Influence Maximization (IM) seeks a seed set to maximize information dissemination in a network. Elegant IM algorithms could naturally extend to cases where each node is equipped with a specific weight, reflecting individual gains to measure its importance. In prevailing literature, these gains are typically assumed to remain constant throughout diffusion and are solvable through explicit formulas based on node characteristics and network topology. However, this assumption is not always feasible due to two key challenges: 1) \textit{Unobservability}: The individual gains of each node are primarily evaluated by the difference between the outputs in the activated and non-activated states. In practice, we can only observe one of these states, with the other remaining unobservable post-propagation. 2) \textit{Environmental sensitivity}: Beyond nodes’ inherent properties, individual gains are also sensitive to the activation status of surrounding nodes, which change dynamically during propagation even when the network topology is fixed. To address these uncertainties, we introduce a Causal Influence Maximization (CauIM) framework, leveraging causal inference techniques to model dynamic individual gains. We propose two algorithms, G-CauIM and A-CauIM, where the latter incorporates a novel acceleration technique. Theoretically, we establish the generalized lower bound of influence spread and provide robustness analysis. Empirically, experiments on synthetic and real-world datasets validate the effectiveness and reliability of our approach.

The graph neural network-based prediction models have demonstrated remarkable utility in traffic prediction, and their efficacy is highly determined by the quality of the provided graphs. Consequently, there is an increasing demand for employing graph structure learning (GSL) techniques to optimize or generate the graphs. However, existing GSL techniques for traffic prediction encounter various issues, including the absence of temporal dynamicity, noisy connections, and insufficient supervisory information. To address these limitations, this article proposes a novel two-stage graph generation framework called contrastive learning-based graph structure denoising network (CLGSDN). This framework formulates the graph generation task as a probabilistic observation-inference process: using the self-learning adjacency matrix and time delayed self-attention (TDSA) methods to generate a series of graph observations, then inferring the optimal graph based on observations. The self-learning adjacency matrix is responsible for learning all potential connections in the graph, while TDSA enables the graph to change with traffic flow. In addition, CLGSDN identifies and eliminates noisy connections by modeling negative samples of the graph (edges), and defines virtual labels to achieve spatiotemporal graph contrastive learning (ST-GCL) in traffic prediction. The experimental results show that CLGSDN significantly enhances current mainstream traffic prediction models by providing reliable and efficient graphs. As such, it has significant implications for a wide range of applications, including traffic management, logistics, and smart transportation systems.

Access Control Lists (ACLs) are crucial for ensuring the security and integrity of modern cloud and carrier networks by regulating access to sensitive information and resources. However, previous software and hardware implementations no longer meet the requirements of modern datacenters. The emergence of FPGA-based SmartNICs presents an opportunity to offload ACL functions from the host CPU, leading to improved network performance in datacenter applications. However, previous FPGA-based ACL designs lacked the necessary flexibility to support different rulesets without hardware reconfiguration while maintaining high performance. In this paper, we propose HACL, a heterogeneous and adaptive architecture for decision-tree-based ACL engine on FPGA. By employing techniques such as tree decomposition and recirculated pipeline scheduling, HACL can accommodate various rulesets without reconfiguring the underlying architecture. To facilitate the efficient mapping of different decision trees to memory and optimize the throughput of a ruleset, we also introduce a heterogeneous framework with a compiler in CPU platform for HACL. We implement HACL on a typical SmartNIC and evaluate its performance. The results demonstrate that HACL achieves a throughput exceeding 260 Mpps when processing 100K-scale ACL rulesets, with low hardware resource utilization. By integrating more engines, HACL can achieve even higher throughput and support larger rulesets.

Long-term user responses, i.e., clicks or purchases on e-commerce platforms, are crucial for sequential recommender systems. Recent off-policy evaluation methods involve these responses by simultaneously maximizing expected cumulative rewards. However, two aspects of these methods require further consideration. Firstly, from the system's point of view, candidates with various values are interchangeable, which may result in contradictory future recommendations despite having the same interaction history. Secondly, rewards are manually designed, which necessitates a trial-and-error approach to strike a balance between training stabilization and reward distinction. To address these issues, we propose a new sequential recommender system called NCM4Rec. Specifically, for the distinction problem, NCM4Rec achieves counterfactual consistency via a neural causal model, which is learnable yet equally expressive as classic structural causal models. Such consistency is maintained by a Gumbel-Max design. For the representing problem, NCM4Rec encodes different types of responses as one-hot vectors and captures the long-term preference via adversarial learning. As a consequence, NCM4Rec is both adaptive and identifiable. Both theoretical analyses of the consistency and empirical studies over two real-world datasets demonstrate the effectiveness of our method.

