3D immersive telepresence systems have dramatically transformed the way users communicate, yet existing point cloud and mesh-based approaches require large amounts of data transmission. Although recent 3D facial semantic-driven techniques can be used to reduce the network burden, they face the critical problem of the high computational cost of facial semantic extraction. To solve the problem, we design and implement an innovative bimodal semantic-driven real-time 3D telepresence system which leverages the low-cost audio-driven semantics for facial expression extraction and head movement semantics for 3D interaction. To improve the efficiency and accuracy of semantic information processing, we propose a speech separation module and optimize the data reception strategy. To optimize the quality of video content reconstruction, we employ frame interpolation and super-resolution techniques, and further propose an online resource scheduling algorithm to balance the rendering, interpolation, and super-resolution processes with limited terminal resources. Experimental results demonstrate that our system can achieve 1K rendering resolution and ~46FPS frame rate with ultra-low network bandwidth (375kbps, approximately 0.32% compared to the point cloud-based approach) and low latency (about 78% semantic feature extraction latency compared to the 3D facial semantic-driven approach) in the campus network.

3D Gaussian Splatting (3DGS), as the cutting-edge static three-dimensional (3D) content generation technology, revolutionizes the speed and fidelity of 3D model construction and provides immense potential for various applications, including e-commerce, 3D exhibitions, and virtual tourism. However, our pioneering analysis of firsthand user experiments uncovers a critical challenge: the unique user behavior patterns of static 3D scenarios render existing immersive video streaming solutions inadequate. To be concrete, the frequent switches between active and inactive states impair viewport prediction accuracy, while the fast glance and slow view pattern provides an opportunity for further quality of experience (QoE) improvement. To tackle these problems, this paper introduces innovative designs for static 3D video streaming. Specifically, we devise a viewport prediction and error correction mechanism on the client side to restore the user viewport with a low cost. Furthermore, we design a dynamic frame rate and bitrate control algorithm to improve user QoE under various network conditions. We implement the first 3DGS-enabled immersive static 3D video streaming system based on an edge-rendered architecture, ensuring efficient rendering and encoding on the edge server while providing broad accessibility for various client-side devices through a web browser. Extensive testing under real-world network conditions and with various kinds of devices demonstrates that the proposed approach exhibits robust and rapid adaptability to fluctuating network conditions, improving user QoE by over 20%, reducing interactive latency by 89%, and minimizing the stall duration by 26% compared to existing low-latency streaming solutions.

In the realm of 360° video streaming, how to deliver optimal viewing experience to users with minimal bandwidth cost has become an emerging challenge. Our research is driven by a comprehensive analysis of real-world user's head movement datasets of 360° video, revealing significant viewport shifts even during the playback of individual 360° video chunks. However, prevailing 360° video streaming algorithms fail to account for such viewport variations, thereby resulting in a substantial degradation of user experience. To this end, we propose OMMS, a multiple-control-based adaptive 360° video streaming algorithm. OMMS adopts an integrated approach by introducing multiple supplementary controls alongside the main control, enabling adaptation to changes in the user's viewport. Experimental results demonstrate that OMMS yields a significant improvement in video quality by 34%, while effectively reducing bandwidth resource consumption.

Apart from the promising potential, federated learning (FL) faces challenges, such as high communication costs and client heterogeneity. Although numerous works have been proposed to address these issues, they lack a holistic perspective to balance all requirements. Moreover, these solutions have not fully utilized the underlying computation capability and network resources, resulting in suboptimal tradeoffs between communication efficiency and inference accuracy. To overcome these challenges, we propose FDL: a federated distillation (FD) learning framework that combines FD and FL to fully utilize computation and network resources. We theoretically prove the convergence bound of the proposed FDL framework. Furthermore, to minimize the training time while maintaining inference accuracy, we design HAD: a heterogeneity-aware FL/FD selection algorithm that determines the total communication rounds and selects the set of FL and FD nodes in each communication round. The optimality of HAD is also theoretically proved. The FDL framework and HAD algorithm together minimize the training time while satisfying the inference accuracy in a heterogeneous and dynamic environment. Extensive experiments on various learning algorithms and data sets show that the proposed FDL-HAD solution can obtain the optimal selection decision in overwhelmingly less selection time compared with the Gurobi solver and can reduce the overall training time by at least 44.8% compared with FL solutions with the same inference accuracy.

