) of the system, while maintaining or improving the energy savings over state-of-the-art power management mechanisms. To validate this predictive policy, a web application running a real workload profile was deployed in an Apache server cluster testbed running Linux. © 2011 Springer Science+Business Media, LLC

Power and performance control of soft real-time web server clusters

This paper presents a novel way to control power consumption and performance in a multi-tier server cluster designed for e-commerce applications. The requests submitted to these server systems have a soft real-time constraint, given that although some can miss a pre-defined deadline, the system can still meet an agreed upon performance level. Clusters of servers are extensively used nowadays and, with the steep increase in the total power consumption in these systems, economic and environmental problems have been raised. We present ways of decreasing power expenditure, and show the implementation of a SISO (Single Input Single Output) controller that acts on the speed of all server nodes capable of dynamic voltage and frequency scaling (DVFS), with QoS (Quality of Service) being the reference setpoint. For QoS, we use the request tardiness, defined as the ratio of the end-to-end response time to the deadline, rather than the usual metric that counts missed deadlines. We adjust the servers operating frequencies to guarantee that a pre-defined p-quantile of the tardiness probability distribution of the requests meet their deadlines. Doing so we can guarantee that the QoS will be statistically p. We test this technique in a prototype multi-tier cluster, using open software, commodity hardware, and a standardized e-commerce application to generate a workload close to that of the real world. The main contribution of this paper is to empirically show the robustness of the SISO controller, presenting a sensibility analysis of its parameters. Experimental results show that our implementation outperforms other published state-of-the-art cluster implementations. © 2010 Elsevier B.V. All rights reserved

Power optimization for dynamic configuration in heterogeneous web server clusters

To reduce the environmental impact, it is essential to make data centers green, by turning off servers and tuning their speeds for the instantaneous load offered, that is, determining the dynamic configuration in web server clusters. We model the problem of selecting the servers that will be on and finding their speeds through mixed integer programming; we also show how to combine such solutions with control theory. For proof of concept, we implemented this dynamic configuration scheme in a web server cluster running Linux, with soft real-time requirements and QoS control, in order to guarantee both energy-efficiency and good user experience. In this paper, we show the performance of our scheme compared to other schemes, a comparison of a centralized and a distributed approach for QoS control, and a comparison of schemes for choosing speeds of servers. © 2009 Elsevier Inc. All rights reserved

Optimized management of power and performance for virtualized heterogeneous server clusters

This paper proposes and evaluates an approach for power and performance management in virtualized server clusters. The major goal of our approach is to reduce power consumption in the cluster while meeting performance requirements. The contributions of this paper are: (1) a simple but effective way of modeling power consumption and capacity of servers even under heterogeneous and changing workloads, and (2) an optimization strategy based on a mixed integer programming model for achieving improvements on power-efficiency while providing performance guarantees in the virtualized cluster. In the optimization model, we address application workload balancing and the often ignored switching costs due to frequent and undesirable turning servers on/off and VM relocations. We show the effectiveness of the approach applied to a server cluster test bed. Our experiments show that our approach conserves about 50 of the energy required by a system designed for peak workload scenario, with little impact on the applications' performance goals. Also, by using prediction in our optimization strategy, further QoS improvement was achieved. © 2011 IEEE