Log anomaly detection plays a pivotal role in ensuring system stability and security, particularly in largescale environments characterized by the generation of log data at exceptionally high volumes and velocities. Conventional approaches often struggle to effectively filter log information and fully leverage temporal dynamics, resulting in challenges such as information loss, semantic drift, and heightened computational overhead. To overcome these limitations, we present QYXLAD, an innovative log anomaly detection framework. QYXLAD enhances log representation accuracy by seamlessly integrating temporal and semantic information. It introduces a MPMM(Multi-level Progressive Masking Mechanism)-based Feature Fusion designed to capture temporal dependencies and semantic features across diverse pattern combinations, thereby significantly improving the sensitivity and precision of anomaly detection. Furthermore, QYXLAD utilizes a Mamba-based classifier for anomaly identification. Comprehensive theoretical analysis and empirical evaluations demonstrate that QYXLAD achieves state-of-the-art performance on multiple public log datasets, surpassing existing methods in key metrics such as precision, recall, and F1-score. These results underscore the framework’s efficacy and superiority in addressing log anomaly detection challenges.

DHCP is widely deployed in WLANs to automatically assign IP addresses to WiFi devices when users connect to the WLANs. However, frequent user mobility brings big challenges to the DHCP performance. Recently proposed IP configuration (e.g., IP lease time, size of IP address pool) decisions on DHCP are based on traditional models to study user mobility patterns which lead to poor DHCP performance since the online time of individuals varies due to their personal pReferences and the number of crowds differs spatially and temporally. In this paper, we propose PredHCP, a predictive configuration framework on DHCP to improve the DHCP performance. Specifically, PredHCP utilizes an attention-based recurrent neural network (ARNN) to learn sequential patterns of individual mobility and accurately predicts user online time to ensure the effective IP lease time configuration. Meanwhile, PredHCP introduces a spatio-temporal graph neural network (STGNN) to learn both spatial and temporal dependencies of crowd migration and accurately predict crowd size in each area to ensure effective IP pool configuration. We conduct comprehensive experiments on real network traces for a month to evaluate the performance of PredHCP. Experimental results show that PredHCP can accurately predict user mobility patterns by achieving lower prediction errors. By accurately modeling mobility patterns, PredHCP makes effective IP configuration to ensure high DHCP performance. Large-scale simulation results show that PredHCP can save up to 69% IP addresses and the IP efficiency is 41% which outperforms existing methods by 6%.

BGP anomaly has a major impact on the stability, availability, and efficiency of the Internet, thus locating the network element which initiates the anomaly is crucial for network recovery. However, BGP is an information-hiding protocol and BGP anomalies are heterogeneous, which limit the ability to perform root cause localization. This paper proposes an approach to automatically detect large-scale BGP anomaly and locate the root cause in an efficient and generalized fashion. Firstly, time-series analysis and clustering strategy are performed on BGP traffic to ascertain "when" and "where" anomaly happened to start the investigation. Next, based on interdomain routing’s propagation property, a subtopology is constructed which not only reveals the dynamic evolution during anomaly, but also helps narrow down the scale of the Internet for further root cause analysis. Last, by taking deep analysis in the routing characteristics of the root cause from different anomalous scenarios, a routing dependence-based algorithm is developed to realize the localization goal. The proposed method is validated on three different real-world, large-scale BGP anomalies. Results demonstrate its effectiveness and robustness, and in most cases the root causes identified with maximum likelihood are consistent with ground-truths. Further investigations reveal some insights which are helpful for network administrations.

With the increasing diversity of user demands for network resources, efficient and flexible task scheduling schemes have gained greater importance. Given that existing works about dependent task scheduling primarily focus on optimizing QoS objectives without considering the impact of user preferences on decision results, and the majority of prior research neglects the underlying relationships among dependent tasks. In this paper, we introduce a Graph Neural Networks (GNN) based dependent task scheduling algorithm (GDTA) to enhance user satisfaction and propose a QoE model to assess user-centered quality of experience. This novel approach incorporates GNN for the purpose of generating embeddings for tasks and networks, leveraging its inherent capability in extracting graph-based features. Compared with baseline algorithms across diverse task parallelisms and network topologies, our method achieves higher QoE scores and shows superior stability and generalization on unseen graph datasets.

