In recent years, there has been an increasing expectation to understand and utilize information from physical space. However, the vast amount of information, including historical data, makes it inevitable that some information will be missing in the virtual space. Active inference, based on the free energy principle, is a control framework that captures the uncertainty and ambiguity of information, allowing for the observation of objects and inference of one’s own actions. In active inference, an agent probabilistically infers the state of the environment by combining prior beliefs about the state with observational results. Then, the agent estimates the state change for each action as if it is selected and finally selects the most appropriate action. This enables the estimation of the state of the environment and the control of actions to reduce uncertainty within a single framework, even when information is uncertain. In this paper, to address the missing information in virtual space, we aim to appropriately engage sensing in the physical space by applying active inference to the search for individuals using multidirectional cameras and drones. The target of the search continues to move, making continuous sensing difficult and leading to missing information about the target. Therefore, when a multidirectional camera cannot sense the target, it estimates the target’s position and controls the capture range of other cameras and the movement direction of drones to more efficiently capture the target. Simulation results show that the time to capture the target is reduced compared to cases where active inference is not used.

Achieving safe and efficient navigation in urban environments remains a significant challenge for autonomous driving, primarily due to frequent occlusions that hinder perception. Cooperative perception (CP) has emerged as a promising solution, offering enhanced sensing capabilities by enabling information sharing among vehicles. In this work, we propose an occlusion-aware motion planning framework that integrates CP to optimize vehicle speed, minimizing risk while ensuring efficient navigation. The proposed framework operates in a sequential pipeline. At each time step, sensor features, vehicle motion data, and contextual map information from an edge server are shared among vehicles to enable cooperative object tracking and occlusion analysis. To quantify the risks posed by occluded areas, we introduce a probabilistic representation of potentially hidden objects, which dynamically adapts to varying viewpoints. In addition, an association module is designed to establish correspondences between potentially hidden object and observed objects, thereby improving the accuracy of risk assessment. Finally, predictive information from both observation and motion spaces is incorporated into the planning and control module to guide reference speed planning and vehicle maneuvering. Extensive simulation results demonstrate that our approach significantly improves safety and driving efficiency in complex occlusion scenarios, outperforming baseline methods that rely solely on onboard sensors or single-view perception fusion.

Cooperative perception has attracted sustained attention, promising groundbreaking contributions to transportation safety and efficiency. It enables vehicles to share environmental information in addressing limited visibility, thus improving individual perception performance. However, most related studies only focus on detection, and ways to explicitly enhance object tracking capabilities through multi-vehicle cooperation still lack sufficient exploration. In this paper, we propose a cooperative 3D multi-object tracking (MOT) system that leverages complementary information from multiple vehicles to alleviate the problem of temporary tracking failures. Specifically, we design a data association module to assist the ego vehicle in leveraging received information to promptly compensate for its missed objects. To avoid erroneous associations, we maintain an object ID mapping set for each communication link to discover the correspondence between objects tracked by different vehicles. We conduct experiments on the V2V4Real dataset and utilize the official pre-trained network checkpoints to generate detection candidates as inputs. Experimental results demonstrate that the proposed method performs favorably against the baseline without bringing a communication burden, as well as its generalizability for various detectors.

This paper proposes a 3D multi-object tracker suitable for collaborative perception scenarios. Our tracker aims to associate detection candidates obtained from the ego-vehicle after performing collaborative perception over time. It considers the temporal asynchronous information exchanged among connected vehicles and focuses on dealing with objects that fail to track due to missed detection. To achieve this, we propose a two-stage data association module with a supplementary mechanism. It adapts the association strategy to track objects according to their state robustly. Specifically, the first stage works on most general objects. The second stage aims to associate spatiotemporal asynchronous detection candidates or tracked objects consecutively missed multiple times. A supplementary mechanism is applied to temporarily missed objects by the detector. We conduct experiments on the DAIR-V2X dataset and use the detection candidates generated by a collaborative detection module. Experimental results demonstrate that the proposed method outperforms baselines in tracking performance while achieving comparable speed.

In this paper, we mathematically generalize probabilistic Grant Free (GF) scheduling models so that they can be easily extended to deal with various problems in URLLC use cases. To demonstrate the extensibility of the generalized models, we introduce the reliability parameter α to the models and show that the extended models can be more rigorous against failure scenarios, which provides more flexible options in the design of GF scheduling.

