Emotional Support Conversation (ESC) aims to alleviate individuals’ negative emotions through multi-turn dialogues, where effective strategy planning and response generation are essential. However, existing methods often suffer from limitations in both planning reasonable support strategies and effectively expressing them in responses. To the end, we propose a novel LLM-based Emotional Support Conversation Agent (ESCA) with a plug-in strategy planner and a strategy-aligned prompt generator. The strategy planner cooperates with four aspects of the seeker’s state, including emotion intensity, trust degree, dialogue behavior, and stage of change, to enhance the rationality and effectiveness of the strategy prediction. To ensure that predicted strategies are better conveyed, the prompt generator integrates strategy-aligned instructions, knowledge, and context to generate the soft prompt for guiding the LLM to generate supportive responses. In addition to supervised fine-tuning, the prompt generator is further optimized by reinforcement learning. Experimental results demonstrate that ESCA significantly improves both response quality and the success rate of achieving the ESC task goal.

Motivated by real-world applications, we study the problem of decentralized online learning with dynamic feedback delays in the presence of malicious data generators under different threat models. In this problem, multiple agents collaborate to classify the features of streaming data samples generated online and receive dynamically delayed feedback on the ground-truth labels. While some data generators are benign, others—due to internal motives or external factors such as cyberattacks—may maliciously manipulate data features to compromise the classification performance. In this work, we first investigate the targeted attacks by malicious data generators, i.e., feature manipulation with aims to gain preferred classification outcomes from the agents. In response, we propose two robust algorithms,RDOC-TOandRDOC-TC, countering ordinary and clairvoyant adversaries that can access certain outdated and the latest classification models of the agents, respectively. Subsequently, we address the untargeted attacks by malicious data generators, which aim to disrupt the classification outcomes without targeting any particular class, by proposing another algorithm,RDOC-U. Our theoretical analysis establishes that all three proposed algorithms achieve sublinear regret bounds. The evaluations conducted in the application of network traffic classification with two real-world datasets demonstrate the competitiveness of the proposed algorithms compared to advanced baselines.

High-definition (HD) cameras for surveillance and road traffic have experienced tremendous growth, demanding intensive computation resources for real-time analytics. Recently, offloading frames from the front-end device to the back-end edge server has shown great promise. In multi-stream competitive environments, efficient bandwidth management and proper scheduling are crucial to ensure both high inference accuracy and high throughput. To achieve this goal, we propose BiSwift, a bi-level framework that scales the concurrent real-time video analytics by a novel adaptive hybrid codec integrated with multi-level pipelines, and a global bandwidth controller for multiple video streams. The lower-level front-back-end collaborative mechanism (called adaptive hybrid codec) locally optimizes the accuracy and accelerates end-to-end video analytics for a single stream. The upper-level scheduler aims to accuracy fairness among multiple streams via the global bandwidth controller. The evaluation of BiSwift shows that BiSwift is able to real-time object detection on 9 streams with an edge device only equipped with an NVIDIA RTX3070 (8G) GPU. BiSwift improves 10%~21% accuracy and presents$1.2\sim 9\times $throughput compared with the state-of-the-art video analytics pipelines.

The unprecedented advancements in quantum technology have opened new prospects for the widespread adoption of quantum applications, placing new demands on the information transmission capabilities of large-scale quantum networks. Long-distance and stable entanglements are deemed as the lifeline in quantum network communication. However, some weaknesses, e.g., quantum decoherence, scarce quantum memory, and uneven-quality entanglement, of the quantum entanglement hinder the development. In this paper, we proposeSophon, an online transmission framework for quantum networks, which utilizes high-dimensional entanglements to concurrently transmit multi-qubit data to satisfy the transmission requirements of the real-time request set. We first model the quantum network with multi-qubit entanglement represented by$W$quantum state and then formulate the Entanglement Routing and Qubit Provisioning (ERQP) problem as a global-local optimization process. To solve theERQPproblem, we distributedly regard each network node as an RL agent for resource provisioning and extend the step-updating of the Markov Decision Process by introducing a centralized controller for entanglement route selection to optimize local and global objectives, respectively. Extensive simulations demonstrate, on the self-made simulation platform,Sophonachieves a$21.89\%-66.52\%$decrease in the communication cost, and is more robust on different scales of the network topology and the request set than the baselines.

Traditional computation offloading and resource allocation strategies encounter several issues that lead to poor service experience and resource wastage. The resource allocation scheme lacks the flexibility to adapt to the time-varying offloading demands of User Equipment (UEs). Furthermore, there is an imbalance between UEs seeking better service and Service Providers (SPs) aiming to minimize cost expenditures. In this paper, we propose a knowledge-defined networking-based Multi-Layer Computation Offloading and Resource Allocation strategy optimization (ML-CORA) architecture. Based on the ML-CORA, we design a Multi-Layer Local-Global Collaborative computation offloading and resource allocation strategy optimization (ML2GC) algorithm. The basic level of the ML2GC algorithm expresses and optimizes computation offloading demands from the perspective of UE (local), while the meta level optimizes the resource allocation strategy on demand from the perspective of the SP (global), achieving a collaborative multi-objective optimization for a win-win system between UEs and SPs. The two-layer structure of the ML2GC algorithm outputs continuous and discrete actions respectively, which improves the flexibility and efficiency of the algorithm while effectively balancing the interests of all parties and promoting efficient resource utilization. Simulation results based on the real-world dataset of Shanghai Telecom indicate that the ML2GC algorithm significantly improves both social welfare and resource utilization compared to baseline algorithms.

Multimodal sentiment analysis (MSA) is attracting increasing attention from researchers. Existing studies on MSA typically rely on surface-level feature extraction and fusion that can be directly obtained from multimodal data, which may often ignore the underlying semantic connection between images and texts. Recent progress in large multimodal models (LMMs) has demonstrated their impressive reasoning abilities, which can be leveraged to improve traditional MSA approaches by providing a deeper understanding of the sematic connection of the modalities. Toward this issue, in this paper, we propose a novel framework called MPT that combines traditional MSA approaches with Multi-Perspective Thinking from LMMs to improve prediction outcomes. Specifically, MPT instructs the traditional multimodal deep learning models to understand multiple-perspective rationales for different sentiment polarities, augmenting its knowledge base and enhancing its ability to make more accurate predictions. Extensive experiments on four refined datasets show that MPT can not only deliver better performance compared with existing methods, but also demonstrate good cross-modal understanding ability for recognizing user sentiment. The codes and datasets can be accessed here: https://github.com/RMJHQwQ/MPT.

Forecasting stock prices is complex and challenging. Uncovering correlations among stocks has proven to enhance stock price forecasting. However, existing correlation discovery methods, such as concept-based methods, are slow, inaccurate, and limited by their reliance on predefined concepts and manual analysis. In this paper, we propose COGRASP, a novel approach for stock price forecasting that constructs stock co-occurrence graphs automatically by analyzing rapidly updated sources such as reports, newspapers, and social media. Besides, we aggregate forecasts across multiple timescales (i.e., long-, medium-, and short-term) to capture multi-timescale trends fluctuations, thereby enhancing price forecasting accuracy. In experiments with real-world open-source stock market data, COGRASP outperforms state-of-the-art methods.

Waypoint planning is critical for Unmanned Aerial Vehicle (UAV) operations, particularly for surveillance and monitoring applications. With the rapid development and deployment of UAVs, an increasing number of industrial UAVs are equipped with multiple cameras to enhance monitoring capabilities and operational efficiency. Meanwhile, a new type of camera supporting adjustable pan and zoom is emerging and rapidly being deployed. While UAVs equipped with multi-adjustable cameras enhance flexibility and precision in capturing dynamic scenes, they also introduce new challenges in optimizing waypoints to ensure efficient coverage and accurate data collection. In this paper, we propose WiPlan, which aims to determine the optimal UAV waypoints while dynamically adjusting the pan (horizontal rotation) and zoom (focal length) of cameras to maximize overall monitoring utility. This problem involves two coupled NP-hard problems, making it significantly more complex to solve compared to previous work. In tackling this challenge, we construct WiPlan as a two-level optimization problem. The results show that our algorithm improves monitoring utility by at least$1.73 \times$compared to state-of-the-art algorithms. Moreover, we test WiPlan using a real-world outdoor field, which includes 23 objects and a two-camera UAV with 7 waypoints. The results demonstrate that our algorithm successfully monitors 65 % of the maximum possible monitored targets, outperforming the baselines by factors of$4.25 \times$and$15 \times$, respectively.

KV cache eviction has emerged as an effective solution to alleviate resource constraints faced by LLMs in long-context scenarios. However, existing token-level eviction methods often overlook two critical aspects: (1) their irreversible eviction strategy fails to adapt to dynamic attention patterns during decoding (the saliency shift problem), and (2) they treat both marginally important tokens and truly unimportant tokens uniformly, despite the collective significance of marginal tokens to model performance (the marginal information over-compression problem). To address these issues, we design two compensation mechanisms based on the high similarity of attention matrices between LLMs with different scales. We propose SmallKV, a small model assisted compensation method for KV cache compression. SmallKV can maintain attention matching between different-scale LLMs to: 1) assist the larger model in perceiving globally important information of attention; and 2) use the smaller model’s attention scores to approximate those of marginal tokens in the larger model. Extensive experiments on benchmarks including GSM8K, BBH, MT-Bench, and LongBench demonstrate the effectiveness of SmallKV. Moreover, efficiency evaluations show that SmallKV achieves 1.75 - 2.56 times higher throughput than baseline methods, highlighting its potential for efficient and performant LLM inference in resource constrained environments.

Influential users in online social networks have been given a lot of attention, which could maximize information diffusion in a centralized dissemination process. However, there exist users that can significantly promote diversified information communication among users, which is a pluralistic interaction process. Structural hole (SH) spanners serve as a bridge between social groups, promoting interactions between users and allowing ordinary users to receive diverse information. Filling structural holes, SH spanners have more opportunities to obtain innovative content. Identifying SH spanners is challenging due to their inconspicuous characteristics and privacy policy restrictions. In this article, we are the first to study the SH spanner identification problem using the behavior data generated by users, releasing the dependency on the entire graph structure to find SH spanners. We propose DeepHole to design both the semantic and the sequential behavior analysis modules on users’ generated content, utilizing the TextCNN and Transformer models, respectively. The semantic and sequential embeddings are effective in distinguishing the SH spanners from ordinary users. We conduct comprehensive evaluations using the Yelp and Foursquare datasets simultaneously. Results show that DeepHole can achieve a satisfying performance in detecting SH spanners labeled by both constraint and effective size metrics, with AUC values of 0.923 and 0.933 for Yelp and 0.828 and 0.815 for Foursquare.

With the rapid development of emerging services such as cellular vehicle-to-everything and immersive video service, network connections have further evolved from tangible physical connections to intangible virtual connections such as content, services, and computing resources, and the application scenarios have become more abundant. The mobile ultra-service, which is characterized by ultra-low latency, ultra-high reliability, and ubiquitous mobility, is becoming one of the most representative traffic types. However, the existing mobile network architecture has not evolved sufficiently to meet the specific requirements of these mobile ultra-services, the mobility anchors introduce unnecessary node and link latency, leaving space for further optimization. A compact network architecture (ComArch) is proposed in this paper for ultralow-latency services with ubiquitous mobility. ComArch is designed with a mapping control plane and a generalized forwarding plane to collaboratively implement packet forwarding in mobile scenarios. The generalized forwarding plane handles packet forwarding, while the mapping control plane manages terminals’ identifier and locator mapping entries. The node latency introduced by mobility anchors is eliminated, and an efficient routing scheme is proposed to find the optimal mandatory nodes in the forwarding path, thereby reducing unnecessary link latency. Experimental results show that ComArch can effectively reduce end-to-end delay while saving resources.

With the flourishing of global services, low-latency analytics on large-volume geo-distributed data has been a regular requirement for application decision-making. Serverless computing, with its rapid function start-up and lightweight deployment, provides a compelling way for geo-distributed analytics. However, existing research focuses on elastic resource scaling at the stage granularity, struggling to heterogeneous resource demands across component functions in wide-area settings. The neglect potentially results in the cost inefficiency and Service Level Objective (SLO) violations. In this paper, we advocate for fine-grained function orchestration to exploit wide-area resource elasticity. We thereby present Demeter, a fine-grained function orchestrator that saves job execution costs for geo-distributed serverless analytics while ensuring SLO compliance. By learning from volatile and bursty environments, Demeter jointly makes per-function placement and resource allocation decisions using a well-optimized multi-agent reinforcement learning algorithm with a pruning mechanism. It prevent the irreparable performance loss by function congestion control. Ultimately, we implement Demeter and evaluate it with the realistic workloads. Experimental results reveal that Demeter outperforms the baselines by up to 46.6% on cost, while reducing SLO violation by over 23.7% and bringing it to below 15%.

Multimodal social network user sentiment analysis aims to determine users' emotional polarity (positive or negative) by mining the associations between multiple data types such as images and texts. Existing public datasets are mainly constructed from English social media platforms, while Chinese social media datasets for multimodal user sentiment analysis are extremely scarce. In terms of the posts published by Chinese social media users, it is not rare that the emotional polarity delivered by the image and the textual content is inconsistent. Given such emotional inconsistency between images and texts, how to effectively identify users' true emotion polarity is still challenging. Toward the above issues, in this paper, we firstly construct a Chinese social media dataset CH-Mits for multimodal user sentiment analysis. In order to evaluate the usability of the dataset, we conceive and implement a novel model called PEMNet, and compare it with state-of-the-art models based on the CH-Mits dataset. In the end, we analyze the performance of PEMNet on selected samples with emotional inconsistency between images and texts. The constructed dataset and codes for PEMNet are available at https://github.com/Marblrdumdore/CH-Mits.

Access networks, even with advanced 5G technology, often face bottlenecks when supporting concurrent real-time Ultra High Definition (UHD) video streams with high bandwidth and low latency (e.g., under 10 ms of one-way delay) requirements. Traditionally, end systems employ a combination of flow and congestion control mechanisms to control the sending rate to avoid overwhelming the receiver and the network. However, such control efforts induce prolonged tail delays, thereby sharply reducing the number of UHD video streams meeting delivery deadlines, and sometimes even zero. These outcomes are largely due to the inaccurate network status estimation associated with the control mechanisms. To address this challenge, we propose CFC, a deadline-oriented flow control mechanism that employs cross-layer status estimation to maximize user satisfaction with deadlines. CFC accurately assesses cross-layer information, including flow status and 5G access network status at minimal expense, thus ensuring the deadlines through effective concurrent flow control. Our experiments, conducted in both simulation and testbed settings, demonstrate significant improvements in delay and load-balancing for both reliable and unreliable transmissions.

High-definition (HD) cameras for surveillance and road traffic have experienced tremendous growth, demanding intensive computation resources for real-time analytics. Recently, offloading frames from the front-end device to the back-end edge server has shown great promise. In multi-stream competitive environments, efficient bandwidth management and proper scheduling are crucial to ensure both high inference accuracy and high throughput. To achieve this goal, we propose BiSwift, a bi-level framework that scales the concurrent real-time video analytics by a novel adaptive hybrid codec integrated with multi-level pipelines, and a global bandwidth controller for multiple video streams. The lower-level front-back-end collaborative mechanism (called adaptive hybrid codec) locally optimizes the accuracy and accelerates end-to-end video analytics for a single stream. The upper-level scheduler aims to accuracy fairness among multiple streams via the global bandwidth controller. The evaluation of BiSwift shows that BiSwift is able to real-time object detection on 9 streams with an edge device only equipped with an NVIDIA RTX3070 (8G) GPU. BiSwift improves 10%∼21% accuracy and presents 1.2∼ 9× throughput compared with the state-of-the-art video analytics pipelines.

In the era of global services, low-latency analytics on large-volume geo-distributed data has been a regular demand for application decision-making. Serverless computing facilitates fast function start-up and deployment, making it an attractive way for geo-distributed analytics. We argue that the serverless paradigm holds the potential to breach current performance bottlenecks via fine-grained function orchestration. However, how to configure it for geo-distributed analytics remains ambiguous. To fill this gap, we present Demeter, a scalable fine-grained function orchestrator for geo-distributed serverless analytics systems. Demeter aims to minimize the composite cost of co-existing jobs while meeting the user-specific Service Level Objectives (SLO). To handle the volatile environments and learn the diverse function demands, a Multi-Agent Reinforcement Learning (MARL) solution is used to co-optimize the per-function placement and resource allocation. The MARL extracts holistic and compact states via hierarchical graph neural networks, and then designs a novel actor network to shrink the huge decision space and model complexity. Finally, we implement Demeter and evaluate it using realistic workloads. The experimental results reveal that Demeter significantly saves costs by 23.3%∼32.7%, while reducing SLO violations by over 27.4%, surpassing state-of-the-art solutions.

Inferring individual human mobility at a given time is not only beneficial for personalized location-based services but also crucial for tracking trajectory of the confirmed cases in the COVID-19 pandemic. However, individual-generated trajectory data from mobile Apps are characterized by implicit feedback, which means only a few individual-location interactions can be observed. Existing studies based on such sparse trajectory data are not sufficient to infer an individual’s missing mobility in his/her historical trajectory and further predict an individual’s future mobility at a given time under a unified framework. To address this concern, in this article, we propose a temporal-context-aware framework that incorporates multiple factors to model the time-sensitive individual-location interactions in a bottom-up way. Based on the idea of feature fusion, the driving effect of heterogeneous information on an individual’s mobility is gradually strengthened, so that the temporal-spatial context when a check-in occurs can be accurately perceived. We leverage Bayesian personalized ranking (BPR) to optimize the model, where a novel negative sampling method is employed to alleviate data sparseness. Based on three real-world datasets, we evaluate the proposed approach with regard to two different tasks, namely, missing mobility inference and future mobility prediction at a given time. Experimental results encouragingly demonstrate that our approach outperforms multiple baselines in terms of two evaluation metrics. Furthermore, the predictability of individual mobility within different time windows is also revealed.