Fueled by the explosive growth of ultra-low-latency and real-time applications with specific computing and network performance requirements, the computing force network (CFN) has become a hot research subject. The primary CFN challenge is to leverage network resources and computing resources. Although recent advances in deep reinforcement learning (DRL) have brought significant improvement in network optimization, these methods still suffer from topology changes and fail to generalize for those topologies not seen in training. This paper proposes a graph neural network (GNN) based DRL framework to accommodate network traffic and computing resources jointly and efficiently. By taking advantage of the generalization capability in GNN, the proposed method can operate over variable topologies and obtain higher performance than the other DRL methods.

Resource Public Key Infrastructure (RPKI) defends against BGP prefix hijacking by signing Route Origin Authorizations (ROAs) and filtering malicious BGP routes with ROAs. However, RPKI's low deployment weakens its defense against prefix hijacking. In the absence of a large fraction of Autonomous Systems (ASes) signing ROAs, the incentive to use filtering to eliminate hijacked prefixes commensurately decreases. There is a cyclic dependency here because reduced filtering in turn lessens the incentive for non-adoptees to become adoptees. Previous studies on RPKI deployment are mainly from the measurement perspective or focus on the deployment of large Internet Service Providers (ISPs). The above circular dependency problem inside the RPKI has not been fully studied. To improve RPKI's defense, this paper studies the circular dependency problem from an evolutionary game theory perspective. We model the strategy evolution of ASes choosing to deploy signing alone, deploy filtering alone, or deploy both signing and filtering. The results show that when the deployment rates of signing and filtering reach a certain range, the evolution can reach an ideal deployment state at a faster speed. Therefore, to increase the probability of evolution reaching the ideal deployment state, we propose RPKIN to widen this interval and reduce the minimum deployment rate required for signing and filtering.

Incorporating wireless power transfer (WPT) into mobile-edge computing (MEC) is an effective way to enhance the self-sustainability of the MEC network. However, it is susceptible to the effect of double-far-near. Meanwhile, inspired by the fact that nonorthogonal multiple access (NOMA) has shown its great potential in improving the spectral efficiency of the network over orthogonal multiple access, this article investigates a cooperative computation offloading scheme for a basic three-node wireless-powered MEC scenario with NOMA, where users overcome the effect of double-far-near through joint communication and computation cooperation. Specifically, we consider a “harvest-then-offload” protocol composed of three stages. In the first stage, users harvest wireless energy signals from the access point (AP). In the second and third stages, based on NOMA, users offload their partial computing tasks on the same time–frequency resource block by using the harvested energy, in which the near user can cooperatively compute and forward the computing task of the far user. We formulate an optimization problem to minimize the transmission energy consumption of the AP by jointly optimizing the transmission power, the offloading coefficient, and the duration of each stage. Due to the nonconvexity of the formulated problem, we first transform it into an equivalent one by using the variable substitution method. Then, we propose an efficient iterative algorithm based on the successive convex approximation (SCA) and Lagrangian duality method to solve this transformed problem. Simulation results show that the proposed algorithm has good convergence and the proposed scheme can achieve the best performance compared with several baseline schemes, which highlights the superiority of the proposed scheme.

Software Defined Network (SDN) has been widely used in modern network architecture. The SD-WAN is considered as a technology that has a potential to revolutionize the WAN service usage by utilizing the SDN philosophy. Attacking SDN router and controller can affect the network and block the entire services. In this paper, we propose a machine learning based anomalous traffic detection framework named OADSD over SD-WAN that can achieve task independent and has the ability of adapting to the environment. The OADSD adopts Distributed Dynamic Feature Extraction (DDFE) to extract representative features directly from the raw traffic, and proposes the On-demand Evolving Isolation Forest (OEIF) to make the system adapt to an environment. We provide a theoretical analysis of the performance of the OADSD. We also conduct comprehensive experiments to evaluate the performance of the OADSD with real world public datasets as well as a small real testbed. Our experiments under real world public datasets show that, the OADSD can accurately detect various kinds of attacks with a high performance. Compared with the state-of-the-art systems, the OADSD can achieve up to 60% accuracy improvement.