Learning user preferences by modeling historical purchase behaviors has significantly succeeded in existing recommender systems. Most use trained models to make predictions for users, and they assume that the training data samples and test data sample sets come from the same distribution. However, in practical applications, the distribution of users’ true preferences may be more complicated, and data drift can easily make the trained model invalid on the test dataset. In this case, to accurately model user preferences based on their historical behavior, two difficulties need to be addressed. First, it is difficult to model various purchase behavior shift situations due to their complexity. Second, inferring users’ true preferences from the complicated shifting cases is challenging. To solve the above problems, we build a robust recommender system to predict possible user purchase shifts and make recommendations for users. First, we propose a simulating strategy to cover possible scenarios when user purchase behavior shifts. Second, we build a novel voting framework to ensure the robustness of predicting results based on learned preferences. Extensive experiments were conducted, and the results demonstrate the outstanding performance of the proposed method on MovieLens-1M and LastFM datasets, providing at most 37.31% and 30.95% relative performance gains, respectively.

Even though conventional methods have contributed a lot to understanding information diffusion to a certain extent, they are limited by the neglect of considering survivors (i.e., non-participants). As the counterpart to participants, survivors exert analogously a vital role in cascade analysis, which represent the inaccessible scope of the message. To characterize participants and survivors simultaneously and assemble them into the macro-level cascade representation, we propose an end-to-end model, named complement coupling network, which utilizes multi-gating mechanism to coalesce inhomogeneous input. We first design a novel strategy for sampling survivors, which extracts a sequence of emblematic survivors corresponding to the sequence of observed participants. Afterwards, the complement gate is designed to weigh the contributions of the participant and survivor to the cascade at each timestamp. The reset and output gates are reformed to update the cell state and output the cascade snapshot, respectively. Furthermore, an attention mechanism keyed by the source node is introduced to assemble all snapshots within the observation window for predicting multiple subsequent activated users. Extensive experiments on two real-world datasets demonstrate that the proposed model significantly outperforms state-of-the-art approaches.

Sarcasm detection is crucial for natural language processing in various applications, such as affective computing and opinion mining. Multi-modal sarcasm detection, which combines information from different modalities, has attracted increasing attention in recent years. However, many current methods concatenate image and text features directly without considering the contextual information between the cross-modal alignment and single-modal features simultaneously. Inspired by this observation, we propose a novel Global-Aware Attention Network (GAAN) for multi-modal sarcasm detection. Specifically, we investigate a cross-modal multi-granularity alignment module that captures align context features through coarse-grained and fine-grained attention. More importantly, considering the complementary effects of single-modal contextual information in sarcasm detection, we fuse textual, visual context features and alignment context features to obtain the global context features. We conducted extensive experiments on public datasets, and the results compared to the baselines illustrate that our proposed model obtains state-of-the-art performance in multi-modal sarcasm detection.

Aspect-oriented multimodal sentiment analysis aims to identify the sentiment associated with a given aspect using text and image inputs. Existing methods have focused on the interaction between aspects, text, and images, achieving significant progress through cross-modal transformers. However, they still suffer from three problems: (1) Ignoring the dependency relationships between objects within the image modality; (2) Failing to consider the role of syntactic dependency relationships within the text modality in capturing aspect-related opinion words; (3) Neglecting the inherent dependency relationships between modalities. To address these issues, we propose a fine-grained cross-modal graph convolutional network model (FCGCN). Specifically, we construct intra-modality dependency relationships using syntactic and spatial relationships and fuse the two modalities through semantic similarity calculation. We then design a GCN-Attention layer to capture richer multimodal fusion information. Additionally, an aspect-oriented transformer module is introduced to capture aspect features interactively. Experimental results on the Twitter datasets show that our FCGCN model consistently outperforms state-of-the-art methods.

Aspect-based sentiment analysis (ABSA) aims to identify the sentiment of one or more aspects in the text. Existing methods pay attention to the syntactic structure, and significant progress has been achieved by using graph convolutional network (GCN). However, they ignore internal connections between different types of syntactic structure from a fine-grained perspective, which may lead to underexploring critical syntactic information of sentences. Additionally, the aspect-oriented syntactic dependency is omitted that generally provides penetrating insights of the corresponding sentiment. To tackle these problems, we propose a multi-grained (both coarse-grained and fine-grained) syntactic dependency-aware graph convolutional network model (named MSD-GCN). Particularly, in the initial representation layer, we redesign the aspect-enhanced coarse-grained dependency graph and construct five fine-grained dependency graphs by taking into account the types of syntactic structure. Moreover, we explore a multi-grained syntactic enhancement layer, which employs GCN and attention mechanism over multi-grained dependency graphs to capture more abundant syntactic information. Experimental results on five datasets illustrate that our proposed MSD-GCN model outperforms other representative ones in terms of Accuracy and Macro-Averaged F1.

In-network caching is a key feature of information-centric networking (ICN), in which routers take charge of caching passing contents. Such cache design enables efficient content distribution, but the benefit comes at a non-trivial cost, given adding workload to routers. With the emergence of edge computing, more edge devices with large storages are available, which is a good opportunity for new caching design. To this end, we propose a novel Edge-oriented Collaborative Caching (ECC) in ICN. In ECC, edge devices (such as edge server, micro datacenter, etc.) cache contents while routers only maintain cache indexes which are used to redirect subsequent requests towards the cached content. This enables ECC to work in a lightweight and collaborative fashion. We propose an optimization method to properly create cache indexes, considering both content popularity and cache benefit. Moreover, we also present a failure recovery mechanism to ensure system robustness. Simulation results show when deploying the same total cache capacity, ECC outperforms typical ICN caching schemes in terms of response latency, server load and bandwidth consumption of links.

