Showing papers by "Xiaoming Fu published in 2023"
TL;DR: In this paper , the authors study the problem of online SFC control across geo-distributed datacenters, which is to dynamically place required VNFs on datacenter nodes and find routing paths between each adjacent VNF pair for each NFV service flow that varies over time.
Abstract: Network Function Virtualization (NFV) provides the possibility to implement complex network functions from dedicated hardware to software instances called Virtual Network Functions (VNF) by leveraging the virtualization technology. Service Function Chaining (SFC) is therefore defined as a chain-ordered set of placed VNFs that handles the traffic of the delivery and control of a specific application. Due to the advantages of flexibility, efficiency, scalability, and short deployment cycles, NFV has been widely recognized as the next-generation network service provisioning paradigm. In this paper, we study the problem of online SFC control across geo-distributed datacenters, which is to dynamically place required VNFs on datacenter nodes and find routing paths between each adjacent VNF pair for each NFV service flow that varies over time. To that end, we first formulate this problem as an offline optimization problem whose goal is to minimize the average delay such that each datacenter's average cost does not exceed a given expense value. Considering that the offline optimization requires complete offline network information which is difficult to obtain or predict in practice, we present an online SFC control framework without requiring any future information about the traffic demands. More specifically, we leverage the Lyapunov optimization technique to formulate the problem as a series of one-time slot offline optimization problems and then apply a primal-decomposition method to solve each one-time slot problem. Simulation results reveal that our proposed online SFC control framework can efficiently reduce long-term average delay while keeping datacenter's long-term average cost consumption low.
1 citations
TL;DR: In this article , a customized Deep Reinforcement Learning (DRL) algorithm was proposed to solve the VNF placement and Service Function Chaining (SFC) routing problem, which is to find the path to deliver traffic for the virtual network functions placed on different nodes.
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.
1 citations
TL;DR: In this article , the authors propose a method to compute globally injective parameterizations with arbitrary positional constraints on disk topology meshes using a scaffold mesh that reduces the globally-injective constraint to a locally flip-free condition.
Abstract: Abstract We propose a novel method to compute globally injective parameterizations with arbitrary positional constraints on disk topology meshes. Central to this method is the use of a scaffold mesh that reduces the globally injective constraint to a locally flipfree condition. Hence, given an initial parameterized mesh containing flipped triangles and satisfying the positional constraints, we only need to remove the flips of a overall mesh consisting of the parameterized mesh and the scaffold mesh while always meeting positional constraints. To successfully apply this idea, we develop two key techniques. Firstly, an initialization method is used to generate a valid scaffold mesh and mitigate difficulties in eliminating flips. Secondly, edge-based remeshing is used to optimize the regularity of the scaffold mesh containing flips, thereby improving practical robustness. Compared to state-of-the-art methods, our method is much more robust. We demonstrate the capability and feasibility of our method on a large number of complex meshes.
1 citations
09 May 2023
TL;DR: In this paper , the concept, purpose and significance of alveloplasty in implant supported full-arm fixed restoration, technology evolution and process to provide reference for clinical practice is discussed.
Abstract: Implant-supported full-arch fixed prosthesis is hot in edentulous therapy currently. Appropriate contour of bone is the premise of good restoration outcome. Alveoloplasty is an important part during treatment procedure. Alveoloplasty can be used to obtain bone platform for implant insertion, create adequate prosthetic space, achieve good Aesthetic effect, and form appropriate soft tissue morphology. The design of alveoloplasty has evolved from traditional plaster models and cone beam CT to three-dimensional (3D) virtual patients. The surgical techniques of alveoloplasty have also undergone the evolution from free-hand to static guide or dynamic navigation. This article elaborates on the concept, purpose and significance of alveloplasty in implant supported full-arch fixed restoration, technology evolution and process to provide reference for clinical practice.
TL;DR: In this article , a case of a 53-year-old male patient with SPG48 presenting spastic paraplegia, infertility, hearing impairment, cognitive abnormalities and peripheral neuropathy was described.
Abstract: Hereditary spastic paraplegias (HSP) are inherited neurodegenerative disorders characterized by progressive paraplegia and spasticity in the lower limbs. SPG48 represents a rare genotype characterized by mutations in AP5Z1, a gene playing a role in intracellular membrane trafficking. This study describes a case of a 53-year-old male patient with SPG48 presenting spastic paraplegia, infertility, hearing impairment, cognitive abnormalities and peripheral neuropathy. The Sanger sequencing revealed a homozygous deletion in the chr 7:4785904-4786677 region causing a premature stop codon in exon 10. The patient's brother was heterozygous for the mutation. The brain magnetic resonance imaging found a mild brain atrophy and white matter lesions. In the analysis of the auditory thresholds, we found a significant hearing decrease in both ears.
TL;DR: In this paper , the authors propose an end-to-end scheme, VSiM, for supporting mobile video streaming applications in heterogeneous wireless networks, which allocates bottleneck bandwidth among multiple users based on their mobility profiles and quality of experience (QoE)-related knowledge to achieve max-min QoE fairness.