Transformer-based methods are recently popular in vision tasks because of their capability to model global dependencies alone. However, it limits the performance of networks due to the lack of modeling local context and global-local correlations of multi-scale features. In this paper, we present MISSFormer, a Medical Image Segmentation tranSFormer. MISSFormer is a hierarchical encoder-decoder network with two appealing designs: 1) a feed-forward network in transformer block of U-shaped encoder-decoder structure is redesigned, ReMix-FFN, which explore global dependencies and local context for better feature discrimination by re-integrating the local context and global dependencies; 2) a ReMixed Transformer Context Bridge is proposed to extract the correlations of global dependencies and local context in multi-scale features generated by our hierarchical transformer encoder. The MISSFormer shows a solid capacity to capture more discriminative dependencies and context in medical image segmentation. The experiments on multi-organ, cardiac segmentation and retinal vessel segmentation tasks demonstrate the superiority, effectiveness and robustness of our MISSFormer. Specifically, the experimental results of MISSFormer trained from scratch even outperform state-of-the-art methods pre-trained on ImageNet, and the core designs can be generalized to other visual segmentation tasks. The code has been released on Github: https://github.com/ZhifangDeng/MISSFormer.

In the inter-domain network, route leaks can disrupt the Internet traffic and cause large outages. The accurate detection of route leaks requires the sharing of AS business relationship information. However, the business relationship information between ASes is confidential. ASes are usually unwilling to reveal this information to the other ASes, especially their competitors. In this paper, we propose a method named FL-RLD to detect route leaks while maintaining the privacy of business relationships between ASes by using a blockchain-based federated learning framework, where ASes can collaboratively train a global detection model without directly disclosing their specific business relationships. To mitigate the lack of ground-truth validation data in route leaks, FL-RLD provides a self-validation scheme by labeling AS triples with local routing policies. We evaluate FL-RLD under a variety of datasets including imbalanced and balanced datasets, and examine different deployment strategies of FL-RLD under different topologies. According to the results, FL-RLD performs better in detecting route leaks than the single AS detection, whether the datasets are balanced or imbalanced. Additionally, the results indicate that selecting ASes with the most peers to first deploy FL-RLD brings more significant benefits in detecting route leaks than selecting ASes with the most providers and customers.

In recent years, microgrids have attracted much attention as one of the typical applications of the Internet of Things (IoT). With the development of renewable energy, a bilayer energy-trading scenario of microgrids has emerged. In the upper layer, the microgrids trade energy with each other, and in the lower layer, users trade energy within the microgrid. Traditional management schemes based on the cooperation mode or island mode of the microgrids can no longer meet the needs of energy trading. In this article, we propose an energy-trading management solution for the bilayer energy-trading scenario. In order to maximize the bilayer’s [i.e., users’ and microgrid operators’ (MGOs)] utility functions under the condition of the local energy supply and demand, we propose a multiobjective optimization model. To solve the optimization model, we give a solution energy-trading management algorithm (ETMA) based on the nondominated sorting genetic algorithm (NSGA). Moreover, the proposed energy-trading framework that is based on the multiblockchain ensures the security and privacy of energy trading, and a consensus mechanism named the Delegated Proof of Reputation (DPoR) is proposed to urge users to complete the transaction according to their promised trading schemes. Experiments based on the real data of Guizhou are done to evaluate the performance of the proposed optimization model. The results show that our proposed model can achieve better performance in terms of users’ and MGOs’ utility functions in the bilayer energy-trading scenario compared with the traditional models.

The development of the Internet of Things (IoT) has brought various IoT services, which facilitate and enrich human life. All these services are at risk of privacy leakage. The privacy-preserving issue of IoT query, which is a typical service, has attracted much attention. A superior candidate for solving the above issue is classical cryptographic schemes based on the computational difficulty. With the advent of quantum computation, the security of such schemes may be broken by the strong ability of some advanced quantum algorithms. How to design a secure scheme for privacy-preserving query under the threat of quantum computation is crucial. To this end, we construct a general architecture of privacy-preserving query in IoT scenarios. Besides trust or honesty-but-curious models, we present a scheme for privacy-preserving query which is also valid in the scenario of untrusted devices. We provide a detailed security analysis. The result shows our scheme achieves private preservation of both service providers and clients.