Truck-drone systems, wherein trucks carrying drones drive to pre-planned positions and then free drones equipped with cameras to monitor a known number of objects with reported positions, have been used for various scenarios. An object's quality of monitoring (QoM) by a camera is defined as a function of camera focal length and monitoring distance. Improving the QoM would help downstream tasks, including object detection and recognition. The monitoring utility is the fusion of all the QoMs of an object from multiple cameras. This paper optimizes theDeploymentOfTrucksAndDrones forObject monitoring (DOTADO) problem,i.e., deploying a truck-drone system, where each drone is equipped with a varifocal camera, to maximize the overall monitoring utility for all objects. Firstly, we model the hybrid system and define monitoring quality and utility. Then, we discretize the solution space into deployment strategies with performance bound. To select deployment strategies, we prove the submodularity of the problem and propose a two-level greedy algorithm with a bounded approximation ratio. Finally, we devise an optimal method to adjust the strategy for energy saving and communication improvement without losing monitoring utility. We perform both simulations and field experiments to verify the proposed framework.

Applying the serverless paradigm to edge computing improves edge resource utilization while bringing the benefits of flexible scaling and pay-as-you-go to latency-sensitive applications. This extends the boundaries of serverless computing and improves the quality of service for Function-as-a-Service users. However, as an emerging cloud computing paradigm, serverless edge computing faces pressing challenges, with one of the biggest obstacles being delay caused by excessively long container cold starts. Cold start delay is defined as the time between when a serverless function is triggered and when it begins to execute, and its existence seriously impacts resource utilization and Quality of Service (QoS). In this paper, we study how to minimize the total system cost by caching function containers and selecting routes for neighboring functions via edge or public clouds. We prove that the proposed problem is NP-hard even in the special case where the user request contains only one function, and that the unpredictability of user requests and the impact between adjacent time decisions require that the problem to be solved in an online fashion. We then design the Online Lazy Caching algorithm, an online algorithm with a worst-case competitive ratio using a randomized dependent rounding algorithm to solve the problem. Extensive simulation results show that the proposed online algorithm can achieve close-to-optimal performance in terms of both total cost and cold start cost compared to other existing algorithms, with average improvements of 31.6% and 51.7%.

The quality of the video stream is the 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 address 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, selects a few frames adaptively via Deep Reinforcement Learning (DRL) to enhance the quality and inference then reuse on the unselected ones. Next, we extend AccDecoder to AccDecoder$+$by formulating the resolution-involved Markov decision process (MDP) to achieve resolution adaptation; it aims to trade accuracy and latency corresponding under various video resolutions. Proved by experiments, 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 contributes 6-21% accuracy improvement and a latency reduction of 20-80% than baselines. Compared with AccDecoder, AccDecoder$+$achieves an additional 2-7% accuracy improvement.

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.

Nowadays, the emerging short video streaming applications have gained substantial attention. With the rapidly burgeoning demand for short video streaming services, maximizing their Quality of Experience (QoE) is an onerous challenge. Current video preloading algorithms cannot determine video preloading sequence decisions appropriately due to the impact of users’ swipes and bandwidth fluctuations. As a result, it is still ambiguous how to improve the overall QoE while mitigating bandwidth wastage to optimize short video streaming services. In this article, we devise Gamora, a buffer-aware short video streaming system to provide a high QoE of users. In Gamora, we first propose an unordered preloading algorithm that utilizes a Deep Reinforcement Learning (DRL) algorithm to make video preloading decisions. Then, we further devise an Asymmetric Imitation Learning (AIL) algorithm to guide the DRL-based preloading algorithm, which enables the agent to learn from expert demonstrations for fast convergence. Finally, we implement our proposed short video streaming system prototype and evaluate the performance of Gamora on various real-world network datasets. Our results demonstrate that Gamora significantly achieves QoE improvement by 28.7%–51.4% compared to state-of-the-art algorithms, while mitigating bandwidth wastage by 40.7%–83.2% without sacrificing video quality.

Video analytics play a pivotal role in enhancing the safety of intelligent surveillance and autonomous driving. However, the transmission of vast video data and the computational demands of video analytics present challenges within traditional cloud computing paradigms. To address latency concerns, dynamic video analytics often leverage edge deployments. Nevertheless, the efficient allocation of resources at the edge, balancing cost-effectiveness and accuracy, becomes crucial, especially when multiple video analytics services concurrently operate within the system. This paper introduces an edge-centric incentive mechanism designed to encourage greater participation from edge nodes in offloading tasks. The key focus is on addressing the dynamic nature of edge resources and optimizing system returns through a rational pricing mechanism. We propose a decentralized Soft Actor-Critic algorithm grounded in game theory (DSACG) to autonomously learn the optimal pricing strategy. A comprehensive theoretical analysis, supported by extensive simulations, substantiates the effectiveness of our proposed solution.

The 360-degree video streaming service provides a unique immersive viewing experience for users, who can freely change their Field-of-View (FoV) to view different portions of the videos. However, the demands for high throughput and low latency for 360-degree video pose substantial challenges to the current network infrastructure. Super Resolution (SR) is the procedure for reconstructing high-resolution images from low-resolution ones. Hence, caching video content on the network edge in advance, which is near end users, and applying the SR technique can significantly alleviate the transmission latency. In this article, we describeNOVA, an efficientNeural-OptimizedViewportAdaptive 360-degree video streaming system to improve the Quality of Experience (QoE) of users. In NOVA, we first design a foveated rendering SR approach to super-resolve video tiles utilizing computational resources at the edge. Subsequently, we present a meta-learning-based Multi-Agent Reinforcement Learning (MARL) algorithm to select SR depths and video tiles inside users’ viewports for agile video tile adaptation to optimize overall QoE under frequent network fluctuations. Finally, we implement the holistic prototype of NOVA and evaluate its performance on various real-world network datasets. Extensive experiments illustrate that compared to the state-of-the-art algorithms, NOVA improves average user-perceived QoE by up to 27%.

Communication is supposed to improve multi-agent collaboration and overall performance in cooperative Multi-agent reinforcement learning (MARL). However, such improvements are prevalently limited in practice since most existing communication schemes ignore communication overheads (e.g., communication delays). In this paper, we demonstrate that ignoring communication delays has detrimental effects on collaborations, especially in delay-sensitive tasks such as autonomous driving. To mitigate this impact, we design a delay-aware multi-agent communication model (DACOM) to adapt communication to delays. Specifically, DACOM introduces a component, TimeNet, that is responsible for adjusting the waiting time of an agent to receive messages from other agents such that the uncertainty associated with delay can be addressed. Our experiments reveal that DACOM has a non-negligible performance improvement over other mechanisms by making a better trade-off between the benefits of communication and the costs of waiting for messages.

The pulse pattern generated by real-time cloud VR applications poses a new challenge to bandwidth-limited wireless access networks. Concurrent pulse flows can lead to significant congestion in access points, making network providers unable to guarantee the quality of experience (QoE) for all users. To maximize the number of satisfied users, we propose a cross-layer assisted traffic scheduling mechanism, CTS, to manage flows into the edge network. We evaluated CTS using a trace-driven simulator, and the experimental results prove that CTS achieves the theoretical maximum of satisfied users and maintains a more balanced load for the network.

Recent 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.

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 address 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.

In the traditional video streaming service provisioning paradigm, viewers typically request video content through a central Content Delivery Network (CDN) server. However, because of the uncertain wide area network delays, the (remote) viewers usually suffer from long video streaming delay, which affects the quality of experience. Multi-Access Edge Computing (MEC) offers a way to shorten the video streaming delay by building small-scale cloud infrastructures at the network edge, which are in close proximity to the viewers. In this paper, we present novel centralized and distributed algorithms for the video content placement problem in MEC. In the proposed centralized video content placement algorithm, we leverage the Lyapunov optimization technique to formulate the video content placement problem as a series of one-time-slot optimization problems and apply an Alternating Direction Method of Multipliers (ADMM)-based method to solve each of them. We further devise a distributed Multi-Agent Reinforcement Learning (MARL)-based method with value decomposition mechanism and parallelization policy update method to solve the video content placement problem. The value Decomposition mechanism deals with the credit assignment among multiple agents, which promotes the cooperative optimization of the global target and reduces the frequency of information exchange. The parallelization of policy network can speed up the convergence process. Simulation results verify the effectiveness and superiority of our proposed centralized and distributed algorithms in terms of performance.

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.

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.

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 optimize multiple users’ experiences jointly, but users often suffer short connection durations and frequent handoffs because of their high mobility. This paper proposes an end-to-end scheme, VSiM, to support 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. 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 on average of the averaged QoE.

With the rapid development of the Internet, more and more computing-intensive applications with high requirements on computing delay and energy consumption have emerged. Recently, the use of mobile-edge computing servers for auxiliary computing is considered as an effective way to reduce latency and energy consumption. In addition, applications such as autonomous driving will generate a large number of repetitive tasks. Using a cache to store the computational results of popular tasks can avoid the overhead caused by repetitive processing. In this article, we study the problem of computation offloading for users in multiple regions. The optimization goal is expressed as choosing offloading strategies and caching strategies to minimize the total delay and energy consumption of all regions. We first use the deep reinforcement learning (DRL) deep deterministic policy gradient (DDPG) framework to solve the problem of computational offloading in a single region. We also show the inefficiency of existing collaborative caching approaches in multiple regions, and propose a new collaborative caching algorithm (CCA) to improve the overall cache hit rate of the system. Finally, we integrate the DDPG and CCA algorithms to form a holistic efficient caching and offloading strategy for all regions. The simulation results show that the proposed algorithm can significantly improve the cache hit rate, and has an excellent performance in reducing the total system overhead.

This paper considers the fundamental problem of deployment of Unmanned AerialVehIcles for aniSotropic monItoringTasks (VISIT), that is, given a set of objects with determined coordinates and directions in 2D area, deploy a fixed number of UAVs by adjusting their coordinates and orientations such that the overall monitoring utility for all objects is maximized. We develop a theoretical framework to address VISIT problem. First, we establish monitoring model whose quality of monitoring (QoM is anisotropic with monitoring angle and varying with various monitoring distance. To the best of our knowledge, we are the first considering the anisotropy of monitoring angle. Then, we propose a framework consisting of area discretization and Monitoring Dominating Set (MDS) extraction to reduce the infinite solution space of VISIT to a limited one with performance bound. Finally, we model the reformulated problem as maximizing a monotone submodular function subject to a matroid constraint, and present a greedy algorithm with$1-1/e-\epsilon$approximation ratio. We conduct both simulations and field experiments to evaluate our framework, and the results show that our algorithm outperforms comparison algorithms by at least 41.3 percent.

Numerous applications in 3D underwater sensor networks (UWSNs), such as pollution detection, disaster prevention, animal monitoring, navigation assistance, and submarines tracking, heavily rely on accurate localization techniques. However, due to the limited batteries of sensor nodes and the difficulty for energy harvesting in UWSNs, it is challenging to localize sensor nodes successfully within a short sensor node lifetime in an unspecified underwater environment. Therefore, we propose the Adaptive Energy-Efficient Localization Algorithm (Adaptive EELA) to enable energy-efficient node localization while adapting to the dynamic environment changes. Adaptive EELA takes a fuzzy game-theoretic approach, whereby the Stackelberg game is used to model the interactions among sensor and anchor nodes in UWSNs and employs the adaptive neuro-fuzzy method to set the appropriate utility functions. We prove that a socially optimal Stackelberg–Nash equilibrium is achieved in Adaptive EELA. Through extensive numerical simulations under various environmental scenarios, the evaluation results show that our proposed algorithm accomplishes a significant energy reduction, e.g., 66% lower compared to baselines, while achieving a desired performance level in terms of localization coverage, error, and delay.

Many organizations nowadays have multiple sites at different geographic locations. Typically, transmitting massive data among these sites relies on the interconnection service offered by ISPs. Segment Routing over IPv6 (SRv6) is a new simple and flexible source routing solution which could be leveraged to enhance interconnection services. Compared to traditional technologies, e.g., physical leased lines and MPLS-VPN, SRv6 can easily enable quick-launched interconnection services and significantly benefit from traffic engineering with SRv6-TE. To parse the SRv6 packet headers, however, hardware support and upgrade are needed for the conventional routers of ISP. In this paper, we study the problem of SRv6 incremental deployment to provide a more balanced interconnection service from a traffic engineering view. We formally formulate the problem as an SRID problem with integer programming. After transforming the SRID problem into a graph model, we propose two greedy methods considering short-term and long-term impacts with reinforcement learning, namely GSI and GLI. The experiment results using a public dataset demonstrate that both GSI and GLI can significantly reduce the maximum link utilization, where GLI achieves a saving of 59.1% against the default method.

Many hardware-based security middleboxes have been deployed in the networks to defend against different threats. However, these hardware middleboxes are hard to upgrade or migrate. The emergence of network functions virtualization (NFV), which realizes various security functions in the form of virtual network functions (VNFs), brings many benefits to network security. To improve the security level further, several VNFs are coordinated in a pre-defined order to form service function chains (SFCs). It is expected that the SFCs are embedded properly with low cost, including the VNF setup cost and the flow routing cost. In this paper, we find that when an SFC is required by multiple flows for the identical network security threats, the total cost could be reduced by embedding an aggregated service function tree (ASFT) instead of multiple independent SFCs. We formally characterize the integer programming model of this problem and prove that it is NP-hard. Then we propose a performance-guaranteed approximation algorithm and prove that the algorithm could find the optimal solution in a special case. Extensive experiments indicate that our method can reduce the total cost by$22.0\%$and$24.1\%$against two compared algorithms, respectively.

Network Function Virtualization (NFV) has been emerging as an appealing solution that transforms from dedicated hardware implementations to software instances running in a virtualized environment. In NFV, the requested service is implemented by a sequence of Virtual Network Functions (VNF) that can run on generic servers by leveraging the virtualization technology. These VNFs are pitched with a predefined order, and it is also known as the Service Function Chaining (SFC). Considering that the delay and resiliency are two important Service Level Agreements (SLA) in a NFV service, in this paper, we first investigate how to quantitatively model the traversing delay of a flow in both totally ordered and partially ordered SFCs. Subsequently, we study how to calculate the VNF placement availability mathematically for both unprotected and protected SFCs. After that, we study the delay-sensitive Virtual Network Function placement and routing problem with and without resiliency concerns. We prove that this problem is NP-hard under two cases. We subsequently propose an exact Integer Nonlinear Programming (INLP) formulation and an efficient heuristic for this problem in each case. Finally, we evaluate the proposed algorithms in terms of acceptance ratio, average number of used nodes and total running time via extensive simulations.

Due to the limitation of public school systems, many students pursue private supplementary tutoring for improving their academic performance. Different from public schools, the private online education provides diverse courses and satisfy differentiated demands of the students. Students’ behavior and performance in online supplementary learning are relevant to not only personal attributes, but also some factors such as city levels, grades and family situation. Existing studies mostly rely on panel survey/questionnaire data and few studied online private tutoring. In this paper, with 11,392 anonymous K-12 students’ 3-year learning data from one of the world’s largest online extra-curricular education platforms, we uncover students’ online learning behaviors and infer the impact of students’ home location, family socioeconomic situation and attended school’s reputation/rank on the students’ private tutoring course participation and learning outcomes. Further analysis suggests that such impact may be largely attributed to the inequality of access to educational resources in different cities and the inequality in family socioeconomic status. Finally, we study the predictability of students’ performance and behaviors using machine learning algorithms with different groups of features, showing students’ online learning performance can be predicted with MAE< 10%.

Existing Internet routing protocols much focus on providing interconnection service for independent autonomous systems (ASes) rather than end-to-end low latency transmission. Nowadays, a growing number of applications and platforms have high requirements for low latency. However, developing new routing protocols in the wide area network that provides low latency routing service is very challenging, and remains an open problem due to the obstacles of compatibility, feasibility, scalability and efficiency. On the other hand, the ignorance of latency performance results in triangle inequality violations (TIV). In this paper, we leverage TIV and a new routing technology, SRv6, to build a new distributed routing protocol, SRv6 underlay federation (SRUF), which aims to provide low-latency routing services in network core. We design a novel method to find alternative paths with lower latency between any pair of ASes in SRUF. This method can achieve high scalability as it incurs only$O(n)$bandwidth overhead in each member of SRUF. SRv6 is then employed to steer the flows along the selected indirect low-latency paths, while keeping compatibility to legacy routing systems. The experimental results with realworld datasets demonstrate that SRUF can effectively reduce the average end-to-end delay by 5.4% ~ 58.9%.

Heterogeneous information network (HIN) embedding, learning the low-dimensional representation of multi-type nodes, has been applied widely and achieved excellent performance. However, most of the previous works focus more on static heterogeneous networks or learning node embedding within specific snapshots, and seldom attention has been paid to the whole evolution process and capturing all temporal dynamics. In order to fill the gap of obtaining multi-type node embeddings by considering all temporal dynamics during the evolution, we propose a novel temporal HIN embedding method (THINE). THINE not only uses attention mechanism and meta-path to preserve structures and semantics in HIN but also combines the Hawkes process to simulate the evolution of the temporal network. Our extensive evaluations with various real-world temporal HINs demonstrate that THINE achieves state-of-the-art performance in both static and dynamic tasks, including node classification, link prediction, and temporal link recommendation.

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 quality of service (QoS) requirements. Given the importance of NFV, many approaches have been proposed to solve the VNF placement and traffic routing problem. However, those prior approaches mainly assume that the state of the network is static and known, disregarding real-time network variations. To bridge that gap, in this paper, we formulate the VNF placement and traffic routing problem as a Markov Decision Process model to capture the dynamic network state transitions. In order to jointly minimize the delay and cost of NFV providers and maximize the revenue, we devise a customized Deep Reinforcement Learning (DRL) algorithm, called A-DDPG, for VNF placement and traffic routing in a real-time network. A-DDPG uses the attention mechanism to ascertain smooth network behavior within the general framework of network utility maximization (NUM). The simulation results show that A-DDPG outperforms the state-of-the-art in terms of network utility, delay, and cost.