TCP is an important factor affecting user-perceived performance of Internet applications. Diagnosing the causes behind TCP performance issues in the wild is essential for better understanding the current shortcomings in TCP. This paper presents a TCP flow performance analysis framework that classifies causes of TCP stalls. The framework forms the basis of a tool that we use to analyze packet-level traces of three services (cloud storage, software download, and web search) deployed by a popular service provider. We find that as many as 20% of the flows are stalled for half of their lifetime. Network-related causes, especially timeout retransmissions, dominate the stalls. A breakdown of the causes for timeout retransmission stalls reveals that double retransmission and tail retransmission are among the top contributors. The importance of these causes depends however on the specific service. Based on these observations, we propose smart-retransmission time out (S-RTO), a mechanism that mitigates timeout retransmission stalls through careful and gentle aggression for retransmission. S-RTO is evaluated in a controlled network and also in a production network. The results consistently show that it is effective at improving TCP performance, especially for short flows.

In-networking caching, as one of the critical features of the Information-Centric Networking (ICN), has been widely investigated. On-path caching is a light-weight implementation of in-networking caching. Existing studies on on-path caching strategy are mainly confined to the local information and neglect the interaction between multi-node cache decisions, which incurs cache redundancy and lead to performance degradation. To this end, we analyze the relationship between multi-node decisions and propose a lightweight on-path cooperative caching strategy, called GAC. In GAC, caching decisions are divided into two phases. In the first round, the upstream node makes caching decision based on the downstream caching information. In the second round, according to the feedback from upstream nodes, each node is given an opportunity to optimize the caching decision it made before. We evaluate the performance of our scheme through extensive simulations regarding a wide range of performance metrics. The experimental results indicate GAC can achieve significant performance improvement compared with representative approaches in terms of server load reduction ratio, average hop reduction ratio and average cache hit ratio. Meanwhile, GAC can significantly reduce the number of cache evictions.

Packet-level caching is difficult to implement in the traditional caching system. The emergence of Information-centric networking (ICN) has alleviated this problem. However, the chunklevel caching is still facing severe scalability issues. In this paper, we analyze the issues which limit the implementation of chunk-level caching and propose a chunk-level caching optimization approach called BGCC. In BGCC, we reduce the consumption of fast memory by creating the index with group prefixes instead of the chunk prefixes, while the group-level popularity is also used to optimize caching decision. We evaluate the performance of our scheme through extensive simulation experiments regarding a wide range of performance metrics. The experimental results indicate BGCC can reduce the fast memory usage and achieve significant improvement in terms of server load reduction ratio, average hop reduction ratio and average cache hit ratio, compared with current chunk-level caching schemes.

Ethernet plays an important role in the layer 2 network. Unfortunately, the tremendous Address Resolution Protocol (ARP) broadcast traffic among massive hosts limits the scale of Ethernet. Recently, Software-Defined Network (SDN) has been proposed to suppress broadcast traffic by centralized control. However, existing approaches based on SDN suffer from an adaptability limitation as they cannot independently obtain ARP table entries. In this paper, we propose SDARP, a Software-Defined Address Resolution Proxy, to suppress broadcast traffic by centrally processing all ARP packets. To overcome the adaptability limitation, SDARP centrally obtains and maintains ARP table entries by independently resolving the header of ARP messages. SDARP is a SDN application. We prototype SDARP based on the open-source SDN controller RYU, and conduct experiments on the Mininet-based virtual testbed. The emulation results demonstrate that SDARP is transparent to hosts, effectively reduces ARP traffic to 7.1%, eliminates the broadcast storm and reduces the response time of the Internet Control Message Protocol to 35.9%.

Software Defined Networking (SDN), the novel paradigm of decoupling the control logic from packet forwarding devices, has been drawing considerable attention from both academia and industry. As the latency between a controller and switches is a significant factor for SDN, selecting appropriate locations for controllers to shorten the latency becomes one grand challenge. In this paper, we investigate multi-controller placement problem from the perspective of latency minimization. Distinct from previous works, the network partition technique is introduced to simplify the problem. Specifically, the network partition problem and the controller placement problem are first formulated. An optimized K-means algorithm is then proposed to address the problem. Extensive simulations are conducted and results demonstrate that the proposed algorithm can remarkably reduce the maximum latency between centroid and their nodes compared with the standard K-means. Specifically, the maximum latency can reach 2.437 times shorter than the average latency achieved by the standard K-means.

