A microscope for the data center

Nowadays, data centers are large energy consumers and the trend for next years is expected to increase further, considering the growth in the order of cloud services. A large portion of this power consumption is due to the control of physical parameters of the data center (such as temperature and humidity). However, these physical parameters are tightly coupled with computations, and even more so in upcoming data centers, where the location of workloads can vary substantially due, for example, to workloads being moved in the cloud infrastructure hosted in the data center. Therefore, managing the physical and compute infrastructure of a large data center is an embodiment of a Cyber-Physical System (CPS). In this paper, we describe a data collection and distribution architecture that enables gathering physical parameters of a large data center at a very high temporal and spatial resolution of the sensor measurements. We think this is an important characteristic to enable more accurate heat-flow models of the data center and with them, find opportunities to optimize energy consumptions. Having a high-resolution picture of the data center conditions, also enables minimizing local hot-spots, perform more accurate predictive maintenance (failures in all infrastructure equipments can be more promptly detected) and more accurate billing. We detail this architecture and define the structure of the underlying messaging system that is used to collect and distribute the data. Finally, we show the results of a preliminary study of a typical data center radio environment.

Odin: a service for gamification of learning activities

Existing gamification services have features that preclude their use by e-learning tools. Odin is a gamification service that mimics the API of state-of-the-art services without these limitations. This paper describes Odin, its role in an e-learning system architecture requiring gamification, and details its implementation. The validation of Odin involved the creation of a small e-learning game, integrated in a Learning Management System (LMS) using the Learning Tools Interoperability (LTI) specification.

Partilha de Pontos de Interesse, Vídeos e Experiências Sensoriais Baseada no Contexto do Utilizador

A evolução tecnológica das últimas décadas na área das Tecnologias da Informação e Comunicação (TIC) contribuiu para a proliferação de fontes de informação e de sistemas de partilha de recursos. As diversas redes sociais são um exemplo paradigmático de sistemas de partilha tanto de informação como de recursos (e.g. audiovisuais). Essa abundância crescente de recursos e fontes aumenta a importância de sistemas capazes de recomendar em tempo útil recursos personalizados, tendo por base o perfil e o contexto do utilizador. O objetivo deste projeto é partilhar e recomendar locais, artigos e vídeos em função do contexto do utilizador assim como proporcionar uma experiência mais rica de reprodução dos vídeos partilhados, simulando as condições de gravação dos vídeos. Este sistema teve como inspiração dois projetos anteriormente desenvolvidos de partilha e recomendação de locais, artigos e vídeos turísticos em função da localização do utilizador. O sistema desenvolvido consiste numa aplicação distribuída composta por um módulo cliente Android, que inclui a interface com o utilizador e o consumo direto de serviços externos de suporte, e um módulo servidor que controla o acesso à base de dados central e inclui o serviço de recomendação baseado no contexto do utilizador. A comunicação entre os módulos cliente e servidor utiliza um protocolo do nível de aplicação dedicado. As recomendações geradas pelo sistema têm por base o perfil de utilizador, informação contextual (posição do utilizador, data e hora atual e velocidade atual do utilizador) e podem ser geradas a pedido do utilizador ou automaticamente, caso sejam encontrados pontos de interesse de grande relevância para o utilizador. Os pontos de interesse recomendados são apresentados com recurso ao Google Maps, incluindo o período de funcionamento, artigos complementares e a reprodução imersiva dos vídeos relacionados. Essa imersão tem em consideração as condições meteorológicas, temporais e espaciais aquando da gravação do vídeo.

Architecture to Support Quality of Service in Arrowhead Systems

Presented at INForum - Simpósio de Informática (INFORUM 2015). 7 to 8, Sep, 2015. Covilhã, Portugal.

Quality-of-service aware routing for static and mobile IPv6-based low-power and lossy sensor networks using RPL

The Internet of Things (IoT) has emerged as a paradigm over the last few years as a result of the tight integration of the computing and the physical world. The requirement of remote sensing makes low-power wireless sensor networks one of the key enabling technologies of IoT. These networks encompass several challenges, especially in communication and networking, due to their inherent constraints of low-power features, deployment in harsh and lossy environments, and limited computing and storage resources. The IPv6 Routing Protocol for Low Power and Lossy Networks (RPL) [1] was proposed by the IETF ROLL (Routing Over Low-power Lossy links) working group and is currently adopted as an IETF standard in the RFC 6550 since March 2012. Although RPL greatly satisfied the requirements of low-power and lossy sensor networks, several issues remain open for improvement and specification, in particular with respect to Quality of Service (QoS) guarantees and support for mobility. In this paper, we focus mainly on the RPL routing protocol. We propose some enhancements to the standard specification in order to provide QoS guarantees for static as well as mobile LLNs. For this purpose, we propose OF-FL (Objective Function based on Fuzzy Logic), a new objective function that overcomes the limitations of the standardized objective functions that were designed for RPL by considering important link and node metrics, namely end-to-end delay, number of hops, ETX (Expected transmission count) and LQL (Link Quality Level). In addition, we present the design of Co-RPL, an extension to RPL based on the corona mechanism that supports mobility in order to overcome the problem of slow reactivity to frequent topology changes and thus providing a better quality of service mainly in dynamic networks application. Performance evaluation results show that both OF-FL and Co-RPL allow a great improvement when compared to the standard specification, mainly in terms of packet loss ratio and average network latency. 2015 Elsevier B.V. Al

PIASA: A power and interference aware resource management strategy for heterogeneous workloads in cloud data centers

Cloud data centers have been progressively adopted in different scenarios, as reflected in the execution of heterogeneous applications with diverse workloads and diverse quality of service (QoS) requirements. Virtual machine (VM) technology eases resource management in physical servers and helps cloud providers achieve goals such as optimization of energy consumption. However, the performance of an application running inside a VM is not guaranteed due to the interference among co-hosted workloads sharing the same physical resources. Moreover, the different types of co-hosted applications with diverse QoS requirements as well as the dynamic behavior of the cloud makes efficient provisioning of resources even more difficult and a challenging problem in cloud data centers. In this paper, we address the problem of resource allocation within a data center that runs different types of application workloads, particularly CPU- and network-intensive applications. To address these challenges, we propose an interference- and power-aware management mechanism that combines a performance deviation estimator and a scheduling algorithm to guide the resource allocation in virtualized environments. We conduct simulations by injecting synthetic workloads whose characteristics follow the last version of the Google Cloud tracelogs. The results indicate that our performance-enforcing strategy is able to fulfill contracted SLAs of real-world environments while reducing energy costs by as much as 21%.