In 5G, the network is divided into slices to provide communications with different characteristics, such as low latency and reliable communications (URRLC), multiple connections (MTC), and high speed and high capacity communications (eMBB), for different applications. Although the selection of network slices is often static, in practice, dynamic slice selection is required depending on the application situation. However, there are issues such as the slice change itself changing the application situation and the delay associated with the slice change. In this paper, we realize dynamic slice selection by recognizing the rough situation and the mapping between the recognized situation and the slice. The Bayesian Attractor Model (BAM) is used for recognition to achieve consistent recognition and is extended to the Dirichlet Process Mixture Model (DPMM) to achieve automatic attractor construction. The mapping between situations and slices is also automatically learned by using feedback. As an application of dynamic slice selection, we also show slice selection based on the video streaming situation. Through numerical examples, we show that our method can keep the quality of video streaming high while reducing slice changes.

Increasing bandwidth requirements in vehicles are pushing the backbone architectures to use faster switching technologies like Ethernet. However, the traditional Ethernet cannot satisfy the strict latency requirements in a vehicle. As a solution, switched Ethernet variants like Time-Sensitive Networking (TSN) and Audio Video Bridging (AVB) are being standardized for automotive Ethernet. Moreover, it is expected that the intravehicle backbone architectures will shift to a zonal architecture for more centralizing the processing and decreasing the costs. In this paper, we present the recent trends, advances and challenges in intra-vehicle backbone networks. Moreover, we compare a TSN+AVB Ethernet backbone architecture with an alternative cut-through switching optical backbone network architecture by simulation and show that the cut-through switching optical architecture may achieve lower latency.

In future beyond 5G networks, the demand on the network would be more diversified and personalized. The variety of underlying physical network would also be accelerated by utilization small and heterogeneous cells and the co-existence of cellular networks and other types of wireless networks. User mobility and unexpected application service utilization would highly fluctuate the network demand spatio-temporally. However, per-service and static network slicing considered in 5G networks cannot accommodate such requirements. In this paper, we propose a novel network architecture, User-Oriented Network slicing Architecture (UONA). The proposed architecture has the following two major characteristics. One is that we maintain network slices on a per-user basis, not on a per-service basis. Such high-resolution of network slices would satisfy various and personal requirements from users. The other is decoupling the process of generating network slices into two subprocess, providing subslices by subslice providers and constructing end-to-end user network slices from the subslices by network slice brokers.We introduce the overall design of UONA and explain its advantages, as well as the research challenges to realize it. We also demonstrate the effectiveness of per-user configuration of network subslice by presenting the preliminary numerical evaluation results.

Network virtualization is expected to handle various forms of network traffic induced by Internet of Things applications and other Internet-based services. Because traffic patterns change with time, virtual networks should be dynamically reconstructed to accommodate increasing traffic and to free unused resources. However, collecting all traffic information is difficult when applications are deployed on a wide-area network. It is therefore necessary to consider uncertainty of information due to data incompleteness or traffic dynamics. Our research group has proposed a virtual network reconstruction method based on a Bayesian attractor model that deals with uncertain information in decision-making. However, this method requires advance knowledge of the assumed environment as an attractor. When the environment changes, attractors must also be changed. In this study, we use control feedback to automatically update attractors when the environment changes. Simulation-based evaluations demonstrate that the proposed method deals with unknown situations while maintaining noise tolerance.

A key challenge for developing networked Cyber-Physical System is how to integrate and process the virtual and real-world information from locally or remotely connected humans/robotics with a tolerable application latency. In this paper, we investigate the improvement of application latency by introducing edge computing environments and the resulting service quality through some experiments. A network-oriented mixed-reality (MR) application that operates a remote robot through users' gestures and displays location-aware information through edge server is implemented as a simplified implementation of cyber-physical networking applications. The results of our experiments reveal that service quality suddenly gets worse when application latency becomes around 1 [sec]. In other words, the service quality of the network-oriented MR application is expected to be improved by introducing edge computing when latency of application running on cloud computing environments is about 1 [sec].