Obtaining accurate and timely estimates of socioeconomic status at fine geographical resolutions is essential for global sustainable development and the fight against poverty. However, data related to local socio-economic dynamics in rural villages is often either unavailable or outdated. To fill this gap, predicting local economic well-being with satellite imagery and machine learning has shown promising results. While state-of-the-art analyses currently mostly focus on predicting the levels of socio-economic status, finding temporal changes in rural villages’ economic well-being is essential for tracking the impacts of public policies (targeting e.g., poverty alleviation or access to various public services). In this paper, we propose 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. We can distinguish between “Decline”, “Stagnation” and “Growth” in these outcomes with an F1 score of 58.8% using a Logistic Regression model. Regression-based approaches achieve an R2of up to 32.5% when predicting actual changes in these outcomes. Our approach demonstrates the feasibility of satellite-based estimates for measuring changes in local socio-economic dynamics.

Cloud radio access network (C-RAN) is a promising 5G network architecture by establishing baseband units (BBU) pools to perform baseband processing functionalities and deploying remote radio heads (RRHs) for wireless signal transmission and reception. Mobile-edge computing (MEC) offers a way to shorten the service delay by building small-scale cloud infrastructures at the network edge. By co-locating the BBU pool with edge cloud at the so-called BBU node, we can take full advantages of C-RAN and MEC for better spectrum utilization and delay-guaranteed services. In this article, we first study how to allocate each user's task to the BBU node and find the path from his/her accessing RRH node to the BBU node such that the maximum service delay among all the requests is minimized. We then consider this problem with survivability concerns, which is to use both primary and backup BBU nodes to issue the request such that the primary path and backup path are link disjoint. We analyze the complexities of these two problems and prove they are NP-hard in general. Subsequently, we devise a randomized approximation algorithm and an efficient heuristic to solve the considered problems, respectively. The simulation results show that the proposed algorithms outperform two benchmark heuristics in terms of acceptance ratio and maximum service delay.

Dating apps have gained tremendous popularity during the past decade. Compared with traditional offline dating means, dating apps ease the process of partner finding significantly. While bringing convenience to hundreds of millions of users, dating apps are vulnerable to become targets of adversaries. In this article, we focus on malicious user detection in dating apps. Existing methods overlooked the signals hidden in the textual information of user interactions, particularly the interplay of temporal-spatial behaviors and textual information, leading to limited detection performance. To tackle this, we propose DatingSec, a novel malicious user detection system for dating apps. Concretely, DatingSec leverages long short-term memory neural networks (LSTM) and an attentive module to capture the interplay of users' temporal-spatial behaviors and user-generated textual content. We evaluate DatingSec on a real-world dataset collected from Momo, a widely used dating app with more than 180 million users. Experimental results show that DatingSec outperforms state-of-the-art methods and achieves an F1-score of 0.857 and AUC of 0.940.

Mobile Edge Computing (MEC) offers a way to shorten the cloud servicing delay by building the small-scale cloud infrastructures at the network edge, which are in close proximity to the end users. Moreover, Network Function Virtualization (NFV) has been an emerging technology that transforms from traditional dedicated hardware implementations to software instances running in a virtualized environment. In NFV, the requested service is implemented by a sequence of Virtual Network Functions (VNF) that can run on generic servers by leveraging the virtualization technology. Service Function Chaining (SFC) is defined as a chain-ordered set of placed VNFs that handles the traffic of the delivery and control of a specific application. NFV therefore allows to allocate network resources in a more scalable and elastic manner, offer a more efficient and agile management and operation mechanism for network functions and hence can largely reduce the overall costs in MEC. In this paper, we study the problem of how to place VNFs on edge and public clouds and route the traffic among adjacent VNF pairs, such that the maximum link load ratio is minimized and each user's requested delay is satisfied. We consider this problem for both totally ordered SFCs and partially ordered SFCs. We prove that this problem is NP-hard, even for the special case when only one VNF is requested. We subsequently propose an efficient randomized rounding approximation algorithm to solve this problem. Extensive simulation results show that the proposed approximation algorithm can achieve close-to-optimal performance in terms of acceptance ratio and maximum link load ratio.

The Bulk Synchronous Parallel (BSP) paradigm is gaining tremendous importance recently due to the popularity of computations as distributed machine learning and graph computation. In a typical BSP job, multiple workers concurrently conduct iterative computations, where frequent synchronization is required. Therefore, the workers should be scheduled simultaneously and their placement on different computing devices could significantly affect the performance. Simply retrofitting a traditional scheduling discipline will likely not yield the desired performance due to the unique characteristics of BSP jobs. In this work, we deriveSPIN, a novel scheduling designed for BSP jobs with placement-sensitive execution to minimize the makespan of all jobs. We first prove the problem approximation hardness and then present howSPINsolves it with a rounding-based randomized approximation approach. Our analysis indicatesSPINachieves a good performance guarantee efficiently. Moreover,SPINis robust against misestimation of job execution time by theoretically bounding its negative impact. We implementSPINon a production-trace driven testbed with 40 GPUs. Our extensive experiments show thatSPINcan reduce the job makespan and the average job completion time by up to$3\times $and$4.68\times $, respectively.SPINalso demonstrates better robustness to execution time misestimation compared with state-of-the-art heuristic baselines.

Network Function Virtualization (NFV) has been emerging as an appealing solution that transforms complex network functions from dedicated hardware implementations to software instances running in a virtualized environment. Due to the numerous advantages such as flexibility, efficiency, scalability, short deployment cycles, and service upgrade, NFV has been widely recognized as the next-generation network service provisioning paradigm. In NFV, the requested service is implemented by a sequence of Virtual Network Functions (VNF) that can run on generic servers by leveraging the virtualization technology. These VNFs are pitched with a predefined order through which data flows traverse, and it is also known as the Service Function Chaining (SFC). In this article, we provide an overview of recent advances of resource allocation in NFV. We generalize and analyze four representative resource allocation problems, namely, (1) the VNF Placement and Traffic Routing problem, (2) VNF Placement problem, (3) Traffic Routing problem in NFV, and (4) the VNF Redeployment and Consolidation problem. After that, we study the delay calculation models and VNF protection (availability) models in NFV resource allocation, which are two important Quality of Service (QoS) parameters. Subsequently, we classify and summarize the representative work for solving the generalized problems by considering various QoS parameters (e.g., cost, delay, reliability, and energy) and different scenarios (e.g., edge cloud, online provisioning, and distributed provisioning). Finally, we conclude our article with a short discussion on the state-of-the-art and emerging topics in the related fields, and highlight areas where we expect high potential for future research.

Online social networks (OSNs) have become a commodity in our daily life. As an important concept in sociology and viral marketing, the study of social influence has received a lot of attentions in academia. Most of the existing proposals work well on dominant OSNs, such as Twitter, since these sites are mature and many users have generated a large amount of data for the calculation of social influence. Unfortunately, cold-start users on emerging OSNs generate much less activity data, which makes it challenging to identify potential influential users among them. In this work, we propose a practical solution to predict whether a cold-start user will become an influential user on an emerging OSN, by opportunistically leveraging the user’s information on dominant OSNs. A supervised machine learning-based approach is adopted, transferring the knowledge of both the descriptive information and dynamic activities on dominant OSNs. Descriptive features are extracted from the public data on a user’s homepage. In particular, to extract useful information from the fine-grained dynamic activities that cannot be represented by the statistical indices, we use deep learning technologies to deal with the sequential activity data. Using the real data of millions of users collected from Twitter (a dominant OSN) and Medium (an emerging OSN), we evaluate the performance of our proposed framework to predict prospective influential users. Our system achieves a high prediction performance based on different social influence definitions.

Accurate prediction is highly important for clinical decision making and early treatment. In this paper, we study the imbalanced data problem in prediction, a key challenge existing in the healthcare area. Imbalanced datasets bias classifiers towards the majority class, leading to an unsatisfied classification prediction performance on the minority class, which is known as imbalance problem. Existing imbalance learning methods may suffer from issues like information loss, overfitting, and high training time cost. To tackle these issues, we propose a novel ensemble learning method called Multiple bAlance Subsets Stacking (MASS) by exploiting a multiple balance subsets construction strategy. Furthermore, we improve MASS with introducing parallelism (Parallel MASS) to reduce the training time cost. We evaluate MASS on three real-world healthcare datasets, and experimental results demonstrate that its prediction performance outperforms the state-of-art methods in terms of AUC, F1-score and MCC. Through the speedup analysis, Parallel MASS reduces the training time cost greatly on large dataset, and its speedup increases as the data size grows.

The Bulk Synchronous Parallel (BSP) paradigm is gaining tremendous importance recently because of the pop-ularity of computations such as distributed machine learning and graph computation. In a typical BSP job, multiple workers concurrently conduct iterative computations, where frequent synchronization is required. Therefore, the workers should be scheduled simultaneously and their placement on different computing devices could significantly affect the performance. Simply retrofitting a traditional scheduling discipline will likely not yield the desired performance due to the unique characteristics of BSP jobs. In this work, we derive SPIN, a novel scheduling designed for BSP jobs with placement-sensitive execution to minimize the makespan of all jobs. We first prove the problem approximation hardness and then present how SPIN solves it with a rounding-based randomized approximation approach. Our analysis indicates SPIN achieves a good performance guarantee efficiently. Moreover, SPIN is robust against misestimation of job execution time by theoretically bounding its negative impact. We implement SPIN on a production-trace driven testbed with 40 GPUs. Our extensive experiments show that SPIN can reduce the job makespan and the average job completion time by up to 3× and 4.68×, respectively. Our approach also demonstrates better robustness to execution time misestimation compared with heuristic baselines.

Large private and government networks are often subjected to attacks like data extrusion and service disruption. Existing anomaly detection systems use offline supervised learning and employ experts for labeling. Hence they cannot detect anomalies in real-time. Even though unsupervised algorithms are increasingly used nowadays, they cannot readily adapt to newer threats. Moreover, many such systems also suffer from high cost of storage and require extensive computational resources. In this paper, we propose ADA: Adaptive Deep Log Anomaly Detector, an unsupervised online deep neural network framework that leverages LSTM networks and regularly adapts to newer log patterns to ensure accurate anomaly detection. In ADA, an adaptive model selection strategy is designed to choose pareto-optimal configurations and thereby utilize resources efficiently. Further, a dynamic threshold algorithm is proposed to dictate the optimal threshold based on recently detected events to improve the detection accuracy. We also use the predictions to guide storage of abnormal data and effectively reduce the overall storage cost. We compare ADA with state-of-the-art approaches through leveraging the Los Alamos National Laboratory cyber security dataset and show that ADA accurately detects anomalies with high F1-score ~95% and it is 97 times faster than existing approaches and incurs very low storage cost.

Content Centric Networking (CCN) and Named Data Networking (NDN) are popular ICN proposals that are widely accepted in the ICN community; however, they do not provide an efficient pub/sub mechanism. Hence, a content oriented pub/sub system named COPSS was developed to enhance the CCN/NDN protocols with efficient pub/sub capabilities. Internet houses powerful devices like routers and servers that can operate with the full-fledged implementation of such ICN protocols. However, Internet of Things (IoT) has become a growing topic of interest in recent years with billions of resource constrained devices expected to connect to the Internet in the near future. The current design to support IoT relies mainly on IP which has limited address space and hence cannot accommodate the increasing number of devices. Even though, IPv6 provides a large address space, IoT devices operate with constrained resources and hence, IPv6 protocol and its headers will induce additional overhead for their operation. Interestingly, we observed that IoTs are information centric in nature and therefore, ICN could be the more suitable candidate to support IoT environments. Although NDN and COPSS are designed for the Internet, their current full fledged implementations cannot be used by the resource constrained IoT devices. Therefore, CCN-lite was designed to provide a light weight, inter-operable version of the CCNx protocol to support the IoT devices. However, we show that communication in the IoT networks resemble pub/sub communication paradigm. However, CCN-lite like its ancestors (CCN/NDN) lacks the support for an efficient pub/sub mechanism while COPSS cannot be directly applied to the constrained IoT networks. Therefore, in this work, we develop COPSS-lite, an efficient and light weight implementation of pub/sub along with multi-hop routing to support the IoT networks. Essentially, COPSS-lite enhances CCN-lite with pub/sub capability with minimal overhead and further enables multi-hop connections by incorporating the famous RPL protocol for low power and lossy networks. Through evaluation using the real world sensor devices from the IoT Lab, we demonstrate the benefits of COPSS-lite in comparison with stand alone CCN-lite. Our results show that COPSS-lite is compact, operates on all platforms that support CCN-lite and significantly improves the performance of constrained devices in the IoT environments.

Identifying an unfamiliar caller's profession is important to protect citizens' personal safety and property. Due to limited data protection of many popular online services in some countries such as taxi hailing or takeouts ordering, many users encounter an increasing number of phone calls from strangers. This may aggravate the situation that criminals pretend to be delivery staff or taxi drivers, bringing threats to the society. Additionally, many people nowadays suffer from excessive digital marketing and fraud phone calls because of personal information leakage. However, previous works on malicious call detection only focused on binary classification, and do not work for identification of multiple professions. We observed that web service requests issued from users' mobile phones which may show their Apps preferences, spatial and temporal patterns, and other profession related information. This offers us a hint to identify unfamiliar callers. In fact, some previous works already leveraged raw data from mobile phones (which includes sensitive information) for personality studies. However, accessing users' mobile phone raw data may violate the more and more strict private data protection policies or regulations (e.g. GDPR 71). Using appropriate statistical methods to eliminate private information and preserve personal characteristics, provides a way to identify mobile phone callers without privacy concern. In this paper, we exploit privacy-preserving mobile data to develop a model which can automatically identify the callers who are divided into four categories of users: normal users (other professions), taxi drivers, delivery and takeouts staffs, telemarketers and fraudsters. The validation results over an anonymized dataset of 1,282 users with a period of 3 months in Shanghai City prove that the proposed model could achieve an accuracy of 75+%.

Ensuring high availability (HA) for software-based networks is a critical design feature that will help the adoption of software-based network functions (NFs) in production networks. It is important for NFs to avoid outages and maintain mission-critical operations. However, HA support for NFs on the critical data path can result in unacceptable performance degradation. We present REINFORCE, an integrated framework to support efficient resiliency for NF service chains. REINFORCE includes timely failure detection and consistent failover mechanisms. REINFORCE replicates state to standby NFs (local and remote) while enforcing correctness. It minimizes the number of state transfers by exploiting the concept of external synchrony, and leverages opportunistic batching and multi-buffering to optimize performance. Experimental results show that, even at line-rate packet processing (10 Gbps), REINFORCE achieves chain-level failover across servers in a LAN within 10ms, incurring less than 10% performance overhead, and adds average latency only ~400 μs, with a worst-case latency of less than 1ms. REINFORCE also recovers from software failures within the same node in less than 100 μs, incurring less than 1% performance overhead and adds less than 5 μs latency during normal operation.

Managing Network Function (NF) service chains requires careful system resource management. We propose NFVnice, a user space NF scheduling and service chain management framework to provide fair, efficient and dynamic resource scheduling capabilities on Network Function Virtualization (NFV) platforms. The NFVnice framework monitors load on a service chain at high frequency (1000Hz) and employs backpressure to shed load early in the service chain, thereby preventing wasted work. Borrowing concepts such as rate proportional scheduling from hardware packet schedulers, CPU shares are computed by accounting for heterogeneous packet processing costs of NFs, I/O, and traffic arrival characteristics. By leveraging cgroups, a user space process scheduling abstraction exposed by the operating system, NFVnice is capable of controlling when network functions should be scheduled. NFVnice improves NF performance by complementing the capabilities of the OS scheduler but without requiring changes to the OS's scheduling mechanisms. Our controlled experiments show that NFVnice provides the appropriate rate-cost proportional fair share of CPU to NFs and significantly improves NF performance (throughput and latency) by reducing wasted work across an NF chain, compared to using the default OS scheduler. NFVnice achieves this even for heterogeneous NFs with vastly different computational costs and for heterogeneous workloads.

Load balancing in datacenter networks (DCNs) is an important and challenging task for datacenter managers. A number of sophisticated technologies have been proposed to improve load balancing performance in a complicated circumstance, i.e., with various traffic characteristics. Many approaches need a high cost to implement, such as changing switch hardware. The efficiency problem has not been well addressed. MPTCP was proposed as a low-cost approach to improve data transmission in DCNs, which uses subflows to balance workloads across multiple paths. However, current MPTCP is not satisfying, especially when there are rack-local flows or many-to-one short flows. In this article, we propose DCMPTCP to improve the efficacy of MPTCP. We gradually develop three mechanisms. First, DCMPTCP identifies rack-local traffic and eliminates unnecessary subflows to reduce the overhead. Second, DCMPTCP estimates flow length and establishes subflows in a smarter way. Third, DCMPTCP strengthens explicit congestion notification to improve the congestion control performance on inter-rack many-to-one short flows. We have implemented DCMPTCP in both the Linux kernel and ns-3 simulator. Our comprehensive testbed experiments and simulations show that DCMPTCP outperforms MPTCP in both 1 Gbps testbed, and 10 Gbps large-scale simulation network.

The currently trending paradigms of edge and fog computing attempt to provide services close to the end user, to meet the demands of latency-sensitive applications and to limit bandwidth consumption in the network core. One open issue is the pricing of edge and fog resources. Current pricing schemes are usually oligopolistic and not fair. In this work, we propose DMC, a marketplace that can dynamically determine the fair price for arbitrary resource types and instances based on supply and demand existing at that period. Unlike the state-of-the-art solutions, DMC performs integral allocation of resources and thereby avoids the unbounded integrality gap. Additionally, DMC provides differential pricing among instances to allow varying prices based on the perceived value of a resource. We evaluate DMC with both heavy and non-heavy tailed distributions to reflect diverse buying interests and the number of resources sold to demonstrate the feasibility of our solution for several realistic scenarios. We observe that (i) DMC arrives at market-clearing prices; (ii) DMC generates 10x to 100x more profit than state-of-the-art solutions, while still maximizing the Nash Social Welfare to achieve prices that are fair to both buyers and the resource providers; and (iii) the computation time for DMC does not exceed 10 seconds even in the case of 500 resource types with 500 buyers each, making it applicable for real-time use cases.

