Papers & Conferences
Papers
StreamK3s: A K3s-Based Data Stream Processing Platform for Simplifying Pipeline Creation, Deployment, and Scaling
June 2024
Ioannis Korontanis, Antonios Makris, Alexandros Kontogiannis, Iraklis Varlamis, Konstantinos Tserpes
SoftwareX
In today’s technology-driven era, applications focused on data stream processing are increasingly in need of user-friendly platforms, especially with the growing popularity of serverless computing solutions. Developers seek features such as straightforward pipeline definition, component reusability, throughput-based automatic scaling, and efficient resource utilization, highlighting the demand for such capabilities. This paper introduces a platform that addresses these requirements, leveraging technologies like K3s, RabbitMQ, and KEDA. In contrast to conventional platforms, the presented solution excels through its seamless integration of adaptability and user-friendliness, surpassing fundamental capabilities. The platform’s simple self- configuration streamlines the deployment of developer-created functions, improving efficiency and guaranteeing a seamless experience. This allows developers to focus on creating functions without the burden of managing complex configurations.
MAGES 4.0: Accelerating the world’s transition to VR training and democratizing the authoring of the medical metaverse
June 2024
Antonis Protopsaltis, Maria Pateraki, Manos Kamarianakis, George Papagiannakis
IEEE Computer Graphics and Applications
In this work, we propose MAGES 4.0, a novel software development kit to accelerate the creation of collaborative medical training applications in virtual/augmented reality (VR/AR). Our solution is essentially a low-code metaverse authoring platform for developers to rapidly prototype high-fidelity and high-complexity medical simulations. MAGES breaks the authoring boundaries across extended reality, since networked participants can also collaborate using different VR/AR as well as mobile and desktop devices, in the same metaverse world. With MAGES we propose an upgrade to the outdated 150-year-old master–apprentice medical training model. Our platform incorporates, in a nutshell, the following novelties: 1) 5G edge-cloud remote rendering and physics dissection layer, 2) realistic real-time simulation of organic tissues as soft-bodies under 10 ms, 3) a highly realistic cutting and tearing algorithm, 4) neural network assessment for user profiling and, 5) a VR recorder to record and replay or debrief the training simulation from any perspective.
EdgeCloud Mon: A lightweight monitoring stack for K3s clusters
May 2024
Ioannis Korontanis, Antonios Makris, Konstantinos Tserpes
SoftwareX
Platforms that utilize resources spanning from the Edge and Cloud continuum demand a monitoring system capable of reporting diverse metrics for various applications on heterogeneous resources. EdgeCloud Mon guarantees consistent monitoring of application components, whether deployed on Edge or Cloud resources, while also addressing the multitude of computing power variations within the Edge and Cloud continuum. Consequently, this monitoring mechanism is lightweight enough to operate efficiently on Edge resources yet robust enough to deliver accurate results for both Cloud and Edge resources.
Pro-active component image placement in Edge computing environments
April 2024
Antonios Makris, Evangelos Psomakelis, Emanuele Carlini, Matteo Mordacchini, Patrizio Dazzi, Theodoros Theodoropoulos, Konstantinos Tserpes
Future Generation Computer Systems
Edge computing has attracted a lot of attention both from industry and academia in recent years and is considered as a key enabler for addressing the increasingly strict requirements of Next Generation applications. Contrary to Cloud computing, in Edge computing the computation are placed closer to the end-users into the so-called Edge, to facilitate low-latency and high-bandwidth applications and services that would not be feasible using cloud and far remote processing alone. However, the distributed, dynamic and heterogeneous environment in the Edge computing along with the diverse applications’ requirements make service placement in such infrastructure a challenging issue. One important aspect of Edge computing is the management of the placement of the applications in the network system so as to minimize each application’s runtime, given the resources of system’s devices and the capabilities of the system’s network. To this end, we propose an empirical experimental analysis, by comparing the results of different placements strategies and various edge communication networks. In particular, we model the problem of proactive placement of application images as a Minimum Vertex Cover problem. Our results demonstrate that the Greedy implementation seems to offer the best tradeoff in terms of performance, cost function and execution time.
Joint Task and Computing Resource Allocation in Distributed Edge Computing Systems via Multi-Agent Deep Reinforcement Learning
March 2024
Yan Chen, Yanjing Sun, Hao Yu, Tarik Taleb
IEEE Transactions on Network Science and Engineering
Edge servers can collaborate to enhance service capability. However, cloud servers may be unable to execute centralized management due to unpredictable communications. In such systems, distributed task and resource management are vital but challenging due to heterogeneity and various restrictions. Therefore, this paper studies such edge systems and formulates the distributed joint task and computing resource allocation problem for maximizing the quality of experience (QoE). Given the restrictions on real-time state observations and resource management involving other facilities, we decompose it into sub-problems of distributed task allocation and computing resource allocation. After formulating the problem as a partially observed Markov decision process, we propose a two-step approach that depends on multi-agent (MA) deep reinforcement learning. First, each edge server performs a policy to allocate tasks for its associated users according to a partial observation. We employ the MA deep deterministic policy gradient to tackle vast spaces of discrete actions. Besides, we incorporate the action entropy of massive users' task allocation to enhance exploration. Then, we prove that the QoE-maximized computing resource allocation is a problem of maxing a sum of sigmoids, and we address it by sigmoidal programming. Simulation results reveal that the proposed approach dramatically improves the system QoE and reduces the average service latency. Besides, the proposed solution outperforms benchmarks in training and convergence.
Conferences
8th Annual Virtual Reality and Healthcare Global Symposium
February 2024
ORAMA
8th Annual Virtual Reality and Healthcare Global Symposium
ORAMA’s talk focusing on “Generative AI for Authoring Medical XR Training Applications”
Capgemini Portugal
September 2023
DOTES
Online
Presenting Cyango to Capgemini Portugal
ACM SIGGRAPH 2023
August 2023
ORAMA
ACM SIGGRAPH 2023
ORAMA presented a talk regarding “Computational Medical XR: Spatial, Neural and Wearable Computing Converging to Transform Healthcare” and provided a workshop regarding computational medical XR.
IEEE International Conference on Joint Cloud Computing (JCC)
July 2023
HUA
IEEE International Conference on Joint Cloud Computing (JCC)
HUA moderated the IEEE International Conference on Joint Cloud Computing (JCC) 2023 forum for researchers and practitioners
European Conference on Networks and Communications (EuCNC) and the 6G Summit 2023
June 2023
OneSource, DOTES, HPE, CNR
European Conference on Networks and Communications (EuCNC) and the 6G Summit
ONESOURCE, DOTES, HPE and CNR presented and demonstrated the CHARITY framework.