Recently, network function virtualization has been focused on achieving a flexible deployment of networking services. Despite the fact that the heart of network function virtualization is its software implementation, there are fewer studies on how network functions are implemented as software. In this study, we investigate the evolution of functional connectivity in network functions using an implementation of Internet protocol suite in the Linux kernel. We constructed a call graph for the Linux kernel and analyzed the change of connectivity between the protocol components based on the directory structure of Linux kernel. Our results on the connectivity analysis show that new sub-directories appears for new emerging technologies, such as “bluetooth” and “sctp”, and they rely mostly on the function of sub-directories “core” and “ipv4”. Since the number of functions in sub-directories “core” and “ipv4” is increasing, we defined a functional core for the call graph to see whether there is a core part which has lower variability during the kernel development or not. The result shows that the functional core consists of mostly 50-70 functions and has lower variability comparing with the increase of a number of network-related functions during the kernel development.

A real-world sensing application, which senses and analyzes the situation in the real world via sensor devices, has attracted increasing attention for providing new services to mobile users. In this paper, for targeting the application, we propose a highly adaptive resource control method that can control the amount of local computing resources, which is provided by the Mobile Edge Computing technology and the amount of network resources necessary for service provisioning. A basic idea is to express various information, such as the amount of sensor information and the amount of user access, into a simple potential field, and then update the potential field in a short cycle in a self-organized manner. Numerical results show that our resource control method based on the potential field is adaptive for the movement of users.

Our research group has proposed a VNT control method based on attractor selection. Since the number of attractors composing an attractor set is limited, it is important to decide what kind of attractors should be prepared. The existing method prepares attractors such that their topological characteristics are different from each other. However, since the existing method does not incorporate current traffic information for designing attractors, it is likely that VNT control needs additional time to find a good VNT. In this paper, we examine several strategies for updating attractors and evaluate the effectiveness of the strategies in terms of the number of steps to find a solution. Our basic approach is to check each attractor to see whether it is adaptive under the current traffic demand or not through offline simulations. Evaluation results show that removal of inappropriate attractors is most effective; the strategy reduces the number of steps to find a solution by 60 %.

We propose an architecture that increases the resiliency against DDoS attacks by leveraging virtual network functions (VNF) and software defined networking (SDN). In the first step, the proposed architecture places the virtual network functions (VNF) optimally by solving a linear program. In the second step, in order to add preemptive protection against DDoS attacks, special filter VNFs and secondary paths passing through these filter VNFs are set up by solving another linear program. Under a DDoS attack, SDN controller switches the routes affected by the attack to the secondary paths for filtering DDoS traffic in order to prevent over-utilization. The simulation results show that the proposed architecture can absorb higher amount of DDoS traffic with low impact on the average hop count.

When wavelength division multiplexing with path (circuit) switching is used on an optical network, even the failure of a single link may tear down many lightpaths and cause abrupt and significant changes in the utilization of many links in the corresponding VNT (virtual network topology). In this paper, we propose an algorithm called MFLDA (Minimum Flow Logical topology Design Algorithm) for designing VNT candidates, which can accommodate a wide range of traffic patterns. Moreover, we show that the variant called MFLDA-FO (MFLDA with Failure Optimization) can design VNT candidates, which have lower probability of congestion right after the failure of multiple nodes compared to HLDA, which is one of the best performing VNT design algorithms in the literature. Furthermore, we show that when these VNT candidates are used as attractors in an attractor selection algorithm, the average time to recover from difficult failure scenarios is less than using the attractors designed by HLDA algorithm. Unlike HLDA, our VNT design algorithm and the attractor selection algorithm does not require the traffic matrix and the topology information after the failure.

SDI (Software Defined Infrastructure) provides virtualized infrastructures to customers by slicing computing resources and network resources. One of the important problems for deploying SDI framework is to control the assignment of physical resources to a virtual network against changes of traffic demand and service demand. For this problem, VNE (Virtual Network Embedding) problem that maps a virtual network to physical resources has been addressed, but a centralized calculation was assumed. It is difficult to adopt the centralized approaches as a size of infrastructure increases and a number of VN requests increases. This is because the identification of current situation becomes more complicated. In this paper, we present a VNE method that works with a little information for the large, complicated, and uncertain SDI frameworks. To achieve this, the proposed method applies biological “Yuragi” principle. The term Yuragi is a Japanese word whose English translation means a small perturbation to the system. Yuragi is a mechanism of adaptability of organisms and is often expressed as attractor selection model. This paper develops Yuragi-based VNE method that deals with node attribute and has a generality of a performance objective and runs in multi-slice environments. Simulation results show that the Yuragi-based method decreases VN migrations by about 29% than a heuristic method to adapt the fluctuations in resource requirements.