Emerging technologies, such as mobile-edge computing (MEC) and next-generation communications are crucial for enabling rapid development and deployment of the Internet of Things (IoT). With the increasing scale of IoT networks, how to optimize the network and allocate the limited resources to provide high-quality services remains a major concern. The existing work in this direction mainly relies on models that are of less practical value for resource-limited IoT networks, and can hardly simulate the dynamic systems in real time. In this article, we integrate digital twins with edge networks and propose the digital twin edge networks (DITENs) to fill the gap between physical edge networks and digital systems. Then, we propose a blockchain-empowered federated learning scheme to strengthen communication security and data privacy protection in DITEN. Furthermore, to improve the efficiency of the integrated scheme, we propose an asynchronous aggregation scheme and use digital twin empowered reinforcement learning to schedule relaying users and allocate spectrum resources. Theoretical analysis and numerical results confirm that the proposed scheme can considerably enhance both communication efficiency and data security for IoT applications.

Emerging technologies, such as digital twins and 6th generation (6G) mobile networks, have accelerated the realization of edge intelligence in industrial Internet of Things (IIoT). The integration of digital twin and 6G bridges the physical system with digital space and enables robust instant wireless connectivity. With increasing concerns on data privacy, federated learning has been regarded as a promising solution for deploying distributed data processing and learning in wireless networks. However, unreliable communication channels, limited resources, and lack of trust among users hinder the effective application of federated learning in IIoT. In this article, we introduce the digital twin wireless networks (DTWN) by incorporating digital twins into wireless networks, to migrate real-time data processing and computation to the edge plane. Then, we propose a blockchain empowered federated learning framework running in the DTWN for collaborative computing, which improves the reliability and security of the system and enhances data privacy. Moreover, to balance the learning accuracy and time cost of the proposed scheme, we formulate an optimization problem for edge association by jointly considering digital twin association, training data batch size, and bandwidth allocation. We exploit multiagent reinforcement learning to find an optimal solution to the problem. Numerical results on real-world dataset show that the proposed scheme yields improved efficiency and reduced cost compared to benchmark learning methods.

The rapid development of artificial intelligence and 5G paradigm, opens up new possibilities for emerging applications in industrial Internet of Things (IIoT). However, the large amount of data, the limited resources of Internet of Things devices, and the increasing concerns of data privacy, are major obstacles to improve the quality of services in IIoT. In this article, we propose the digital twin edge networks (DITENs) by incorporating digital twin into edge networks to fill the gap between physical systems and digital spaces. We further leverage the federated learning to construct digital twin models of IoT devices based on their running data. Moreover, to mitigate the communication overhead, we propose an asynchronous model update scheme and formulate the federated learning scheme as an optimization problem. We further decompose the problem and solve the subproblems based on the deep neural network model. Numerical results show that our proposed federated learning scheme for DITEN improves the communication efficiency and reduces the transmission energy cost.

Network resource management relies on trusted infrastructures. However, current trusted infrastructures have centralized or hierarchical structures where root nodes often have privileges over subtrees. Hacked or malicious roots may affect subtrees by modifying IP ownership, routing and so on. Therefore, a trusted and decentralized network resource management becomes crucial. The emergence of blockchain such as Ethereum becomes a leading candidate for achieving trusted and decentralized network resource management. In this paper, based on Ethereum, we design a trusted authentication scheme that includes voting policy, endorsement, credibility model and rebinding mechanism to ensure the credibility of entities. Our scheme can allocate, delegate and transfer network resources in a trusted and decentralized style. The experimental results verify the feasibility and security of our scheme. When an entity is hacked or malicious, its endorsement authentication is revoked and the entity does not have the right to manage the network resources. In addition, the overhead increased by the trusted authentication scheme is within a reasonable range.