The notion of socioeconomic status (SES) of a person or family reflects the corresponding entity's social and economic rank in society. Such information may help applications like bank loaning decisions and provide measurable inputs for related studies like social stratification, social welfare and business planning. Traditionally, estimating SES for a large population is performed by national statistical institutes through a large number of household interviews, which is highly expensive and time-consuming. Recently researchers try to estimate SES from data sources like mobile phone call records and online social network platforms, which is much cheaper and faster. Instead of relying on these data about users' cyberspace behaviors, various alternative data sources on real-world users' behavior such as mobility may offer new insights for SES estimation. In this paper, we leverage Smart Card Data (SCD) for public transport systems which records the temporal and spatial mobility behavior of a large population of users. More specifically, we develop S2S, a deep learning based approach for estimating people's SES based on their SCD. Essentially, S2S models two types of SES-related features, namely the temporal-sequential feature and general statistical feature, and leverages deep learning for SES estimation. We evaluate our approach in an actual dataset, Shanghai SCD, which involves millions of users. The proposed model clearly outperforms several state-of-art methods in terms of various evaluation metrics.

Segment Routing is a source routing based architecture that provides an opportunity to include a list of instructions called segments in the packet headers. The segments may allow the inclusion of detours for responding to Traffic Engineering needs or Service Function Chaining implementations. Even though there is an increasing interest towards enhancing and adopting Segment Routing, the administrators are still burdened with the task of manually write and maintain the segment lists. Such type of management presents several challenges ranging from error-prone configurations to increased response time for network updates. In this paper, we present a Segment Routing management framework named Busoni, which automates and simplifies the process of segments lists management. Additionally, we also provide programming tools to compose and manage Segment Routing policies that operate efficiently, even under multi-tenancy environments. Using different use cases, we show the programming capabilities offered by our framework.

Road accidents and traffic congestion are two critical problems for global transport systems. Connected vehicles (CV) and automated vehicles (AV) are among the most heavily researched and promising automotive technologies to reduce road accidents and improve road efficiency. However, both AV and CV technologies have inherent shortcomings, for example, line of sight sensing limitation of AV sensors and the dependency of high penetration rate for CVs. In this paper we present a cooperative connected intelligent vehicles (CAV) framework. It is motivated by the observation that vehicles are increasingly intelligent with various levels of autonomous functionalities. The vehicles intelligence is boosted by more sensing and computing resources. These sensor and computing resources of CAV vehicles and the transport infrastructure could be shared and exploited. With resource sharing and cooperation CAVs can have comprehensive perception of driving environments, and novel cooperative applications can be developed to improve road safety and efficiency (RSE). The key feature of the cooperative CAV system is the cooperation within and across the key players in the road transport systems and across system layers. For example, the various levels of cooperation include cooperative sensing, cooperative RSE applications and cooperation among the vehicles and among the vehicles and infrastructure. We will present the potentials that could be brought by cooperative CAV, the roadmap for research and development, the preliminary research results and open issues.

Currently, cloud computing is a primary enabler of new paradigms such as edge and fog computing. One open issue is the pricing of services or resources. Current pricing schemes are usually oligopolistic and not fair. In this work, we propose OFM, an online learning based marketplace that dynamically determines the price for arbitrary resource types based on supply and demand existing at that period. Unlike state of the art solutions, OFM can handle an arbitrary number of customers and resource types at every instance of time. It further performs integral allocation of resources and thereby avoids the unbounded integrality gap. We evaluate OFM with both real and synthetic datasets to reflect varying buying interests, the number of resources sold and market volatility to demonstrate the feasibility of our solution for several realistic scenarios. We observe that (i) OFM achieves about 9% of optimal prices and maximizes the Nash social welfare (NSW); (ii) OFM converges faster and works with different data distributions; and (iii) OFM scales for a large number of resources and buyers and computational time is in the order of microseconds, making it applicable for real-time use cases especially in edge markets.

Mobile edge computing (MEC) offers a way to shorten the cloud servicing delay by building the small-scale cloud infrastructures, such as cloudlets at the network edge, which are in close proximity to end users. On one hand, it is energy consuming and costly to place each cloudlet on each access point (AP) to process the requested tasks. On the other hand, the service provider should provide delay-guaranteed service to end users, otherwise they may get revenue loss. In this paper, we first model how to calculate the task completion delay in MEC and mathematically analyze the energy consumption of different equipments in MEC. Subsequently, we study how to place cloudlets on the network and allocate each requested task to cloudlets and public cloud with the minimum total energy consumption without violating each task's delay requirement. We prove that this problem is NP-hard and propose a Benders decomposition-based algorithm to solve it. We also present a software-defined network (SDN)-based framework to deploy the proposed algorithm. Extensive simulations reveal that the proposed algorithm can achieve an (close-to-)optimal performance in terms of energy consumption and acceptance ratio compared with two benchmark heuristics.

With an increasing demand for flexible management in software-defined networks (SDNs), it becomes critical to minimize the network policy update time. Although major SDN controllers are now optimized for rapid network update at the control plane, there is still room for data plane optimization in terms of update time, when using TCAM-based physical SDN commodity-off-the-shelf switches. A slow update directly affects network performance and creates bottlenecks. To minimize the flow entry update time, a dependency graph, a kind of directed acyclic graph (DAG), can be used for the access management of flow entries at the switch. Thanks to the DAG, unnecessary entry movements, which are the main factor slowing down flow entry updates, can be avoided. However, existing algorithms show limitations when updates become very frequent. We propose a new flow entry update algorithm, called FastRule, that exploits a greedy strategy with an efficient data structure to accelerate flow entry update with a DAG approach. Moreover, we also adjust our algorithm for other flow table layouts to make it scalable. We elaborate on the correctness of FastRule and test our algorithm using a hardware switch. Compared with existing algorithms, the evaluation shows that our algorithm is about 100x faster than state-of-the-art solutions with a flow table of 1k size.

With an increasing demand for resilience in software-defined networks (SDN), it becomes critical to minimize service recovery delay upon route failures. Fast reroute (FRR) mechanisms are widely used in IP and MPLS networks by computing the recovery path before a failure occurs. The centralized control plane in SDN can potentially enhance path computation, so that FRR path computation can better scale in SDN than in traditional networks. However, the traditional FRR path computation algorithms could lead to a poor performance in large-scale SDN. The problem can become more severe for a highly dynamic network, which often sees dozens of failures or configuration changes in any single day. We propose a new algorithm that exploits pruned searching to quickly compute recovery paths for all-pair switches/hosts upon a link failure. For applications requiring stringent path robustness levels, we also extend this algorithm to quickly find the shortest guaranteed-cost path, which ensures that the recovery path used upon on-path link failures has the minimum cost. Compared with traditional solutions, our evaluations show that our algorithm is about 8 ~ 81 times faster than the practical implementation, 1.93 ~ 3.11 times faster than the state-of-the-art solution. Our results also show that the shortest guaranteed-cost path can reduce the cost of the recovery path significantly. Moreover, we design a prototype to show how to deploy our algorithm in an OpenFlow network.

Summary With the popularity of mobile devices (such as smartphones and tablets) and the development of the Internet of Things, mobile edge computing is envisioned as a promising approach to improving the computation capabilities and energy efficiencies of mobile devices. It deploys cloud data centers at the edge of the network to lower service latency. To satisfy the high latency requirement of mobile applications, virtual machines (VMs) have to be correspondingly migrated between edge cloud data centers because of user mobility. In this paper, we try to minimize the network overhead resulting from constantly migrating a VM to cater for the movement of its user. First, we elaborate on two simple migration algorithms (M‐All and M‐Edge), and then, two optimized algorithms are designed by classifying user mobilities into two categories (certain and uncertain moving trajectories). Specifically, a weight‐based algorithm (M‐Weight) and a mobility prediction–based heuristic algorithm (M‐Predict) are proposed for the two types of user mobilities, respectively. Numerical results demonstrate that the two optimized algorithms can significantly lower the network overhead of user mobility–induced VM migration in mobile edge computing environments.

In software-defined networking (SDN), high-level abstractions typically offer a useful means to avoid writing network applications and policies on lower levels. However, abstractions are typically developed for a specific use case, which in turn results in an abundance of existing abstractions for different networking tasks. As a consequence, orchestrating these abstractions to implement a common network policy becomes an arduous task. To address this challenge, plain data representations of the network and its control infrastructure have been proposed recently, which offer programmable ad-hoc abstractions to administrators. However, these frameworks suffer from quite complex programming requirements and impractical performance in terms of latency, as they are based on relational database engines. In this paper, we address these shortcomings by introducing Gavel, an SDN controller that at its heart facilitates a plain data representation based on a graph database. By exploiting the native graph support of the database engine, Gavel significantly eases application and policy writing. Additionally, we show by experimental evaluation of several typical applications on multiple different topologies that Gavel offers significant performance improvements over the state-of-the-art solutions.

Ensuring high availability (HA) for software-based networks is a critical design feature that will help the adoption of software-based network functions (NFs) in production networks. It is important for NFs to avoid outages and maintain mission-critical operations. However, HA support for NFs on the critical data path can result in unacceptable performance degradation. We present REINFORCE, an integrated framework to support efficient resiliency for NFs and NF service chains. REINFORCE includes timely failure detection and consistent failover mechanisms. REINFORCE replicates state to standby NFs (local and remote) while enforcing correctness. It minimizes the number of state transfers by exploiting the concept of external synchrony, and leverages opportunistic batching and multi-buffering to optimize performance. Experimental results show that, even at line-rate packet processing (10 Gbps), REINFORCE achieves chain-level failover across servers in a LAN (or within the same node) within 10ms (100/μs), incurring less than 10% (1%) performance overhead, and adds average latency of only ~400/μs (5/μs), with a worst-case latency of less than 1ms (10/μs).

Information-centric networking (ICN) is a promising paradigm to support in-network big data sharing. However, it suffers from the problem of the segmented cached chunks resulting in low throughput and the large Interest packet overhead (IPO). The existing work cannot address these problems well, largely due to their insufficient considerations on the interplay between caching and data transport mechanisms. Through our experiments, we observe that ICN data chunks are cached in a distributed manner. Based on these observations, we propose consecutive data chunk caching (ConCaching) and adaptive data chunk retrieval (ACUR) to bridge the gap between caching and transport, where intermediate nodes on transmission path only cache the consecutive data chunks, while users can adjust the range of requested data chunks to maximize the throughput. Through the intensive simulations, we show that the proposed mechanisms can achieve better performance, in terms of higher throughput and substantial reduction in IPO compared with the existing pipeline mechanism in ICN.

This paper discusses the usage of micro-level behavioural data for understanding macro-level housing market behavior. We use sentiment analysis to examine local newspaper articles discussing real estate at a suburb level in inner-west Sydney, Australia. We calculate the media sentiment index by using two different methods, and compare them with each other and the housing price index. The use of media sentiment index can serve as a finer-grained guiding tool to facilitate decision making for home buyers, investors, researchers and policy makers.

Load balancing in datacenter networks (DCNs) is an important and challenging task for datacenter managers. A number of sophisticated technologies have been proposed to improve load balancing performance in a complicated circumstance, i.e., with various traffic characteristics. Many approaches need a high cost to implement, such as changing switch hardware. The efficiency problem has not been well addressed. MPTCP was proposed as a low-cost approach to improve data transmission in DCNs, which uses subflows to balance workloads across multiple paths. However, current MPTCP is not satisfying, especially when there are rack-local flows or many-to-one short flows. In this paper, we propose DCMPTCP to improve the efficacy of MPTCP. We gradually develop three mechanisms. First, DCMPTCP identifies rack-local traffic and eliminates unnecessary subflows to reduce the overhead. Second, DCMPTCP estimates flow length and establishes subflows in a smarter way. Third, DCMPTCP strengthens explicit congestion notification to improve the congestion control performance on inter-rack many-to-one short flows. DCMPTCP has a good compatibility and is easy to deploy. We implement DCMPTCP in ns-3 simulator and evaluate the performance by comprehensive simulations. The results show that DCMPTCP achieves ~65-771X and ~10-15X better FCT than MPTCP for rack-local and inter-rack traffic respectively.

With an increasing demand for flexible management in software-defined networks (SDNs), it becomes critical to minimize the network policy update time. Although major SDN controllers are now optimized for rapid network update at the control plane, there is still room for data plane optimization in terms of update time, when using TCAM-based physical SDN commodity-off-the-shelf switches. A slow update directly affects network performance creating bottlenecks. To minimize flow entry update time, a dependency graph, a kind of DAG (directed acyclic graph), can be used for the access management of flow entries at the switch. Thanks to the DAG, unnecessary entry movements, which are the main factor slowing down flow entry updates, can be avoided. However, existing algorithms show limitations when updates become very frequent. We propose a new flow entry update algorithm, called FastRule, that exploits a greedy strategy with an efficient data structure to accelerate flow entry update with a DAG approach. Moreover, we also adjust our algorithm for other flow table layouts to make it scalable. We elaborate on the correctness of FastRule and test our algorithm using a hardware switch. Compared with existing algorithms, the evaluation shows that our algorithm is about 100x faster than state-of-the-art solutions with a flow table of 1k line size.

Nowadays, multiple heterogeneous cellular networks coexist simultaneously, and mobile devices can freely select the appropriate network for data communication. Our measurement studies show that a barrier exists between heterogeneous cellular networks and applications. Triggering handovers between various cellular networks can break the barrier and present the promise of saving energy for cellular data communication. However, most existing network handover triggering methods do not adequately incorporate the characteristics of applications and may cause unnecessary handovers. In this paper, we propose an application-based handover triggering method called AHT. According to the applications that are used by users, AHT triggers handovers between high-performance and energy-efficient cellular networks, thus conserving energy. Based on the user experience (UX) requirements, AHT classifies applications into UX- sensitive and insensitive ones. AHT determines whether and when to switch to the high-performance network (e.g., LTE) in accordance with the predicted UX-sensitive application and estimated usage time, and triggers handovers to the energy- efficient network (e.g., UMTS) through an idle timer. We evaluate the performance of AHT with real application usage traces. Experimental results show that AHT saves up to 60.7% and 32.8% energy compared with the pure LTE network transmission and the screen-based handover triggering scheme Intelli3G, respectively.

The rapid popularity of mobile devices especially smart phones has changed human life style greatly. In this paper, we examine the consumer behaviors on several e-commerce platforms based on a large-scale dataset of mobile internet access records for about 3.5 months from a major telecom operator in China, which covers 126,388 users from Shanghai. We provide a preliminary study on users' daily and periodic online shopping behaviors, as well as the influence of special online shopping events and gender factors. These findings may be exploited by e-commerce providers e.g., for developing personalized recommendation systems to improve their service quality and profit.

The proliferation of mobile devices especially smart phones brings remarkable opportunities for both industry and academia. In particular, the massive data generated from users» usage logs provide the possibilities for stakeholders to know better about consumer behaviors with the aid of data mining. In this paper, we examine the consumer behaviors across multiple platforms based on a large-scale mobile Internet dataset from a major telecom operator, which covers 9.8 million users from two regions among which 1.4 million users have visited e-commerce platforms within one week of our study. We make several interesting observations and examine users» cultural differences from different regions. Our analysis shows among the multiple e-commerce platforms available, most mobile users are loyal to their favorable sites; people (60%) tend to make quick decisions to buy something online, which usually takes less than half an hour. Furthermore, we find that people in residential areas are much easier to perform purchases than in business districts and purchases take place during non-work time. Meanwhile, people with medium socioeconomic status like browsing and purchasing on e-commerce platforms, while people with high and low socioeconomic status are much easier to conduct purchases online. We also show the predictability of cross-platform shopping behaviors with extensive experiments on the basis of our observed data. Our discoveries could be a good guide for e-commerce future strategy making.

Internet of Things (IoT) is a growing topic of interest. Billions of IoT devices are expected to connect to the Internet in the near future. These devices differ from the traditional devices operated in the Internet. In this paper, we argue that an information-centric networking (ICN), a new networking paradigm, is a more suitable architecture for the IoT compared to the currently prevailing IP-based network. We observe that recent works that propose to use ICN for IoT, either do not cover the need to integrate sensor networks with the Internet to realize IoT or do so inefficiently. There is a need to understand effective ways to integrate the various heterogeneous sensor networks with the Internet without affecting their current mode of operation. In this paper, we study the essential requirements for integrating sensor networks to the Internet. We provide an architecture with gateways for paving a way for the sensor networks to become a part of the IoT family. We further provide a naming schema for efficient operation of the resource constrained sensor networks, discuss mobility, security, communication patterns, and propose the most suitable choices for IoT networks.

The social triad—a group of three people—is one of the simplest and most fundamental social groups. Extensive network and social theories have been developed to understand its structure, such as triadic closure and social balance. Over the course of a triadic closure—the transition from two ties to three among three users, the strength dynamics of its social ties, however, are much less well understood. Using two dynamic networks from social media and mobile communication, we examine how the formation of the third tie in a triad affects the strength of the existing two ties. Surprisingly, we find that in about 80% social triads, the strength of the first two ties is weakened although averagely the tie strength in the two networks maintains an increasing or stable trend. We discover that (1) the decrease in tie strength among three males is more sharply than that among females, and (2) the tie strength between celebrities is more likely to be weakened as the closure of a triad than those between ordinary people. Furthermore, we formalize a triadic tie strength dynamics prediction problem to infer whether social ties of a triad will become weakened after its closure. We propose a TRIST method—a kernel density estimation (KDE)-based graphical model—to solve the problem by incorporating user demographics, temporal effects, and structural information. Extensive experiments demonstrate that TRIST offers a greater than 82% potential predictability for inferring triadic tie strength dynamics in both networks. The leveraging of the KDE and structural correlations enables TRIST to outperform baselines by up to 30% in terms of F1-score.

