Stochastic Models for Planning VLE Moodle Environments based on Containers and Virtual Machines

Moodle Virtual Learning Environments (VLEs) represent tools of a pedagogical dimension where the teacher uses various resources to stimulate student learning. Content presented in hypertext, audio or vídeo formats can be adopted as a means to facilitate the learning. These platforms tend to produce high processing rates on servers, large volumes of data on the network and, consequently, degrade performance, increase energy consumption and costs. However, to provide eficiente sharing of computing resources and at the same time minimize financial costs, these VLE platforms typically run on virtualized infrastructures such as Virtual Machines (VM) or containers, which have advantages and disadvantages. Stochastic models, such as stochastic Petri nets (SPNs), can be used in the modeling and evaluation of such environments. Therefore, this work aims to use analytical modeling through SPNs to assess the performance, energy consumption and cost of environments based on containers and VMs. Metrics such as throughput, response time, energy consumption and cost are collected and analyzed. The results revealed that, for example, a cluster with 10 replicas, occupied at their maximum capacity, can generate a 46.54% reduction in energy consumption if containers are used. Additionally, we validate the accuracy of the analytical models by comparing their results with the results obtained in a real infrastructure

Dimensioning the relationship between availability and data center energy flow metrics

The advancement of technology and the growing number of applications available to network users have increased the demand for services hosted in cloud environments. In 2020, more than 4 billion of people access these services through the Internet, a value 7% higher in comparison to the same period in 2019. To support the demand for such services, an environment that provides such conditions for applications available whenever needed has grown in importance. These environments are generally available from large data centers, which consume large amounts of electricity to provide such demand service capacity. In this context, this work proposes an integrated and dynamic strategy that demonstrates the impact of the availability on the energyconsumption of the devices that compose the data center system architecture. In order to accomplish this, colored Petri net models were proposed for quantifying the cost, environmental impact and availability of the electric energy infrastructure ofdata centers. The models presented in this work are supported by the developed prototype. Two case studies illustrate the applicability of the proposed models and strategy. Significant results were obtained, showing an increase close to 100% in the system availability, with practically the same operational cost and environmental impact

Evaluating the impact of maintenance policies associated to SLA contracts on the dependability of data centers electrical infrastructures

Due to the growth of cloud computing, data center environment has grown in importance and in use. Data centers are responsible for maintaining and processing several critical-value applications. Therefore, data center infrastructures must be evaluated in order to improve the high availability and reliability demanded for such environments. This work adopts Stochastic Petri Nets (SPN) to evaluate the impact of maintenance policies on the data center dependability. The main goal is to analyze maintenance policies, associated to SLA contracts, and to propose improvements. In order to accomplish this, an optimization strategy that uses Euclidean distance is adopted to indicate the most appropriate solution assuming conflicting requirements (e.g., cost and availability). To illustrate the applicability of the proposed models and approach, this work presents case studies comparing different SLA contracts and maintenance policies (preventive and corrective) applied on data center electrical infrastructures

Energy consumption and execution time estimation of embedded system applications

Nos últimos anos, a redução do consumo de energia das aplicações dos sistemas embarcados tem recebido uma grande atenção da comunidade científica, visto que, como o tempo de resposta e o baixo consumo de energia são requisitos conflitantes, esses estudos tornam-se altamente necessários. Nesse contexto, é proposta uma metodologia aplicada nas fases iniciais de projeto para dar suporte às decisões relativas ao consumo de energia e ao desempenho das aplicações desses dispositivos embarcados. Al´em disso, esse trabalho propõe modelos temporizados de eventos discretos que são avaliados através de uma metodologia de simulção estocástica com o objetivo de representar diferentes cenários dos sistemas com facilidade. Dessa forma, para cada cenário ´e preciso decidir o n´umero máximo de simulações e o tamanho de cada rodada da simulação, onde ambos os fatores podem impactar no desempenho para se obter tais estimativas. Essa metodologia considera também, um modelo intermediário que representa a descrição do comportamento do sistema e, é através desse modelo que cenários são analisados. Esse modelo intermediário ´e baseado em redes de Petri coloridas temporizadas que permitem não somente a anáise do software, mas também fornece suporte a um conjunto de métodos bem estabelecidos para verificações de propriedades. É nesse contexto que o software, ALUPAS, responsável por estimar o consumo de energia e o tempo de execução dos sistemas embarcados é apresentado. Por fim, um caso de estudo real, assim como tamb´em, exemplos customizados são apresentados com a finalidade de mostrar a aplicabilidade desse trabalho, onde usuários não especializados não precisam interagir diretamente com o formalismo de redes de Petri