Named Data Networking (NDN) is currently a hot research topic in the field of network architecture, and its transport control mechanism is one of the key technologies needed to be studied. Since the transport in NDN network has the characteristic of multi-source, the implicit congestion detection mechanism of the traditional TCP protocol is no longer suitable for the NDN network. In this paper, we propose a novel congestion control protocol for NDN network based on explicit feedback - ECP (Explicit Control Protocol), which detects the condition of network congestion proactively, and sends explicit feedback to the receiver. According to the feedback, the receiver can adjust the sending rate of Interests in order to control the sending rate of Datas from the sender, thus to realize the congestion control of the network. The simulation results based on NdnSIM show that the ECP protocol performs higher transfer efficiency and stability compared to the current NDN transport protocol using TCP implicit detection mechanism.

Nowadays, Information-Centric Networking (ICN) has attracted more and more attention, which allows named data to be cached within the network. Existing works mainly focus on decreasing the redundancy of replicas to enhance the cache hit ratio, while pay less attention to cache benefit maximization and often bring about frequent cache operations. In this paper, we first formulate the content placement problem and find two key factors, i.e., the content popularity and the content placement benefit. Then we propose a heuristic probability-based caching strategy, called MBP (Max-Benefit Probability-based Caching). In MBP, each cache node caches the passing content with certain probability, which is proportional to the content popularity and the content placement benefit. We evaluate MBP via extensive simulations by comparing it with state-of-art caching strategies under tree and graph topologies. The experimental results indicate that MBP can achieve great improvement compared with other caching strategies, in terms of average cache hit ratio, average access hop ratio, caching operation and link stress. Especially, when the cache size is small, MBP can also achieve dramatically performance improvement.

Increased network traffic has put great pressure on edge router. It tends to be more expensive and consumes more resources. Buffer is especially the most valuable resource in the router. Given the potential benefits of reducing buffer sizes, a lot of debate on buffer sizing has emerged in the past few years. The small buffer rule, for example, was challenged at edge router. Instead of buffer sizing, the goal of our work is to find out a way to relieve the pressure of edge router and logically enlarge its buffer size without incurring additional costs. In this paper, taking advantage of the global view of SDN, we proposed Software Defined Backpressure Mechanism (SD-BM) to alleviate the pressure of edge router. Particularly, we gave Refill and Software Defined Networking based RED (RS-RED) algorithm, which makes it possible to enlarge the network buffer logically and offloads traffic from busy egress router to free ingress devices. Simulation results show that it has comparable performance, both in time delay and loss rate, with edge router which has large buffer in traditional way. The results can have consequences for the design of edge router and the related network.

We model the multiple controllers' approach of SDN and analyze the performance of the model by using the queuing theory. Extensive experiments are conducted and the simulation results are compatible with the theoretical analysis which validates the correctness of the simulation. Through the analysis, we find that multiple controllers' approach is an effective way to increase the control plane's scalability of SDN, but it also results to longer sojourn time. In this point of view, the multiple controllers' approach can be regarded as a tradeoff between the scalability and the sojourn time.

CCN has been witnessed as a promising future Internet architecture. In-network caching has been paid much attention, but there is still no consensus on its usage, due to its non-negligible costs. Meanwhile, massive storage and bandwidth resources of end systems still remain underutilized. To this end, we present an End System Caching and Cooperation scheme in CCN, called ESCC to realize content distribution of CCN, without using costly in-network caching. ESCC enables fast content distribution through clients caching and sharing contents with each other. Experiments show that ESCC can achieve better performance than the universal caching. It is also quite simple, efficient, robust and has low overhead. ESCC could be a candidate substitute for the costly and unnecessary universal caching.

Information-centric networking (ICN) is designed to decouple contents from hosts at the network layer, using in-network caching as a key feature to improve the overall performance. However, the en-route caching strategy used in many ICN implementations generally yields redundancies in the cached contents across different routers. There are also some recent works focusing on cache optimization by respectively exploiting either application layer or network layer, which we think is not sufficient to increase cache hit rate and reduce traffic. In this paper, we propose a novel caching scheme (CRCache) that utilizes a cross-layer design to cache contents in a few selected routers based on the correlation of content popularity and the network topology. Specifically, through exploiting information available at both application and network layers, CRCache aims to improve the cache hit rate and reduce the overall network traffic. We conduct a large scale and real traces-driven simulation with an underlying real Internet topology in China, and show that by using CRCache, the overall cache hit rate is increased by 62.5% and network traffic reduction is improved by at least 42% compared with recent single layer schemes.

Path load balancing is used for distributing workload across an array of paths to increase network reliability and optimize link utilization. However, it is not easy to realize the load balancing globally in traditional networks as the whole status of the network is difficult to obtain. To address this problem, we propose the Fuzzy Synthetic Evaluation Mechanism (FSEM), a path load balancing solution based on Software Defined Networking (SDN). In this mechanism, the network traffic is allocated to the paths operated by Open Flow switches, where the flow-handling rules are installed by the central SDN controller. The paths can be dynamically adjusted with the aid of FSEM according to the global view of the network. Experimental results verify that the proposed solution can effectively balance the traffic and avoid unexpected breakdown caused by link failure. The overall network performance is also improved as well.

