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Ioana Giurgiu
Researcher at IBM
Publications - 28
Citations - 878
Ioana Giurgiu is an academic researcher from IBM. The author has contributed to research in topics: Server & Cloud computing. The author has an hindex of 12, co-authored 25 publications receiving 763 citations. Previous affiliations of Ioana Giurgiu include ETH Zurich & National University of Ireland, Galway.
Papers
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Book ChapterDOI
Calling the cloud: enabling mobile phones as interfaces to cloud applications
TL;DR: In this article, the authors present a middleware platform that can automatically distribute different layers of an application between the phone and the server, and optimize a variety of objective functions (latency, data transferred, cost, etc.).
Proceedings ArticleDOI
Predicting Disk Replacement towards Reliable Data Centers
TL;DR: A highly accurate SMART-based analysis pipeline that can correctly predict the necessity of a disk replacement even 10-15 days in advance and uses statistical techniques to automatically detect which SMART parameters correlate with disk replacement.
Proceedings ArticleDOI
Failure Analysis of Virtual and Physical Machines: Patterns, Causes and Characteristics
TL;DR: This study conducts an analysis on 10K virtual and physical machines hosted on five commercial data centers over an observation period of one year to establish a sound understanding of the differences and similarities between failures of physical and virtual machines.
Book ChapterDOI
Dynamic software deployment from clouds to mobile devices
TL;DR: It is argued that the static decisions made in existing work cannot leverage the full potential of application partitioning, so a system that dynamically adapts the application partition decisions is developed to allow for variations in the execution environment.
Proceedings ArticleDOI
MTEX-CNN: Multivariate Time Series EXplanations for Predictions with Convolutional Neural Networks
TL;DR: This work presents MTEX-CNN, a novel explainable convolutional neural network architecture which can not only be used for making predictions based on multivariate time series data, but also for explaining these predictions.