Your gateway to a wealth of knowledge and insights at the forefront of Edge-Cloud innovation.
Our diverse range of publications reflects our commitment to pushing the boundaries of the Edge-Cloud continuum, fostering a deeper understanding of its potential, and disseminating cutting-edge findings to the global community.
Whether you’re an academic researcher, industry expert, technology enthusiast, or simply curious about the evolving landscape of Edge-Cloud convergence, this repository offers you a journey into the heart of CODECO.
Authors: Rute C. Sofia, Doug Dykeman, Peter Urbanetz, Akram Galal, Dave Anirudhdhabhai, Dykeman, Doug, Urbanetz, Peter, Galal, Akram, Dushyan Dave Anirudhdhabhai
Abstract: Container orchestration handles the semi-automated management of applications across Edge-Cloud, providing features such as autoscaling, high availability, and portability. Having been developed for Cloud-based applications, container orchestration faces challenges in the context of decentralized Edge-Cloud environments, requiring a higher degree of adaptability in the verge of mobility, heterogeneous networks, and constrained devices. In this context, this perspective paper aims at igniting discussion on the aspects that a dynamic orchestration approach should integrate to support an elastic orchestration of containerized applications. The motivation for the provided perspective focuses on proposing directions to better support challenges faced by next-generation IoT services, such as mobility or privacy preservation, advocating the use of context awareness and a cognitive, cross-layer approach to container orchestration to be able to provide adequate support to next-generation services. A proof of concept (available open source software) of the discussed concept has been implemented in a testbed composed of embedded devices.
Authors: Biao Hou; Song Yang; Fernando A. Kuipers; Lei Jiao; Xiaoming Fu
Abstract: Recent years have witnessed video streaming grad- ually evolve into one of the most popular Internet applica- tions. With the rapidly growing personalized demand for real- time video streaming services, maximizing their Quality of Experience (QoE) is a long-standing challenge. The emergence of the serverless computing paradigm has potential to meet this challenge through its fine-grained management and highly parallel computing structures. However, it is still ambiguous how to implement and configure serverless components to optimize video streaming services. In this paper, we propose EAVS, an Edge-assisted Adaptive Video streaming system with Serverless pipelines, which facilitates fine-grained management for multiple concurrent video transmission pipelines. Then, we design a chunk-level optimization scheme to solve the video bitrate adap- tation issue. We propose a Deep Reinforcement Learning (DRL) algorithm based on Proximal Policy Optimization (PPO) with a trinal-clip mechanism to make bitrate decisions efficiently for better user-perceived QoE. Finally, we implement the serverless video streaming system prototype and evaluate the performance of EAVS on various real-world network traces. Extensive results show that our proposed EAVS significantly improves user- perceived QoE and reduces the video stall rate, achieving over 9.1% QoE improvement and 60.2% latency reduction compared to state-of-the-art solutions..
Authors: Nan He; Song Yang; Fan Li; Stojan Trajanovski; Liehuang Zhu; Yu Wang; Xiaoming Fu
Abstract: The efficacy of Network Function Virtualization (NFV) depends critically on (1) where the virtual network functions (VNFs) are placed and (2) how the traffic is routed. Unfortunately, these aspects are not easily optimized, especially under time-varying network states with different QoS requirements. Given the importance of NFV, many approaches have been proposed to solve the VNF placement and Service Function Chaining (SFC) routing problem. However, those prior approaches mainly assume that the network state is static and known, disregarding dynamic network variations. To bridge that gap, we leverage Markov Decision Process (MDP) to model the dynamic network state transitions. To jointly minimize the delay and cost of NFV providers and maximize the revenue, we first devise a customized Deep Reinforcement Learning (DRL) algorithm for the VNF placement problem. The algorithm uses the attention mechanism to ascertain smooth network behavior within the general framework of network utility maximization (NUM). We then propose attention mechanism-based DRL algorithm for the SFC routing problem, which is to find the path to deliver traffic for the VNFs placed on different nodes. The simulation results show that our proposed algorithms outperform the state-of-the-art algorithms in terms of network utility, delay, cost, and acceptance ratio.
Published in: IEEE Transactions on Parallel and Distributed Systems (Volume: 34, Issue 4, April 2023). DOI: 10.1109/TPDS.2023.3240404