A networked time series (NETS) is a family of time series on a given graph, one for each node. It has a wide range of applications from intelligent transportation to environment monitoring to smart grid management. An important task in such applications is to predict the future values of a NETS based on its historical values and the underlying graph. Most existing methods require complete data for training. However, in real-world scenarios, it is not uncommon to have missing data due to sensor malfunction, incomplete sensing coverage, and so on. In this article, we study the problem of NETS prediction with incomplete data . We propose networked time series Imputation Generative Adversarial Network (NETS-ImpGAN), a novel deep learning framework that can be trained on incomplete data with missing values in both history and future. Furthermore, we propose Graph Temporal Attention Networks , which incorporate the attention mechanism to capture both inter-time series and temporal correlations. We conduct extensive experiments on four real-world datasets under different missing patterns and missing rates. The experimental results show that NETS-ImpGAN outperforms existing methods, reducing the Mean Absolute Error by up to 25%.

We study the online restless bandit problem, where each arm evolves according to a Markov chain independently, and the reward of pulling an arm depends on both the current state of the corresponding Markov chain and the pulled arm. The agent (decision maker) does not know the transition functions and reward functions, and cannot observe the states of arms even after pulling. The goal is to sequentially choose which arms to pull so as to maximize the expected cumulative rewards collected. In this paper, we propose TSEETC, a learning algorithm based on Thompson Sampling with Episodic Explore-Then-Commit. The algorithm proceeds in episodes of increasing length and each episode is divided into exploration and exploitation phases. During the exploration phase, samples of action-reward pairs are collected in a round-robin fashion and utilized to update the posterior distribution as a mixture of Dirichlet distributions. At the beginning of the exploitation phase, TSEETC generates a sample from the posterior distribution as true parameters. It then follows the optimal policy for the sampled model for the rest of the episode. We establish the Bayesian regret bound $\tilde {\mathcal{O}}(\sqrt{T})$ for TSEETC, where $T$ is the time horizon. We show through simulations that TSEETC outperforms existing algorithms in regret.

Data with missing values is ubiquitous in many applications. Recent years have witnessed increasing attention on prediction with only incomplete data consisting of observed features and a mask that indicates the missing pattern. Existing methods assume that the training and testing distributions are the same, which may be violated in real-world scenarios. In this paper, we consider prediction with incomplete data in the presence of distribution shift. We focus on the case where the underlying joint distribution of complete features and label is invariant, but the missing pattern, i.e., mask distribution may shift agnostically between training and testing. To achieve generalization, we leverage the observation that for each mask, there is an invariant optimal predictor. To avoid the exponential explosion when learning them separately, we approximate the optimal predictors jointly using a double parameterization technique. This has the undesirable side effect of allowing the learned predictors to rely on the intra-mask correlation and that between features and mask. We perform decorrelation to minimize this effect. Combining the techniques above, we propose a novel prediction method called StableMiss. Extensive experiments on both synthetic and real-world datasets show that StableMiss is robust and outperforms state-of-the-art methods under agnostic mask distribution shift.

Overlapping gradient communication with backward computation is a popular technique to reduce communication cost in the widely adopted data parallel S-SGD training. However, the resource contention between computation and All-Reduce communication in GPU-based training reduces the benefits of overlap. With GPU cluster network evolving from low bandwidth TCP to high speed networks, more GPU resources are required to efficiently utilize the bandwidth, making the contention more noticeable. Existing communication libraries fail to account for such contention when allocating GPU threads and have suboptimal performance. In this paper, we propose to mitigate the contention by balancing the overlapped computation and communication time. We formulate an optimization problem that decides the communication thread allocation to reduce overall backward time. We develop a dynamic programming based near-optimal solution and extend it to co-optimize thread allocation with tensor fusion. We conduct simulated study and real-world experiment using an 8-node GPU cluster with 50Gb RDMA network training four representative DNN models. Results show that our method reduces backward time by 10%-20% compared with Horovod-NCCL, by 6%-13% compared with tensor-fusion-optimization-only methods. Simulation shows that our method achieves the best scalability with a training speedup of 1.2x over the best-performing baseline as we scale up cluster size.