Network tomography is a promising technique to achieve fault management in networks where the existing IP-based troubleshooting mechanism cannot be used. Aiming to apply Boolean network tomography to fault management, various heuristic methods for configuring monitoring trails have been proposed to localize link failures in all-optical mesh networks. However, these existing heuristic methods must be executed in a centralized server that administers the entire managed network, and present scalability problems when they are applied to large-scale managed networks. Thus, this paper proposes a novel scheme for achieving lightweight Boolean network tomography in a decentralized manner. The proposed scheme partitions the managed network into multiple management areas and localizes link failures independently within each area. This paper also proposes a heuristic network partition method with the aim of implementing the proposed scheme efficiently. The effectiveness of the proposed scheme is verified using a typical fault management scenario, where all the single-link failures are localized by the monitoring paths the routes of which are predetermined. Simulation results show that the proposed scheme can greatly reduce the computational load on the fault management server when Boolean network tomography is deployed in large-scale managed networks.

Our research group has proposed a VNT control method that is adaptive to traffic changes. The method is based on a dynamical system, called attractor selection, which models behavior where living organisms adapt to unknown changes in their surrounding environments and recover their conditions. One of important things of our VNT control method is how to determine attractors, i.e., VNT candidates, because a VNT configured by our VNT method finally converges on one of the VNT candidates. However, since the number of VNT candidates is limited, it is crucial that the limited number of attractors have diversity so that various kinds of VNTs are searched by attractor selection. In this paper, we propose a method to decide the VNT candidates. Our approach prepares the VNT candidates whose bottleneck links (lightpaths) are different to each other. However, this approach has a problem that it takes a heavy computational time for large-scaled networks. We therefore propose a method that divides a network into clusters for which our algorithm can be applied. Evaluation results show that the VNT candidates prepared by our method can suppress maximum link utilization than the ones prepared in a random manner or by an existing heuristic algorithm.

Our previous VNT control method is adaptive to traffic changes. However, performance of VNT is fundamentally decided by the physical infrastructure, and a physical network should be designed so that the adaptiveness of VNT control method can be enjoyed. In this paper, we propose a design method of WDM networks based on a biological evolution model to have adaptability under various traffic fluctuation and traffic growth. Our method determines a set of nodes where transceivers should be added so that the designed network can obtain a plasticity, which represents changeability against the environmental change. Evaluation results for a topology with 19 nodes show that our method accommodates more patterns of traffic fluctuation comparing with an ad-hoc design method.

This paper proposes a novel approach to separating background (BG) and foreground (FG) traffic based on periodicity analysis. As BG traffic is commonly periodically generated by applications, this trait is leveraged to effectively detect BG traffic. Concretely, the Period Candidate Array (PCA) approach is proposed to extract only necessary information from long and sparse traffic flows, hence quickly detects the flows' periodicity with low computational cost. The PCA works directly with "on-site'' traffic without depending on historical data as in machine learning methods. As a result, the proposed approach can be immediately applied to the real world traffic management systems. In addition, the PCA properly works with latency-included traffic affected by network delays. Experimental results reveal the effectiveness and efficiency of the PCA compared to the conventional methods in terms of computational cost, memory usage, and independence to historical data.

Two network-wide optimization contexts are traffic engineering and topology optimization. Various optimization objective functions and metrics have been proposed for both contexts. Yet, it is hard to evaluate the efficiency of those optimization objectives. Previously, a study analyzed the efficiency of some optimization metrics for traffic engineering by using linear programming (LP). On the other hand, in the topology optimization domain, there has not been any work on evaluation of different metrics. Because, it is hard to evaluate these metrics as the optimization algorithms are objective function tailored heuristics generally. As a result, a fair comparison of different objectives becomes hard. In this work, using machine learning we compare and analyze different traffic optimization objectives for topology optimization.

