The abstract of her disseration is as follows:
Distributed and Parallel Systems
Authors: Juanjuan Li, Rui Qin, Cristina Olaverri-Monreal, Radu Prodan, Fei-Yue Wang
Journal: IEEE Transactions on Intelligent Vehicles
Abstract: As part of TIV’s DHW on Vehicle 5.0, this letter introduces a novel concept, Logistics 5.0, to address high complexities in logistics CyberPhysical-Social Systems (CPSS). Building upon the theory of parallel intelligence and leveraging advanced technologies and methods such as blockchain, scenarios engineering, Decentralized Autonomous Organizations and Operations (DAOs), Logistics 5.0 promises to accelerate the paradigm shift towards intelligent and sustainable logistics. First, the parallel logistic framework is proposed, and the logistics ecosystem is discussed. Then, the human-oriented operating systems (HOOS) are suggested to providing intelligent Logistics 5.0 solutions. Logistics 5.0 serves as a critical catalyst in realizing the “6S” objectives, i.e. Safety, Security, Sustainability, Sensitivity, Service, and Smartness, within the logistics industry
Title: A distributed and energy-efficient KNN for EEG classification with dynamic money-saving policy in heterogeneous clusters
Authors: Juan José Escobar, Francisco Rodríguez, Beatriz Prieto, Dragi Kimovski, Andrés Ortiz, and Miguel Damas
Abstract: Due to energy consumption’s increasing importance in recent years, energy-time efficiency is a highly relevant objective to address in High-Performance Computing (HPC) systems, where cost significantly impacts the tasks executed. Among these tasks, classification problems are considered due to their great computational complexity, which is sometimes aggravated when processing high-dimensional datasets. In addition, implementing efficient applications for high-performance systems is not an easy task since hardware must be considered to maximize performance, especially on heterogeneous platforms with multi-core CPUs. Thus, this article proposes an efficient distributed K-Nearest Neighbors (KNN) for Electroencephalogram (EEG) classification that uses minimum Redundancy Maximum Relevance (mRMR) as a feature selection technique to reduce the dimensionality of the dataset. The approach implements an energy policy that can stop or resume the execution of the program based on the cost per Megawatt. Since the procedure is based on the master-worker scheme, the performance of three different workload distributions is also analyzed to identify which one is more suitable according to the experimental conditions. The proposed approach outperforms the classification results obtained by previous works that use the same dataset. It achieves a speedup of 74.53 when running on a multi-node heterogeneous cluster, consuming only 13.38% of the energy consumed by the sequential version. Moreover, the results show that financial costs can be reduced when energy policy is activated and the importance of developing efficient methods, proving that energy-aware computing is necessary for sustainable computing.
SWForum.eu: The Way Forward: Workshop on Future Challenges in Software Engineering
https://www.flickr.com/photos/198632876@N07/sets/72177720309399251/
During the session, experts delved into the challenges of processing massive amounts of data and explored cutting-edge technologies that can handle such extreme data requirements.
From graph-based solutions to distributed computing frameworks, attendees shared valuable insights into the evolving landscape of data management. The discussion highlighted the need for scalable infrastructure and intelligent algorithms to efficiently process and analyze vast datasets. The future of data management is promising, thanks to innovative approaches showcased in the session. Stay tued as we continue to push the boundaries of data processing and drive advancements in the field through the Graph-Massivizer Project Together, we’re shaping the future of extreme data management!
Our Graph-Massivizer Project is thrilled to be part of the #DataWeek2023 event! Join us for a thought-provoking session on “Are current infrastructures suitable for extreme data processing? Technologies for data management.”
Don’t miss this opportunity to explore cutting-edge solutions and discuss the future of data processing together with Nuria De Lama Dumitru Roman Roberta Turra Radu Prodan Lilit Axner Jan Martinovič Bill Patrowicz Irena Pavlova! ?
? Tuesday 13th
⏰ 15:30 – 17:00
BDVA – Big Data Value Association
Container-based Data Pipelines on the Computing Continuum for Remote Patient Monitoring
Authors: Nikolay Nikolov, Arnor Solberg, Radu Prodan, Ahmet Soylu, Mihhail Matskin, Dumitru Roman
Computer Jounal, Special Issue on Computing in Telemedicine
Abstract: Diagnosing, treatment, and follow-up care of patients is happening increasingly through telemedicine, especially in remote areas where direct interaction is hindered. Over the past three years, following the COVID-19 pandemic, the utility of remote patient care has been further field-tested. Tackling the technical challenges of a growing demand for telemedicine requires a convergence of several fields: 1) software solutions for reliable, secure, and reusable data processing, 2) management of hardware resources (at scale) on the Cloud/Fog/Edge Computing Continuum, and 3) automation of DevOps processes for deployment of digital healthcare solutions with patients. In this context, the emerging concept of \emph{big data pipelines} provides relevant solutions and is one of the main enablers. In what follows, we present a data pipeline for remote patient monitoring and show a real-world example of how data pipelines help address the stringent requirements of telemedicine.
Radu Prodan presented the paper “Proactive SLA-aware Application Placement in the Computing Continuum” at the 37th IEEE International Parallel & Distributed Processing Symposium (IPDPS 2023) <https://www.ipdps.org/> in St. Petersburg, Florida, USA.
Authors: Zahra Najafabadi Samani, Narges Mehran, Dragi Kimovski, and Radu Prodan, Alpen-Adria-Universität Klagenfurt
Abstract: The accelerating growth of modern distributed applications with low delivery deadlines leads to a paradigm shift towards the multi-tier computing continuum. However, the geographical dispersion, heterogeneity, and availability of the continuum resources may result in failures and quality of service degradation, significantly negating its advantages and lowering users’ satisfaction. We propose in this paper a proactive application placement PROS method relying on distributed coordination to prevent the quality of service violations through service-level agreements on the computing continuum. PROS employs a sigmoid function with adaptive weights for the different parameters to predict the service level agreement assurance of devices based on their past credentials and current capabilities. We evaluate PROS using two application workloads with different traffic stress levels up to 90 million services on a real testbed with 600 heterogeneous instances deployed over eight geographical locations. The results show that PROS increases the success rate by 7-33%, reduces the response time by 16-38%, and increases the deadline satisfaction rate by 19-42% compared to the two related work methods. A comprehensive simulation study with 1000 devices and a workload of up to 670 million services confirms the scalability of the results.
Title: Serverless ECG Stream Processing in Federated Clouds with Lambda Architecture
Authors: Sashko Ristov, Marjan Gusev, Armin Hohenegger, Radu Prodan, Dimitar Mileski, Pano Gushev, Goran Temelkov
Computer Jounal, Special Issue on Computing in Telemedicine
Abstract: Although telemedicine has emerged as an everyday necessity for health monitoring, still current solutions are built for a small number of patients or use sensors that do not stream data with high velocity and volume. In this article, we explore a novel architecture for distributing health monitoring computations over distributed cloud regions, both for constantly online patients and offline for several hours daily. We propose a conceptual architecture for a use-case example capable of processing thousands of simultaneous incoming streams with electrocardiogram signals. Current serverless cloud providers limit the concurrency within a single region, and we evaluate the performance of this solution across multiple cloud regions. The results indicate that our new solution can overcome the limitations of a single cloud for online and offline patients, thereby saving their lives in case of detected dangerous arrhythmia.