Peer-to-peer (P2P) systems are imposing a heavy burden on internet services providers (ISPs). P2P caching is an effective way of easing this burden. We focus on the cache deployment problem as it has a significant impact on the effectiveness of caching. An ISP backbone network is usually abstracted to a graph comprising nodes representing core routers and links connecting adjacent core routers. While deploying P2P caches at nodes (NCD, node-based cache deployment) can reduce the amount of P2P traffic transmitted from access networks to the ISP backbone network, deploying P2P caches on links (LCD, link-based cache deployment) can directly reduce the amount of P2P traffic on the ISP backbone network. However, neither NCD nor LCD maximizes the performance of P2P caches. In this paper, we propose a node-link-based cache deployment method (NLCD), which optimally selects nodes or links as deployment locations during the cache deployment process. First, we propose an analysis model and define an optimal cache deployment problem for NLCD. Then, we prove that this problem is NP complete and develop a corresponding deployment algorithm. Experimental results show that the average link utilization of NLCD is 5–15% lower than that of LCD, and 7–30% lower than that of NCD.

The transmission control protocol (TCP) has contributed to the tremendous success of the Internet but it also faces many challenges which are becoming more and more significant as the network grows. Although numerous congestion control algorithms have been proposed to improve the performance of TCP in heterogeneous networks, designing a congestion control algorithm that could achieve high utilisation, ensure fairness and maintain stability remains a great challenge. A novel congestion control algorithm named fair TCP (FTCP) has been proposed to mitigate these challenges. FTCP mitigates these challenges through the following strategies: First, increase the round trip time (RTT)‐fairness by altering TCP's initial congestion control window (cwnd) and adjusting the cwnd's growth rate to make FTCP flows with different RTTs achieve the same throughput. Secondly, balance the transmission efficiency and TCP‐friendliness by dynamically adjusting the aggressiveness of FTCP according to the congestion level of the link. Preliminary experimental evaluations verify that FTCP has obvious advantages in transmission efficiency, RTT‐fairness and TCP‐friendliness comparing to the state‐of‐the‐art congestion control algorithms.

This paper analyzes the TCP incast problem in data centers by focusing on the relationships between the TCP throughput and the congestion control window size of TCP. The root causes of the TCP incast problem are explored and the essence of the current methods to smooth the TCP incast is well explained. To verify our analysis, extensive simulations are conducted. The simulation results are in conformity with the estimated results of our analysis, which verifies the accuracy of our analysis. The analysis as well as the simulation results are helpful for the improvement of the TCP incast problem.

P2P live streaming systems have been widely adopted nowadays. In such systems, flash crowds still remains a big challenge, which often occur when an enormous number of users suddenly arrive to view a newly released program. In a flash crowd scenario, users often suffer from a long startup delay and a high failure rate. In this paper, we propose a topology-construction-based algorithm to alleviate the flash crowd. Specifically, first the tracker server constructs a multiple tree topology with total new peers. Then according to the topology, all new peers join the current P2P system in form of multiple trees. When constructing the topology, the tracker server puts new peers with higher bandwidth and longer waiting time more closer to the root in each tree, in order to reduce the average waiting time of new peers. Moreover, a new analytical model is also devised to evaluate our algorithm. Model analysis and simulation indicate that our method can enhance the joining process of new peers and improve their startup delay and failure rate.

P2P platforms have been criticized because of the heavy strain that they can inflict on costly inter-domain links of network operators. It is therefore mandatory to develop network optimization schemes for controlling the load generated by a P2P platform on an operator network. While many research efforts exist on centralized tracker-based systems, in recent years multiple DHT-based P2P platforms have been widely deployed and considered as commercial services due to their scalability and fault tolerance. Finding network optimization for DHT-based P2P applications has thereby potential large practical impacts. In this paper, we present THash, a simple scheme that implements a distributed and effective network optimization for DHT systems. THash uses standard DHT put/get semantics and utilizes a triple hash method to guide the DHT clients to choose their sharing peers in proper domains. We have implemented THash in a major commercial P2P system (PPLive), using the standard ALTO/P4P protocol as the network information source. We conducted experiments over this network in real operation and observed that compared with Native DHT, THash reduced respectively by 47.4% and 67.7% the inter-PID and inter-AS traffic, while reducing the average downloading time by 14.6% to 24.5%.

Peer-to-Peer (P2P) live streaming systems are becoming popular and are imposing a heavy burden on Internet Services Providers (ISPs). Proxy caching has been shown to be an effective means of reducing operation costs for ISPs. Although many caching algorithms for conventional web applications and P2P file sharing systems have been proposed and deployed, there have been few works on the caching for P2P live streaming. Data requests in P2P live streaming have distinct characteristics; for example, they are concentrated in a limited window which progresses rapidly with time in a monotonic mode. In this paper, we propose a novel caching algorithm for P2P live streaming called Lazy Caching (LC), which is designed based on the characteristics of P2P live streaming. In LC, data requests that encounter cache misses are stored in a Request Buffer (RB) that is processed periodically. LC can better utilize the cache space than traditional caching algorithms at the cost of introducing a response latency which is at most one RB processing period. Experimental results show that the hit ratio of LC, at a processing period of 0.5 second, can be as much as 20 percent higher than that of SLW, and 9 percent higher than that of OPT. We believe that it is worthwhile to achieve this higher hit ratio at the cost of a small response latency increase.