An optical packet and circuit integrated network (OPCInet) will allow diverse services, enhanced functional flexibility, and efficient energy consumption, as optical packet switching (OPS) and optical circuit switching (OCS) links are provided on the same infrastructure. In this paper, we design and develop a control system consisting of header processing for optical packets, signaling and routing for optical path setup, and wavelength resource control and link switching between OPS and OCS for the efficient use of resources. Our onboard optical packet header processor is capable of 16-bit longest prefix matching by embedding a 0.6W (5% of TCAM technology on the same condition), 200 million search-per-second (equivalent to 100 Gbps) forwarding engine LSI, and a statistical memory LSI for retaining the statistical information of processed headers. Reactive control automatically selects a packet or circuit link according to the statistical traffic information from the header processor and works with signaling for optical paths. We present an experimental demonstration of a new control system using a previously developed OPCInet testbed.

This work extends a previously proposed algorithm for virtual topology reconfiguration in all optical networks. Earlier, an algorithm using auto-associative neural memories has been presented. The algorithm stores topologies by assigning equal weights. In this work, we analyzed the effect of weighing topologies differently based on maximum flow, average weighted hop and the age of topology. Although we focus on optical networks, the algorithm and the analysis we present here can be useful in other network domains, such as wireless networks.

Previously, a highly adaptive virtual network topology (VNT) reconfiguration method called Attractor Selection Based (ASB) topology control was presented. ASB has an important drawback: it can only work with binary path tables. We propose a novel VNT controller by adding multistate path capabilities into ASB. However, adding multistate path capabilities reduces topology exploration space. We solve this problem by changing the system dynamics of ASB. With the modification of the system dynamics and the extension from binary to multistate paths, we observed a 60% performance improvement over ASB, and a 21% reduction in processing time in the simulations.

Recent papers in the literature on hybrid optical architectures combining path and packet switching have shown that it can be a good candidate for future optical networks. However, the optimization of the traffic splitting parameters by some metrics is vital to maximize the benefit by the hybrid architecture. Blocking rate is one of the most important performance metrics in a path switching network. In this paper, we propose an analytical method to compute both forward and backward blocking rates in path switching optical WDM networks with destination-initiated reservation. On a mesh topology we show that the results of our analytical method and simulations are close to each other.

Future network should flexibly deal with unexpected network changes caused by the diversification of the network services. In this paper, we propose a managed self-organizing network concept and a dynamic resource allocation mechanism, called as DRAMS. In the managed self-organizing network, multiple virtual networks are accommodated on a single physical network and controlled autonomically based on self-organization. Due to the sharing of limited physical resources, resource contention among virtual networks will be inevitably caused. To cope with this issue, we introduce a dynamic resource allocation to regulate the resource usage of ill-behaved virtual networks. Our research goal is to establish a management mechanism for controllable self-organizing networks. The proposed mechanism allows self-organizing control by virtual networks and just indirectly controls the behavior of virtual network in order to avoid resource contention and improve resource efficiency.

Traffic Engineering (TE) is one efficient approach to handle traffic changes. To perform TE, a server called the Path Computation Element (PCE) collects the traffic information from all nodes within the network. Then, the PCE calculates the routes suitable to the current traffic. However, in a large-scale network, it is difficult for one PCE to collect all traffic information in a short period of time. Thus, it takes time to change the routes according to traffic changes. In this paper, we propose a method that changes the routes suitable to the current traffic soon after the traffic changes. In our method, we hierarchically divide the network into multiple ranges; the ranges of the lowest layer are constructed of a small number of nodes and the ranges of the upper layer are constructed from the multiple ranges of the lower layer. We deploy a PCE for each range. The PCEs in the lowest layer change the routes within a small range in a short interval according to the traffic information within the range to handle the traffic changes that occur in a short period of time. Against the traffic change that cannot be handled in the lower layer, the PCEs in the upper layer change the routes within the large ranges of the upper layer according to the aggregated traffic information collected from the PCEs of the lower layer. We also propose a method to aggregate traffic information and a method to calculate the new routes by using the aggregated traffic information considering the upper bounds of link utilizations. In this method, we aggregate traffic information so that we can calculate the upper bounds of the link utilizations after the route change only from the aggregated traffic information. Then, the PCE obtaining the aggregated traffic information calculates the new routes without causing any new congestion by checking the upper bounds of link utilizations calculated from the aggregated traffic information. In this paper, we evaluate our method by simulation and clarify that our method can mitigate the congestion soon after the traffic changes.

