Distributed and Parallel Systems

Autohors: Auday Al-Dulaimy, Matthijs Jansen, Bjarne Johansson, Animesh Trivedi, Alexandru Iosup, Mohammad Ashjaei, Antonino Galletta, Dragi Kimovski, Radu Prodan, Konstantinos Tserpes, George Kousiouris, Chris Giannakos, Ivona Brandic, Nawfal Ali, Andre B. Bondi, Alessandro V. Papadopoulos

Journal “Internet of things”: https://link.springer.com/journal/43926


In the era of the IoT revolution, applications are becoming ever more sophisticated and accompanied by diverse functional and non-functional requirements, including those related to computing resources and performance levels. Such requirements make the development and implementation of these applications complex and challenging. Computing models, such as cloud computing, can provide applications with on-demand computation and storage resources to meet their needs. Although cloud computing is a great enabler for IoT and endpoint devices, its limitations make it unsuitable to fulfill all design goals of novel applications and use cases. Instead of only relying on cloud computing, leveraging and integrating resources at different layers (like IoT, edge, and cloud) is necessary to form and utilize a computing continuum.

The layers’ integration in the computing continuum offers a wide range of innovative services, but it introduces new challenges (e.g., monitoring performance and ensuring security) that need to be investigated. A better grasp and more profound understanding of the computing continuum can guide researchers and developers in tackling and overcoming such challenges. Thus, this paper provides a comprehensive and unified view of the computing continuum. The paper discusses computing models in general with a focus on cloud computing, the computing models that emerged beyond the cloud, and the communication technologies that enable computing in the continuum. In addition, two novel reference architectures are presented in this work: one for edge-cloud computing models and the other for edge-cloud communication technologies. We demonstrate real use cases from different application domains (like industry and science) to validate the proposed reference architectures, and we show how these use cases map onto the reference architectures. Finally, the paper highlights key points that express the authors’ vision about efficiently enabling and utilizing the computing continuum in the future.

Title: Cataract-1K Dataset for Deep-Learning-Assisted Analysis of Cataract Surgery Videos

Authors: Negin Ghamsarian, Yosuf El-Shabrawi, Sahar Nasirihaghighi, Doris Putzgruber-Adamitsch, Martin Zinkernagel, Sebastian Wolf, Klaus Schoeffmann, and Raphael Sznitman

Abstract: In recent years, the landscape of computer-assisted interventions and post-operative surgical video analysis has been dramatically reshaped by deep-learning techniques, resulting in significant advancements in surgeons’ skills, operation room management, and overall surgical outcomes. However, the progression of deep-learning-powered surgical technologies is profoundly reliant on large-scale datasets and annotations. In particular, surgical scene understanding and phase recognition stand as pivotal pillars within the realm of computer-assisted surgery and post-operative assessment of cataract surgery videos. In this context, we present the largest cataract surgery video dataset that addresses diverse requisites for constructing computerized surgical workflow analysis and detecting post-operative irregularities in cataract surgery. We validate the quality of annotations by benchmarking the performance of several state-of-the-art neural network architectures for phase recognition and surgical scene segmentation. Besides, we initiate the research on domain adaptation for instrument segmentation in cataract surgery by evaluating cross-domain instrument segmentation performance in cataract surgery videos. The dataset and annotations are publicly available in Synapse.


The paper is available here: https://doi.org/10.1038/s41597-024-03193-4

Authors: Reza Farahani (AAU, Austria), and Vignesh V Menon (Fraunhofer HHI, Berlin, Germany)

Venue: The 12th European Workshop on Visual Information Processing (EUVIP 2024)

08-11 September, 2024 in Geneva, Switzerland

Based on the 2023 TPDS editorial data and his excellent performance, Radu Prodan received the 2024 IEEE TPDS Award for Editorial Excellence. His achievement will be recognized by IEEE and his name will appear at the IEEE award website https://next-test.computer.org/digital-library/journals/td/tpds-award-for-editorial-excellence.


Authors: Seyedehhaleh Seyeddizaji, Joze Martin Rozanec, Reza Farahani, Dumitru Roman and Radu Prodan

Venue: The 2nd Workshop on Serverless, Extreme-Scale, and Sustainable Graph Processing Systems Co-located with ICPE 2024

Abstract: While graph sampling is key to scalable processing, little research has tried to thoroughly compare and understand how it preserves features such as degree, clustering, and distances dependent on the graph size and structural properties. This research evaluates twelve widely adopted sampling algorithms across synthetic and real datasets to assess their qualities in three metrics: degree, clustering coefficient (CC), and hop plots. We find the random jump algorithm to be an appropriate choice regarding degree and hop-plot metrics and the random node for CC metric. In addition, we interpret the algorithms’ sample quality by conducting correlation analysis with diverse graph properties. We discover eigenvector centrality and path-related features as essential features for these algorithms’ degree quality estimation, node numbers (or the size of the largest connected component) as informative features for CC quality estimation and degree entropy, edge betweenness and path-related features as meaningful features for hop-plot metric. Furthermore, with increasing graph size, most sampling algorithms produce better-quality samples under degree and hop-plot metrics.