To provide high user QoE while maintaining low bandwidth waste, it is important to design adaptive bitrate and preloading algorithms for short video streaming. Current solutions either have relatively low performance, as observed in heuristic algorithms, or suffer the problem of poor generalization, as seen in deep reinforcement learning (DRL)-based algorithms. To address this issue, we propose CLAPS, a curriculum learning-based DRL model that enhances the generalization of the DRL model across a wide range of data, while ensuring high performance. CLAPS introduces a comprehensive metric that measures the curriculum difficulty by combing the performance gap between an existing heuristic algorithm and the DRL model with the prediction error of network bandwidth. Moreover, we design a training scheduler to control sampling proportion based on Markov transition probabilities to address the model forgetting problem. Extensive evaluations using real video datasets and network traces including 5G, 4G, and Wi-Fi demonstrate that CLAPS outperforms all the baseline algorithms. Specifically, CLAPS improves the overall performance by 10.04%-13.84% and the generalization by 22.52% -35.77% compared to the best-performing DRL baseline.

Mobile edge computing is a promising framework for mobile virtual reality (VR) game. Although there are several existing studies on the edge assisted mobile VR game system, they lack the consideration of provisioning services with satisfactory QoE to a large number of users. In this paper, we consider the problem of providing QoE-oriented edge assisted mobile VR game as a service to multiple users, with a comprehensive QoE concern of both visual and delay aspects. Due to the unique features of mobile VR game, the problem is formulated into a Mixed Integer Quadratically Constrained Quadratic Programming (MIQCQP) problem. We show that the problem is NP-hard with object placement decision and rendering level selection decision quadratically coupling together. To solve this problem, we propose the Alternating Directions Method of Multipliers (ADMM) algorithm which can iteratively decouple the quadratic terms and reform the problem into the efficiently solvable MIQCQP-1 (i.e., MIQCQP with one constraint) problem. Trace driven simulation shows that our algorithm fits the edge assisted mobile VR game scenario well with fast computation time (at least 4 orders of magnitude less computation time compared to Gurobi solver) and good performance (at least 18% of user visual QoE improvement compared to other mobile VR scheme).

To conduct service quality management of industry devices or Internet infrastructures, various deep learning approaches have been used for extracting the normal patterns of multivariate Key Performance Indicators (KPIs) for unsupervised anomaly detection. However, in the scenario of Content Delivery Networks (CDN), KPIs that belong to diverse websites usually exhibit various structures at different timesteps and show the non-stationary sequential relationship between them, which is extremely difficult for the existing deep learning approaches to characterize and identify anomalies. To address this issue, we propose a switching Gaussian mixture variational recurrent neural network (SGmVRNN) suitable for multivariate CDN KPIs. Specifically, SGmVRNN introduces the variational recurrent structure and assigns its latent variables into a mixture Gaussian distribution to model complex KPI time series and capture the diversely structural and dynamical characteristics within them, while in the next step it incorporates a switching mechanism to characterize these diversities, thus learning richer representations of KPIs. For efficient inference, we develop an upward-downward autoencoding inference method which combines the bottom-up likelihood and up-bottom prior information of the parameters for accurate posterior approximation. Extensive experiments on real-world data show that SGmVRNN significantly outperforms the state-of-the-art approaches according to F1-score on CDN KPIs from diverse websites.

One key challenge to maintain a large-scale Content Delivery Network (CDN) is to minimize the service downtime when severe system problems happen (e.g., hardware failures). In this case, a critical step is to quickly and accurately identify the range of users with performance degradation, termed impact identification. Successful impact identification not only helps identify impacted users but also provides meaningful information for troubleshooting. However, current practice of impact identification usually takes network engineers several hours to manually identify impacted users, which may lead to a huge business loss. The main challenges for automatic impact identification in large CDNs include the inaccuracy of underlying anomaly detection, huge search space of impact identification and severe long-tail distribution of user traffic. In this paper we propose iSwift, a system that is specifically designed for impact identification in large-scale CDNs in order to address aforementioned challenges. We evaluate the performance of iSwift on semi-synthetic datasets and the results show that iSwift can achieve a F1-score greater than 0.85 within ten seconds, which significantly outperforms state-of-the-art solutions. Furthermore, iSwift has been deployed in a production CDN around one year as a pilot project and demonstrated its online performance confirmed by the network operators.