P2P platforms have been criticized because of the heavy strain that some P2P services can inflict on costly inter-domain links of network operators. It is therefore necessary to develop network optimization schemes for controlling the load generated by P2P platforms on an operator network. Previous focus on network optimization has been mostly on centralized tracker-based systems. However, in recent years multiple DHT-based P2P networks are widely deployed due to their scalability and fault tolerance, and these networks have even been considered as platforms for commercial services. Thereby, finding network optimization for DHT-based P2P applications has potentially large practical impacts. In this paper, we present THash, a simple scheme to implement an effective distributed network optimization for DHT systems. THash is based on standard DHT put/get semantics and utilizes a triple hash method to guide the DHT clients sharing resources with peers in proper domains. We have implemented THash in a major P2P application (PPLive) by using the standard ALTO/P4P protocol as the network information source. We conducted realistic experiments over the network and observed that compared with Native DHT, THash only generated 45.5% and 35.7% of inter-PID and inter-AS traffic, and at the same time shortened the average downloading time by 13.8% to 22.1%.

P2P live streaming systems have been widely adopted nowadays. However, the flash crowd still poses challenges in such P2P systems, which often occurs when an enormous number of users suddenly arrive to view a newly released live program. Facing so many new users, a P2P streaming system usually can not provide reasonable quality of service and these new users often suffer from a long startup delay and a high service rejection rate. In this paper, we propose a bandwidth-aware peer selection method to alleviate the flash crowd. To use the rare available bandwidths more effectively, we let new peers send more requests to the high-bandwidth parents and less requests to the low-bandwidth parents, aiming to make the upload rate of each parent match well with its upload capacity. Moreover, two analytical models are also constructed to evaluate our method and the traditional random peer selection method. Both model analysis and simulation experiment reveal the merits of our method in tackling the flash crowd, in terms of growth of system scale, average startup delay and rejection rate, compared with the random peer selection method.

Peer-to-Peer (P2P) systems are generating a large portion of the total Internet traffic and imposing a heavy burden on Internet Services Providers (ISPs). Proxy caching for P2P traffic is an effective means of reducing network usage, thereby reducing operation costs for ISPs. Proxy cache storage design has a significant impact on ISPs. While there are several works on how to optimally design cache locations and capacity allocation to each location given a total capacity, few works tell ISPs what is the optimal total P2P cache storage capacity. In this paper, we propose an analysis method to the problem of optimally determining P2P cache size. An analysis methodology is proposed to determine the optimal cache size by considering the monetary costs of cache storage and bandwidth. Guided by our model, a close-form expression is developed to guide an ISP in the cache capacity design. Numerical evaluation results show that ISPs can achieve significant cost saving by deploying P2P cache and by allocating the cache capacity optimally.

Peer-to-Peer (P2P) technology has been widely adopted by various live streaming systems recently, due to its better scalability and lower costs compared with the client-server architecture. However, P2P live streaming systems are still challenged by the flash crowd scenarios, which often occur when a great number of users suddenly arrive and compete for the limited upload bandwidth of a P2P system. In this case, users are usually subject to a long startup delay and are likely to retry multiple times before leave out of impatience. Current studies mainly focus on the measurement of practical systems and model analysis on flash crowd, but there are few specific approaches so far. In this paper, we develop a capacity-aware user access control algorithm to relieve the flash crowd problem. Firstly, we control the peers to enter the system at a proper rate, which avoids too high arrival rate slowing down the increase of system scale. Secondly, to increase the system service capacity as soon as possible, we let the peers with higher capacity enter the system ahead of the peers with lower capacity. Finally, we also consider the waiting time of peers with low capacity and let them in before they lose patience. To evaluate our algorithm, a new analysis model is also proposed. Simulation experiments and model analysis reveal that our algorithm is more effective to increase the system scale, and can achieve shorter user waiting time as well as lower reject rate.

A novel Colored Petri Nets (CP-nets) model based test case generation approach is proposed to makes the best of advantages of the ioco testing theory and the CP-nets modeling, where the Conformance Testing orientated CP-nets (CT-CPN) is proposed for modeling certain software systems, and PN-ioco relation is defined as a new conformance relation, and finally test cases are generated through simulating the system CT-CPN models. CP-nets model simulation based test generation approach reflects the data-dependent control flow of the system behaviors, so all test cases are completely feasible for the actual test executions. Besides, better formal modeling and analytic capabilities in CP-nets modeling quite facilitate validating the accuracy of the system CT-CPN model. For effectively extending the applicability of the Petri nets based testing technologies, our novel CT-CPN model based test generation approach may well become a competent choice.