Authors: Reza Farahani, Frank Loh, Dumitru Roman, and Radu Prodan
Venue: The 2nd Workshop on Serverless, Extreme-Scale, and Sustainable Graph Processing Systems Co-located with ICPE 2024
Abstract: The growing desire among application providers for a cost model
based on pay-per-use, combined with the need for a seamlessly
integrated platform to manage the complex workflows of their
applications, has spurred the emergence of a promising comput-
ing paradigm known as serverless computing. Although serverless
computing was initially considered for cloud environments, it has
recently been extended to other layers of the computing continuum,
i.e., edge and fog. This extension emphasizes that the proximity of
computational resources to data sources can further reduce costs
and improve performance and energy efficiency. However, orches-
trating the computing continuum in complex application workflows,
including a set of serverless functions, introduces new challenges.
This paper investigates the opportunities and challenges introduced
by serverless computing for workflow management systems (WMS)
on the computing continuum. In addition, the paper provides a
taxonomy of state-of-the-art WMSs and reviews their capabilities.

Title: Cloud Storage Tier Optimization through Storage Object Classification

Authors: Akif Quddus Khan, Mihhail Matskin, Radu Prodan, Christoph Bussler, Dumitru Roman, Ahmet Soylu

Abstract: Cloud storage adoption has increased over the years given the high demand for fast processing, low access latency, and ever-increasing amount of data being generated by, e.g., Internet of Things (IoT) applications. In order to meet the users’ demands and provide a cost-effective solution, cloud service providers (CSPs) offer tiered storage; however, keeping the data in one tier is not cost-effective. In this respect, cloud storage tier optimization involves aligning data storage needs with the most suitable and cost-effective storage tier, thus reducing costs while ensuring data availability and meeting performance requirements. Ideally, this process considers the trade-off between performance and cost, as different storage tiers offer different levels of performance and durability. It also encompasses data lifecycle management, where data is automatically moved between tiers based on access patterns, which in turn impacts the storage cost. In this respect, this article explores two novel classification approaches, rule-based and game theory-based, to optimize cloud storage cost by reassigning data between different storage tiers. Four distinct storage tiers are considered: premium, hot, cold, and archive. The viability and potential of the proposed approaches are demonstrated by comparing cost savings and analyzing the computational cost using both fully-synthetic and semi-synthetic datasets with static and dynamic access patterns. The results indicate that the proposed approaches have the potential to significantly reduce cloud storage cost, while being computationally feasible for practical applications. Both approaches are lightweight and industry- and platform-independent.

Computing, https://link.springer.com/journal/607

Radu Prodan has been invited and will participate as a general chair at the ICONIC 2024, April 26-27, 2024, at Lovely Professional University, Punjab, India.

The Conference will provide a platform for scientists, researchers, academicians, industrialists, and students to assimilate the knowledge and get the opportunity to discuss and share insights through deep-dive research findings on the recent disruptions and developments in computing. All technical sessions will largely be steering Network Technologies, Artificial Intelligence and ethics, Advances in Computing, Futuristic Trends in Data Science, Security and Privacy, Data Mining and Information Retrieval.


  • To provide a platform to facilitate the exchange of knowledge, ideas, and innovations among scientists, researchers, academicians, industrialists, and students.
  • To deliberate and disseminate the recent advancements and challenges in the computing sciences.
  • To enable the delegates to establish research or business relations and find international linkage for future collaborations.

Cluster Computing

DFARM: A deadline-aware fault-tolerant scheduler for cloud computing

Authors: Ahmad Awan, Muhammad Aleem, Altaf Hussain, Radu Prodan


Cloud computing has become popular for small businesses due to its cost-effectiveness and the ability to acquire necessary on-demand services, including software, hardware, network, etc., anytime around the globe. Efficient job scheduling in the Cloud is essential to optimize operational costs in data centers. Therefore, scheduling should consider assigning tasks to Virtual Machines (VMs) in a Cloud environment in such a manner that could speed up execution, maximize resource utilization, and meet users’ SLA and other constraints such as deadlines. For this purpose, the tasks can be prioritized based on their deadlines and task lengths, and the resources could be provisioned and released as needed. Moreover, to cope with unexpected execution situations or hardware failures, a fault-tolerance mechanism could be employed based on hybrid replication and the re-submission method. Most of the existing techniques tend to improve performance. However, their pitfall lies in certain aspects such as either those techniques prioritize tasks based on a singular value (e.g., usually deadline), only utilize a singular fault tolerance mechanism, or try to release resources that cause more overhead immediately. This research work proposes a new scheduler called the Deadline and fault-aware task Adjusting and Resource Managing (DFARM) scheduler, the scheduler dynamically acquires resources and schedules deadline-constrained tasks by considering both their length and deadlines while providing fault tolerance through the hybrid replication-resubmission method. Besides acquiring resources, it also releases resources based on their boot time to lessen costs due to reboots. The performance of the DFARM scheduler is compared to other scheduling algorithms, such as Random Selection, Round Robin, Minimum Completion Time, RALBA, and OG-RADL. With a comparable execution performance, the proposed DFARM scheduler reduces task-rejection rates by $2.34 – 9.53$ times compared to the state-of-the-art schedulers using two benchmark datasets.

Journal of Grid Computing

Authors: Zeinab Bakhshi, Guillermo Rodriguez-Navas, Hans Hansson, Radu Prodan


This paper analyzes a persistent storage method’s timing performance for distributed container-based architectures in industrial control applications. The method focuses on ensuring data availability and consistency while accommodating faults. The analysis considers four aspects: placement strategy, design options, data size, and evaluation under faulty conditions. Experimental results considering the timing constraints in industrial applications indicate that the storage solution can meet critical deadlines, particularly under specific failure patterns. Moreover, the method is applicable for evaluating timing constraints in other container-based critical applications that require persistent storage.Further comparison results reveal that, while the method may underperform current centralized solutions under fault-free conditions, it outperforms the centralized solutions in failure scenarios.