Tag Archive for: paper

“Dependency-Aware Microservice Deployment for Edge Computing: A Deep Reinforcement Learning Approach With Network Representation” Paper

/0 Comments/in /by

The popularity of microservices in industry has sparked much attention in the research community. Despite significant progress in microservice deployment for resource-intensive services and applications at the network edge, the intricate dependencies among microservices are often overlooked, and some studies underestimate the importance of system context extraction in deployment strategies. This paper addresses these issues by formulating the microservice deployment problem as a max-min problem, considering system cost and quality of service (QoS) jointly. We first study the attention-based microservice representation (AMR) method to achieve effective system context extraction. In this way, the contributions of different computing power providers (users, edge servers, or cloud servers) in the networks can be effectively paid attention to. Subsequently, we propose the attention-modified soft actor-critic (ASAC) algorithm to tackle the microservice deployment problem. ASAC leverages attention mechanisms to enhance decision-making and adapt to changing system dynamics. Our simulation results demonstrate ASAC’s effectiveness, prioritizing average system cost and reward compared to the other state-of-the-art algorithms.

Find more information here: https://ieeexplore.ieee.org/document/10663201 

“RMF: A Risk Measurement Framework for Machine Learning Models” Paper

/0 Comments/in /by

RMF: A Risk Measurement Framework for ML Models is a new tool designed to help teams assess the vulnerability of their machine learning models to real-world attacks. It evaluates how easy and costly it is to attack a model, helping teams prioritize the right defenses. Especially valuable for AI systems in security-critical sectors like finance and healthcare, RMF supports safer, more resilient deployment of machine learning

Find more here: https://dl.acm.org/doi/10.1145/3664476.3670867 

A Novel Multiple Access Scheme for Heterogeneous Wireless Communications Using Symmetry-Aware Continual Deep Reinforcement Learning Journal Paper

/0 Comments/in /by

The Metaverse holds the potential to revolutionize digital interactions through the establishment of a highly dynamic and immersive virtual realm over wireless communications systems, offering services such as massive twinning and telepresence. This landscape presents novel challenges, particularly efficient management of multiple access to the frequency spectrum, for which numerous adaptive Deep Reinforcement Learning (DRL) approaches have been explored. However, challenges persist in adapting agents to heterogeneous and non-stationary wireless environments. In this paper, we present a novel approach that leverages Continual Learning (CL) to enhance intelligent Medium Access Control (MAC) protocols, featuring an intelligent agent coexisting with legacy User Equipments (UEs) with varying numbers, protocols, and transmission profiles unknown to the agent for the sake of backward compatibility and privacy. We introduce an adaptive Double and Dueling Deep Q-Learning (D3QL)-based MAC protocol, enriched by a symmetry-aware CL mechanism, which maximizes intelligent agent throughput while ensuring fairness. Mathematical analysis validates the efficiency of our proposed scheme, showcasing superiority over conventional DRL-based techniques in terms of throughput, collision rate, and fairness, coupled with real-time responsiveness in highly dynamic scenarios.

Find the paper online here: https://ieeexplore.ieee.org/document/10908203

Transactions on Instrumentation and Measurement entitled: Semantic-Enhanced Digital Twin System for Robot–Environment Interaction Monitoring” paper

/0 Comments/in /by

The publication in IEEE “Transactions on Instrumentation and Measurement entitled: Semantic-Enhanced Digital Twin System for Robot–Environment Interaction Monitoring” is now available!

You may find more information here: https://ieeexplore.ieee.org/document/9380190

Another Look at Side-Channel-Resistant Encoding Schemes paper

/0 Comments/in /by

The publication in IEEE Xplore entitled “Another Look at Side-Channel-Resistant Encoding Schemes”, linked to the aerOS project, explores cutting-edge advancements in edge computing and orchestration. As we push the boundaries of AI-driven, adaptive, and efficient computing infrastructures, this work marks another step toward a more connected and intelligent future.
🔗https://ieeexplore.ieee.org/document/10508964

“Approaching interoperability and data-related processing issues in a human-centric industrial scenario” paper

/0 Comments/in /by

On September 24th, Walter Quadrini presented at the Global IoT&Edge Computing Summit a peer reviewed scientific paper named “Approaching interoperability and data-related processing issues in a human-centric industrial scenario” which won the best paper award by the conference scientific committee. This work highlighted the capabilities of private 5G networks and of Edge to Cloud infrastructures in solving the latency and computational efficiency-related issues that the manufacturing sector is going to face while moving towards a human-centric production. The talks with the AIOTI community which followed the conference also allowed to highlight how the aerOS solution could pave the way towards an effective and robust integration of edge nodes into complex informative systems.