Short video streaming has been increasingly popular in recent years. Due to its unique user behavior of watching and sliding, a critical technique issue is to design a preload algorithm deciding which video chunk to download next, bitrate selection and the pause time, in order to improve user experience while reducing bandwidth wastage. However, designing such a preload algorithm is non-trivial, especially taking into account conflicting goals of improving QoE and reducing bandwidth wastage. In this paper, we propose a deep reinforcement learning-based approach to simultaneously decide the aforementioned three decision variables via learning an optimal policy under a complex environment of varying network conditions and unpredictable user behavior. In particular, we incorporate domain knowledge into the decision procedure via action masking to make decisions more transparent, and accelerate the model training. Experimental results validate the proposed approach significantly outperforms baseline algorithms in terms of QoE metrics and bandwidth wastage.

Rate control is one of the most critical technologies of real-time video coding. It aims to make bit rate allocation and quantization parameter(QP) decisions to minimize video distortion while reducing buffering delay. However, existing solutions suffer from the problem of extra encoding delay and lack joint optimization of the region of interest(ROI) quality and buffer latency. In this paper, we propose a deep reinforcement learning(RL) based low-delay rate control method without using the information of uncoded frames to avoid the extra delay. Particularly, our RL-based rate control algorithm outputs two types of policies. The first is to make frame-level QP decisions to stabilize buffer occupancy and optimize the overall quality, while the other is in charge of adjusting the QP offset between ROI and Non-ROI regions to enhance portrait quality. Extensive experiments verify that our proposed method improves ROI and overall quality while reducing the buffer occupancy variation, compared with baseline algorithms.

The key challenge for large scale software system maintenance is to minimize the troubleshooting time when severe system anomaly (e.g., server failure, link congestion, software bugs) happens. It often takes hours for operators to manually locate the fault and thus degrades the service performance in terms of user experience and economics. Previous root cause localization algorithms are usually time-consuming and error-prone. In this paper, we present AutoRoot, a fast and accurate multi-dimensional root cause localization algorithm. Specifically, AutoRoot uses an adaptive density clustering to improve the accuracy and an effective filtering mechanism to reduce the search time. Extensive experiments using multiple real data traces validate the performance of AutoRoot compared with existing algorithms.

Content Delivery Networks (CDNs) are critical for providing good user experience of cloud services. CDN providers typically collect various multivariate Key Performance Indicators (KPIs) time series to monitor and diagnose system performance. State-of-the-art anomaly detection methods mostly use deep learning to extract the normal patterns of data, due to its superior performance. However, KPI data usually exhibit non-additive Gaussian noise, which makes it difficult for deep learning models to learn the normal patterns, resulting in degraded performance in anomaly detection. In this paper, we propose a robust and noise-resilient anomaly detection mechanism using multivariate KPIs. Our key insight is that different KPIs are constrained by certain time-invariant characteristics of the underlying system, and that explicitly modelling such invariance may help resist noise in the data. We thus propose a novel anomaly detection method called SDFVAE, short for Static and Dynamic Factorized VAE, that learns the representations of KPIs by explicitly factorizing the latent variables into dynamic and static parts. Extensive experiments using real-world data show that SDFVAE achieves a F1-score ranging from 0.92 to 0.99 on both regular and noisy dataset, outperforming state-of-the-art methods by a large margin.