Datagram transport layer security (DTLS) protocol is widely used in Internet of Things (IoT) for providing security services. The computational overhead makes it hard to implement DTLS on resource-constrained IoT devices. The two significant costly computations in the DTLS handshake are the Diffie-Hellman (DH) key exchange and the certificate verification. A simplified handshake protocol of DTLS (DTLShps) is proposed to reduce the computational overhead of the IoT devices for a general scenario of end-to-end communications based on software-defined networking (SDN). First, a controller is utilized to generate a symmetric key dynamically, then encrypt and distribute this key to two communicating IoT devices. Second, the certificate verification is shifted from the IoT device to the more powerful controller. Third, the controller replaces the DTLS server to make a cookie exchange with the DTLS client. Furthermore, the BAN logic and the tool Scyther are used to validate the security of our scheme. The performance evaluation shows that not only the computational overhead and the energy consumption in the IoT devices are effectively decreased but also the overall duration of the whole handshake is reduced.

Driven by technologies such as mobile edge computing and 5G, recent years have witnessed the rapid development of urban informatics, where a large amount of data is generated. To cope with the growing data, artificial intelligence algorithms have been widely exploited. Federated learning is a promising paradigm for distributed edge computing, which enables edge nodes to train models locally without transmitting their data to a server. However, the security and privacy concerns of federated learning hinder its wide deployment in urban applications such as vehicular networks. In this article, we propose a differentially private asynchronous federated learning scheme for resource sharing in vehicular networks. To build a secure and robust federated learning scheme, we incorporate local differential privacy into federated learning for protecting the privacy of updated local models. We further propose a random distributed update scheme to get rid of the security threats led by a centralized curator. Moreover, we perform the convergence boosting in our proposed scheme by updates verification and weighted aggregation. We evaluate our scheme on three real-world datasets. Numerical results show the high accuracy and efficiency of our proposed scheme, whereas preserve the data privacy.

The rapid increase in the volume of data generated from connected devices in industrial Internet of Things paradigm, opens up new possibilities for enhancing the quality of service for the emerging applications through data sharing. However, security and privacy concerns (e.g., data leakage) are major obstacles for data providers to share their data in wireless networks. The leakage of private data can lead to serious issues beyond financial loss for the providers. In this article, we first design a blockchain empowered secure data sharing architecture for distributed multiple parties. Then, we formulate the data sharing problem into a machine-learning problem by incorporating privacy-preserved federated learning. The privacy of data is well-maintained by sharing the data model instead of revealing the actual data. Finally, we integrate federated learning in the consensus process of permissioned blockchain, so that the computing work for consensus can also be used for federated training. Numerical results derived from real-world datasets show that the proposed data sharing scheme achieves good accuracy, high efficiency, and enhanced security.

Data leakage is a growing insider threat to data owners. Several studies have been done on data leakage prevention (DLP). In the era of big data, massive data has been generated constantly by various institutions. In many applications, multiple institutions may be interested in sharing data with each other to extract more value, without leaking private data. It is a new challenge for DLP in the big data scenario to train a global detection model over distributed big data sets without breaking the data privacy of each party. Moreover, as the forms of data become much complicated, the model should also be capable of tolerating data transformation. In this paper, we propose a Collaborative graph-based mechanism for Distributed big data Leakage Detection (CoDLD). CoDLD addresses the collaborative DLP problem in three aspects. First, it transfers the text detection problem into graph space. The local weighted graphs of data owners are iteratively constructed in turn. Second, it protects the privacy of each data owner by using graph masking on local weighted graphs. Third, it applies partition-based method on the graph to perform accurate matching detection efficiently. Experimental results show that our method can perform collaborative data leak detection over distributed data with high accuracy and efficiency.

Data leakage prevention (DLP) uses a series of techniques to detect and prevent the sensitive data leakage caused by insider threat. Current detection methods either fail to achieve high accuracy toward transformed data or fail to reduce computational complexity. To ensure high detection accuracy and reduce computational complexity, we propose a Weighted Context Graph Model (WCGM) in this paper. The main goal of WCGM is three folds. First, the weighted context graph is proposed to build the contextual relation of data, based on which sub-graph matching method is used to calculate similarity features between tested data and pre-defined template. Second, machine learning algorithms are used to classify the tested data based on the similarity features of its context graphs. Third, privacy- preserving graph masking method is proposed to protect the data privacy of data holders. Extensive simulation results show that the proposed WCGM is able to achieve significant enhancement in terms of running time and accuracy.

