5,815 research outputs found
Alioth: A Machine Learning Based Interference-Aware Performance Monitor for Multi-Tenancy Applications in Public Cloud
Multi-tenancy in public clouds may lead to co-location interference on shared
resources, which possibly results in performance degradation of cloud
applications. Cloud providers want to know when such events happen and how
serious the degradation is, to perform interference-aware migrations and
alleviate the problem. However, virtual machines (VM) in
Infrastructure-as-a-Service public clouds are black-boxes to providers, where
application-level performance information cannot be acquired. This makes
performance monitoring intensely challenging as cloud providers can only rely
on low-level metrics such as CPU usage and hardware counters.
We propose a novel machine learning framework, Alioth, to monitor the
performance degradation of cloud applications. To feed the data-hungry models,
we first elaborate interference generators and conduct comprehensive
co-location experiments on a testbed to build Alioth-dataset which reflects the
complexity and dynamicity in real-world scenarios. Then we construct Alioth by
(1) augmenting features via recovering low-level metrics under no interference
using denoising auto-encoders, (2) devising a transfer learning model based on
domain adaptation neural network to make models generalize on test cases unseen
in offline training, and (3) developing a SHAP explainer to automate feature
selection and enhance model interpretability. Experiments show that Alioth
achieves an average mean absolute error of 5.29% offline and 10.8% when testing
on applications unseen in the training stage, outperforming the baseline
methods. Alioth is also robust in signaling quality-of-service violation under
dynamicity. Finally, we demonstrate a possible application of Alioth's
interpretability, providing insights to benefit the decision-making of cloud
operators. The dataset and code of Alioth have been released on GitHub.Comment: Accepted by 2023 IEEE International Parallel & Distributed Processing
Symposium (IPDPS
Integration of heterogeneous data sources and automated reasoning in healthcare and domotic IoT systems
In recent years, IoT technology has radically transformed many crucial industrial and service sectors such as healthcare. The multi-facets heterogeneity of the devices and the collected information provides important opportunities to develop innovative systems and services. However, the ubiquitous presence of data silos and the poor semantic interoperability in the IoT landscape constitute a significant obstacle in the pursuit of this goal. Moreover, achieving actionable knowledge from the collected data requires IoT information sources to be analysed using appropriate artificial intelligence techniques such as automated reasoning. In this thesis work, Semantic Web technologies have been investigated as an approach to address both the data integration and reasoning aspect in modern IoT systems. In particular, the contributions presented in this thesis are the following: (1) the IoT Fitness Ontology, an OWL ontology that has been developed in order to overcome the issue of data silos and enable semantic interoperability in the IoT fitness domain; (2) a Linked Open Data web portal for collecting and sharing IoT health datasets with the research community; (3) a novel methodology for embedding knowledge in rule-defined IoT smart home scenarios; and (4) a knowledge-based IoT home automation system that supports a seamless integration of heterogeneous devices and data sources
Intelligent computing : the latest advances, challenges and future
Computing is a critical driving force in the development of human civilization. In recent years, we have witnessed the emergence of intelligent computing, a new computing paradigm that is reshaping traditional computing and promoting digital revolution in the era of big data, artificial intelligence and internet-of-things with new computing theories, architectures, methods, systems, and applications. Intelligent computing has greatly broadened the scope of computing, extending it from traditional computing on data to increasingly diverse computing paradigms such as perceptual intelligence, cognitive intelligence, autonomous intelligence, and human computer fusion intelligence. Intelligence and computing have undergone paths of different evolution and development for a long time but have become increasingly intertwined in recent years: intelligent computing is not only intelligence-oriented but also intelligence-driven. Such cross-fertilization has prompted the emergence and rapid advancement of intelligent computing
AN EMPIRICAL STUDY OF CONCURRENT FEATURE USAGE IN GO
The Go language includes support for running functions or methods concurrently as goroutines, which are lightweight threads managed directly by the Go language runtime. Go is probably best known for the use of a channel-based, message-passing concurrency mechanism, based on Hoare's Communicating Sequential Processes (CSP), for inter-thread communication. However, Go also includes support for traditional concurrency features, such as mutexes and condition variables, that are commonly used in other languages. In this paper, we analyze the use of these traditional concurrency features, using a corpus of Go programs used in earlier work to study the use of message-passing concurrency features in Go. The goal of this work is to better support developers in using traditional concurrency features, or a combination of traditional and message-passing features, in Go