Abstract: The rapid growth of mobile video traffic and user demand poses a more stringent requirement for efficient bandwidth allocation in mobile networks where multiple users may share a bottleneck link. This provides content providers an opportunity to jointly optimize multiple users’ experiences but users often suffer short connection durations and frequent handoffs because of their high mobility. In this paper, we propose an end-to-end scheme, VSiM, for supporting mobile video streaming applications in heterogeneous wireless networks. The key idea is allocating bottleneck bandwidth among multiple users based on their mobility profiles and Quality of Experience (QoE)-related knowledge to achieve max-min QoE fairness. Besides, the QoE of buffer-sensitive clients is further improved by the novel server push strategy based on HTTP/3 protocol without affecting the existing bandwidth allocation approach or sacrificing other clients’ view quality. VSiM is lightweight and easy to deploy in the real world without touching the underlying network infrastructure. We evaluated VSiM experimentally in both simulations and a lab testbed on top of the HTTP/3 protocol. We find that the clients’ QoE fairness of VSiM achieves more than 40% improvement compared with state-of-the-art solutions, i.e., the viewing quality of clients in VSiM can be improved from 720p to 1080p in resolution. Meanwhile, VSiM provides about 20% improvement of average QoE.
Proceedings Article•
TL;DR: Zhang et al. as discussed by the authors proposed a novel framework called CityTrans to transfer traveler mobility knowledge from hometown city to surrounding city, which considers both the long-term preference in hometown city and short-term interest drift in surrounding city.
Abstract: The increasingly built intercity transportation enables people to visit surrounding cities conveniently. Hence it is becoming a hot research topic to predict where a traveler would visit in a surrounding city based on check-in data collected from location-based mobile Apps. However, as most users rarely travel out of hometown, there is a high skew of the quantity of check-in data between hometown and surrounding cities. Suffering from the severe sparsity of user mobility data in surrounding city, existing approaches do not perform well as they can hardly maintain travelers’ intrinsic preference and meanwhile adapt to travelers’ interest drift. To address these concerns, in this paper, taking cross-city travelers as the medium, we propose a novel framework called CityTrans to transfer traveler mobility knowledge from hometown city to surrounding city, which considers both the long-term preference in hometown city and short-term interest drift in surrounding city. Various attention mechanisms are leveraged to obtain traveler representation enriched by long-term and short-term preferences. Besides, we propose to portray POIs through GNN incorporating POI attributes and geographical information. Finally, the traveler and POI representations are combined for prediction. To train the framework, the transfer loss as well as the prediction loss are jointly optimized. Extensive experiments on real-world datasets validate the superiority of our framework over several state-of-the-art approaches.
TL;DR: In this paper , a novel accelerated decoder for real-time and neural-enhanced video analytics is presented, which selects a few frames adaptively via Deep Reinforcement Learning (DRL) to enhance the quality by neural super-resolution and then up-scale the unselected frames that reference them, which leads to 6-21% accuracy improvement.
Abstract: —The quality of the video stream is key to neural network-based video analytics. However, low-quality video is inevitably collected by existing surveillance systems because of poor quality cameras or over-compressed/pruned video streaming protocols, e.g. , as a result of upstream bandwidth limit. To ad- dress this issue, existing studies use quality enhancers ( e.g. , neural super-resolution) to improve the quality of videos ( e.g. , resolution) and eventually ensure inference accuracy. Nevertheless, directly applying quality enhancers does not work in practice because it will introduce unacceptable latency. In this paper, we present AccDecoder, a novel accelerated decoder for real-time and neural- enhanced video analytics. AccDecoder can select a few frames adaptively via Deep Reinforcement Learning (DRL) to enhance the quality by neural super-resolution and then up-scale the unselected frames that reference them, which leads to 6-21% accuracy improvement. AccDecoder provides efficient inference capability via filtering important frames using DRL for DNN- based inference and reusing the results for the other frames via extracting the reference relationship among frames and blocks, which results in a latency reduction of 20-80% than baselines.
21 Jan 2023
TL;DR: Homo3D as discussed by the authors is a high-performance GPU solver for inverse homogenization problems to design high-resolution 3D microstructures, which makes full use of the parallel computation power of today's GPUs through a software-level design space exploration.
Abstract: We propose a high-performance GPU solver for inverse homogenization problems to design high-resolution 3D microstructures. Central to our solver is a favorable combination of data structures and algorithms, making full use of the parallel computation power of today's GPUs through a software-level design space exploration. This solver is demonstrated to optimize homogenized stiffness tensors, such as bulk modulus, shear modulus, and Poisson's ratio, under the constraint of bounded material volume. Practical high-resolution examples with 512^3=134.2 million finite elements run in less than 40 seconds per iteration with a peak GPU memory of 9 GB on an NVIDIA GeForce GTX 1080Ti GPU. Besides, our GPU implementation is equipped with an easy-to-use framework with less than 20 lines of code to support various objective functions defined by the homogenized stiffness tensors. Our open-source high-performance implementation is publicly accessible at https://github.com/lavenklau/homo3d.