“Federated Deep Reinforcement Learning for Prediction-Based Network Slice Mobility in 6G Mobile Networks” Journal Paper

/in /by

Network slices are generally coupled with services and face service continuity/unavailability concerns due to the high mobility and dynamic requests from users. Network slice mobility (NSM), which considers user mobility, service migration, and resource allocation from a holistic view, is witnessed as a key technology in enabling network slices to respond quickly to service degradation. Existing studies on NSM either ignored the trigger detection before NSM decision-making or didn’t consider the prediction of future system information to improve the NSM performance, and the training of deep reinforcement learning (DRL) agents also faces challenges with incomplete observations. To cope with these challenges, we consider that network slices migrate periodically and utilize the prediction of system information to assist NSM decision-making. The periodical NSM problem is further transformed into a Markov decision process, and we creatively propose a prediction-based federated DRL framework to solve it. Particularly, the learning processes of the prediction model and DRL agents are performed in a federated learning paradigm. Based on extensive experiments, simulation results demonstrate that the proposed scheme outperforms the considered baseline schemes in improving long-term profit, reducing communication overhead, and saving transmission time.

“A novel approach to self-capabilities in IoT industrial automation computing continuum” paper presentation

/in , , /by

aerOS paper entitled “A novel approach to self-capabilities in IoT industrial automation computing continuum” was presented during WF-IoT 2023! The 2023 IEEE 9th World Forum on Internet of Things (WF-IoT 2023) brings the latest from the research and academic community. It included a broad program of papers and presentations on the latest technology developments and innovations in the many fields and disciplines that drive the utility and vitality of IoT solutions and applications. After extensive peer review, the best of the proposals were selected for the conference program, which included technical papers, tutorials workshops and industry sessions designed specifically to advance technologies, end to end solutions, systems and infrastructure, processes, and operating methods, that are contributing to how IoT can reshape the world and overcome the challenges we face, so that all individuals can participate in and enjoy life to the fullest.

“A Survey on In-Network Computing: Programmable Data Plane and Technology Specific Applications” Paper

/in , /by

The aerOS journal paper entitled “A Survey on In-Network Computing: Programmable Data Plane and Technology Specific Applications” is now available!
In comparison with cloud computing, edge computing offers processing at locations closer to end devices and reduces the user experienced latency. The new recent paradigm of in-network computing employs programmable network elements to compute on the path and prior to traffic reaching the edge or cloud servers. It advances common edge/cloud server based computing through proposing line rate processing capabilities at closer locations to the end devices. This paper discusses use cases, enabler technologies and protocols for in-network computing. According to our study, considering programmable data plane as an enabler technology, potential in-network computing applications are in-network analytics, in-network caching, in-network security, and in-network coordination. There are also technology specific applications of in-network computing in the scopes of cloud computing, edge computing, 5G/6G, and NFV. In this survey, the state of the art, in the framework of the proposed categorization, is reviewed. Furthermore, comparisons are provided in terms of a set of proposed criteria which assess the methods from the aspects of methodology, main results, as well as application-specific criteria. Finally, we discuss lessons learned and highlight some potential research directions.
Find more information about the paper here: https://ieeexplore.ieee.org/document/9919270

A Novel Combinatorial Multi-Armed Bandit Game to Identify Online the Changing top-K Flows in Software-Defined Networks – Paper

/in , /by

Identifying the top-K flows that require much more bandwidth resources in a large-scale Software-Defined Network (SDN) is essential for many network management tasks, such as load balancing, anomaly detection, and traffic engineering. However, identifying such top-K flows is not trivial, not only because of the fluctuations in flow bandwidth requirements but also because of the combinatorial explosion of problem instance sizes. In this paper, we weaken the tradeoff between exploration and exploitation and innovatively define the online top-K flows identification problem as identifying the top-K arms in a Combinatorial Multi-Armed Bandit (CMAB) model. Then, we propose a general greedy selection mechanism with some identification strategies that focus on temporal variations in the rewards. Extensive simulation experiments based on real traffic data are conducted to evaluate the performance of different strategies. In addition, the results of numerical simulations demonstrate that our proposed greedy selection mechanism significantly outperforms existing counterparts on top-K arms identification.

Find the paper online here: http://mosaic-lab.org/uploads/papers/6fa449ad-7848-4db5-889f-f81f790b516d.pdf