CDN (Content Delivery Network) has become an important infrastructure of the Internet. However, building an anomaly detection system to monitor and guarantee CDN service quality is non-trivial. Current anomaly detection system usually suffers from undesirable performance in terms of high rate of false positive and false negative, which consequently impacts on its practical deployment. Identifying the root cause of a false detection is critical for diagnosing and improving the performance of anomaly detection. In this paper, we propose a novel feedback mechanism for prediction-based anomaly detection in CDN . Specifically, we introduce a carefully-designed metric named Fittingscore to diagnose whether the prediction model can fit the data well. Further, a threshold adjustment mechanism is proposed to dynamically adjust the thresholds of residual errors. Extensive experiments employing a three-month real CDN dataset collected from a top ISP-operated CDN in China show our proposed method can significantly improve the performance of anomaly detection.

More and more CDN (Content Delivery Network) or video content providers are deploying their cache nodes at the edge of networks in order to improve user perceived QoE (Quality of Experience). However, state-of-the-art ABR (Adaptive Bitrate) algorithms do not take into account the presence of a cache on video delivery path. In this paper, we firstly investigate how these ABR algorithms behave in the context of edge caching system and how caching factors such as hit ratio and access bandwidth impact on the performance of ABR algorithms. Extensive analysis results show introducing an edge cache on video delivery path cannot necessarily improve the performance of ABR algorithms in terms of average QoE. Furthermore, we propose a cache-aware ABR approach taking into account caching information including a cache hit indicator for the past chunks and the availability of the next video chunk on the cache. Experimental results validate the significant benefits of the approach which helps to make a more accurate throughput estimation and is more likely to select the video chunks stored in the cache to increase cache utilization.

Information Centric Networking (ICN) leverages built-in caching capacity, termed as in-network caching, to support fast and efficient content distribution. The profits of ICN brought by its widespread in-network caching infrastructures has been well explored, but how to incentivize network players to deploy the socially optimal number of caches was largely ignored. Network players might not want to deploy or contribute their cache resources, if they do not have clear economic incentives, as cache deployment implies an extra capital expenditure. In this paper, we focus on the economic incentive interactions in caching deployment and usage between several types of network players in ICN. In particular, a Stackerlberg game model is used to capture the interactions between content provider (CP) and Internet service providers (ISPs). By using backward induction, it is proven that an equilibrium of the proposed model exists, under which both ISPs and CP can achieve their maximum utility. Numerical results show the effectiveness of the proposed model and the findings can provide important insights for network players to design efficient content sharing and caching schemes.

Content-Centric Networking (CCN) proposals rethink the communication model around named data. In-network caching and multipath routing are regarded as two fundamental features to distinguish the CCN from the current host-centric IP network. In this paper, we tackle the problem of joint collaborative caching and multipath routing in CCN. We achieve this with an online and offline combination caching scheme based on a local content popularity statistic results. Besides, we place the content heterogeneously along a path and resort to a caching aware dynamic multipath routing in a coordination fashion. The proposed scheme can increase the content diversity and improve the caching utility with the aim of minimizing the user access delay. Simulation experiments have been performed to evaluate the proposed scheme. Simulation results show that the proposed scheme is effective and outperforms the existing caching mechanisms in CCN.

Deploying caches in mobile networks, especially in the radio access network (RAN) is regarded as a promising way to improve mobile user experiences and alleviate the increasing pressure of traffic growth. However, the characteristics of mobile traffic and the performance of RAN caching still remains unclear. In this paper, we extensively analyze the traffic characteristics, the content popularity and the cache performance using a unique dataset collected from a commercial LTE network of China Mobile, from the perspective of mobile access network. The dataset spans nearly a week and consists of a collection of approximately 62.1 millions HTTP sessions, generated by more than 3200 users distributed across three base stations. Based on this realistic dataset, we observe that HTTP traffic can be reduced by 24.4% on average and the hit ratio can reach up to 42.2%, using 100GB cache size. The implications on some fundamental design issues of practical RAN caching systems, including reasonable size of RAN cache, suitable locations of cache deployment and potential benefits of collaborative RAN caching, are further presented. We believe our findings will shed light on practical RAN caching system design.