The routing scalability and IP address exhaustion are two significant issues the current Internet faces. The "locator/identifier (Loc/ID) split" has become a well recognized design principle for future Internet architectures that make Internet routing more scalable. In this paper, a novel Loc/ID split routing and addressing architecture called IPv4+6 is proposed. It not only solves the routing scalability problem but also expands the IP address space. It is easy to deploy, which only needs to make simple changes on DNS and gateway router.

Push-to-Talk over Cellular (PoC) is a one-to-one or one-to-many Push-To-Talk (PTT) service designed to work over the cellular network. In the PTT, a participant may speak as soon as he/she has obtained the permission through pushing a “talk” button. The typical telephone setup delay is avoided. In the traditional floor control protocol defined in the Open Mobile Alliance standard (FCP-OMA) for PoC, at any time only one participant is permitted to speak. The permitted speaker is totally “deaf” and the listeners are fully “dumb” until the speaker releases or is revoked the floor. In this article, we propose an enhanced floor control protocol (EFCP) for PoC application in a packet voice scenario where full-duplex communications can be supported. The proposed protocol is divided into two categories: EFCP-M and EFCP-NM depending on whether media mixing is needed or supported. Through a queuing model, we compare the performance of EFCP against FCP-OMA. Experimental results show that our scheme can significantly reduce the probability that floor requests are denied and the expected waiting time in queue.

Hybrid content distribution network (HCDN) makes use of the highly complementary advantages of conventional CDN (content distribution network) and pure P2P (peer-to-peer). In HCDN, clients can concurrently retrieve content from both CDN and P2P networks. In this paper, service differentiation is formulated as a constraint optimization problem, in which two critical factors are taken into consideration: quality factor of a link and demand factor of a file. With the convex optimization theory, two bandwidth allocation algorithms, HBAA-P for source peer and HBAA-S for surrogate server, are proposed and proved for service differentiation in the hybrid architecture. Some experimental results are illustrated to make sense of the performance features of our approaches.

Adopting two independent models for functional verification and performance analysis respectively could not guarantee the performance models satisfying the functionality correctness. In this paper, a colored Petri nets (CP-nets) based method is proposed to integrate functional verification and performance analysis for network protocols. Firstly, a CP-nets based function model for the protocol is constructed and validated. Then, performance related temporal constrains are added into above model, and data monitor units are generated together to form a corresponding CP-nets based performance model. Finally, based on such performance model, simulation based performance evaluation is executed. Because such coessential CP-nets models are utilized where every occurrence sequence in the performance model corresponds to an occurrence sequence in the functional model, it is guaranteed that both models satisfy the functionality correctness requirements of that protocol. As a representative, an integrated analysis process of TRDP protocol is presented to illustrate the practical effectiveness of our proposed method.

Content distribution networks (CDNs) improve the performance of content delivery by replicating the popular content on surrogate servers deployed at the edge of the Internet. The CDN-P2P architecture, which combines the complementary advantages of both CDN and P2P networks, can improve the quality of service (QoS). In this paper, we propose a k-coordinated decentralized replica placement algorithm (DRPA) based on a gain formulation of the replica placement problem. Although the gain formulation is designed for different types of the CDN-P2P architecture, we focus on the robust ring-based architecture in this study. In our approach, each surrogate server makes the replica placement in terms of the content replicas on k closer surrogate servers, which enhances the system scalability compared to the centralized replica placement heuristics. In addition, according to the simulation results, the proposed algorithm is able to reduce the backbone traffic between the servers and the requesting peers compared to the traditional replica placement algorithms for the pure CDN.

In recent years, structured P2P communications is being widely used for its features of self-organization as well as good scalability and flexibility. To make sure that every node can participate in such a network whether it is behind a NAT or not, we must solve the NAT traversal problem. However, existing NAT traversal methods all need the support of a centralized server which will destroy the distributive characteristic of structured P2P. In this paper, we propose a distributed NAT traversal mechanism called SMBR (Selective-Message Buddy Relaying) for structured P2P. SMBR has two main advantages. The first one is that it does not need the support of a server and thus can maintain the characteristics of structured P2P. Secondly, SMBR uses different mechanisms for the control messages and data according to their size. For control messages, it uses the method of buddy's relay while for data direct connections can be built with the help of the buddy. Using this mechanism, SMBR can achieve a balance between the traversal time and the buddies' load.

P4P (Provider Portal for Applications) integrated P2P (peer-to-peer) network is one of the main trends of P2P networks. While P4P brings advantages to P2P, it also brings new challenges in solving trust problems in P2P networks. In this paper, we consider the P4P's characteristics, domain partition and strategy matrix guidance, and propose a novel domain-based trust model for P2P networks. In our model, we distinguish peer's intra-domain behavior and inter-domain behavior. Peer's intra-domain good behavior does not mean that the peer also behaves well when interacting with other domain's peers, due to P4P's inter-domain traffic control. In addition, we give each domain a trust value, using them to modify the strategy matrix provided by P4P. Simulation results show that our model can effectively distinguish good peers and malicious peers. Even when a peer behaves well in its own domain, our method can make other domains cooperate together to evaluate the peer's bad inter-domain behavior. Lastly, using the domain's trust value to modify the P4P's strategy matrix can reduce the impact of malicious peers' cheating behavior on forming the strategy matrix.