Given a software defined wide-area network (WAN), how to choose location of controllers and how to assign the controllers to forwarding devices are two significant issues. Previously, most of solutions to these two problems focus on the propagation delay but ignore the balance of controllers, because it's difficult to solve them with consideration of balance of controllers. In this paper, a novel approach which can efficiently and accurately solve SDN controller placement problem and assignment problem for WAN is proposed. The SDN controller assignment problem is formulated as a minimum weight matching of bipartite graph, and it also considers the balance of controllers. The Kuhn- Munkres algorithm based solution is used to find optimal matching between switches and controllers. Then, a genetic algorithm is proposed to solve the controller placement problem based on the controller assignment scheme. The performance shows that our approach has good performance in reducing the average propagation delay between SDN forwarding devices and controllers, and it also achieves better balance of controllers.

Vehicular networks play an important role in the intelligent transportation systems which have gained technical supports from car industry. Due to the mobility and the broadcast nature of wireless communication, security of the vehicular networks is a critical issue for the academia and industry. Many solutions have been proposed to target the security provisioning. However, most of them have various shortcomings. Based on the elliptic curve public key cryptography algorithm, in this paper, we propose a new anonymous roaming authentication protocol for the Long Term Evolution‐Advanced (LTE‐A) supported vehicular networks. For a vehicular LTE‐A network, an authentication protocol should be able to fulfill a variety of security requirements, which can be met by our proposal and proved by using Burrows–Abadi–Needham (BAN) logic. Compared with some existing solutions, our scheme has lower communication costs with stronger security functionality. The analyses on the security functions and the performance of the proposed solution show that our scheme is secure and efficient with ability against various types of malicious attacks.

Software Defined Networking (SDN) provides flexible and convenient means to support fine-grained management by the logically centralized control. Thus SDN can not only result in better network capacity utilization but can offer better customer satisfaction. In this paper, we analyze and consolidate the utility theory of network bandwidth allocation to offer better customers' satisfaction in SDNs especially when SDNs are incrementally introduced into an existing network. The utilities are modeled as sigmoid curves, since they are well-known functions and often used to describe the perception of Quality of Service (QoS). We propose an optimization bandwidth allocation strategy to maximize the network utility and thus increase the customer satisfaction. The results show that the network utility based on customer satisfaction improvements are possible with proper bandwidth allocation even only a part of SDN forwarding devices in a network topology. Compared with other bandwidth allocation strategies based on fairness, our strategy is more efficient in fulfilling the basic demand of customers.

A possible approach to address the ossification of the Internet is to set up multiple virtual networks on top of a single shared physical substrate, and each virtual network can be customized to some specific purposes. However, the current realization of virtualized infrastructure, more specifically, the IaaS cloud and network testbed, suffer from the limited programmability or high cost. To overcome these drawbacks, we propose SDViNet, a solution combining the network virtualization concept applied in IaaS cloud and SDN (Software Defined Network)-based networking testbed. It aims to achieve flexible programmable virtual network abstraction, extend the system at scale and underlying technology easily, and utilize the existing cloud platforms and commercial devices at low-cost. SDViNet is comprised of a centralized server, a set of distributed agents and a central OpenFlow controller, which communicates with the underlying packet delivery fabric. Our prototype implementation and case study show that it is feasible to provide a programmable virtual network in the IaaS cloud, while utilizing the virtualized infrastructure in an extensible and lost-cost way.

Finding a path that satisfies multiple Quality-of-Service (QoS) requirements is vital to the deployment of current emerged services. However, existing QoS routing algorithms are not very efficient and effective at finding such path. Moreover, few works focus on two or more QoS constraints. In this paper, we propose an effective fully polynomial approximation scheme (FPAS) for multiconstrainted path optimal problem based on the technique of auxiliary graph construction. By employing the nonlinear definition of the path constraint and limited iteration of the FPAS itself, our FPAS can not only achieve the complexity reduction but generate a preferable path as well. We further analyze the Markov properties of the entire network and obtain some key parameters to reflect the routing characteristic. We experiment with different scale of random networks and compare our FPAS against previous well known studies. Our results show that FPAS can find path with lower complexity and better quality.