Virtualization helps to deploy the functionality of expensive and rigid hardware appliances on scalable virtual resources running on commodity servers. However, optimal resource provisioning for non-trivial services is still an open problem. While there have been efforts to answer the questions of when to provision additional resources in a running service, and how many resources are needed, the question of what should be provisioned has not been investigated, in particular, for complex applications or services, which consist of a set of connected components, where each component in turn potentially consists of multiple component instances (e.g., VMs or containers). Each instance of a component can be run in different flavors (i.e., number of cores or amount of memory), while the service constructed by the combination of these component configurations must satisfy the customer Service Level Objective (SLO). In this work, we offer to service providers an answer to the what to deploy question by introducing Rconf, a system that automatically chooses the optimal combination of component instances for non-trivial network services. In particular, we propose an analytical model based on robust queuing theory that is able to accurately model arbitrary components, and develop an algorithm that finds the combination of their instances, such that the overall utilization of the running instances is maximized while meeting SLO requirements.

Live Virtual Machine (VM) migration offers a couple of benefits to cloud providers and users, but it is limited within a data center. With the development of cloud computing and the cooperation between data centers, live VM migration is also desired across data centers. Based on a detailed analysis of VM deployment models and the nature of VM image data, we design and implement a new migration framework called CBase. The key concept of CBase is a newly introduced central base image repository for reliable and efficient data sharing between VMs and data centers. With this central base image repository, live VM migration and further performance optimizations are made possible. The results from an extensive experiment show that CBase is able to support VM migration efficiently, outperforming existing solutions in terms of total migration time and network traffic.

In an Information-Centric Internet of Things (ICIoT) environment for big data sharing, IoT data can be cached throughout the network. Such distributed data caching poses a challenge on flexible authorization and identity verification. For fine-grained data access authorization in a distributed manner, Ciphertext-Policy Attribute-Based Encryption (CP-ABE) has been identified as a promising approach. However in the existing CP-ABE based scheme, each publisher would need to retrieve the attributes from the centralized server for encrypting data, resulting in high communication overhead. Moreover, valid authorization period and distributed authentication are still not addressed and seamlessly incorporated. In this paper, we propose a Verifiable and Flexible Data Sharing (VFDS) mechanism for ICIoT, which exploits CP-ABE for authorization and Identity-Based Signature (IBS) for the distributed verification of the identities. In VFDS, publishers retrieve the attributes from the nearby cache holders. In addition, the Attribute Manifest (AM) and the Automatic Attribute Update (AAU) realize efficient attribute updates within the distributed caches to achieve valid authorization period. Meanwhile, VFDS provides the public parameters of IBS in local domain, which enables the efficient identity verifications. Our system evaluations show that the VFDS can achieve lower bandwidth cost compared to the existing schemes for both authentication and flexible authorization.

Today, Online Social Networks (OSNs) are ubiquitous means of communication. In order to prevent the misuse of personal user data by OSN providers, various research efforts have produced a multitude of approaches to decentralize OSNs in the past decade. The most critical challenge for these systems is to replace the infrastructure of centralized OSNs. That is, they need to handle the large amounts of data uploaded by users on one end, and requests towards that data on the other end. Typically, existing approaches instrumentalize cloud facilities or user devices for this task. Unfortunately, they introduce either a monetary cost for users or have limited success in making data highly available. In this work we propose SocialGate, the first prototype that makes use of home routers of users as the infrastructure backbone of the OSN to avoid these shortcomings. Measurements and experiments based on real-world data support the feasibility and practicability of our approach.

Customers often suffer from the variability of data access time in cloud storage service, caused by network congestion, load dynamics, etc. One solution to guarantee a reliable latency-sensitive service is to issue requests with multiple download/upload sessions, accessing the required data (replicas) stored in one or more servers. In order to minimize storage costs, how to optimally allocate data in a minimum number of servers without violating latency guarantees remains to be a crucial issue for the cloud provider to tackle. In this paper, we study the latency-sensitive data allocation problem for cloud storage. We model the data access time as a given distribution whose Cumulative Density Function (CDF) is known, and prove that this problem is NP-hard. To solve it, we propose both exact Integer Nonlinear Program (INLP) and Tabu Search-based heuristic. The proposed algorithms are evaluated in terms of the number of used servers, storage utilization and throughput utilization.

A variety of wireless communication links today, such as HSPA+ access in high speed trains, balloon-based aerial wireless networks and satellite Internet connections have high loss rates. In such environments, Multipath TCP (MPTCP) offers a robust solution compared to regular TCP. However, MPTCP and existing schedulers suffer from performance degradation for both constant bit rate (CBR) and bulk traffic. To tackle this challenge, we develop LAMPS, a novel scheduler for MPTCP, which considers both the loss and delay when selecting subflows and chooses segments based on subflows' state. The design goal of LAMPS is to achieve a steady performance for different traffic and significantly reduce the unnecessary bandwidth consumption, especially in case of bursty losses. We have implemented LAMPS and evaluated its performance for Dynamic Adaptive Streaming over HTTP (DASH), CBR, and bulk traffic. Our experiment results show that LAMPS preserves application latency, keeps low memory consumption, and significantly reduces extra bandwidth consumption in the presence of high packet loss rate.

Managing Network Function (NF) service chains requires careful system resource management. We propose NFVnice, a user space NF scheduling and service chain management framework to provide fair, efficient and dynamic resource scheduling capabilities on Network Function Virtualization (NFV) platforms. The NFVnice framework monitors load on a service chain at high frequency (1000Hz) and employs backpressure to shed load early in the service chain, thereby preventing wasted work. Borrowing concepts such as rate proportional scheduling from hardware packet schedulers, CPU shares are computed by accounting for heterogeneous packet processing costs of NFs, I/O, and traffic arrival characteristics. By leveraging cgroups, a user space process scheduling abstraction exposed by the operating system, NFVnice is capable of controlling when network functions should be scheduled. NFVnice improves NF performance by complementing the capabilities of the OS scheduler but without requiring changes to the OS's scheduling mechanisms. Our controlled experiments show that NFVnice provides the appropriate rate-cost proportional fair share of CPU to NFs and significantly improves NF performance (throughput and loss) by reducing wasted work across an NF chain, compared to using the default OS scheduler. NFVnice achieves this even for heterogeneous NFs with vastly different computational costs and for heterogeneous workloads.

Human mobility data has been ubiquitously collected through cellular networks and mobile applications, and publicly released for academic research and commercial purposes for the last decade. Since releasing individual's mobility records usually gives rise to privacy issues, datasets owners tend to only publish aggregated mobility data, such as the number of users covered by a cellular tower at a specific timestamp, which is believed to be sufficient for preserving users' privacy. However, in this paper, we argue and prove that even publishing aggregated mobility data could lead to privacy breach in individuals' trajectories. We develop an attack system that is able to exploit the uniqueness and regularity of human mobility to recover individual's trajectories from the aggregated mobility data without any prior knowledge. By conducting experiments on two real-world datasets collected from both mobile application and cellular network, we reveal that the attack system is able to recover users' trajectories with accuracy about 73%~91% at the scale of tens of thousands to hundreds of thousands users, which indicates severe privacy leakage in such datasets. Through the investigation on aggregated mobility data, our work recognizes a novel privacy problem in publishing statistic data, which appeals for immediate attentions from both academy and industry.

Recent years have witnessed the proliferation of mobile crowdsourcing that brings a new opportunity to leverage human intelligence and movement behaviors to wider application areas. In parallel with the development of online centralized platforms, we look into the realization of self-organized mobile crowdsourcing drawing on opportunistic networks, and propose the Crowd Foraging framework, in which a mobile task requester can proactively recruit a massive crowd of opportunistic encountered mobile workers in real time for quick and high-quality results. We present a comprehensive framework model that fully integrates human behavior factors for modeling task profile, worker arrival, and work ability, and then introduce a service quality concept to indicate the expected service gain that a requester can enjoy when she recruits an arrival worker by jointly considering the work ability of workers as well as timeliness and reward of tasks. Furthermore, we formulate a sequential worker recruitment problem as an online multiple stopping problem to maximize the expected sum of service quality, and accordingly derive an optimal worker recruitment policy through the dynamic programming principle, which exhibits a nice threshold-based structure. We provide data-driven case studies to validate the assumptions used in the policy design, and conduct extensive trace-driven numerical evaluations, which demonstrate that our policy can achieve superior performance (e.g., improve more than 30% performance over classic policies). Besides, our Android prototype shows that the Crowd Foraging framework is cost-efficient, such as requiring less than 7 s and 6 J in terms of time and energy consumption for the optimal threshold calculation in our policy in most cases.

In current cloud computing systems, when leveraging virtualization technology, the customer’s requested data computing or storing service is accommodated by a set of communicated virtual machines (VM) in a scalable and elastic manner. These VMs are placed in one or more server nodes according to the node capacities or failure probabilities. The VM placement availability refers to the probability that at least one set of all customer’s requested VMs operates during the requested lifetime. In this paper, we first study the problem of placing at most$H$groups of$k$requested VMs on a minimum number of nodes, such that the VM placement availability is no less than$\delta$, and that the specified communication delay and connection availability for each VM pair under the same placement group are not violated. We consider this problem with and without Shared-Risk Node Group (SRNG) failures, and prove this problem is NP-hard in both cases. We subsequently propose an exact Integer Nonlinear Program (INLP) and an efficient heuristic to solve this problem. We conduct simulations to compare the proposed algorithms with two existing heuristics in terms of performance. Finally, we study the related reliable routing problem of establishing a connection over at most$w$link-disjoint paths from a source to a destination, such that the connection availability requirement is satisfied and each path delay is no more than a given value. We devise an exact algorithm and two heuristics to solve this NP-hard problem, and evaluate them via simulations.

Network Coordinate (NC) systems provide a scalable means for Internet distance prediction and are useful for various Internet-based services, such as cloud or web-based services. Decentralized, matrix factorization-based NC (MFNC) systems have received particular attention recently. They can serve large-scale distributed services (as opposed to centralized NC systems) and do not need to satisfy the triangle inequality (as opposed to Euclidean-based NC systems). However, because of their decentralized nature, MFNC systems are vulnerable to various malicious attacks. In this paper, we provide the first study on attacks toward MFNC systems, and propose a trust and reputation-based approach calledNCShieldto counter such attacks. It is fully decentralized and can easily be customized. Different from previous approaches, NCShield is able to distinguish between legitimate distance variations and malicious distance alterations. Using four representative data sets from the Internet, we show that NCShield can defend against not only the typical disorder, repulsion and isolation attacks, but also more advanced attacks such as frog-boiling attacks. For example, when selecting node pairs with a shorter distance than a predefined threshold in an online game scenario, even if 30 percent of nodes are malicious, NCShield can reduce the false positive rate from 45.5 to 3.7 percent.

SDN (Software-Defined Networking) promises easier management of complex networks. In abiding by this promise northbound APIs play a major role, as they provide abstractions to ease the process of writing network applications. As more and more of such abstractions emerge, one challenging task is to orchestrate different policies that originate from different abstractions. In an effort to provide automation for this process, recent research has suggested to replace the application-specific abstractions by a primitive, SQL-based data representation of the entire network control infrastructure. This representation can then be transformed into higher-level abstractions. However, the use of relational databases results in performance inferior to existing controllers and still requires complex application writing. To address these issues, in this work we introduce Gavel, a graph database based controller which exploits the natural graph structure of computer networks. Based on results obtained from a prototype implementation, we show that Gavel not only simplifies application writing, but at the same time offers a significantly better performance than existing schemes.

In this paper, we advocate Crowdlet, a novel self-organized mobile crowdsourcing paradigm, in which a mobile task requester can proactively exploit a massive crowd of encountered mobile workers at real-time for quick and high-quality results. We present a comprehensive system model of Crowdlet that defines task, worker arrival and worker ability models. Further, we introduce a service quality concept to indicate the expected service gain that a requester can enjoy when he recruits an encountered worker, by jointly taking into account worker ability, real-timeness and task reward. Based on the models, we formulate an online worker recruitment problem to maximize the expected sum of service quality. We derive an optimal worker recruitment policy through the dynamic programming principle, and show that it exhibits a nice threshold based structure. We conduct extensive performance evaluation based on real traces, and numerical results demonstrate that our policy can achieve superior performance and improve more than 30% performance gain over classic policies. Besides, our Android prototype shows that Crowdlet is cost-efficient, requiring less than 7 seconds and 6 Joule in terms of time and energy cost for policy computation in most cases.

Mobile online social networks (OSNs) are emerging as the popular mainstream platform for information and content sharing among people. In order to provide Quality of Experience (QoE) support for mobile OSN services, in this paper we propose a socially-driven learning-based framework, namely Spice, for media content prefetching to reduce the access delay and enhance mobile user's satisfaction. Through a large-scale data-driven analysis over real-life mobile Twitter traces from over 17,000 users during a period of five months, we reveal that the social friendship has a great impact on user's media content click behavior. To capture this effect, we conduct social friendship clustering over the set of user's friends, and then develop a cluster-based Latent Bias Model for socially-driven learning-based prefetching prediction. We then propose a usage-adaptive prefetching scheduling scheme by taking into account that different users may possess heterogeneous patterns in the mobile OSN app usage. We comprehensively evaluate the performance of Spice framework using trace-driven emulations on smartphones. Evaluation results corroborate that the Spice can achieve superior performance, with an average 67.2% access delay reduction at the low cost of cellular data and energy consumption. Furthermore, by enabling users to offload their machine learning procedures to a cloud server, our design can achieve speed-up of a factor of 1000 over the local data training execution on smartphones.

Information-Centric Networking (ICN) treats content as a first-class entity - each content has a unique identity and ICN routers forward traffic based on content identity rather than the locations of the content. This provides benefits like dynamic request routing, caching and mobility support. The choice of naming schema (flat vs. hierarchical) is a fundamental design choice in ICN which determines the functional separation between the network layer and the application layer. With hierarchical names, the network layer is cognizant of the semantics of hierarchical names. Name space management is also part of network layer. ICN architectures using flat names leave these to the application layer. The naming schema affects the performance and scalability of the network in terms of forwarding efficiency, routing table size and name space size. This paper provides both qualitative and quantitative comparison on the two naming schemas using these metrics, noting that they are interdependent. We seek to understand which naming schema would be better for a high-performance, scalable ICN architecture.

Live Virtual Machine (VM) migration across data centers is an important support technology which will bring benefits to both cloud providers and users. At present, VM storage migration becomes the bottleneck of this technology as it explores only the static data feature of image files without much consideration on data semantics. In this paper, we propose a new space-efficient VM image structure-three-layer structure. According to different functions and features, the data of a VM are separated into an Operating System (OS) layer, a Working Environment (WE) layer, and a User Data (UD) layer. On basis of three-layer image structure, we design a novel VM storage migration system-LayerMover. It tries to improve the migration performance through optimizing current data deduplication methods. To evaluate the potential performance gains of LayerMover, we explore the similarity features of WE layer data. Our experimental results show that the similarity ratio between different images can reach 70%. Our experiments about the VM storage migration performance of LayerMover show that it can be 27% faster than current migration methods for twolayer image structures under our experimental environments. When migrating a VM in a second round or migrating a batch of VMs, LayerMover will see more performance improvement.

The Internet of Things (IoT) is connecting people and smart devices on a scale that was once unimaginable. One major challenge for the IoT is to handle vast amount of sensing data generated from the smart devices that are resource-limited and subject to missing data due to link or node failures. By exploring cloud computing with the IoT, we present a cloud-based solution that takes into account the link quality and spatio-temporal correlation of data to minimize energy consumption by selecting sensors for sampling and relaying data. We propose a multiphase adaptive sensing algorithm with belief propagation (BP) protocol (ASBP), which can provide high data quality and reduce energy consumption by turning on only a small number of nodes in the network. We formulate the sensor selection problem and solve it using both constraint programming (CP) and greedy search. We then use our message passing algorithm (BP) for performing inference to reconstruct the missing sensing data. ASBP is evaluated based on the data collected from real sensors. The results show that while maintaining a satisfactory level of data quality and prediction accuracy, ASBP can provide load balancing among sensors successfully and preserves 80% more energy compared with the case where all sensor nodes are actively involved.

In this paper, we propose device-to-device (D2D) Fogging, a novel mobile task offloading framework based on network-assisted D2D collaboration, where mobile users can dynamically and beneficially share the computation and communication resources among each other via the control assistance by the network operators. The purpose of D2D Fogging is to achieve energy efficient task executions for network wide users. To this end, we propose an optimization problem formulation that aims at minimizing the time-average energy consumption for task executions of all users, meanwhile taking into account the incentive constraints of preventing the over-exploiting and free-riding behaviors which harm user's motivation for collaboration. To overcome the challenge that future system information such as user resource availability is difficult to predict, we develop an online task offloading algorithm, which leverages Lyapunov optimization methods and utilizes the current system information only. As the critical building block, we devise corresponding efficient task scheduling policies in terms of three kinds of system settings in a time frame. Extensive simulation results demonstrate that the proposed online algorithm not only achieves superior performance (e.g., it reduces approximately 30% ~ 40% energy consumption compared with user local execution), but also adapts to various situations in terms of task type, user amount, and task frequency.

