Papers & Conferences

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.
A Deep Transfer Learning-powered EDoS Detection Mechanism for 5G and Beyond Network Slicing
December 2023
C. Benzaid, T. Taleb, A. Sami, and O. Hireche
GLOBECOM 2023 IEEE Global Communications Conference
Network slicing is recognized as a key enabler for 5G and beyond (B5G) services. However, its dynamic nature and the growing sophistication of DDoS attacks put it at risk of Economical Denial of Sustainability (EDoS) attack, causing economic losses to service provider due to the increased elastic use of resources. Motivated by the limitations of existing solutions, we propose FortisEDoS, a novel framework that aims at enabling EDoS-aware elastic B5G services. FortisEDoS integrates a new deep learning-based DDoS anomaly detection model, called CG-GRU, that leverages the capabilities of emerging graph and recurrent neural networks in capturing spatio-temporal correlations to accurately identify malicious behavior, allowing proactive mitigation of EDoS attacks. Moreover, FortisEDoS uses transfer learning to effectively counteract EDoS attacks in newly deployed slices by leveraging the knowledge acquired in previously deployed slice. The experimental results show the superiority of transfer learning-powered CG-GRU in achieving higher detection performance with lower computation overhead, compared to other baseline methods.
Security in Cloud-Native Services: A Survey
26 October 2023
Theodoros Theodoropoulos, Luis Rosa, Chafika Benzaid, Peter Gray, Eduard Marin, Antonios Makris, Luis Cordeiro, Ferran Diego, Pavel Sorokin, Marco Di Girolamo, Paolo Barone, Tarik Taleb, Konstantinos Tserpes
Journal of Cybersecurity and Privacy, Vol.3, No.4
Cloud-native services face unique cybersecurity challenges due to their distributed infrastructure. They are susceptible to various threats like malware, DDoS attacks, and Man-in-the-Middle (MITM) attacks. Additionally, these services often process sensitive data that must be protected from unauthorized access. On top of that, the dynamic and scalable nature of cloud-native services makes it difficult to maintain consistent security, as deploying new instances and infrastructure introduces new vulnerabilities. To address these challenges, efficient security solutions are needed to mitigate potential threats while aligning with the characteristics of cloud-native services. Despite the abundance of works focusing on security aspects in the cloud, there has been a notable lack of research that is focused on the security of cloud-native services. To address this gap, this work is the first survey that is dedicated to exploring security in cloud-native services. This work aims to provide a comprehensive investigation of the aspects, features, and solutions that are associated with security in cloud-native services. It serves as a uniquely structured mapping study that maps the key aspects to the corresponding features, and these features to numerous contemporary solutions. Furthermore, it includes the identification of various candidate open-source technologies that are capable of supporting the realization of each explored solution. Finally, it showcases how these solutions can work together in order to establish each corresponding feature. The insights and findings of this work can be used by cybersecurity professionals, such as developers and researchers, to enhance the security of cloud-native services.
FortisEDoS: A Deep Transfer Learning-empowered Economical Denial of Sustainability Detection Framework for Cloud-Native Network Slicing
25 September 2023
Chafika Benzaïd, Tarik Taleb, Ashkan Sami, Othmane Hireche
IEEE Transactions on Dependable and Secure Computing ( Early Access )
Network slicing is envisaged as the key to unlocking revenue growth in 5 G and beyond (B5G) networks. However, the dynamic nature of network slicing and the growing sophistication of DDoS attacks rises the menace of reshaping a stealthy DDoS into an Economical Denial of Sustainability (EDoS) attack. EDoS aims at incurring economic damages to service provider due to the increased elastic use of resources. Motivated by the limitations of existing defense solutions, we propose FortisEDoS, a novel framework that aims at enabling elastic B5G services that are impervious to EDoS attacks. FortisEDoS integrates a new deep learning-powered DDoS anomaly detection model, dubbed CG-GRU, that capitalizes on the capabilities of emerging graph and recurrent neural networks in capturing spatio-temporal correlations to accurately discriminate malicious behavior. Furthermore, FortisEDoS leverages transfer learning to effectively defeat EDoS attacks in newly deployed slices by exploiting the knowledge learned in a previously deployed slice. The experimental results demonstrate the superiority of CG-GRU in achieving higher detection performance of more than 92% with lower computation complexity. They show also that transfer learning can yield an attack detection sensitivity of above 91%, while accelerating the training process by at least 61%. Further analysis shows that FortisEDoS exhibits intuitive explainability of its decisions, fostering trust in deep learning-assisted systems.
Intelligent Multi-Domain Edge Orchestration for Highly Distributed Immersive Services: An Immersive Virtual Touring Use Case
02 July 2023
Tarik Zakaria Benmerar, Theodoros Theodoropoulos, Diogo Fevereiro, Luis Rosa, João Rodrigues, Tarik Taleb, Paolo Barone, Konstantinos Tserpes, Luis Cordeiro
2023 IEEE International Conference on Edge Computing and Communications (EDGE)
Edge cloud technologies in tandem with AI-enabled solutions can contribute to overcoming the challenges that pertain the distributed execution of immersive services and contribute towards providing a positive experience for the end-users. Intelligent resource management, orchestration, and prediction systems can optimize the deployment of services, adapt to changing demands, and ensure that the services are running smoothly. This paper introduces a novel architectural paradigm capable of facilitating multi-domain edge orchestration for highly distributed immersive services by incorporating a plethora of AI solutions and technological enablers that can support multi-domain edge deployments. The proposed architecture is designed to operate on the basis of multi-level specification blueprints, which decouple the simple high-level user-intent infrastructure definition from the AI-driven orchestration and the final execution plan. The Application Management Framework (AMF) offers a visual language and tool that can be used as an alternative to a formal method for creating the intent blueprint. In the frame of this work, the latter is validated by an immersive virtual touring use-case scenario.
CHARITY Project presents the highlights of the project developments
May 2022
Fermin Calvo
Second workshop on the future of XR: Current ecosystem and upcoming opportunities
Fermin Calvo presented the highlights of the CHARITY project during the "2nd Workshop on the future of XR: Current ecosystem and upcoming opportunities" organised by H2020 ARETE and H2020 iv4xr projects.
Cyango Presentation and what we are doing with CHARITY
April 2022
João Rodrigues
7th International XR Conference - ISCTE
João Rodrigues from Dotesfera presented their ongoing work on CHARITY and Cyango, the application behind the VR Tour creator use case.
CHARITY’s Holographic Assistant Use Case
December 2021
Uwe Herzog
Horizon Cloud Summit 2021
Uwe Herzog as coordinator of the CHARITY Project presented at “‘Success Stories and Use Cases from the European Cloud Community” session of H-Cloud Summit 2021, the CHARITY’s holographic assistant use case.
The future of XR Services in the wake of 6G
November 2021
Luis Cordeiro
International Symposium on 6G Networking
Luis Cordeiro from OneSource presented at the International Symposium on 6G Networking in Lisbon the future of XR Services, their upcoming network and computing challenges and the role of 6G in addressing them.
Cyango and VR Tour Creator
November 2021
João Rodrigues
Web Summit Lisbon 2021
Dotesfera participated on Web Summit Lisbon 2021, a popular technology event where it showcased Cyango, storytelling VR tool