Modularity structure is often found in many different types of complex networks. Especially, this topological property is believed to provide a certain degree of robustness to networks. This belief leads us naturally to investigate the impact of modularity structure on the behavior of networks. In this paper, we carry out two simulation studies to monitor traffic dynamic in modularized and non-modularized power law networks. In the first simulation, coarse-grained traffic is flooded through networks. This study demonstrates that a modularized structure localizes damages and stops the malicious effect to the whole system. In the second simulation, more finegrained traffic is used to investigate how a modularity structure impacts on the saturation of networks. This result shows that a strong modularity structure is saturated much faster than nonmodularized topology as traffic load increases, and additionally we show how this early saturation in modularized structure can be overcome.

Obtaining current traffic matrices is essential to traffic engineering (TE) methods. Because it is difficult to monitor traffic matrices, several methods for estimating them from link loads have been proposed. The models used in these methods, however, are incorrect for some real networks. Thus, methods improving the accuracy of estimation by changing routes also have been proposed. However, existing methods for estimating the traffic matrix by changing routes, however, can only capture long-term variations and cannot obtain current traffic matrices accurately. In this paper, we propose a method for estimating current traffic matrices by using route changes introduced by a TE method. In this method, we first estimate the long-term variations of traffic by using the link loads monitored the last M times. Then, we adjust the estimated long-term variations so as to fit the current link loads. In addition, when the traffic variation trends change and the estimated long-term variations cannot match the current traffic, our method detects mismatches. Then, so as to capture the current traffic variations, the method re-estimates the long-term variations after removing information about the end-to-end traffic causing the mismatches. For this paper, we evaluated our method through simulation. The results show that our method can estimate current traffic matrices accurately even when some end-to-end traffic changes suddenly.

We show that by applying rate-based pacing at the edge nodes, very small optical RAM buffers can be enough for high utilization and low packet drop ratio inside core optical packet-switched (OPS) networks.

Overlay networks achieve new functionality and enhance network performance by allowing routing to be controlled at the application layer. However, these approaches result in degradations of underlying networks due to the selfish behavior of overlay networks. In this paper, we investigate the stability of virtual network topology (VNT) control under the overlay networks that perform dynamic routing updates. We reveal that the dynamics of routing on overlay networks causes a high fluctuation in the traffic demand matrix, which leads to significant instability of VNT control. To overcome the instability induced by the overlay routing, we introduce hysteresis to the VNT control. Simulation results indicate that the hysteresis mechanism improves the network stability, but cannot always improve the network performance. We therefore extend the hysteresis mechanism and show that the proposed method improves both the network stability and the performance when the amount of traffic for overlay network is not large.

In this paper, we present a practical VNT (virtual network topology) reconfiguration method for large-scale IP and optical networks with traffic matrix estimation considerations. We newly introduce a partial VNT reconfiguration algorithm with multiple transition stages. By dividing the whole VNT transition sequence into multiple transitions, estimation errors are calibrated at each stage by using network state information of prior stages. Because estimation errors are mainly due to the fewer information in the estimated traffic matrix calculation, our approach tries to increase the constraint conditions for traffic matrix estimation by introducing partial reconfiguration, and to relax the impact of estimation errors by limiting the number of optical-paths reconfigured at each stage. We also investigate the effectiveness of our proposal through simulations and clarify the robustness against estimation errors by using partial reconfiguration.

One of the difficulties of optical packet switched networks is buffering optical packets in the network. Currently, one solution that can be used for buffering in the optical domain is using long fiber lines called Fiber Delay Lines (FDL). However, FDLs provide only a small and fixed amount of delay. Thus, burstiness of Internet traffic and over-utilizations cause high packet drop rates in small and fixed delayed OPS networks. Recently, we proposed a new network architecture using a XCP-based congestion control algorithm for OPS WDM networks with pacing at edge nodes for minimizing the buffer requirements at core nodes. In this paper, we investigate input and output optical switch architectures for minimizing the size of optical switching fabric with the proposed network architecture. We show the number of FDLs and switch size requirements of architectures depending on FDL granularity and packet size distribution.