Abstract In wireless sensor networks, one of the primary requirements is that sensor data acquired from the physical world can be interchanged with all interested collaborative entities in a secure, reliable manner. Because of highly unpredictable nature of the environments caused by malicious attacks or potential threats, minimizing transmission cost between source and sink nodes with jointly considering the security of the whole network is a critical issue. This paper considers two optimization problems of deriving the minimum cost paths from multiple source nodes to the sink node under the guaranteed level of the vulnerability. The link or node vulnerability is defined as a metric, which characterizes the degree of link or node sharing among paths. With the defined link vulnerability, the link vulnerability‐constrained minimum cost paths problem is first formulated, and two polynomial‐time algorithms are developed for deriving the optimal paths. For the node‐vulnerability‐constrained minimum cost paths problem, we adopt the network conversion and then achieve the optimal solution with previous proposed algorithms. The necessary condition for solution existence, the optimality of the proposed algorithms, and the related properties of tree network are further theoretically analyzed. Extensive simulations show the significant performance improvements achieved by our proposed algorithms.Copyright © 2014 John Wiley & Sons, Ltd.

The explosive growth of online videos brings a great pressure on LTE mobile networks caused by huge mobile data traffics. Deploying caches at the evolved packet core (EPC) and radio access network (RAN) in LTE mobile networks can efficiently relieve this pressure by the way of reducing duplicate content transmissions. In this paper, we propose collaborative caching algorithms for the LTE mobile network with caches deployed at the EPC and RAN to reduce the bandwidth cost of Internet access and improve the end-user experience. We decompose the caching problem into a content placement subproblem and a request routing subproblem. We solve the content placement subproblem with a greedy placement algorithm by utilizing its matroid and submodular properties. As for the request routing subproblem, we solve it by transforming it into the problem of maximizing a submodular function subject to a matroid constraint and present a greedy algorithm. Both the proposed algorithms can achieve at least 1=2 of the optimal solution. Experiment results show that our proposed algorithms can significantly improve the performance in terms of access cost and supported request compared with three reference algorithms.

To reduce data access latency, network traffic volume and server load, in-network caching was proposed and has become an intrinsic component of the content-centric network (CCN) architecture. The content-oriented characteristics of in-network caching, such as arbitrary topology, volatile content locations and line speed requirements, make routers content-aware and supportive of fast content distribution. Meanwhile, they also raise new challenges in content placement and request routing, namely, how to optimally make content storage decisions and provision individual router's bandwidth to serve user requests, so as to minimize the bandwidth cost under storage and link capacity limit. To address this problem, we build a distributed in-network caching model to formulate the content placement and request routing in CCN, aiming at minimizing the bandwidth cost with strict storage and bandwidth constraints. Based on the proposed model, we design a scalable, adaptive and low-complexity in-network caching scheme for content placement and request routing and analyze the performance gains via simulations on a real ISP network topology and traffic traces. The experimental results show the proposed model and scheme are superior. Compared with the existing works, we also observe significant performance enhancements in terms of hit ratio of requests, reduction of server load, and bandwidth cost.

In this paper, we try to design a collaborative caching scheme in an edge Content Centric Networking (CCN). Specifically, we first decompose the arbitrary network into clusters based on dominating set. Then, we place the content heterogeneously within each cluster based on the content popularity results and resort to a dynamic request routing. Simulation results show that the proposed outperforms the existing caching mechanisms in CCN.

Quality of Experience (QoE) support becomes more demanding due to the increasing popularity of multimedia services. However, QoE-based multimedia provisioning will impose a high pressure on server capacity and network bandwidth, which becomes one of the major concerns for both content providers and network operators. A common approach for efficient and scalable multimedia service provisioning in content distribution networks (CDN) is to replicate certain “hot” service or content from the original server to multiple replica servers, usually closer to end users. However, how to direct distributed user requests to an appropriate replica server to fundamentally improve the user perceived service quality largely remains unknown. In this paper, we present a user-oriented QoE-driven server selection scheme to solve the aforementioned problem. Experiments carried out on a real-world network have been performed to evaluate the proposed scheme, and the results show that the proposed scheme can improve QoE perceived by end users at least 24.6%.

