An Overview of User-level Usage Monitoring in Cloud Environment

Cloud computing monitors applications, virtual and physical resources to ensure performance capacity, workload management, optimize future application updates and so on. Current state-of-the-art monitoring solutions in the cloud focus on monitoring in application/service level, virtual and physical (infrastructure) level. While some of the researchers have identified the importance of monitoring users, there is still need for developing solutions, implementation and evaluation in this domain. In this paper, we propose a novel approach to extract end-user usage of cloud services from their interactions with the interfaces provided to access the services called User-level Usage Monitoring. We provide the principles necessary for the usage data extraction process and analyse existing cloud monitoring techniques from the identified principles. Understanding end-user usage patterns and behaviour can help developers and architects to assess how applications work and which features of the application are critical for the users

End-to-End Personalized AAL Services for Elderly with Chronic Conditions

In the recent years, the number of the elderly living with chronic diseases (e.g., high blood pressure, diabetes, etc.) is continuously growing, potentially affecting their independence. Therefore, many efforts have been given in AAL applications for supporting the monitoring activities and, particularly, focusing to the issue of gathering continuous information at home, standardizing formats, extracting further information from raw sensors data using different techniques to reconstruct a context. One aspect that is rather important but less developed in current investigation is the design of personalized end-to-end services for classes of users of such technology being them either primary user (old people) or secondary users (medical doctors, caregiver, relatives)

Systems and Methods for Measuring and Improving End-User Application Performance on Mobile Devices

In today's rapidly growing smartphone society, the time users are spending on their smartphones is continuing to grow and mobile applications are becoming the primary medium for providing services and content to users. With such fast paced growth in smart-phone usage, cellular carriers and internet service providers continuously upgrade their infrastructure to the latest technologies and expand their capacities to improve the performance and reliability of their network and to satisfy exploding user demand for mobile data. On the other side of the spectrum, content providers and e-commerce companies adopt the latest protocols and techniques to provide smooth and feature-rich user experiences on their applications. To ensure a good quality of experience, monitoring how applications perform on users' devices is necessary. Often, network and content providers lack such visibility into the end-user application performance. In this dissertation, we demonstrate that having visibility into the end-user perceived performance, through system design for efficient and coordinated active and passive measurements of end-user application and network performance, is crucial for detecting, diagnosing, and addressing performance problems on mobile devices. My dissertation consists of three projects to support this statement. First, to provide such continuous monitoring on smartphones with constrained resources that operate in such a highly dynamic mobile environment, we devise efficient, adaptive, and coordinated systems, as a platform, for active and passive measurements of end-user performance. Second, using this platform and other passive data collection techniques, we conduct an in-depth user trial of mobile multipath to understand how Multipath TCP (MPTCP) performs in practice. Our measurement study reveals several limitations of MPTCP. Based on the insights gained from our measurement study, we propose two different schemes to address the identified limitations of MPTCP. Last, we show how to provide visibility into the end- user application performance for internet providers and in particular home WiFi routers by passively monitoring users' traffic and utilizing per-app models mapping various network quality of service (QoS) metrics to the application performance.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/146014/1/ashnik_1.pd

A Proposal for Architectural Framework Using Internet of Things with Fog Computing for an Air Quality Monitoring System

Air Monitoring becomes a systematic approach for sensitivity and finding out the cir-cumstances of the atmosphere. The major concern of air quality monitoring is to meas-ure the concentration of pollution and other important parameter related to the con-tamination and provides information in real-time to make decisions at right time to cure lives and save the environment. This paper proposes an Architectural Framework for the air quality monitoring system based on Internet-of-Things (IoT) and via Fog computing techniques with novel methods to obtain real-time and accurate measurements of con-ventional air quality monitoring. IoT-based real-time air pollution monitoring system is projected to at any location and stores the measured value of various pollutants over a web server with the Internet. It can facilitate the process and filter data near the end of the IoT nodes in a concurrent manner and improving the Latency issue with the quality of services

The evolution of Wide-Area DInSAR: from regional and national services to the European Ground Motion Service

