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Anthony Deese - One of the best experts on this subject based on the ideXlab platform.

  • Implementation of Unsupervised k-Means Clustering Algorithm Within amazon web services Lambda
    2018 18th IEEE ACM International Symposium on Cluster Cloud and Grid Computing (CCGRID), 2018
    Co-Authors: Anthony Deese
    Abstract:

    This work demonstrates how an unsupervised learning algorithm based on k-Means Clustering with Kaufman Initialization may be implemented effectively as an amazon web services Lambda Function, within their serverless cloud computing service. It emphasizes the need to employ a lean and modular design philosophy, transfer data efficiently between Lambda and DynamoDB, as well as employ Lambda Functions within mobile applications seamlessly and with negligible latency. This work presents a novel application of serverless cloud computing and provides specific examples that will allow readers to develop similar algorithms. The author provides compares the computation speed and cost of machine learning implementations on traditional PC and mobile hardware (running locally) as well as implementations that employ Lambda.

  • CCGrid - Implementation of unsupervised k-means clustering algorithm within amazon web services lambda
    2018 18th IEEE ACM International Symposium on Cluster Cloud and Grid Computing (CCGRID), 2018
    Co-Authors: Anthony Deese
    Abstract:

    This work demonstrates how an unsupervised learning algorithm based on k-Means Clustering with Kaufman Initialization may be implemented effectively as an amazon web services Lambda Function, within their serverless cloud computing service. It emphasizes the need to employ a lean and modular design philosophy, transfer data efficiently between Lambda and DynamoDB, as well as employ Lambda Functions within mobile applications seamlessly and with negligible latency. This work presents a novel application of serverless cloud computing and provides specific examples that will allow readers to develop similar algorithms. The author provides compares the computation speed and cost of machine learning implementations on traditional PC and mobile hardware (running locally) as well as implementations that employ Lambda.

Durmus Koc - One of the best experts on this subject based on the ideXlab platform.

Barr Ylva - One of the best experts on this subject based on the ideXlab platform.

  • Molnlagring i landskapet. Fallstudie över amazon web services och Eskilstuna Logistikpark
    2020
    Co-Authors: Norlin Anna-klara, Ringvall Ronja, Barr Ylva
    Abstract:

    Denna uppsats har utgjorts av en fallstudie över vilka effekter etableringen av Eskilstuna logistikpark, och mer specifikt amazon web services datahall, har haft på det lokala landskapet i samhället Kjula, Eskilstuna kommun. Uppsatsen har utgått från en motsättning mellan å ena sidan en utbredd uppfattning om så kallade molntjänster som viktlösa och platslösa och å andra sidan datalagringens fysiska och resurskrävande dimensioner. De specifika syftet med uppsatsen har varit att i en lokal kontext undersöka och synliggöra hur etableringen av en datahall påverkar ett landskap och vilka aktiviteter som kan ta plats i det. Uppsatsen har vidare haft som ambition att bidra till en diskussion om informationstekniken och datalagringens resursanspråk, inte minst i form av landyta, och hur det med dessa kan följa intressekonflikter. Studiens har utgått från en multimetoddesign där såväl kvalitativa samtalsintervjuer som en vandrande observation i Eskilstuna logistikpark har använts för att samla in empiri. Såväl intervjuguide som “blickriktning” vid observationen har baserats på utvalda delar av Torsten Hägerstrands landskapsteori. I analysskedet kompletterades detta perspektiv med begrepp från politisk ekologi för att synliggöra intressekonflikter mellan intervjudeltagarnas berättelser samt belysa hur ett maktförhållande mellan centrum och periferi kan uppstå vid ianspråktagande av markyta. Det visade sig vara svårt att utifrån empiri urskilja vilka effekter just amazon web services har haft på landskapets aktiviteter, och därmed utökades kartläggningen till att gälla etableringen av hela Eskilstuna logistikpark. Samtalsintervjuerna och den vandrande observationen resulterade i slutsatsen att etableringen av Eskilstuna logistikpark har gynnat en ny typ av kommersiella aktiviteter som har haft en mer totalt utträngande effekt på andra aktiviteter i landskapet. Samtidigt påvisar studien att kommuner som Eskilstuna inte har mycket val i att upplåta sina landskap till den här typen av infrastruktur om de vill fortsätta existera och kunna tillgodose sina medborgares behov. Det finns dock en slags motsättning mellan denna vilja att få kommunen att leva och den utträngning av lokala aktiviteter och lokalt liv som datalagringens infrastruktur kräver.This dissertation is based upon a case study that examined the effects of the establishment of Eskilstuna Logistics Park, and more specifically the amazon web services data center, on the local landscape in the society of Kjula in Eskilstuna municipality. The departure point for this dissertation is the contradiction between the general perception of, on the one hand, services of The Cloud as weightless and detached, and on the other hand the physical and resource-intensive infrastructure that upholds these services. The specific purpose of the dissertation has been to study and make visible how the establishment of a data center affects the landscape, and the activities in the landscape, in a local context. As a wider purpose we had the ambition to contribute to a discussion about the resource requirements of information technology and data storage, especially from a land use perspective, and how these requirements can cause conflicts of interests. The study has used a multi-method design where qualitative interviews and a walking observation have been used to collect empirical material. The structure of the interviews and the observation have been shaped by the landscape theory of Torsten Hägerstrand. This theory has been complemented with concepts from political ecology to make visible the conflicting interests that were revealed in the interviews. Further, the concepts have contributed to revealing how different uses of land can work to establish a power dynamic between the center and the periphery. Based on the empirical data it proved to be hard to single out the specific effects on the landscape of amazon web services. Therefore, the focus of inquiry was broadened to include the whole of Eskilstuna Logistics Park. The research resulted in the conclusion that the establishment of Eskilstuna Logistics Park has contributed to the emergence of new kinds of commercial activities, which have had an exclusionary effect on other types of activities in the landscape. At the same time, the result of the study indicates that if municipalities like Eskilstuna want to continue to exist and be able to provide their inhabitants with services, they have little choice but to make their landscape available for this kind of infrastructure. Nevertheless, there is a contradiction between these efforts to keeping the municipality alive, and the negative impacts of the data center infrastructure on local life