Evaluating the impact of maintenance policies associated to SLA contracts on the dependability of data centers electrical infrastructures

Due to the growth of cloud computing, data center environment has grown in importance and in use. Data centers are responsible for maintaining and processing several critical-value applications. Therefore, data center infrastructures must be evaluated in order to improve the high availability and reliability demanded for such environments. This work adopts Stochastic Petri Nets (SPN) to evaluate the impact of maintenance policies on the data center dependability. The main goal is to analyze maintenance policies, associated to SLA contracts, and to propose improvements. In order to accomplish this, an optimization strategy that uses Euclidean distance is adopted to indicate the most appropriate solution assuming conflicting requirements (e.g., cost and availability). To illustrate the applicability of the proposed models and approach, this work presents case studies comparing different SLA contracts and maintenance policies (preventive and corrective) applied on data center electrical infrastructures

Análise de desempenho do ambiente virtual de aprendizagem na nuvem privada apache cloudstack

Cloud computing is a paradigm that offers computing resources dynamically over the Internet. Universities and schools are increasingly adopting Virtual Learning Environments (VLE) to facilitate student-teacher communication, so there is a need for VLE benchmarking on cloud computing software. This paper performs the performance evaluation of the private cloud VLE. A methodology was proposed to perform the measurements and modeling of the system. The model was developed in stochastic petri net. This paper presents three case studies performed to illustrate the applicability of the proposed methodology and models in a real, lab-mounted environment with Moodle configured in a private cloud with Apache CloudStack. The results show that the performance model was practical and efficient to quantify the metrics of interest.A computação em nuvem é um paradigma que oferece recursos computacionais de forma dinâmica por meio da Internet. As universidades e escolas vêm adotando cada vez mais os Ambientes Virtuais de Aprendizagem (AVA) para facilitar a comunicação entre alunos e professores e, assim, existe uma necessidade de uma avaliação de desempenho do AVA em softwares de computação em nuvem. Dessa forma essa pesquisa visa propor uma estratégia baseada em modelos para a avaliação de desempenho do ambiente virtual de aprendizagem Moodle em nuvem privada. A metodologia para a realização das medições e modelagem do sistema foram desenvolvidos em rede de Petri estocástica. Este trabalho apresenta três estudos de casos realizados para ilustrar a aplicabilidade da metodologia e dos modelos propostos em um ambiente real, montado em laboratório, com o Moodle configurado em uma nuvem privada com Apache CloudStack. Os resultados mostram que o modelo de desempenho foi prático e eficiente para quantificar as métricas de interesse atestando a qualidade do serviço provido além de auxiliar os projetistas a estimar as configurações demandas para dar suporte ao sistema em diferentes situações e com diversos números de usuários

Stochastic Models for Planning VLE Moodle Environments based on Containers and Virtual Machines

Moodle Virtual Learning Environments (VLEs) represent tools of a pedagogical dimension where the teacher uses various resources to stimulate student learning. Content presented in hypertext, audio or vídeo formats can be adopted as a means to facilitate the learning. These platforms tend to produce high processing rates on servers, large volumes of data on the network and, consequently, degrade performance, increase energy consumption and costs. However, to provide eficiente sharing of computing resources and at the same time minimize financial costs, these VLE platforms typically run on virtualized infrastructures such as Virtual Machines (VM) or containers, which have advantages and disadvantages. Stochastic models, such as stochastic Petri nets (SPNs), can be used in the modeling and evaluation of such environments. Therefore, this work aims to use analytical modeling through SPNs to assess the performance, energy consumption and cost of environments based on containers and VMs. Metrics such as throughput, response time, energy consumption and cost are collected and analyzed. The results revealed that, for example, a cluster with 10 replicas, occupied at their maximum capacity, can generate a 46.54% reduction in energy consumption if containers are used. Additionally, we validate the accuracy of the analytical models by comparing their results with the results obtained in a real infrastructure