Modelling, Monitoring, Control and Optimization for Complex Industrial Processes
This reprint includes 22 research papers and an editorial, collected from the Special Issue "Modelling, Monitoring, Control and Optimization for Complex Industrial Processes", highlighting recent research advances and emerging research directions in complex industrial processes. This reprint aims to promote the research field and benefit the readers from both academic communities and industrial sectors
Identification of Micro- and Submicron (Nano) Plastics in Water Sources and the Impact of COVID-19 on Plastic Pollution
One of the most significant environmental issues that our society may deal with this century could be plastics. The world's water bodies, as well as land and air, are becoming more and more contaminated by plastic due to the ongoing and expanding manufacturing of these synthetic materials, as well as the lack of an effective strategy for managing plastic waste. The fact that plastics break down into smaller particles (micro and nanoplastics) by action of environmental physical and chemical reactions, and do not degrade biologically in a reasonable time, is a cause of concern as plastics are believed to cause harm in animals, plants and humans.To identify the types of plastics prevalent in aquatic habitats, a number of procedures have been developed, from sampling to identification. After a water body has been sampled using nets, pumps, or other tools, depending on the type of sample taken, it is usually necessary to treat the samples for separation and purification. The next stage is to employ analytical techniques to identify the synthetic contaminants. The most common approaches are microscopy, spectroscopy, and thermal analysis. This thesis gives an overview of where in the environment microplastics (MPs) and nanoplastics (NPs) can be found and summarizes the most important technologies applied to analyse the importance of plastics as a contaminant in water bodies. The development of standardised analytical procedures is still necessary as most of them are not suitable for the identification of particles below 50 μm due to resolution limitations. The preparation and analysis of samples are usually time-consuming factors that shall be considered. Particularly for MP and NP analysis in aqueous samples, thermal analysis methods based on sample degradation are generally not considered to be the most effective approach. Nevertheless, Pyrolysis - Gas Chromatography Time-of-Flight Mass Spectrometry (Py-GCToFMS) is used in this thesis to propose a novel approach as due to its unique detection abilities, and with a novel filtration methodology for collection, it enables the identification of tiny particle sizes (>0.1 μm) in water samples.PTFE membranes were selected to filter the liquid samples using a glass filtration system. This way, the synthetic particles will be deposited on the membranes and will allow the study and analysis of the precipitated material. PTFE is a readily available, reasonably priced, and adaptable product that makes sample preparation quick and simple.The three plastics under study—polypropylene (PP), polystyrene (PS), and polyvinyl chloride (PVC)—can be identified from complex samples at trace levels thanks to the employment of these widely used membranes and the identification of various and specific (marker) ions. The technique was examined against a range of standards samples that contained predetermined concentrations of MPs and NPs. Detection levels were then determined for PVC and PS and were found to be below <50 μg/ L, with repeatable data showing good precision (RSD <20 %). The examination of a complex matrix sample taken from a nearby river contributed to further validate this innovative methodology; the results indicated the existence of PS with a semi-quantifiable result of 250.23 g/L. Because of this, PY-GCToFMS appears to be a method that is appropriate for the task of identifying MPs and NPs from complex mixtures.This thesis also focuses on the environmental challenge that disposable plastic face masks (DPFMs) pose, which has been made significantly worse due to the COVID-19 pandemic. By the time this thesis was written, the production of disposable plastic facemasks had reached to approximately 200 million a day, in a global effort to tackle the spread of the new SARS-CoV-2 virus. This thesis investigates the emissions of pollutants from several different DPFM brands (medical and non-medical) that were submerged in water to replicate the conditions in the environment after these DPFMs have been discarded. The DPFM leachates were filtered using inorganic membranes type and characterized using Fourier transform infrared spectroscopy (FTIR), Scanning electron microscopy coupled with energy-dispersive X-ray spectroscopy (SEM-EDS), Light/Optical Microscopy (LM/OM), Inductively coupled plasma mass spectrometry (ICP-MS) and Liquid chromatography–mass spectrometry (LC-MS). Micro and nano scale polymeric fibres, particles, siliceous fragments and leachable inorganic and organic chemicals were observed from all of the