Derya Ucuz - One of the best experts on this subject based on the ideXlab platform.

  • Comparison of the IoT Platform Vendors, Microsoft Azure, amazon web services, and Google Cloud, from Users’ Perspectives
    2020 8th International Symposium on Digital Forensics and Security (ISDFS), 2020
    Co-Authors: Aina’u Shehu Muhammed, Derya Ucuz
    Abstract:

    The largest Internet of Things (IoT) cloud platform vendors are Microsoft Azure, amazon web services, and Google Cloud. These companies are known as the big three, and have all agreed to join the IoT domain and concentrate on improving the services on their IoT platforms. While these platform descriptions are extensive, users are constantly experiencing difficulties in making the right choice of which platform to use, between the three. This paper presents a comparison of the big three, using the constraints of hubs, analytics, and security. The study also provides some recommendations as to which IoT cloud platform vendor is more ideal, notwithstanding the limitations of the study. In view of these results, users will be able to more confidently select vendors, based on their demands and goals.

  • ISDFS - Comparison of the IoT Platform Vendors, Microsoft Azure, amazon web services, and Google Cloud, from Users’ Perspectives
    2020 8th International Symposium on Digital Forensics and Security (ISDFS), 2020
    Co-Authors: Aina'u Shehu Muhammed, Derya Ucuz
    Abstract:

    The largest Internet of Things (IoT) cloud platform vendors are Microsoft Azure, amazon web services, and Google Cloud. These companies are known as the big three, and have all agreed to join the IoT domain and concentrate on improving the services on their IoT platforms. While these platform descriptions are extensive, users are constantly experiencing difficulties in making the right choice of which platform to use, between the three. This paper presents a comparison of the big three, using the constraints of hubs, analytics, and security. The study also provides some recommendations as to which IoT cloud platform vendor is more ideal, notwithstanding the limitations of the study. In view of these results, users will be able to more confidently select vendors, based on their demands and goals.

Sanjay Gowda - One of the best experts on this subject based on the ideXlab platform.

  • Lessons Learned and Cost Analysis of Hosting a Full Stack Open Data Cube (ODC) Application on the amazon web services (AWS)
    IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium, 2018
    Co-Authors: Syed R Rizvi, Brian Killough, Andrew Cherry, Sanjay Gowda
    Abstract:

    The Open Data Cube (ODC) initiative, with support from the Committee on Earth Observation Satellites (CEOS) System Engineering Office (SEO) has developed a state-of-the-art suite of software tools and products to facilitate the analysis of Earth Observation data. This paper presents a short summary and cost analysis of our experience using amazon web services (AWS) to host one such software product, the CEOS Data Cube (CDC) web-based User Interface (UI). In order to provide adaptability, flexibility, scalability, and robustness, we leverage widely-adopted and well-supported technologies such as the Django web framework and the AWS Cloud platform. The UI has empowered users by providing features that assist with streamlining data preparation, data processing, data visualization, and the sub-setting of Analysis Ready Data (ARD) products in order to achieve a wide variety of Earth imaging objectives.

  • IGARSS - Lessons Learned and Cost Analysis of Hosting a Full Stack Open Data Cube (ODC) Application on the amazon web services (AWS)
    IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium, 2018
    Co-Authors: Syed R Rizvi, Brian Killough, Andrew Cherry, Sanjay Gowda
    Abstract:

    The Open Data Cube (ODC) initiative, with support from the Committee on Earth Observation Satellites (CEOS) System Engineering Office (SEO) has developed a state-of-the-art suite of software tools and products to facilitate the analysis of Earth Observation data. This paper presents a short summary and cost analysis of our experience using amazon web services (AWS) to host one such software product, the CEOS Data Cube (CDC) web-based User Interface (UI). In order to provide adaptability, flexibility, scalability, and robustness, we leverage widely-adopted and well-supported technologies such as the Django web framework and the AWS Cloud platform. The UI has empowered users by providing features that assist with streamlining data preparation, data processing, data visualization, and the sub-setting of Analysis Ready Data (ARD) products in order to achieve a wide variety of Earth imaging objectives.