Cloud Workload Prediction by Means of Simulations

Clouds hide the complexity of maintaining a physical infrastructure with a disadvantage: they also hide their internal workings. Should users need to know about these details e.g., to increase the reliability or performance of their applications, they would need to detect slight behavioural changes in the underlying system. Existing solutions for such purposes offer limited capabilities. This paper proposes a technique for predicting background workload by means of simulations that are providing knowledge of the underlying clouds to support activities like cloud orchestration or workflow enactment. We propose these predictions to select more suitable execution environments for scientific workflows. We validate the proposed prediction approach with a biochemical application

Facilitating self-adaptable inter-cloud management

Cloud Computing infrastructures have been developed as individual islands, and mostly proprietary solutions so far. However, as more and more infrastructure providers apply the technology, users face the inevitable question of using multiple infrastructures in parallel. Federated cloud management systems offer a simplified use of these infrastructures by hiding their proprietary solutions. As the infrastructure becomes more complex underneath these systems, the situations (like system failures, handling of load peaks and slopes) that users cannot easily handle, occur more and more frequently. Therefore, federations need to manage these situations autonomously without user interactions. This paper introduces a methodology to autonomously operate cloud federations by controlling their behavior with the help of knowledge management systems. Such systems do not only suggest reactive actions to comply with established Service Level Agreements (SLA) between provider and consumer, but they also find a balance between the fulfillment of established SLAs and resource consumption. The paper adopts rule-based techniques as its knowledge management solution and provides an extensible rule set for federated clouds built on top of multiple infrastructures. © 2012 IEEE

Contribution of micro‐PIXE to the characterization of settled dust events in an urban area affected by industrial activities

This study aimed to identify possible sources of settled dust events that occurred in an urban area nearby an industrial park, which alarmed the local population. Settled dust was collected in January 2019 and its chemical characterization was assessed by micro-PIXE, focusing on a total of 29 elements. Comparison with chemical profiles of particulate matter from different types of environment was conducted, along with the assessment of crustal enrichment factors and Spearman correlations, allowing to understand which sources were contributing to this settled dust event. A nearby industrial area’s influence was identified due to the contents of Fe, Cr and Mn, which are typical tracers of iron and steel industries.info:eu-repo/semantics/publishedVersio

Characterization of a settled dust event in an urban area affected by industrial activities

Trabalho apresentado em 7th Iberian Meeting Aerosol Science and Technology – RICTA19, 2019, Lisbon, PortugalN/

Chemical characterization of atmospheric particulate matter and source apportionment in an urban-industrial area of the Lisbon Metropolitan Area (Portugal)

Trabalho apresentado em 11th International Aerosol Conference (IAC2022), 4-9 de setembro 2022, Atenas, GréciaN/

Citizens involvement in the assessment of atmospheric contamination in an industrial area

Trabalho apresentado em 25th International Clean Air and Environment Conference CASANZ 2021, 17-21 de maio 2021, onlineN/

Biomonitoring PM using transplanted lichens in an urban-industrial area of the Lisbon Metropolitan Area and integration with reference monitoring method

Trabalho apresentado em 11th International Aerosol Conference (IAC2022), 4-9 setembro 2022, Atenas, GréciaN/

Cloud Workload Prediction based on Workflow Execution Time Discrepancies

Infrastructure as a service clouds hide the complexity of maintaining the physical infrastructure with a slight disadvantage: they also hide their internal working details. Should users need knowledge about these details e.g., to increase the reliability or performance of their applications, they would need solutions to detect behavioural changes in the underlying sys- tem. Existing runtime solutions for such purposes offer limited capabilities as they are mostly restricted to revealing weekly or yearly behavioural periodicity in the infrastructure. This article proposes a technique for predicting generic background workload by means of simulations that are capable of providing additional knowledge of the underlying private cloud systems in order to support activities like cloud orchestration or workflow enactment. Our technique uses long-running scientific workflows and their behaviour discrepancies and tries to replicate these in a simulated cloud with known (trace-based) workloads. We argue that the better we can mimic the current discrepancies the better we can tell expected workloads in the near future on the real life cloud. We evaluated the proposed prediction approach with a biochemical application on both real and simulated cloud infrastructures. The proposed algorithm has shown to produce significantly (_20%) better workload predictions for the future of simulated clouds than random workload selection

Source apportionment of PM2.5 in the pre-pandemic versus pandemic period in an area near Lisbon: lessons for air quality management

Trabalho apresentado em European Aerosol Conference - EAC2023, 3-8 setembro 2023, Málaga, EspanhaN/