Distributed and Parallel Systems
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!
BDVA – Big Data Value Association
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.
Authors: Martin Molan, Junaid Ahmed Khan, Andrea Bartolini, Roberta Turra, Giorgio Pedrazzi, Michael Cochez, Alexandru Iosup, Dumitru Roman, Jože Rožanec, Ana Lucia Vărbănescu, Radu Prodan
COMPSAC 2023: 1st IEEE International Workshop on Digital Twins for Metaverse
Abstract: Modeling and understanding an expensive next-generation data center operating at a sustainable exascale performance remains a challenge yet to solve. The paper presents the approach taken by the Graph-Massivizer project, funded by the European Union, towards a sustainable data center, targeting a massive graph representation and analysis of its digital twin. We introduce five interoperable open-source tools that support this undertaking, creating an automated, ustainable loop of graph creation, analytics, optimization, sustainable resource management, and operation, emphasizing state-of-the-art progress. We plan to employ the tools for designing a massive data center graph, representing a digital twin describing spatial, semantic, and temporal relationships between the monitoring metrics, hardware nodes, cooling equipment, and jobs. The project aims to strengthen Bologna Technopole as a leading European supercomputing and big data hub offering sustainable green computing for improved societally relevant science throughput.
Authors: Akif Quddus Khan, Nikolay Nikolov, Mihhail Matskin‡, Radu Prodan, Christoph Bussler, Dumitru Roman, Ahmet Soylu
COMPSAC 2023: 8th IEEE International Workshop on Distributed Big Data Management
Abstract: Cloud computing has become an increasingly popular choice for businesses and individuals due to its flexibility, scalability, and convenience; owever, the rising cost of cloud resources has become a significant concern for many. The pay-per-use model used in cloud computing means that costs can accumulate quickly, and the lack of visibility and control can result in unexpected expenses. The cost structure becomes even more complicated when dealing with hybrid or multi-cloud environments. For businesses, the cost of cloud computing can be a significant portion of their IT budget, and any savings can lead to better financial stability and competitiveness. In this respect, it is essential to manage cloud costs effectively. This requires a deep understanding of current resource utilization, forecasting future needs, and optimising resource utilization to control costs. To address this challenge, new tools and techniques are being developed to provide more visibility and control over cloud computing costs. In this respect, this paper explores a graph-based solution for modelling cost elements and cloud resources and potential ways to solve the resulting constraint problem of cost optimisation. We primarily consider utilization, cost, performance, and availability in this context. Such an approach will eventually help organizations make informed decisions about cloud resource placement and manage the costs of software applications and data workflows deployed in single, hybrid, or multi-cloud environments.
Dragi Kimovski participated in the EU Concentration and Consultation event on the computing continuum, held in Brussels on May 10-11, 2023. During the event, he delivered a presentation on the groundbreaking DataCloud H2020 Project and actively engaged in open panels and poster sessions.