Mobile-edge cloud computing is a new paradigm to provide cloud computing capabilities at the edge of pervasive radio access networks in close proximity to mobile users. In this paper, we first study the multi-user computation offloading problem for mobile-edge cloud computing in a multi-channel wireless interference environment. We show that it is NP-hard to compute a centralized optimal solution, and hence adopt a game theoretic approach for achieving efficient computation offloading in a distributed manner. We formulate the distributed computation offloading decision making problem among mobile device users as a multi-user computation offloading game. We analyze the structural property of the game and show that the game admits a Nash equilibrium and possesses the finite improvement property. We then design a distributed computation offloading algorithm that can achieve a Nash equilibrium, derive the upper bound of the convergence time, and quantify its efficiency ratio over the centralized optimal solutions in terms of two important performance metrics. We further extend our study to the scenario of multi-user computation offloading in the multi-channel wireless contention environment. Numerical results corroborate that the proposed algorithm can achieve superior computation offloading performance and scale well as the user size increases.

Geo-distributed clouds provide an intriguing platform to deploy online social network (OSN) services. To leverage the potential of clouds, a major concern of OSN providers is optimizing the monetary cost spent in using cloud resources while considering other important requirements, including providing satisfactory quality of service (QoS) and data availability to OSN users. In this paper, we study the problem of cost optimization for the dynamic OSN on multiple geo-distributed clouds over consecutive time periods while meeting predefined QoS and data availability requirements. We model the cost, the QoS, as well as the data availability of the OSN, formulate the problem, and design an algorithm named${\tt cosplay}$. We carry out extensive experiments with a large-scale real-world Twitter trace over 10 geo-distributed clouds all across the US. Our results show that, while always ensuring the QoS and the data availability as required,${\tt cosplay}$can reduce much more one-time cost than the state-of-the-art methods, and it can also significantly reduce the accumulative cost when continuously evaluated over 48 months, with OSN dynamics comparable to real-world cases.

Opportunistic Mobile Social Networks (MSNs) as a kind of social network in which nodes are opportunistically connected. Many data routing strategies have been proposed for opportunistic MSNs. Most of them apply a utility for relay selection that a node with a higher utility value is selected as relay. However, such utility-based routing strategies run into dead end problem in which the data is stuck into a node with local maximal utility value. In this paper, we propose a virtual routing method named VIRO, which exerts conformal mapping to convert the local topology of dead end into a virtual geometric map to guarantee data dissemination to the next relay. We first convert the utility into geometric planner, and then conduct the discrete Ricci Flow bypass the gap between the dead end and next relay. Extensive experiment results suggest VIRO can reduce the ratio of dead end effectively so that the data delivery ratio is enhanced up to 42% by increasing only a slight amount of delay and cost.

Information Centric Networks (ICN) enable accessing data oblivious of its location, by allowing end-systems to retrieve content based on names. But, architectures such as Named Data Networking (NDN) and Content Oriented Publish/Subscribe System (COPSS) do not yet provide a mechanism for end-system applications to obtain these names. There is a need for an object resolution system that addresses a most important and as yet unimplemented component of obtaining a name in ICN. In this paper, we propose ORICE, an architectural design for Object Resolution services in Information-Centric Environment that satisfies this need. The architecture enables intelligent resolution service by placing the service in the application layer and allows for the service diversity by separating the name space management from resolution service. Through preliminary evaluation, we show that with the help of ORICE, the states stored in the network can be dramatically reduced while ensuring complete data delivery.

AS-level end-to-end paths are of great value for ISPs and a variety of network applications. Although tools like traceroute may reveal AS paths, they require the permission to access source hosts and introduce additional probing traffic, which is not feasible in many applications. In contrast, AS path inference based on BGP control plane data and AS relationship information is a more practical and cost-effective approach. However, this approach suffers from a limited accuracy and high traffic, especially when AS paths are long. In this paper, we bring a new angle to the AS path inference problem by exploiting the metrical tree-likeness or low hyperbolicity of the Internet, part of the complex network properties of the Internet. We show that such property can generate a new constraint that narrows down the searching space of possible AS paths to a much smaller size. Based on this observation, we propose two new AS path inference algorithms, namely HyperPath and Valley-free HyperPath. With intensive evaluations on AS paths from real-world BGP Routing Information Bases, we show that the proposed new algorithms can achieve superior performance, in particular, when AS paths are long paths. We demonstrate that our algorithms can significantly reduce inter-AS traffic for P2P applications with an improved AS path prediction accuracy.

Named data networking (NDN) is a new paradigm for the future Internet wherein interest and data packets carry content names rather than the current IP paradigm of source and destination addresses. Security is built into NDN by embedding a public key signature in each data packet to enable verification of authenticity and integrity of the content. However, existing heavyweight signature generation and verification algorithms prevent universal integrity verification among NDN nodes, which may result in content pollution and denial of service attacks. Furthermore, caching and location-independent content access disables the capability of a content provider to control content access, e.g., who can cache a content and which end user or device can access it. We propose a lightweight integrity verification (LIVE) architecture, an extension to the NDN protocol, to address these two issues seamlessly. LIVE enables universal content signature verification in NDN with lightweight signature generation and verification algorithms. Furthermore, it allows a content provider to control content access in NDN nodes by selectively distributing integrity verification tokens to authorized nodes. We evaluate the effectiveness of LIVE with open source CCNx project. Our paper shows that LIVE only incurs average 10% delay in accessing contents. Compared with traditional public key signature schemes, the verification delay is reduced by over 20 times in LIVE.

We study the problem of group formation in online social networks. In particular, we focus on one of the most important human groups-the triad-and try to understand how closed triads are formed in dynamic networks, by employing data from a large microblogging network as the basis of our study. We formally define the problem of triadic closure prediction and conduct a systematic investigation. The study reveals how user demographics, network characteristics, and social properties influence the formation of triadic closure. We also present a probabilistic graphical model to predict whether three persons will form a closed triad in a dynamic network. Different kernel functions are incorporated into the proposed graphical model to quantify the similarity between triads. Our experimental results with the large microblogging dataset demonstrate the effectiveness (+10 percent over alternative methods in terms of F1-Score) of the proposed model for the prediction of triadic closure formation.

This paper investigates the rating of network paths, i.e., acquiring quantized measures of path properties such as round-trip time and available bandwidth. Compared to fine-grained measurements, coarse-grained ratings are appealing in that they are not only informative but also cheap to obtain. Motivated by this insight, we first address the scalable acquisition of path ratings by statistical inference. By observing similarities to recommender systems, we examine the applicability of solutions to a recommender system and show that our inference problem can be solved by a class of matrix factorization techniques. A technical contribution is an active and progressive inference framework that not only improves the accuracy by selectively measuring more informative paths, but also speeds up the convergence for available bandwidth by incorporating its measurement methodology. Then, we investigate the usability of rating-based network measurement and inference in applications. A case study is performed on whether locality awareness can be achieved for overlay networks of Pastry and BitTorrent using inferred ratings. We show that such coarse-grained knowledge can improve the performance of peer selection and that finer granularities do not always lead to larger improvements.

With the increasing number of location-dependent applications, positioning and tracking a mobile device becomes more and more important to enable pervasive and context-aware service. While extensive research has been performed in physical localization and logical localization for satellite, GSM and WiFi communication networks where fixed reference points are densely-deployed, positioning and tracking techniques in a sparse disruption tolerant network (DTN) have not been well addressed. In this paper, we propose a decentralized cooperative method called PulseCounting for DTN localization and a probabilistic tracking method called ProbTracking to confront this challenge. PulseCounting evaluates the user walking steps and movement orientations using accelerometer and electronic compass equipped in cellphones. It estimates user location by accumulating the walking segments, and improves the estimation accuracy by exploiting the encounters of mobile nodes. Several methods to refine the location estimation are discussed, which include the adjustment of trajectory based on reference points and the mutual refinement of location estimation for encountering nodes based on maximum-likelihood. To track user movement, the proposed ProbTracking method uses Markov chain to describe movement patterns and determines the most possible user walking trajectories without full record of user locations. We implemented the positioning and tracking system in Android phones and deployed a testbed in the campus of Nanjing University. Extensive experiments are conducted to evaluate the effectiveness and accuracy of the proposed methods, which show an average deviation of 9m in our system compared to GPS.

Socially aware services often have a large user base and data of users have to be partitioned and replicated over multiple geographically distributed clouds. Choosing in which cloud to place data, however, is difficult. Effective data placements entail meeting multiple system objectives, including reducing the usage of cloud resources, providing good service quality to users, and even minimizing the carbon footprint, while facing critical challenges such as the interconnection of social data, the conflicting requirements of different objectives, and the customized multi-cloud data access policies. In this paper, we study multi-objective optimization for placing users' data over multiple clouds for socially aware services. We build a model framework that can accommodate a range of different objectives, and based on this model we formulate the optimization problem. Leveraging graph cuts, we propose an optimization approach that decomposes our original problem into two simpler subproblems and solves them alternately in multiple rounds. We carry out evaluations using a large group of real-world geographically distributed users with realistic interactions, and place users' data over 10 clouds all across the US. We demonstrate results that are significantly superior to standard and de facto methods in all objectives, and also show that our approach is capable of exploring trade-offs among objectives, converges fast and scales to a huge user base.

Accurately determining the geographic location of an Internet host is important for location-aware applications such as location-based advertising and network diagnostics. Despite their fast response time, widely used database-driven geolocation approaches provide only inaccurate locations. Delay measurement based approaches improve the estimation accuracy but still suffer from a limited precision (about 10 km) and a long response time (tens of seconds) to localize a single PC, which cannot meet the demand of precise and real-time geolocation for location-aware applications. In this paper, we propose a new geolocation approach, Checkin-Geo, which exploits geolocation resources fundamentally different from existing database-driven (using DNS, Whois, etc.) or network delay measurement based approaches. In particular, we leverage the location data that users are willing to share in location-sharing services and logs of user logins from PCs for real-time and accurate geolocation. Experimental results show that compared to existing geolocation techniques, Checkin-Geo achieves 1) a median estimation error of 799 meters (an order of magnitude smaller than existing approaches), and 2) a negligible response time, which are promising for accurate location-aware applications.

It is common to deploy stationary sensors in large geographical environments for monitoring purposes. In such cases, the monitored data are subject to data loss due to poor link quality or node failures. Fortunately, the sensing data are highly correlated both spatially and temporally. In this paper, we consider such networks in general, and jointly take into account the link quality estimates, and the spatio-temporal correlation of the data to minimise energy consumption by selecting sensors for sampling and relaying data. In particular, we propose a multi-phase adaptive sensing algorithm with belief propagation protocol (ASBP), which can provide high data quality and reduce energy consumption by turning on only a small number of nodes in the network. We explore the correlation of data, formulate the sensor selection problem and solve it using constraint programming (CP) and greedy search. Bayesian inference technique is used to reconstruct the missing sensing data. We show that while maintaining a satisfactory level of data quality and prediction accuracy, ASBP successfully provides load balancing among sensors and preserves 80% more energy compared to the case where all sensor nodes are actively involved.

Disasters result in disruptions to the communication infrastructure, typically forming a multitude of fragmented networks. This makes it difficult to exchange even simple while critical messages with large portions of the affected population, including first-responders and government authorities. In this paper, we focus on a content driven data retrieval model and propose an enhanced information-centric network (ICN) approach to provide communication resilience to such disruption-prone, delay-tolerant networks. The message exchange between communities and mules takes into account the limited available resources and the fact that the interest and the data might traverse completely different paths, unlike the assumption in many of the existing ICN solutions. Moreover, we argue for the separation of the the logical faces from the actual physical interfaces taking into account the fact that the data mules behave as mobile routers and are connected to different nodes at different time periods, unlike the assumptions made in existing ICN solutions. Our preliminary evaluations show that our solution is able to outperform other approaches such as “Epidemic” and “SprayAndWait” with respect to the latency, response probability and overall performance while improving the performance (latency and response probability) for high priority messages.

Cloud datacenters typically require tenants to specify the resource demands for the virtual machines (VMs) they create using a set of pre-defined, fixed configurations, to ease the resource allocation problem. Unfortunately, this leads to low resource utilization of cloud datacenters as tenants are obligated to conservatively predict the maximum resource demand of their applications. We argue that instead of such a static VM resource allocation, a finer-grained dynamic resource allocation and scheduling can substantially improve the utilization of the datacenter resources by increasing the number of jobs accommodated and correspondingly, the cloud datacenter provider's revenue. The dynamic real-time scheduling of jobs can also ensure that the performance goals for the tenant VMs are achieved. Examining a typical publicly available cluster data center trace, we observe that a large number of jobs are short. Only a small proportion of jobs are long and which require substantial compute or memory resources. We propose an optimization based approach that exploits this division between the short and long jobs to dynamically allocate a cloud datacenter's resources to achieve significantly better utilization by increasing the number of jobs accommodated by the datacenter. We use a constraint programming solution to schedule the long jobs, and use simple heuristics to quickly, yet quite accurately schedule the short jobs. Using trace-driven simulations based on public traces collected on provider cluster we show that the overall revenue for the cloud provider can be improved by 30% over the traditional static VM resource allocation based on the coarse granularity specifications. We are able to increase the number of jobs accommodated using dynamic scheduling by 18%. We also compare the performance of our approach to multi-resource (CPU and memory) first-fit and best-fit algorithms and to the optimal offline solution, and demonstrate that our solution achieves within 76% of the offline optimal solution.

Concomitant with the tremendous growth of online social networking (OSN) platforms are increasing concerns from users about their privacy and the protection of their data. As user data management is usually centralized, OSN providers nowadays have the unprecedented privilege to access every user's private data, which makes large-scale privacy leakage at a single site possible. One way to address this issue is to decentralize user data management and replicate user data at individual end-user machines across the OSN.

A Sybil attack can inject many forged identities (called Sybils) to subvert a target system. Because of the severe damage that Sybil attacks can cause to a wide range of networking applications, there has been a proliferation of Sybil defense schemes. Of particular attention are those that explore the online social networks (OSNs) of users in a victim system in different ways. Unfortunately, while effective Sybil defense solutions are urgently needed, it is unclear how effective these OSN-based solutions are under different contexts. For example, all current approaches have focused on a common, classical scenario where it is difficult for an attacker to link Sybils with honest users and create attack edges; however, researchers have found recently that a modern scenario also becomes typical where an attacker can employ simple strategies to obtain many attack edges. In this work we analyze the state of OSN-based Sybil defenses. Our objective is not to design yet another solution, but rather to thoroughly analyze, measure, and compare how well or inadequate the well-known existing OSN-based approaches perform under both the classical scenario and the modern scenario. Although these approaches mostly perform well under the classical scenario, we find that under the modern scenario they are vulnerable to Sybil attacks. As shown in our quantitative analysis, very often a Sybil only needs a handful of attack edges to disguise itself as a benign node, and there is only a limited success in tolerating Sybils. Our study further points to capabilities a new solution must possess; in particular, in defense against Sybils under the modern scenario, we anticipate a new approach that enriches the structure of a social graph with more information about the relations between its users can work more effectively.

With increasing frequency, users raise concerns about data privacy and protection in centralized Online Social Networks (OSNs), in which providers have the unprecedented privilege to access and exploit every user's private data at will. To mitigate these concerns, researchers have suggested to decentralize OSNs and thereby enable users to control and manage access to their data themselves. However, previously proposed decentralization approaches suffer from several drawbacks. To tackle their deficiencies, we introduce the Self-Organized Universe of People (SOUP). In this demonstration, we present a prototype of SOUP and share our experiences from a real-world deployment.

In this paper, we revisit the design of mega data centers, which are usually built by a number of modularized containers. Due to technical innovation and vendor diversity, heterogeneity widely exists among data-center containers in practice. To embrace this issue, we propose uFix, which is a scalable, flexible, and modularized network architecture to interconnect heterogeneous data-center containers. The intercontainer connection rule in uFix is designed in such a way that it can flexibly scale to a huge number of servers with stable server/switch hardware settings. uFix allows modularized and fault-tolerant routing by completely decoupling intercontainer routing from intracontainer routing. We implement a software-based uFix prototype on a Linux platform. Both simulation and prototype-based experiment show that uFix enjoys high network capacity, gracefully handles server/switch failures, and causes lightweight CPU overhead onto data-center servers.

The vast majority of application traffic in modern data center networks (DCNs) can be classified into two categories: throughput-sensitive large flows and latency-sensitive small flows. These two types of flows have the conflicting requirements on link buffer occupancy. Existing data transfer proposals either do not fully utilize the path diversity of DCNs to improve the throughput of large flows, or cannot achieve a controllable link buffer occupancy to meet the low latency requirement of small flows. Aiming to balance throughput with latency, we develop the eXplicit MultiPath (XMP) congestion control scheme for DCNs. XMP comprises two components: the BOS algorithm brings link queue buffers consumed by large flows under control, while the TraSh algorithm is responsible for shifting traffic from more congested paths to less congested ones, thus achieving high throughput. We implemented XMP and evaluated its performance on traffic shifting, fairness, goodput, buffer occupancy and link utilization by conducting comprehensive experiments and simulations. The results show that XMP outperforms existing schemes and achieves a reasonable tradeoff between throughput and latency.

This work demonstrates the benefit brought by COPSS for an efficient notification service, including the convenience of hierarchical group management, network efficiency and timeliness in delivering the notification. The work will also demonstrate a simple first-step authorization. Future work will include a more complete authorization, authentication, encryption and mobility support for a scalable notification service.

Welcome to the 21stedition of the International Conference on Network Protocols (ICNP) in Göttingen, Germany. We are pleased you can join us in the historic building of the Mathematical Institute at the Georg-August-University, a place that was the home to pioneers such as David Hilbert, Felix Klein, Emmy Noether and Carl-Friedrich Gauss. At this renowned university where the first telegraph was invented by its professors Carl-Friedrich Gauss and Wilhelm Weber and used to communicate with each other using a three kilometer wire between two office buildings 180 years ago, we are looking forward to an inspiring four days of workshops, paper presentations, discussions and social networking among researchers from all over the world with an interest in modern computer networking protocols.