To reduce network access latency, network traffic volume, and server load, caching capacity was proposed as a component of eNodeBs in the radio access network (RAN). These eNodeB caches require less transport energy but bring additional caching energy through providing each eNodeB with caching capacity. It is thus challenging for eNodeBs to make content placement and request routing decisions so as to minimize the total energy consumption, especially when considering different caching hardware technologies, arrival rate of requests and content popularity. To address this problem, we first build an energy model to formulate the problem of minimizing energy consumption at eNodeB caches. Then a Lagrangian relaxation technique is adopted to find a near-optimal solution to the proposed model. Based on the proposed model and the obtained solution, we design a practical scheme for coordinating the content placement and request routing, and thus ensure the minimum-energy eNodeB caches. Compared with the existing works, our proposal significantly reduces the energy consumption by approximately 28% while keeps a good network performance. Our results also indicate that caching hardware technologies and content popularity greatly affect the content placement, therefore are crucial to the energy efficiency of eNodeB caches.

Information-Centric Networking (ICN) is a new emerging concept, in which the principal paradigm shifts from the traditional end-to-end connection to the information-centric communication model. In ICN, information unit, such as content or service, is distributed in different sites or data centers to provide large-scale services. A common supporting approach for scalable service provisioning is deploying multiple replica servers throughout the network. Accordingly, an efficient and flexible scheme is needed to direct distributed requests to an appropriate replica server. In this paper, we first propose an incrementally deployable ICN architecture based on edge/core separation. And then, a practical anycast-based service provisioning scheme is presented with joint considerations of both servers contexts and the underlying network conditions. Extensive experiments have been performed to evaluate the proposed scheme. Experiment results show that efficient context-aware distributed service provisioning can be achieved.

Information-Centric Networking (ICN) enables caching of addressable content chunks in every cache-equipped router. So it is crucial to make the cached content to be visible locally, in order to satisfy the subsequent request. We achieve this goal in this paper with a novel caching scheme, which combines the content placement with dynamic request routing by selectively creating trails along the content delivery path. In addition, in-network caching requires search or replacement of cached chunks at line-speed, which resorts to the low complexity collaborative caching scheme and limits the cache size at each network level. Coordinate with the feasible cache size allocation, the proposed scheme automatically distributes the content to the proper cache location according to its popularity. Therefore, the proposed self assembly caching scheme could reduce caching redundancy and in turn, make more efficient utilization of available cache resources. The simulation results show that the proposed scheme outperforms existing algorithms.

In wireless sensor networks (WSNs), sensor nodes are usually powered by batteries of limited capacity, which results in dynamic changes of available paths for data aggregation due to node failures caused by energy depletion. For transmitting certain amount of data generated by source node, the overall transmission cost is affected by two major factors, using sequence of available paths and amount of data imposed on each path, which becomes a major issue significantly influencing the efficient usage of the networks. To address this issue, we consider the optimization problem of how to minimize the overall transmission cost of given data delivered from the source node to the sink node in the energy-constrained WSN. Specifically, we first describe the problem on the basis of the minimum cost flow theory and derive the upper bound for the data amount in terms of the number of packets that can be successfully transmitted from the source node to the sink node. Then, we propose specific algorithms to derive the optimal paths and their optimal data amounts, and then achieve the minimized overall transmission cost for the certain amount of data. Extensive simulations show that significant performance enhancement can be achieved by using our proposed algorithms.

Content Centric Networking (CCN) is a new emerging network architecture, shifting from an end-to-end connection to a content-centric communication model. A number of content-oriented characteristics, such as name-based routing, arbitrary topology and ubiquitous caching make caching in CCN different from the previous works. In this paper, we focus on the collaborative caching in CCN and propose a dominating-set-based collaborative caching mechanism, which jointly considers the content placement and request routing in a tightly-couple manner. Extensive experiments have been performed to evaluate the proposed scheme. Simulation results show that the proposed CollaCache proposal outperforms the existing caching and routing mechanisms in CCN.