It is very risky to improve the performance of network protocols without the assurance of its functional correctness, especially for protocols that with complicated and concurrent behaviors. However, in most of current model based protocol engineering projects, two independent models are adopted for individual functional verification and performance analysis, which could not guarantee the performance model satisfying the functionality correctness, and usually cost more in protocol design and maintenance. In this paper, we propose a colored Petri nets (CP-nets) based method to integrate functional verification and performance analysis procedures, and focus on the BitTorrent protocol as a representative example to illustrate the practical effectiveness of our proposed methodology. That is, the functional CP-nets models of BitTorrent protocol are constructed and validated firstly, and then performance related temporal constrains are added into above models to form its performance CP-nets models for corresponding simulation based performance analysis. Because such closely related CP-nets models are utilized where every occurrence sequence in the performance model corresponds to an occurrence sequence in its functional model, it is guaranteed that both models satisfy the functionality requirements of protocol systems. Besides, model maintenance becomes more convenient.

The convergence of IP network over broadcasting transmission systems is a feasible solution towards the next generation wireless broadband multimedia network for large numbers of users and Internet service providers. Taking the characteristics of IP over broadcasting network into account, this paper proposes a novel Dynamic Differentiated Service Management (DDSM) to provide QoS guarantee for delivering IP applications on the IP-Broadcasting Gateway equipment. In the DDSM model, the queue threshold is regulated dynamically according to the level of emergency and priority; the scheduling weight derives from the queue saturation during a regulating period. As shown in the performance evaluation and simulation analysis, DDSM significantly improves the resource utility of the IP over broadcasting system, and reduces the packet drop probability and the packet delay. Thus DDSM achieves the effective differentiated service of the diverse IP applications over broadcasting network.

Location based routing protocols are often used to deliver packets in VANET for the sake of scalability and lower overhead. Before exchanging information between moving vehicles, the location of destination node should be discovered. However, the existing location service employed in MANET is not very suitable for vehicular ad hoc network. In this paper, a vehicle location service protocol (VLS) is proposed to support inter-vehicle communication (IVC) in cities. The information in digital maps is utilized to help realize location service. We present a method of partitioning the network and constructing distributed location servers, which can avoid selecting servers in void areas and decrease the average location discovery delay. In order to reduce the location update cost, forwarding trees and adaptive update policies are used to send messages. In addition, the cost of location update and discovery delay can be balanced through different parameters setting for a given network. Different scenarios are simulated to compare the performance of GLS, GHLS and VLS. The experiment results show that VLS outperforms others in urban environments.

The Hybrid CDN-P2P architecture, or HCDN, which combines the complementary advantages of CDN and P2P networks, has been proposed to reduce the deployment cost and to improve the quality of service in file sharing and video streaming applications. A replica placement algorithm (RPA) decides where to replicate the specific data. Existing RPAs for pure CDN do not work efficiently in the HCDN architecture because they do not take into consideration the contribution of the peers at the P2P distribution level. In this article, a heuristic RPA that takes into account the effects of P2P distribution is proposed for HCDN. The performance of our proposed algorithm is evaluated and the impact of some key metrics is analyzed. The experimental result shows the clear performance benefits of our approach.

Most of existing transfer protocols performs poorly for transferring bulk data over fast long distance network. A key reason is the congestion control algorithm. This paper designs a novel congestion control algorithm, namely congestion detection and rate adaptation (CDRA) algorithm, which adjusts sending rate according to the congestion level of terminal in order to maximize transfer performance. For implementation, a modified protocol based on the UDP-based protocol Tsunami, called robust Tsunami (RTsunami), was developed. The experimental results show that the CDRA algorithm is efficient and the RTsunami protocol outperforms Tsunami.

Key management is a significant part of secure wireless communication. In IEEE 802. Hi standard, 4-way handshake protocol is designed to exchange key materials and generate a fresh pairwise key for subsequent data transmissions between the mobile supplicant and the authenticator. Due to several design flaws, original 4-way handshake protocol cannot provide satisfying security and performance. In this study, we adopt formal specification and verification methods to analyze the 4-way handshake protocol. We give its formal models utilizing two kinds of High-level Petri Nets. Based on these formal models, we use two verification methods, model checking and insecure states deduction, to perform an integrated security verification process. The verification results confirm that the 4- way handshake protocol is vulnerable to Denial-of-Service attack during handshake. To repair such vulnerability, we propose an improved key management scheme named enhanced two-way handshake protocol. According to security analysis and performance evaluation, our proposal could provide stronger security capability and cost less computation and communication time.

Based on pre-IP level access control, a method called authenticated stateful auto-configuration (ASAC) for MIPv6 is proposed in this paper, in which both security issue and handover performance are considered. In order not to introduce any vulnerability into network, ASAC information is authenticated and signed by mobile node and backend server. To meet the requirement for handover performance, ASAC is combined together with authentication procedure. At the end of this paper a result analysis is presented.