A Sybil attack can inject many forged identities (called Sybils) to subvert a target system. Among various defense approaches, of particular attention are those that explore the online social networks (OSNs) of users in a target system to detect or tolerate Sybil nodes. Albeit different in their working principle, all these approaches assume it is difficult for an attacker to create attack edges to connect Sybils with honest users. However, researchers have found that an attacker can employ simple strategies to obtain many attack edges. In this work we revisit the state-of-the-art, OSN-based Sybil defenses, and point out their strengths and weaknesses due to the impact of the new properties. We find these defense approaches are vulnerable to attackers under the new scenario, and in many cases a Sybil node only needs to obtain a handful of attack edges to disguise itself as a benign node.

Error correction techniques play an important role to guarantee reliable communication in wireless networks. The widely used error-correcting codes (ECCs) such as Hamming code introduce the benefit of error correction without retransmitting the data packet, but they suffer from high redundancy and communication overhead. In the recent years, error estimating code (EEC) was proposed to estimate the bit-error-rate (BER) of a packet efficiently with very low data redundancy. However, the ability of error correction using EEC remains unexplored. In this paper, we argue that EEC can be used to recover erroneous bits from the data packet. To show the capacity of error recovery with EEC, we propose an error correction scheme based on the parity check information provided by the EEC bits. We first introduce a filtering algorithm to rule out the correct data bits and obtain a set of suspicious bits containing most of the errors. Then we apply a polynomial randomized algorithm called Rand_flipping to examine the suspicious bits and flip the most promising erroneous bits aiming to minimize the total numbers of errors in the packet. Theoretical analysis proves that under some constraints the proposed Rand_flipping algorithm can correct most of the erroneous bits with probability higher than 1-1/e. Extensive experiments based on a real WiFi trace are conducted, which shows that the proposed algorithm corrects over 80% erroneous bits of the trace in practice.

With a huge number of users and a very large scale of data, an Online Social Network (OSN) service has to partition its data among multiple servers inside a data center. As data are often partitioned randomly, the response time in accessing the data is however unpredictable. Researchers have proposed social locality to address this concern: if a server hosts the master replica of a user's data, it must also host a replica (either master or slave) of every friend of this user, thus enabling convenient access of all of them on the same server. However, doing so comes with two overheads: the replication storage and the traffic of maintaining replica consistency. Existing work focuses on the former, but overlooks the latter that can consume considerable network resources. In this paper, we study social-locality-aware partitioning of the OSN data while meeting diverse performance goals of data center networks. We formulate the traffic optimization problem and propose a new traffic-aware data partitioning algorithm. Through the evaluations with a large-scale, real-world Twitter trace, we further show that, compared with state-of-the-art algorithms, our algorithm significantly reduces traffic without deteriorating the load balance among servers and causing extra replication storage.

New applications based on cloud computing, such as data synchronization for large chain departmental stores and bank transaction records, require very high-speed data transport. Although a number of high-bandwidth networks have been built, existing transport protocols or their variants over such networks cannot fully exploit the network bandwidth. Our experiments show that the fixed-size application level buffer employed in the receiver side is a major cause of this deficiency. A buffer that is either too small or too large impairs the transfer performance. Due to the varied natures of network conditions and of real-time packet processing (i.e., consuming) speed at the receiver, it is important to ensure that the buffer size is dynamically adjusted according to the perceived execution situation during runtime. In this paper, we propose Rada, a dynamic receiving buffer adaptation scheme for high-speed data transfer. Rada employs an exponential moving average aided scheme to quantify the data arrival rate and consumption rate in the buffer. Based on these two rates, we develop a linear aggressive increase conservative decrease scheme to adjust the buffer size dynamically. Moreover, a weighted mean function is employed to make the adjustment adaptive to the available memory in the receiver. Theoretical analysis is provided to demonstrate the rationale and parameter bounds of Rada. The performance of Rada is also theoretically compared with potential alternatives. We implement Rada in a Linux platform and extensively evaluate its performance in a variety of scenarios. Experimental results conform to the theoretical results, and show that Rada outperforms the static buffer scheme in terms of throughput, memory footprint, and fairness.

Content-Centric Networking (CCN) seeks to meet the content-centric needs of users. In this paper, we propose hybrid-COPSS, a hybrid content-centric architecture. We build on the previously proposed Content-Oriented Publish/Subscribe System (COPSS) to address incremental deployment of CCN and elegantly combine the functionality of content-centric networks with the efficiency of IP-based forwarding including IP multicast. Furthermore, we propose an approach for incremental deployment of caches in generic query/response CCN environments that optimizes latency and network load. To overcome the lack of inter-domain IP multicast, hybrid-COPSS uses COPSS multicast with shortcuts in the CCN overlay. Our hybrid approach would also be applicable to the Named Data Networking framework.

Bots are the root cause of many security problems on the Internet, as they send spam, steal information from infected machines, and perform distributed denial-of-service attacks. Many approaches to bot detection have been proposed, but they either rely on end-host installations, or, if they operate on network traffic, require deep packet inspection for signature matching.

Information-Centric Networking provides substantial flexibility for users to obtain information without knowing the source of information or its current location. With users increasingly focused on an online world, an emerging challenge for the network infrastructure is to support Massively Multiplayer Online Role Playing Game (MMORPG). Currently, MMORPG is built on IP infrastructure with the primary responsibility resting on servers for disseminating control messages and predicting/retrieving objects belonging to each player's view. Scale and timeliness are major challenges of such a server-oriented gaming architecture. Limited server resources significantly impair the user's interactive experience, requiring game implementations to limit the number of players in a single game instance. We propose Gaming over COPSS (G-COPSS), a distributed communication infrastructure using a Content-Oriented Pub/Sub System (COPSS) to enable efficient decentralized information dissemination in MMORPG, jointly exploiting the network and end-systems for player management and information dissemination. G-COPSS aims to scale well in the number of players in a single game, while still meeting users' response time requirements. We have implemented G-COPSS on top of the open-source CCNx implementation. We use a simple game with a hierarchical map to carefully micro benchmark the implementation and the processing involved in managing game dynamics. We have also micro benchmarked the game based on NDN and a server with an IP infrastructure. We emulate an application that is particularly emblematic of MMORPG -- Counter-Strike -- but one in which all players share a hierarchical structured map. Using trace-driven simulation, we demonstrate that G-COPSS can achieve high scalability and tight timeliness requirements of MMORPG. The simulator is parameterized based on micro benchmarks of our implementation. Our evaluations show that G-COPSS provides orders of magnitude improvement in update latency and a factor of two reduction in aggregate network load compared to a server-based implementation.

With the aid of multipath transport protocols, a multihomed host can shift some of its traffic from more congested paths to less congested ones, thus compensating for lost bandwidth on some paths by moderately increasing transmission rates on other ones. However, existing multipath proposals achieve only coarse-grained load balancing due to a rough estimate of network congestion using packet losses. This paper formulates the problem of multipath congestion control and proposes an approximate iterative algorithm to solve it. We prove that a fair and efficient traffic shifting implies that every flow strives to equalize the extent of congestion that it perceives on all its available paths.We call this result “Congestion Equality Principle”. By instantiating the approximate iterative algorithm, we develop weighted Vegas (wVegas), a delay-based algorithm for multipath congestion control, which uses packet queuing delay as congestion signals, thus achieving fine-grained load balancing. Our simulations show that, compared with loss-based algorithms, wVegas is more sensitive to changes of network congestion and thus achieves more timely traffic shifting and quicker convergence. Additionally, as it occupies fewer link buffers, wVegas rarely causes packet losses and shows better intra-protocol fairness.

Geo-distributed IaaS (Infrastructure-as-a-Service) clouds provide an intriguing platform to deploy Online Social Network (OSN) services. To leverage the potential of clouds, a major task of OSN providers is optimizing the monetary cost spent on cloud resource utilization while providing satisfactory Quality of Service (QoS) to OSN users. We thus study the problem of cost optimization for the dynamic OSN on multiple geo-distributed clouds over consecutive time periods, with its QoS meeting the pre-defined requirement. We model the QoS as well as the cost of an OSN, formulate the problem, and design a solution named cosplay. Our experiments with a large-scale Twitter trace show that, while always ensuring the QoS as required, cosplay can achieve superior one-time cost reduction compared with the state of the art, and can also reduce the accumulative cost significantly when continuously evaluated over 48 months with dynamics comparable to real-world OSNs.

In order to minimize user perceived latency while ensuring high data availability, cloud applications desire to select servers from one of the multiple data centers (i.e., server clusters) in different geographical locations, which are able to provide desired services with low latency and low cost. This paper presents CloudGPS, a new server selection scheme of the cloud computing environment that achieves high scalability and ISP-friendliness. CloudGPS proposes a configurable global performance function that allows Internet service providers (ISPs) and cloud service providers (CSPs) to leverage the cost in terms of inter-domain transit traffic and the quality of service in terms of network latency. CloudGPS bounds the overall burden to be linear with the number of end users. Moreover, compared with traditional approaches, CloudGPS significantly reduces network distance measurement cost (i.e., from O(N) to O(1) for each end user in an application using N data centers). Furthermore, CloudGPS achieves ISP-friendliness by significantly decreasing inter-domain transit traffic.

While network coordinate (NC) systems provide scalable Internet distance estimation service and are useful for various Internet applications, decentralized, matrix factorization-based NC (MFNC) systems have received particular attention recently. They can serve large-scale distributed applications (as opposed to centralized NC systems) and do not need to assume triangle inequality (as opposed to Euclidean-based NC systems). However, because of their decentralized nature, MFNC systems are vulnerable to various malicious attacks. In this paper, we provide the first study on attacks toward MFNC systems, and propose a decentralized trust and reputation approach, called NCShield, to counter such attacks. Different from previous approaches, our approach is able to distinguish between legitimate distance variations and malicious distance alterations. Using four representative data sets from the Internet, we show that NCShield can defend against attacks with high accuracy. For example, when selecting node pairs with a shorter distance than a predefined threshold in an online game scenario, even if 30% nodes are malicious, NCShield can reduce the false positive rate from 45.5% to 3.7%.

Content-Centric Networks (CCN) provide substantial flexibility for users to obtain information without regard to the source of the information or its current location. Publish/subscribe (pub/sub) systems have gained popularity in society to provide the convenience of removing the temporal dependency of the user having to indicate an interest each time he or she wants to receive a particular piece of related information. Currently, on the Internet, such pub/sub systems have been built on top of an IP-based network with the additional responsibility placed on the end-systems and servers to do the work of getting a piece of information to interested recipients. We propose Content-Oriented Pub/Sub System (COPSS) to achieve an efficient pub/sub capability for CCN. COPSS enhances the heretofore inherently pull-based CCN architectures proposed by integrating a push based multicast capability at the content-centric layer. We emulate an application that is particularly emblematic of a pub/sub environment - Twitter - but one where subscribers are interested in content (e.g., identified by keywords), rather than tweets from a particular individual. Using trace-driven simulation, we demonstrate that our architecture can achieve a scalable and efficient content centric pub/sub network. The simulator is parameterized using the results of careful micro benchmarking of the open source CCN implementation and of standard IP based forwarding. Our evaluations show that COPSS provides considerable performance improvements in terms of aggregate network load, publisher load and subscriber experience compared to that of a traditional IP infrastructure.

Social networking platforms such as Facebook, MySpace, and Twitter have seen a significant increase in user population and user provided information. However, users are increasingly concerned about identity and data privacy since information is controlled by single companies. To address this issue researchers investigated alternative solutions, where the users' data, e.g. profile information, comments and messages, is stored at user-controlled nodes. Although these solutions provide a plausible means for avoiding privacy leaking in central instances, they raise a new challenge to design a cost-effective storage replica scheme which ensures a high data availability even when some users are offline. In this paper we present Gemstone, a social network platform where the data replication scheme leverages a learning mechanism based on social relationships, online patterns of peers and user experiences. Our preliminary evaluation shows that compared to related works, it achieves higher data availability while requiring a smaller number of data replicas.

Large military ad hoc networks are often characterized by the interconnection of heterogeneous domains. The same trend is emerging in civilian MANETs (e.g., search and rescue, vehicular networks). In these networks it is important to be able to efficiently propagate information across domains in multicast mode (e.g., situation awareness dissemination, commands, streams). Several multicast protocols have been developed for single domain MANET. However, few can be extended to inter-domain operation. In fact, multicast routing across different MANET domains faces the challenges of node motion, topology changes, dynamic gateway election and, possibly, connectivity interruption. To overcome these challenges, especially to achieve routing scalability and at the same time maintains efficient routing, this paper proposes the Geo-assisted Multicast Inter-domain Routing (GMIDR) protocol based on geographical assistance and cluster technology. Intensive simulation results show that the GMIDR protocol is scalable and stable with various numbers of multicast group members, and it outperforms other multicast protocols. A military use case scenario simulation shows that GMIDR can be utilized efficiently in the large scale networks crossing multiple domains. Geocast by applying GMIDR shows the flexibility of the protocol.

Opportunistic spectrum access, which allows secondary users opportunistically access unused licensed channels to exploit instantaneous spectrum availability, is a promising approach to achieve efficient spectrum utilization and mitigate spectrum scarcity. To address the challenge of dynamic spectrum access, one of the most important issues is cooperative spectrum reallocation among secondary users to minimize spectrum handoffs. In this paper, we present a low-complexity approach based on conflict graph to optimize spectrum reallocation by local coordination. In order to reduce communication overhead, we propose two heuristic spectrum selection methods named local observation and metric maximum. Experimental results show two benefits of the proposed schemes. On one hand, the coordination approach can dynamically improve the total system throughput (approximately doubled at most). On the other hand, our heuristic strategies decrease the number of spectrum handoff by up to 20% compared with existing strategies.

Supporting Massively Multiplayer Online Role Playing Games (MMORPG) is a significant challenge. MMORPGs have become very popular because of their attractive structuring and incorporation of realistic and creative scenarios. World of Warcraft and Counter-Strike are examples of such games and are characterized by high interactivity (need very low network latency). Every action an individual player performs typically needs to be communicated to all the related players and the players need to react according to the 'current' environment and the cumulative actions of all the players. Games like Second Life involve a large number of players and require a persistent view of the world that is usually managed by a dedicated server (e.g., one that is hosted by the game's publisher). The load on such a server for player management and communication can be significant, and is likely to be a source of substantial latency.

In this paper we introduce LENS, a novel spam protection system based on the recipient's social network, which allows correspondence within the social circle to directly pass to the mailbox and further mitigates spam beyond social circles. The key idea in LENS is to select legitimate and authentic users, called Gatekeepers (GKs), from outside the recipients social circle and within pre-defined social distances. Unless a GK vouches for the emails of potential senders from outside the social circle of a particular recipient, those e-mails are prevented from transmission. In this way LENS drastically reduces the consumption of Internet bandwidth by spam. Using extensive evaluations, we show that LENS provides each recipient reliable email delivery from a large fraction of the social network. We also evaluate the computational complexity of email processing with LENS deployed on two Mail Servers (MSs) and compared it with the most popular content-based filter i.e SpamAssassin. LENS proved to be fast in processing emails (around 2-3 orders of magnitude better than SpamAssassin) and scales efficiently with increasing community size and GKs.

Data center containers are regarded as the basic units to build mega data centers. In practice, heterogeneity exists among data center containers, because of technical innovation and vendor diversity. In this paper, we propose uFix, a scalable, flexible and modularized network architecture to interconnect heterogeneous data center containers. The inter-container connection rule in uFix is designed in such a way that it can flexibly scale to a huge number of servers with stable server/switch hardware settings. uFix allows modularized and fault-tolerant routing by completely decoupling inter-container routing from intra-container routing. We implement a software-based uFix stack on the Linux platform. Simulation and experiment results show that uFix enjoys high network capacity, gracefully handles server/switch failures, and brings light-weight CPU overhead onto data center servers.

As the physical link speeds grow and the size of routing table continues to increase, IP address lookup has been a challenging problem at routers. There have been growing demands in achieving high-performance IP lookup cost-effectively. Existing approaches typically resort to specialized hardwares, such as TCAM. While these approaches can take advantage of hardware parallelism to achieve high-performance IP lookup, they also have the disadvantage of high cost. This paper investigates a new way to build a cost-effective IP lookup scheme using graphics processor units (GPU). Our contribution here is to design a practical architecture for high-performance IP lookup engine with GPU, and to develop efficient algorithms for routing prefix update operations such as deletion, insertion, and modification. Leveraging GPU's many-core parallelism, the proposed schemes addressed the challenges in designing IP lookup at GPU-based software routers. Our experimental results on real-world route traces show promising gains in IP lookup and update operations.

With users increasingly focused on an online world, an emerging challenge for the network infrastructure is the need to support Massively Multiplayer Online Role Playing Games (MMORPG). This is an application domain that is attracting more players than ever before, very often with players distributed over a metropolitan area. Currently, MMORPG are built on an IP infrastructure with the primary responsibility on servers to do the work of disseminating control messages and having to predict/retrieve objects in each player's view. Limited server resources significantly impair the user's interactive experience. Modern fast-paced action games that run on a client/server architecture limit the number of players who can interact simultaneously since the server needs to handle the frequent updates and disseminate them. Scale and timeliness are major challenges of such a server-oriented gaming architecture. We propose Gaming over COPSS (G-COPSS), a communication infrastructure using a Content-Oriented Pub/Sub System (COPSS) to enable efficient decentralized information dissemination in MMORPG, exploiting the network and the end-systems for player management and information dissemination. We emulate an application that is particularly emblematic of MMORPG - Counter-Strike - but one in which all the players share a hierarchical structured map. Using trace-driven simulation, we demonstrate that G-COPSS can achieve high scalability and tight timeliness requirements of MMORPG. The simulator is parameterized using the results of careful microbenchmarking of the open-source CCN implementation and of standard IP-based forwarding. Our evaluations show that G-COPSS provides considerable performance improvement in terms of aggregate network load and update latency compared to that of a traditional IP server-based infrastructure.