In wireless sensor networks, one of the primary requirements is that sensor data derived from the physical world can be interchanged with all interested collaborative entities in a secure and reliable manner. Due to highly unpredictable environments where sensor nodes are usually deployed, minimizing the transmisstion cost with jointly taking into account the security of the whole network poses a challenging task. With this end, this paper considers an optimization problem of deriving the minimum cost paths from multiple source nodes, which are deployed in the area of interest, to the sink node under the constraint that the vulnerability of the whole network is under the given level. The vulnerability is defined as a metric which characterizes the degree of edge and node sharing among different paths. With the defined vulnerability, vulnerability-constrained minimum cost paths problem is formulated and two polynomial-time algorithms are developed for deriving the optimal paths. The necessary condition for the existence of the optimal solution, and the optimality of the proposed algorithms are analyzed in the theoretical. Extensive simulations show the significant performance enhancements achieved by our proposed algorithms.

Information-Centric Network (ICN) aims to achieve the shift of communication model from host-centric to information-centric. In ICN, anycast plays an important role and is even regarded as a primary network primitive in some architectures such as DONA and SCAFFOLD. However, network layer anycast based on route-by-name in these architectures may severely worsen the problem of routing scalability as IP anycast does. On the other hand, the widely used application layer anycast based on lookup-by-name in current Internet exists many flaws such as inaccuracy of server selection and weak responsiveness to dynamic network conditions. Accordingly, how to achieve efficient and flexible anycast becomes a research challenge in future internet. In this paper, a novel anycast routing scheme based on ID/LOC separation is proposed. Then, a decentralized server selection algorithm based on Ant Colony Optimization (ACO) is further investigated. Experiments show that efficient and flexible anycast can be achieved to combine the advantages of route-by-name anycast and lookup-by-name anycast.

Network virtualization provides a powerful way to run multiple heterogeneous virtual networks (VNs) at the same time on a shared substrate network. A major challenge in network virtualization is the efficient virtual network embedding: mapping virtual nodes and virtual edges onto substrate networks. Previous researches have presented several heuristic algorithms, which fail to consider the topology attributes of substrate and virtual networks. However, the topology information affects the performance of the embedding obviously. In this paper, for the first time, we exploit the topology attributes of substrate and virtual networks, introduce network centrality analysis into the virtual network embedding, and propose virtual network embedding algorithms based on closeness centrality. Due to considering the topology information, our study is more reasonable than the existing work in coordinating node and edge embedding. In addition, with the guidance of topology quantitative evaluation, the proposed network embedding approaches largely improve the network utilization efficiency and decrease the embedding complexity. Experimental results demonstrate the usability and feasibility of the proposed approach.

Content Centric Networking (CCN) is a new network infrastructure around content dissemination and retrieval, shift from host addresses to named data. Each CCN router has a cache to store the chunks passed by it. Therefore the caching strategy about chunk placement can greatly affect the whole CCN performance. This paper proposes an implicit coordinate chunk caching location and searching scheme (CLS) in CCN hierarchical infrastructure. In CLS, there is at most one copy of a chunk cached on the path between a server and a leaf router. This copy is pulled down one level towards the leaf router by a request or pushed up one level towards the server by the cache eviction. Thus, it is possible to store more diverse contents in the whole CCN and improve the network performance. Plus, in order to reduce the server workload and file download time, a caching trail of chunk is created to direct the following request where to find the chunk. Extensive test-bed experiments have been performed to evaluate the proposed scheme in terms of a wide range of performance metrics. The results show that the proposed scheme outperforms existing algorithms.

Content-Centric Networking (CCN), a typical future Internet architecture focused on content dissemination and retrieval, brings a paradigm shift in network model by addressing named-data instead of host location. Many fields related to CCN become the research hotspots, such as caching and name-based routing. However, little work has addressed on its transmission protocol in literature. In this paper, we proposed an effective Congestion Control Scheme (CCS) in CCN through combining a router-driven congestion forecast with a requester-driven Interest control protocol. Each router detects the network congestion status and then informs the requester by setting Congestion Information Bits into the returned Data packet passed by. Then the requester dynamically adjusts its Interest sending window according to the received feedback congestion information. Furthermore, in our scheme, a Fair Queuing algorithm is used on each router, allowing max-min fair share of the link capacity. A performance evaluation using simulation designed on OMNet++ network simulator showed that the proposed CCS causes fewer packet losses, decreases Data packet delay, treats each flow fairly and maximizes the use of bandwidth.