Zobrazeno 1 - 10
of 63
pro vyhledávání: '"Brandić, Ivona"'
Edge computing provides resources for IoT workloads at the network edge. Monitoring systems are vital for efficiently managing resources and application workloads by collecting, storing, and providing relevant information about the state of the resou
Externí odkaz:
http://arxiv.org/abs/2405.07806
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 scientific workflows. As the need for computing power increases, quantum co
Externí odkaz:
http://arxiv.org/abs/2404.10389
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 usually outsourced into HPC facilities, where we have started to experien
Externí odkaz:
http://arxiv.org/abs/2403.18579
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 enables Artificial Intelligence (AI) at the edge by creating models wi
Externí odkaz:
http://arxiv.org/abs/2403.16930
Autor:
De Maio, Vincenzo, Kanatbekova, Meerzhan, Zilk, Felix, Friis, Nicolai, Guggemos, Tobias, Brandic, Ivona
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 computing being among the most prominent and promising technologies.
Externí odkaz:
http://arxiv.org/abs/2403.00885
Publikováno v:
Euro-Par 2023: Parallel Processing Workshops. Euro-Par 2023. Lecture Notes in Computer Science, vol 14351. Springer, Cham
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 are the cornerstone of urgent analytics in distributed computing. Qua
Externí odkaz:
http://arxiv.org/abs/2402.15542
Publikováno v:
Euro-Par 2023: Parallel Processing pp 411-425. Springer Nature Switzerland, Cham (2023)
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 rapidly growing amount of data on resource-constrained edge devices. Symbo
Externí odkaz:
http://arxiv.org/abs/2309.03014
Autor:
Tundo, Alessandro, Mobilio, Marco, Ilager, Shashikant, Brandić, Ivona, Bartocci, Ezio, Mariani, Leonardo
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 resource-constrained devices populating the Internet of Things. The prolifera
Externí odkaz:
http://arxiv.org/abs/2309.00022
Autor:
Gill, Sukhpal Singh, Xu, Minxian, Ottaviani, Carlo, Patros, Panos, Bahsoon, Rami, Shaghaghi, Arash, Golec, Muhammed, Stankovski, Vlado, Wu, Huaming, Abraham, Ajith, Singh, Manmeet, Mehta, Harshit, Ghosh, Soumya K., Baker, Thar, Parlikad, Ajith Kumar, Lutfiyya, Hanan, Kanhere, Salil S., Sakellariou, Rizos, Dustdar, Schahram, Rana, Omer, Brandic, Ivona, Uhlig, Steve
Autonomic computing investigates how systems can achieve (user) specified control outcomes on their own, without the intervention of a human operator. Autonomic computing fundamentals have been substantially influenced by those of control theory for
Externí odkaz:
http://arxiv.org/abs/2203.04159
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 the smart grid. Although there have been many works on MG energy managem
Externí odkaz:
http://arxiv.org/abs/2111.11868