Network coordinate (NC) systems provide a lightweight and scalable way for predicting the distances, i.e., round-trip latencies among Internet hosts. Most existing NC systems embed hosts into a low dimensional Euclidean space. Unfortunately, the persistent occurrence of Triangle Inequality Violation (TIV) on the Internet largely limits the distance prediction accuracy of those NC systems. Some alternative systems aim at handling the persistent TIV, however, they only achieve comparable prediction accuracy with Euclidean distance based NC systems. In this paper, we propose an NC system, so-called Phoenix, which is based on the matrix factorization model. Phoenix introduces a weight to each reference NC and trusts the NCs with higher weight values more than the others. The weight-based mechanism can substantially reduce the impact of the error propagation. Using the representative aggregate data sets and the newly measured dynamic data set collected from the Internet, our simulations show that Phoenix achieves significantly higher prediction accuracy than other NC systems. We also show that Phoenix quickly converges to steady state, performs well under host churn, handles the drift of the NCs successfully by using regularization, and is robust against measurement anomalies. Phoenix achieves a scalable yet accurate end-to-end distances monitoring. In addition, we study how well an NC system can characterize the TIV property on the Internet by introducing two new quantitative metrics, so-called RERPLand AERPL. We show that Phoenix is able to characterize TIV better than other existing NC systems.

In this paper, we describe a path-based intrinsic monitoring protocol that can efficiently associate SNMP based MIB information to a network path within a single administrative domain. Our method is based on intrinsic monitoring, a lightweight metric collection protocol that makes use of the IPv6 Router Alert hop-by-hop option. The main advantage of our approach is that operators can rapidly associate node specific MIB metrics to paths within the network. This can dramatically reduce the overhead associated with correlating node specific MIB information to topology information and network paths. It provides the network operator with a tool that can be used to focus on monitoring individual paths in the network, where the nodes along the path are not known beforehand. We compare the performance overhead associated with our proposed approach to conventional SNMP get/response message exchanges. The results demonstrate a dramatic reduction in the collection time of node specific metrics. Our approach also implicitly relates collected metrics to the network path, thus removing the need to correlate metrics to topology and path information.

The wide availability of WiFi hot-spots provides mobile users with convenient access to the Internet. However, hot-spots cover only small areas and are often operated by a local authority. A user that roams between hot-spots effectively traverses different operators and access networks. Due to deployment complexity, IP mobility is usually not supported by the mobile node or the network. In this paper we propose an approach that enables access routers to provide seamless mobility support without any changes to the mobile node or other network entities. Our approach provides a simple, flexible, and deployable framework for mobility support and scales well with the number of involved access routers. By simulation and mathematical analysis, we show that even with lower deployment requirements and less support from the network, our approach performs well in terms of routing stretch, handover latency, and signaling overhead.

Network Coordinate (NC) systems provide a lightweight and useful way for scalable Internet distance prediction while serving as an important component in many Peer-to-Peer applications. Most of the existing NC systems utilize the Euclidean distance model, which is largely impaired by the persistent occurrence of Triangle Inequality Violation (TIV) in the Internet. Recently, matrix factorization (MF) based NC systems, which can completely remove the TIV constraint, provide an alternative approach towards better prediction accuracy. Phoenix, an NC system based on the MF model, well explores the advantage of the MF model and becomes the most accurate NC system so far. However, through experimental study, we find that the prediction accuracy of Phoenix for short links is significantly worse compared with the overall prediction accuracy. Based on the observation, we propose a new NC system, named Pancake, aiming at reducing the high prediction error for short links. By introducing a two-level architecture, Pancake achieves much higher prediction accuracy than other selected existing NC systems. Through extensive experiments, we demonstrate that Pancake reduces the 90th percentile relative error by up to 25.37% from Phoenix. Moreover, Pancake converges very fast and is robust to different dimension values. For further insights, we study the performance of Pancake using a dynamic data set in addition to the widely used aggregate data sets. With varying RTTs over time, Pancake outperforms other NC systems consistently.

The requirement for supporting DVD-like functions raises new challenges to the design of P2P VoD systems. The uncertainty of frequent user DVD-like interactivity makes it difficult to ensure user perceived Quality of Experience (QoE) for real-time streaming services over distributed self-organized P2P overlay networks. Most existing solutions are based on the unreasonable assumption that all the users in P2P VoD systems have the same preference. Few attention has been paid to personalization, which accommodates the differences between users. In this paper, we present a video model which characterizes the personalization information for users' contents and preferences. Based on this model, we develop APEX, a practical personalization framework for P2P VoD applications. APEX makes the personalization practical by using a hybrid architecture which leverages the offline pattern mining on the server side and online collaborative filtering on the peer side. Furthermore, APEX helps peers to personalize navigation, prefetching and membership management, aiming at improving QoE for DVD-like functions by reducing response latency and optimizing content sharing. Both theoretical analysis and comprehensive simulations show that APEX outperforms most existing schemes in terms of accumulated hit ratio, response latency, and searching efficiency.

Delay-insensitive applications such as P2P file sharing, data center backups and software updates generate substantial amounts of traffic. This traffic, transported potentially over multiple TCP connections, competes with traffic from other possibly interactive applications. Today, with TCP, they compete on an equal level for each individual connection. In this paper, we propose a new TCP variant for such delay-insensitive applications, which we call Network Friendly TCP (NF-TCP). NF-TCP is responsive to available bandwidth, seeking to quickly and efficiently utilize the same in a congestion-free situation, while backing-off more aggressively than standard TCP on encountering competing traffic in a congested network. NF-TCP uses a novel combination of utilizing measurement of available bandwidth and ECN-based congestion avoidance techniques to ensure that it is truly friendly to existing TCP connections. We evaluate the performance of NF-TCP through ns-2 simulations and present the initial results enlightening the friendly nature of NF-TCP compared to standard TCP.

Online microblogging services, as exemplified by Twitter, have become immensely popular during the latest years. However, current microblogging systems severely suffer from performance bottlenecks and malicious attacks due to the centralized architecture. As a result, centralized microblogging systems may threaten the scalability, reliability as well as availability of the offered services, not to mention the high operational and maintenance cost.

The forwarding paths in an IP network may change due to a link failure, network equipment maintenance, or reconfiguration of link weights. The forwarding tables in the routers need then to be updated. These updates may cause transient loops and link overflow, if they are not performed in an appropriate order. While existing proposals achieve loop-free updates, transient link overflow is still a problem during the updating process. In this paper, we present a method that compares the initial and final forwarding paths, and obtains the updatable nodes that do not cause any transient loop or link overflow. However, this goal is not always achievable, therefore, we propose an algorithm to update the forwarding tables that will refrain the link overflows to a minimal level. The performance study on a real topology with two setups confirms that our approach achieves smaller link overflow than by using a previously proposed approach.

To ensure state synchronization of signalling operations, many signaling protocol designs choose to establish ldquosoftrdquo state that expires if it is not refreshed. The approaches of refreshing state in multi-hop signaling system can be classified as either end-to-end (E2E) or hop-by-hop (HbH). Although both state refresh approaches have been widely used in practical signaling protocols, the design tradeoffs between state synchronization and signaling cost have not yet been fully investigated. In this paper, we investigate this issue from the perspectives of state refresh and state removal. We propose simple but effective Markov chain models for both approaches and obtain closed-form solutions which depict the state refresh performance in terms of state consistency and refresh message rate, as well as the state removal performance in terms of state removal delay. Simulations verify the analytical models. It is observed that the HbH approach yields much better state synchronization at the cost of higher signaling cost than the E2E approach. While the state refresh performance can be improved by increasing the values of state refresh and timeout timers, the state removal delay increases largely for both E2E and HbH approaches. The analysis here shed lights on the design of signaling protocols and the configuration of the timers to adapt to changing network conditions.

Recent trends in applications require per-flow treatment instead of the packet-based treatment inherent in the present day Internet. This requirement is analogous to trying to make a phone call in a standard PSTN line or cellular network and either receiving a busy tone or call accept. This paper explores the potential of providing flow-based admission control and termination functionality to an MPLS-based DiffServ network by applying pre-congestion notification (PCN). MPLS-based DiffServ networks are state of the art technologies used in many carrier backbones to provide quality of service for different traffic classes. The standard PCN scheme as developed by the IETF requires three code-points, one of which finds application in differentiating between PCN and non-PCN traffic, while the other two are used for PCN congestion marking to indicate admission control and flow termination, respectively. An MPLS-based DiffServ network on the other hand is limited as only two code-points are available. To overcome this limitation, we propose various schemes, namely: 1) one-codepoint signaling for admission control (AC), 2) one-codepoint signaling for flow termination (FT), and 3) one-codepoint signaling for 3 states (no congestion, AC and FT). The three schemes use the available code-point for different purposes and require different behaviors from the involved PCN nodes. We evaluate them using simulations. The results suggest that the third approach is able to effectively signal the three states without incurring much inaccuracy. The success rate with respect to the quality of experience (QoE) of existing flows, error recovery and throughput is not compromised either. To the best of our knowledge, this is the first known attempt to study the use of PCN in an MPLS-based DiffServ network.

Proxy Mobile IP (PMIP) provides a solution for network-based localized mobility management which in contrast to host-based mobility solutions, like Mobile IP (MIP), does not require changes to the end-hosts. It also avoids tunneling overhead on the interface which connects a mobile node to it's access network. Within a PMIP-enabled mobility domain, the mobile node is able to maintain the same IP address when it moves. However, if the mobile node leaves this domain the mobility support breaks.

Recent deployment of IP based multimedia distribution, especially broadcast TV distribution has increased the importance of simple and fast restoration during IP network failures for service providers. In this paper, we propose and evaluate a simple but efficient method for fast rerouting of IP multicast traffic during link failures in managed IPTV networks. More specifically, we devise an algorithm for tuning IP link weights so that the multicast routing path and the unicast routing path between any two routers are failure disjoint, allowing us to use unicast IP encapsulation for undelivered multicast packets during link failures. We demonstrate that, our method can be realized with minor modification to the current multicast routing protocol (PIM-SM). We run our prototype implementation in Emulab which shows our method yields to good performance.

It is well-known that wireless links are error-prone and require retransmissions for recovering frames from errors and losses. Network coding (NC) has been proposed for more efficient MAC-layer retransmissions in WLANs. However, existing schemes employed the reception report mechanism, which is both inefficient and expensive. Furthermore, they considered neither fairness nor the effects of time-varying heterogeneous wireless networks. These issues are critical for achieving full benefit of network coding. Without addressing them, these schemes may even impair system performance. In this paper, a novel MAC-layer retransmission scheme, namely XOR rescue (XORR) is proposed. It estimates the reception status without extra overheads and devises a new coding metric, which accommodates the effects of the frames size and the channel condition. Finally, XORR employs NC-aware fair opportunistic scheduling, which is theoretically proven to be fair, i.e. not only the service time is evenly allocated, but also it always improves the expected goodput for every wireless station. It is further verified by theoretic analyses, extensive simulations and testbed experiments. Our results show that XORR outperforms the non-coding fair opportunistic scheduling and 802.11 by 25% and 40%, respectively.

This paper presents an evaluation of the quality of Service Signalling Layer Protocol (QoS-NSLP) of the NSIS (next steps in signalling) protocol suite. The QoS-NSLP in combination with the NSIS Transport Layer Protocol (NTLP) is proposed by the Internet Engineering Task Force (IETF) as an alternative to the Resource reSerVation Protocol (RSVP). We describe our implementations of the software architecture, both on a network simulator and on a Linux implementation. Both implementations are used in a complimentary manner to illustrate the performance of the QoS-NSLP protocol. The results show the performance of QoS-NSLP with respect to resource consumption, packet processing time, session set up time, refresh interval and protocol overhead. Furthermore, we analyse the protocol performance during route change scenarios.

With an increasing number of personalized information and services offered on the Internet, especially the World Wide Web, effective identity management solutions are demanded by application providers. Instead of a Web-based stand-alone approach, we extend existing network-based AAA mechanisms to be useable for identity management by Web applications. Our proposal, Diameter WebAuth, allows to seamlessly integrate Web-based services into a Diameter infrastructure for authentication, authorization, credit-control and identity management purposes. Diameter WebAuth offers comparable features to Web-based identity management solutions, benefits from the maturity and wide deployment of the Diameter protocol, and takes advantage of existing AAA setups.

Real-time interactive multimedia applications are highly delay-sensitive, and packets that are out of delay boundaries are usually obsolete. However the current Internet Protocol lacks a way to control the lifetime of the packets explicitly. We propose a packet lifetime control mechanism called Explicit Delay Control (EDC) that embeds a Maximum Tolerable Delay (MTD) field in an IPv4 option. At each network node, the MTD is deducted by the singlehop delay. Packets that expire their lifetime are discarded and non-congestion related delay losses are signaled to the sender to reduce inaccuracy in delay estimations and to adapt to path changes. We implemented EDC in the Linux kernel. Our evaluation has shown that EDC is an effective scheme to ensure the “legality” of the packets, reduce the waste of bandwidth and processing time in the networks, and alleviate congestions.

The General Internet Signaling Transport (GIST) protocol is currently being developed as the base protocol component in the IETF Next Steps In Signaling (NSIS) protocol stack to support a variety of signaling applications. We present our study on the protocol overhead and performance aspects of GIST. We quantify network-layer protocol overhead and observe the effects of enhanced modularity and security in GIST. We developed a first open source GIST implementation at the University of Göttingen, and study its performance in a Linux testbed. A GIST node serving 45,000 signaling sessions is found to consume average only 1.1 ms for processing a signaling message and 2.4 KB of memory for managing a session. Individual routines in the GIST code are instrumented to obtain a detailed profile of their contributions to the overall system processing. Important factors in determining performance, such as the number of sessions, state management, refresh frequency, timer management and signaling message size are further discussed. We investigate several mechanisms to improve GIST performance so that it is comparable to an RSVP implementation.

The pre-shared key based mechanisms for Transport Layer Security (TLS) were recently standardized by the IETF to extend the set of ciphersuites by utilizing existing key management infrastructures. The benefit of pre-shared based mechanisms is the avoidance or reduction of the cryptographic operations used in public-key based mechanisms. However, so far there are no performance measurements for pre-shared key based ciphersuites available. In this paper, we present a systematic analysis and performance comparison between the pre-shared key exchange mechanisms and the standard public key exchange mechanisms in TLS. Our performance metrics are processing time and transmitted amount of data for a handshake establishment. Furthermore, the interaction between the overall TLS handshake duration and the network environment is evaluated. The results for different key exchange mechanisms are comparatively studied and the design choices of pre-shared key based key exchange mechanisms have been validated. Experimental results give details about the performance improvement of the pre-shared key based mechanisms compared to the standard public key based mechanisms.

With today's penetration in volume and variety of information flowing across the Internet, data and services are experiencing various issues with the TCP/IP infrastructure, most notably availability, reliability and mobility. Therefore, a critical infrastructure is highly desireable, in particular for multimedia streaming applications. So far the proposed approaches have focused on applying application-layer routing and path monitoring for reliability and on enforcing stateful packet filters in hosts or network to protect against Denial of Service (DoS) attacks. Each of them solves its own aspect of the problem, trading scalability for availability and reliability among a relatively small set of nodes, yet there is no single overall solution available which addresses these issues in a large scale.We propose an alternative overlay network architecture by introducing a set of generic functions in network edges and end hosts. We conjecture that the network edge constitutes a major source of DoS, resilience and mobility issues to the network, and propose a new solution to this problem, namely the General Internet Signaling Transport (GIST) Overlay Networking Extension, or GONE. The basic idea of GONE is to create a half-permanent overlay mesh consisting of GONE-enabled edge routers, which employs capability-based DoS prevention and forwards end-to-end user traffic using the GIST messaging associations. GONE's use of GIST on top of SCTP allows multi-homing, multi-streaming and partial reliability, while only a limited overhead for maintaining the messaging association is introduced. In addition, upon the services provided by GONE overlays, hosts are identified by their unique host identities independent of their topologies location, and simply require (de-)multiplexing instead of the traditional connection management and other complex functionality in the transport layer. As a result, this approach offers a number of advantages for upper layer end-to-end applications, including intrinsic provisioning of resilience and DoS prevention in a dynamic and nomadic environment.

The ping-pong type of movement is a typical motion manner in mobile IPv6 networks, which will bring frequent handovers and thus increase signaling burden. On the other hand, reducing handover delay in this case seems to be more significant. In this paper, we propose a fast seamless handover scheme for the ping-pong type of movement as an extension to the hierarchical mobile IPv6. Based on the simulation results, it can be observed that, by setting the reservation active flag (RAF) and the offline count down timer (CDT), the scheme significantly reduces QoS signaling cost and handover delay. Furthermore, the simulations work out an optimized CDT for acquiring better cost performance of resource reservation

RSVP is a reservation setup protocol designed specifically to support QoS signaling in the Internet. However, RSVP end-to-end signaled QoS for the Internet has not become a reality. Moreover, there are many other applications demanding different signaling services. The paper analyses the features of RSVP version 1 we believe to be essential, and its complexity due to QoS-oriented design and multicast support as an indispensable component in a signaling protocol, deriving the design principles to be covered in a more generic signaling protocol. Based on this analysis, we present a light-weight version of RSVP, RSVP Lite, which clearly separates the signaled data from signaling messages and removes the multicast capability from the mandatory components of RSVP. RSVP Lite is intended to be applicable to a wide range of networking environments, while providing the flexibility to serve for generic signaling purposes and incremental deployment in the Internet.

QoS signaling protocol is one of the key components in Internet QoS architectures to establish, maintain, and remove reservation states in network nodes. This paper gives an overview of the recent efforts underway on next steps in QoS signaling protocols, namely RSVP extensions with mobility support, QoS-conditionalized handoff protocol, the layered architecture RSVP lite and the cross-application signaling protocol (CASP). These efforts address main issues with existing approaches differently: modularity, complexity and mobility support, with a focus on protocol behaviors based on different design principles. The paper also provides pointers to standards effort towards general Internet signaling and other service-specific signaling protocols.