TL;DR: In this article , a two-stage procedure is proposed to construct sparse integer-constrained cone singularities with low distortion constraints for conformal parameterizations, which achieves fewer cones and lower parameterization distortion than state-of-theart methods.
Abstract: We propose a practical method to construct sparse integer-constrained cone singularities with low distortion constraints for conformal parameterizations. Our solution for this combinatorial problem is a two-stage procedure that first enhances sparsity for generating an initialization and then optimizes to reduce the number of cones and the parameterization distortion. Central to the first stage is a progressive process to determine the combinatorial variables, i.e., numbers, locations, and angles of cones. The second stage iteratively conducts adaptive cone relocations and merges close cones for optimization. We extensively test our method on a data set containing 3885 models, demonstrating practical robustness and performance. Our method achieves fewer cone singularities and lower parameterization distortion than state-of-the-art methods.
TL;DR: In this paper , a robust and automatic method to construct manifold cages for 3D triangular meshes is proposed, which consists of two phases: (1) constructing manifold cages satisfying the tightness, enclosing, and intersection-free requirements and (2) reduce mesh complexities and approximation errors without violating the enclosing and intersection free requirements.
Abstract: We propose a robust and automatic method to construct manifold cages for 3D triangular meshes. The cage contains hundreds of triangles to tightly enclose the input mesh without self-intersections. To generate such cages, our algorithm consists of two phases: (1) construct manifold cages satisfying the tightness, enclosing, and intersection-free requirements and (2) reduce mesh complexities and approximation errors without violating the enclosing and intersection-free requirements. To theoretically make the first stage have those properties, we combine the conformal tetrahedral meshing and tetrahedral mesh subdivision. The second step is a constrained remeshing process using explicit checks to ensure that the enclosing and intersection-free constraints are always satisfied. Both phases use a hybrid coordinate representation, i.e., rational numbers and floating point numbers, combined with exact arithmetic and floating point filtering techniques to guarantee the robustness of geometric predicates with a favorable speed. We extensively test our method on a data set of over 8500 models, demonstrating robustness and performance. Compared to other state-of-the-art methods, our method possesses much stronger robustness.
TL;DR: In this article , an approach that utilizes pixel-wise differences in satellite images to classify temporal changes in average and median consumption expenditures (and income) in rural villages in Thailand and Vietnam between 2007 and 2017 was proposed.
TL;DR: In this paper , the interplay between edge computing and artificial intelligence (AI) for 6G mobile communicaton networks is discussed. But the authors focus on the novel area of edge computing.
Abstract: The papers in this special issue focus on the novel area of the interplay between edge computing and artificial intelligence (AI) for 6G mobile communicaton networks. While 5G mobile communication has gradually opened the curtain of internet of everything and brings vertical transform to change the society, 6G is believed to open a new era of “Internet of Intelligence” with connected people, connected things, and connected intelligence, solving human challenges in many aspects and helping perfect the world we all live in. To empower 6G networks with AI capabilities, large volumes of multi-modal data of physical surroundings will be continuously generated by the mobile and IoT devices which reside at the network edge. Driving by this trend, there is an urgent need to push the AI frontiers to the network edge so as to fully unleash the potential 6G networks. To meet this demand, edge computing, an emerging paradigm that pushes computing tasks and services from the network core to the network edge, has been widely recognized as an indispensable part for the upcoming 6G networks.
TL;DR: Wang et al. as discussed by the authors proposed an edge-assisted adaptive video streaming system with serverless pipelines, which facilitates fine-grained management for multiple concurrent video transmission pipelines, and designed a chunk-level optimization scheme to address video bitrate adaptation.
Abstract: ÐRecent years have witnessed video streaming gradually evolve into one of the most popular Internet applications. 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 address video bitrate adaptation. 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 QoE. Finally, we implement the serverless video streaming system prototype and evaluate the performance of EAVS on various real-world network traces. Our results show that EAVS significantly improves 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.
TL;DR: In this article , the authors propose to use nonlinear shape functions represented as neural networks in numerical coarsening to achieve generalization capability as well as good accuracy, which greatly improves training efficiency and inference robustness.
Abstract: We propose to use nonlinear shape functions represented as neural networks in numerical coarsening to achieve generalization capability as well as good accuracy. To overcome the challenge of generalization to different simulation scenarios, especially nonlinear materials under large deformations, our key idea is to replace the linear mapping between coarse and fine meshes adopted in previous works with a nonlinear one represented by neural networks. However, directly applying an end-to-end neural representation leads to poor performance due to over-huge parameter space as well as failing to capture some intrinsic geometry properties of shape functions. Our solution is to embed geometry constraints as the prior knowledge in learning, which greatly improves training efficiency and inference robustness. With the trained neural shape functions, we can easily adopt numerical coarsening in the simulation of various hyperelastic models without any other preprocessing step required. The experiment results demonstrate the efficiency and generalization capability of our method over previous works.