681 citations · 844 across the 18 of their papers we have counts for
19 papers
Paving the Way to Hybrid Quantum-Classical Scientific Workflows
Sandeep Suresh Cranganore, Vincenzo De Maio, Ivona Brandic +1
The increasing growth of data volume, and the consequent explosion in demand for computational power, are affecting scientific computing, as shown by the rise of extreme data scien…
FLIGAN: Enhancing Federated Learning with Incomplete Data using GAN
Paul Joe Maliakel, Shashikant Ilager, Ivona Brandic
Federated Learning (FL) provides a privacy-preserving mechanism for distributed training of machine learning models on networked devices (e.g., mobile devices, IoT edge nodes). It…
On Optimizing Hyperparameters for Quantum Neural Networks
Sabrina Herbst, Vincenzo De Maio, Ivona Brandic
The increasing capabilities of Machine Learning (ML) models go hand in hand with an immense amount of data and computational power required for training. Therefore, training is usu…
Training Computer Scientists for the Challenges of Hybrid Quantum-Classical Computing
Vincenzo De Maio, Meerzhan Kanatbekova, Felix Zilk +3
As we enter the post-Moore era, we experience the rise of various non-von-Neumann-architectures to address the increasing computational demand for modern applications, with quantum…
Streaming IoT Data and the Quantum Edge: A Classic/Quantum Machine Learning Use Case
Sabrina Herbst, Vincenzo De Maio, Ivona Brandic
With the advent of the Post-Moore era, the scientific community is faced with the challenge of addressing the demands of current data-intensive machine learning applications, which…
SymED: Adaptive and Online Symbolic Representation of Data on the Edge
Daniel Hofstätter, Shashikant Ilager, Ivan Lujic +1
The edge computing paradigm helps handle the Internet of Things (IoT) generated data in proximity to its source. Challenges occur in transferring, storing, and processing this rapi…
An Energy-Aware Approach to Design Self-Adaptive AI-based Applications on the Edge
Alessandro Tundo, Marco Mobilio, Shashikant Ilager +3
The advent of edge devices dedicated to machine learning tasks enabled the execution of AI-based applications that efficiently process and classify the data acquired by the resourc…
AI for Next Generation Computing: Emerging Trends and Future Directions
Sukhpal Singh Gill, Minxian Xu, Carlo Ottaviani +19
Autonomic computing investigates how systems can achieve (user) specified control outcomes on their own, without the intervention of a human operator. Autonomic computing fundament…
Multi-agent Bayesian Deep Reinforcement Learning for Microgrid Energy Management under Communication Failures
Hao Zhou, Atakan Aral, Ivona Brandic +1
Microgrids (MGs) are important players for the future transactive energy systems where a number of intelligent Internet of Things (IoT) devices interact for energy management in th…
On the Future of Cloud Engineering
David Bermbach, Abhishek Chandra, Chandra Krintz +7
Ever since the commercial offerings of the Cloud started appearing in 2006, the landscape of cloud computing has been undergoing remarkable changes with the emergence of many diffe…
Towards an Integrated Platform for Big Data Analysis
Mahdi Bohlouli, Frank Schulz, Lefteris Angelis +4
The amount of data in the world is expanding rapidly. Every day, huge amounts of data are created by scientific experiments, companies, and end users' activities. These large data…
Performance-Based Pricing in Multi-Core Geo-Distributed Cloud Computing
Dražen Lučanin, Ilia Pietri, Simon Holmbacka +3
New pricing policies are emerging where cloud providers charge resource provisioning based on the allocated CPU frequencies. As a result, resources are offered to users as combinat…
A Cloud Controller for Performance-Based Pricing
Dražen Lučanin, Ilia Pietri, Ivona Brandic +1
New dynamic cloud pricing options are emerging with cloud providers offering resources as a wide range of CPU frequencies and matching prices that can be switched at runtime. On th…
Pervasive Cloud Controller for Geotemporal Inputs
Dražen Lučanin, Ivona Brandic
The rapid cloud computing growth has turned data center energy consumption into a global problem. At the same time, modern cloud providers operate multiple geographically-distribut…
Using Meta-heuristics and Machine Learning for Software Optimization of Parallel Computing Systems: A Systematic Literature Review
Suejb Memeti, Sabri Pllana, Alecio Binotto +2
While modern parallel computing systems offer high performance, utilizing these powerful computing resources to the highest possible extent demands advanced knowledge of various ha…
Challenges and Recommendations for Preparing HPC Applications for Exascale
Erika Abraham, Costas Bekas, Ivona Brandic +5
While the HPC community is working towards the development of the first Exaflop computer (expected around 2020), after reaching the Petaflop milestone in 2008 still only few HPC ap…
Energy-Aware Cloud Management through Progressive SLA Specification
Dražen Lučanin, Foued Jrad, Ivona Brandic +1
Novel energy-aware cloud management methods dynamically reallocate computation across geographically distributed data centers to leverage regional electricity price and temperature…
Take a break: cloud scheduling optimized for real-time electricity pricing
Dražen Lučanin, Ivona Brandić
Cloud computing revolutionised the industry with its elastic, on-demand approach to computational resources, but has lead to a tremendous impact on the environment. Data centers co…
Energy Efficient Service Delivery in Clouds in Compliance with the Kyoto Protocol
Drazen Lucanin, Michael Maurer, Toni Mastelic +1
Cloud computing is revolutionizing the ICT landscape by providing scalable and efficient computing resources on demand. The ICT industry - especially data centers, are responsible…