Recently, the significantly increased IPv6 address length has posed a greater challenge on wire-speed router for packet classification (PC). Most conventional IPv4-based PC algorithms are no longer suitable for IPv6 PC. The performance and capacity of many IPv6 algorithms and classification devices depend upon properties of the IPv6 classifiers. However, there are no publicly available IPv6 real classifiers due to the security and confidentiality reasons. Besides, since IPv6 implementation is still in the initial phase, IPv6 real classifiers at present are too small to depict the feature. Thus, researchers who work on IPv6 PC can only generate synthesized classifiers by ad hoc methods. In response to this problem, we present ClassBenchv6, a suite of tools for benchmarking IPv6 PC algorithms and devices. In this paper, we analyze the intrinsic relationship among the major characteristics of real classifiers and the associated IPv4/IPv6 RFC documents. Based on the analysis, we predict the high-level characteristics for IPv6 classifiers. Our ClassBenchv6 can generate different kinds of classifiers applied to different fields and be used to estimate newly proposed IPv6 PC algorithms comprehensively. It is simple, easy to configure and scalable for further IPv6 deployment. While we have already found ClassBenchv6 to be very useful in our own research, we seek to eliminate the significant access barriers to realistic test vectors and initiate a broader discussion to guide the refinement of the tools. ClassBenchv6 is publicly available at: http://code.google.eom/p/classbenchv6/.

Internet migration from IPv4 to IPv6 has introduced more challenge to IP address lookup problem. Nowadays, some existing address lookup algorithms work well for IPv4 addresses, however, few of them can scale well to IPv6 both in lookup and update speed. As IPv6 uses 128-bit addresses, schemes whose lookup time grows with address length become less attractive. In this paper, a novel level based IPv6 routing lookup algorithm, called level-based routing lookup (LRL) algorithm, is proposed. This proposed algorithm is specially designed based on the extensive research on the characteristics of IPv6 prefix length distribution, which is composed of four levels. Different structures are used for different levels. Intensive experiments have been done to evaluate the algorithm. The results show that the new algorithm excels in following aspects: good support of IPv6, extremely excellent fast lookup speed, stable memory consumption and competitive update speed.

Recently, the significantly increased IPv6 address length has posed a greater challenge on wire-speed router for IP lookup. As a result, even the most efficient IPv4 lookup scheme can not meet the demand in IPv6. In this paper, we make a thorough observation on the characteristic of IPv4/IPv6 routing table and propose a novel division technique for routing table, with which the traditional LPM (longest prefix matching) problem can be changed to an exact matching one for majority prefixes. Based on this technique, we propose a DBH (dynamic binary hash) scheme for dynamic IPv6 routing lookup, which achieves O(log W) (W stands for the address length) lookup performance. Key feature of the new scheme is its ability to have a performance guarantee of 7 hash probes in the worst cast of IPv6 routing lookup, meanwhile it can support incremental update. This makes it more attractive in reality. The performance evaluation has shown that the newly proposed scheme can achieve an average lookup memory access number of 2.7 for current IPv6 lookup. It is below 35% of best existing dynamic algorithm. Besides, the memory cost by the data structure itself is extremely small and the average update memory access number is very few.

In this paper, a local optimization framework is proposed, which is able to include system constraints including channel availability, receiver availability and tuning overhead by linear mathematical formulations so that it is sufficient to obtain the optimal performance in terms of message delay. A mixed integer linear programming (MILP) based scheme for passive star coupled WDM optical networks is presented. Based on the new solution, the wavelength assignment and message sequence that guarantee the delivery of the given traffic request, while minimizing the average delay can be achieved. Moreover, the negative effect of the tuning overhead has been incorporated into the new algorithm, which has been ignored in most of the previous work. Numerical results obtained suggest that the proposed scheme is a promising approach for optimizing the network performance in terms of average message delay.

Relatively large transmitter/receiver tuning overhead is one major difficulty when designing a medium access control scheme for a single hop passive star-coupled WDM optical network. Most schemes proposed so far have ignored the tuning overhead. To overcome this difficulty, two algorithms, namely continuous channel scheduling (CCS) and continuous channel-minimum scheduling latency (CC-MSL) is proposed to reduce the negative impact of tuning overhead as much as possible. Extensive simulations have been conducted. And the results show that there is significant improvement in average delay, which is achieved by the new proposed algorithms.