tested DPFMs. For non-medical DPFMs, traces of concerning heavy metals were detected in association with silicon containing fragments (i.e. lead up to 6.79 μg/L). ICP-MS also confirmed the presence of other leachable metals like cadmium (up to 1.92 μg/L), antimony (up to 3.93 μg/L) and copper (up to 4.17 μg/L). LC-MS analysis identified organic species related to plastic additives; polyamide-66 monomer and oligomers (nylon-66 synthesis), surfactant molecules, and dye-like molecules were all tentatively identified in the leachate. The question of whether DPFMs are safe to use daily and what implications may be anticipated after their disposal into the environment is brought up by the toxicity of some of the chemicals discovered.The previous approach is expanded to medical DPFMs with the utilisation of Field Emission Gun Scanning Electron Microscope (FEG-SEM) in order to get high resolution images of the micro and nanoparticles deposited on the membranes. It is also incorporated the use of 0.02 μm pore size inorganic membranes to better identify the nanoparticles released.Separated aqueous samples were also obtained by submerging medical DPFMs for 24 hours to be analysed using ICP-MS and LC-MS.Both particles and fibres in the micro and nano scale were found in all 6 DPFMs brands of this study. EDS analysis revealed the presence of particles containing different heavy metals like lead, mercury, and arsenic among others. ICP-MS analysis results confirmed traces of heavy metals (antimony up to 2.41 μg/L and copper up to 4.68 μg/L). LC-MS analysis results identified organic species related to plastic additives and contaminants; polyamide-66 monomer and oligomers (nylon-66 synthesis), surfactant molecules, and polyethylene glycol were all tentatively identified in the leachate. The toxicity of some of the chemicals found raises the question of whether DPFMs are safe to be used on a daily basis and what consequences are to be expected after their disposal into the environment
A Decision Support System for Economic Viability and Environmental Impact Assessment of Vertical Farms
Vertical farming (VF) is the practice of growing crops or animals using the vertical dimension via multi-tier racks or vertically inclined surfaces. In this thesis, I focus on the emerging industry of plant-specific VF. Vertical plant farming (VPF) is a promising and relatively novel practice that can be conducted in buildings with environmental control and artificial lighting. However, the nascent sector has experienced challenges in economic viability, standardisation, and environmental sustainability. Practitioners and academics call for a comprehensive financial analysis of VPF, but efforts are stifled by a lack of valid and available data.
A review of economic estimation and horticultural software identifies a need for a decision support system (DSS) that facilitates risk-empowered business planning for vertical farmers. This thesis proposes an open-source DSS framework to evaluate business sustainability through financial risk and environmental impact assessments. Data from the literature, alongside lessons learned from industry practitioners, would be centralised in the proposed DSS using imprecise data techniques. These techniques have been applied in engineering but are seldom used in financial forecasting. This could benefit complex sectors which only have scarce data to predict business viability.
To begin the execution of the DSS framework, VPF practitioners were interviewed using a mixed-methods approach. Learnings from over 19 shuttered and operational VPF projects provide insights into the barriers inhibiting scalability and identifying risks to form a risk taxonomy. Labour was the most commonly reported top challenge. Therefore, research was conducted to explore lean principles to improve productivity.
A probabilistic model representing a spectrum of variables and their associated uncertainty was built according to the DSS framework to evaluate the financial risk for VF projects. This enabled flexible computation without precise production or financial data to improve economic estimation accuracy. The model assessed two VPF cases (one in the UK and another in Japan), demonstrating the first risk and uncertainty quantification of VPF business models in the literature. The results highlighted measures to improve economic viability and the viability of the UK and Japan case.
The environmental impact assessment model was developed, allowing VPF operators to evaluate their carbon footprint compared to traditional agriculture using life-cycle assessment. I explore strategies for net-zero carbon production through sensitivity analysis. Renewable energies, especially solar, geothermal, and tidal power, show promise for reducing the carbon emissions of indoor VPF. Results show that renewably-powered VPF can reduce carbon emissions compared to field-based agriculture when considering the land-use change.
The drivers for DSS adoption have been researched, showing a pathway of compliance and design thinking to overcome the ‘problem of implementation’ and enable commercialisation. Further work is suggested to standardise VF equipment, collect benchmarking data, and characterise risks. This work will reduce risk and uncertainty and accelerate the sector’s emergence
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