Nowadays enormous amounts of energy are consumed by Cloud infrastructures and this trend is still growing. An existing solution to lower this consumption is to turn off as many servers as possible, but these solutions do not involve the user as a main lever to save energy. We introduce a system that proposes to the user to run her application with degraded performance. A user choosing an energy-efficient run promotes a better consolidation of the Virtual Machines in the Cloud and thus may help turning off more servers. We experimented our system on Grid' 5000 and we used the Montage workflow as a benchmark. Experimentation results show promising outcomes. In energy-efficiency mode, the energy consumed can be significantly reduced to the cost of a low increase of the execution time.
The fast growth of demand for computing and storage resources in data centers has considerably increased their energy consumption. Improving the utilization of data center resources and integrating renewable energy, such as solar and wind, has been proposed to reduce both the brown energy consumption and carbon footprint of the data centers. In this paper, we propose a novel framework oPportunistic schedulIng broKer infrAstructure (PIKA) to save energy in small mono-site data centers. In order to reduce the brown energy consumption, PIKA integrates resource overcommit techniques that help to minimize the number of powered-on Physical Machines (PMs). On the other hand, PIKA dynamically schedules the jobs and adjusts the number of powered-on PMs to match the variable renewable energy supply. Our simulations with a real-world job workload and solar power traces demonstrate that PIKA saves brown energy consumption by up to 44.9% compared to a typical scheduling algorithm.
The technology of Security Information and Event Management (SIEM) becomes one of the most important research applications in the area of computer network security, including distributed networks of internet enabled objects (as in the Internet of Things). The overall functionality of SIEM systems depends largely on the quality of solutions implemented at the data storage level, which is purposed for the representation of heterogeneous security events, their storage in the data repository and the extraction of relevant data for the analytical modules of SIEM systems. An ontological approach at present becomes more applicable for realizing these tasks in various spheres of information security. The paper discusses the possibilities of applying the ontological approach for implementation of the data repository of SIEM systems for distributed networks of Internet enabled objects. Based on the analysis of existing SIEM systems and standards, the choice of ontological approach is done, an example of the ontological data model of vulnerabilities is presented, a hybrid architecture of the ontological repository is proposed and the issues of developing and testing the repository for attack modelling and secure evaluation tasks are discussed.
Future urban computing scenarios will be characterized by the close integration of ICT devices and humans. Citizens, by using their own capabilities augmented and integrated with ICT technologies, could thus collaboratively realize a sort of socio-technical super organism to support high-levels of collective "urban" intelligence and various forms of collective actions. This position paper, also with the help of a representative case study, identifies the key engineering challenges for future urban super organisms and proposes a two-tier architecture for the infrastructure to support the activities of novel urban super organisms.
New applications are motivating and informing the design of sensor/actuator networks, and, more broadly, research in cyber-physical systems (CPS). Our knowledge of many physical systems is uncertain, so that sensing and actuation must be mediated by inference of the structure and parameters of physical-system models. One CPS application domain of growing interest is ecological systems, motivated by the need to understand plant survival and growth as a function of genetics, environment, and climate change. For this effort to be successful, we must be able to infer coupled, data-driven predictive models of plant growth dynamics in response to climate drivers that allow incorporation of uncertainty. We are developing an architecture and implementation for precise fine-scale control of irrigation in an array of geographically-distributed outdoor gardens on an elevational gradient of over 1500 m, allowing design of experiments that combine control of temperature and water availability. This paper describes a system architecture and implementation for this class of cyber-eco systems, including sensor/actuator node design, site-level networking, data assimilation, inference, and distributed control. Among its innovations are a modular, parallel-processing node hardware design allowing real-time processing and heterogeneous nodes, energy-aware hardware/software design, and a networking protocol that builds in trade-offs between energy conservation and latency. Throughout, we emphasize the changes in system architecture required as missions evolve from sensing-only to sensing, inference, and control. We also describe our developmental implementation of the architecture and its planned deployment. Future extensions will likely add negative control of precipitation using active rain-out shelters and additional plant-level control of air or soil temperature.