This study is focused on wide-area deformation monitoring initiatives based on the differential interferometric SAR technique (DInSAR). In particular, it addresses the use of advanced DInSAR (A-DInSAR) techniques, which are based on large sets of synthetic aperture radar (SAR) and Copernicus Sentinel-1 images. Such techniques have undergone a dramatic development in the last twenty years: they are now capable to process big sets of SAR images and can be exploited to realize a wide-area A-DInSAR monitoring. The study describes several initiatives to establish wide-area ground motion services (GMS), both at county- and region-level. In the second part of the study, some of the key technical aspects related to wide-area A-DInSAR monitoring are discussed. Finally, the last part of the study is devoted to the European ground motion service (EGMS), which is part of the Copernicus land monitoring service. It represents the most important wide-area A-DInSAR deformation monitoring system ever developed. The study describes its main characteristics and its main products. The end of the production of the first EGMS baseline product is foreseen for the last quarter of 202

Individual monitoring for internal exposures in Europe: Conclusions of an EURADOS action

Once the EC Directive 96/29 has been implemented into national regulation across Europe, the coordination of dosimetry laboratories for the monitoring of occupational exposures becomes the principal aim to achieve. Within this framework the European Radiation Dosimetry Group, EURADOS, carried out an Action on ‘Harmonisation of Individual Monitoring' (2000-2004) to promote coordination in the field of individual monitoring of occupational exposures throughout Europe. With reference to internal exposures, the main aims were the completion of a catalogue of internal dosimetry services and an inventory of methods and techniques used for individual monitoring at European internal dosimetry facilities. At the end of this EURADOS Action, a report was published in Radiation Protection Dosimetry in 2004. The information collected related to various topics: the equipments used for the measurement of internal exposures, calibration and sensitivity data, the methods applied for the assessment of internal doses, Quality Control procedures, Quality Assurance Programmes in the facilities and legal requirements. The information to be presented here will give a general overview of the actual status of individual monitoring for internal exposures in Europ

Latency-driven Network Slices Orchestration

This paper has been presented at: IEEE Conference on Computer Communications Workshops ( INFOCOM'19 )The novel concept of network slicing is envisioned to allow service providers to open their infrastructure to vertical industries traditionally alien to mobile networks, such as automotive, health or factories. In this way multiple vertical services can be delivered over the same physical facilities by means of advanced network virtualization techniques. However, the vertical service requirements heterogeneity (e.g., high throughput, low latency, high reliability) calls for novel orchestration solutions able to manage end-to-end network slice resources across different domains while satisfying stringent service level agreements. In this demonstration we will show a novel orchestration solution able to handle one of the most stringent requirements: end-to-end latency. Our testbed-evolution of the work presented in [1]-implements all the resource brokerage schemes and allocation operations necessary to complete the life-cycle management of network slices. In addition, the novel overbooking concept is applied to pursue the overall revenue maximization when admitting network slices. Finally, an advanced network slicing monitoring system will be provided as a user-friendly dashboard allowing users to interact with the proposed solution.This work was supported by the H2020 5G-Transformer Project under Grant 761536 and by the H2020-MSCA-ITN-2015 5G-AURA Project under Grant 675806

Towards a Practical and Cost-Effective Water Monitoring System

In recent years, there has been increasing awareness of the preservation, protection and sustainable use of natural resources. Water resources, being one of the most important, face major threats due to contamination by pollutants of various types and origins. Maintaining the quality of water resources requires more robust, reliable and more frequent monitoring than traditional data collection techniques based on manual sampling methods. This article, which is the result of ongoing research, proposes a practical and cost-effective solution for a surface water monitoring system, using a robotics platform and cloud services. The proposed solution allows for scalability and will accommodate a wide range of end-user specifications. To allow for continuous operation in longer activities, the design of a versatile real-time water quality monitoring system should also take into consideration the question of its energy requirements and self-sufficiency.This work was supported by Centro 2020, Portugal 2020 and European Union (EU) under the grants, CENTRO-01-0145-FEDER-024052E – Libélula: Mobile robotic surface water quality monitoring system.info:eu-repo/semantics/publishedVersio

USING DEEP LEARNING AND UAV IMAGERY TO DETECT ELKHORN CORAL IN ST. CROIX’S EAST END MARINE PARK

Elkhorn coral, or Acropora palmata, is an important reef building species that promotes species abundance and other ecological services to the communities in the US Virgin Islands. We captured high resolution imagery of a reef in St. Croix’s East End Marine Park using a Wingtra One UAV. We then used deep learning techniques to detect individual coral colonies. We compared two deep learning models, FasterRCNN and MaskRCNN, and found that the models achieved accuracy shores up to 0.78. These scores improved when examining only larger corals in shallow waters. The model was able to both detect Elkhorn coral and distinguish it from other corals and features. This will be a useful method for measuring coral abundance and monitoring the success of restoration efforts