In this paper, we analyze the behavior of GMPLSRSVP-TE and its variants with a Markov model and analyze the performance of RSVP-TE. From the results, we demonstrate that resource utilization of RSVP-TE can be equivalent to that of a hard-state protocol when the loss probability of signaling messages is low. We also investigate the effectiveness of message retransmission and show that using message retransmission leads to poor resource utilization in some cases.

Famous rule-of-thumb states that a buffer sized at B = RTT × BW, where RTT is the average round trip time and BW is the bandwidth of output link is necessary in order to achieve high utilization with TCP flows. However, as the link speeds continue increasing with technological advances, this buffer requirement starts becoming an important cost factor on routers of electronic networks. On the other hand, bursty nature of TCP limits further decreasing the buffer requirements, because it brings a high packet drop rate in small buffered networks. In this paper, we evaluate several transmission control algorithms in small buffered networks. The algorithms include TCP Reno, TCP NewReno, Highspeed TCP with SACK, and XCP. Simulation results show that all non-paced TCP and XCP variants perform poorly. Furthermore, the results show that even rule-of-thumb buffers are not enough for XCP in some cases because of high burstiness of XCP. We evaluate the effectiveness of pacing method at sender side for making transmission control algorithms suitable for very small buffered networks. We show that pacing alone is not enough for XCP to make suitable for small buffered networks, so we introduce a suitable parameter set. Simulation results show that buffer requirements on routers are greatly reduced by paced XCP with suitable parameter settings, while keeping the high fairness, fast convergence, and high utilization.

In a distributed wavelength-routed network, a lightpath request is blocked when its assigned wavelength is already occupied by another lightpath request. Conventional studies assume that a wavelength for the lightpath is selected randomly in the distributed lightpath setup method. However, this random selection method causes unnecessary blocks of lightpath requests, even when the arrival rate of requests is low. In this paper, we develop a novel method for assigning wavelengths, based on the first-fit algorithm. In our proposed method, the intermediate nodes forecast the wavelength that will be selected at the destination node, so that the subsequent lightpath requests avoid the forecasted wavelengths. The forecasted wavelength is thus kept available until the corresponding request reserves it, which prevents wavelength conflicts with other lightpath requests. Computer-simulated performance comparison showed that our method reduces the blocking probability by more than one order of magnitude compared to random selection.

Wavelength conversion is effective in reducing the connection blocking probability and increasing the link utilization in wavelength-routed WDM networks. However, wavelength converters are expensive in the foreseeable future, which means only a limited number of converters can be deployed in a network. In such case, placement algorithms are used to determine the locations of a given number of converters such that the connection blocking probability is minimized. This paper developed a low-complexity analytical model to reflect the impact of the converter locations on the network blocking probability. Based on this model, an algorithm is developed for converter placement. Since an analytical approach is taken, the algorithm has the advantage of high efficiency, allocation of 500 converters in two exiting networks with 14 and 19 nodes takes no more than 1 second using a personal computer. Simulations show that the proposed approach outperforms the best existing algorithm in terms of blocking probability.

With the dramatic increase of bandwidth from WDM technology, packet switching has caused a bottleneck for multi-hop networks, where electronic switches cannot scale up to high capacity while optical packet switches are still immature due to lack of optical memory. This paper proposes virtual fully connected (VFC) architecture for WDM networks to provide high performance edge-to-edge all optical transportation capability. With moderate wavelength resources, VFC architecture emulates a fully connected network by transporting traffic between edge nodes without intermediate buffering, thus a single-hop network is realized and high speed packet switches are avoided, which facilitates performance guarantee and reduces network cost. A scheduling algorithm is developed for contention resolution and bandwidth allocation, which is proved to provide 100% throughput as well as bandwidth guarantee under arbitrary traffic. Simulations show that VFC network achieves good delay performance under both uniform and non-uniform load.

With the increase of capacity and functionality in WDM networks, the control planes have become more and more important in state monitoring, resource management, and connection maintenance. The capability of fast failure restoration in a control plane not only enables effective network management but also helps efficient recovery of the transport plane. This work presents a signaling network that is able to achieve fast restoration under multiple failures. The mechanism employs distributed path restoration and introduces path probe using failure notification packets, which brings the benefits of low computation as well as short restoration time. Parallel processing architecture is developed and the complexity is analyzed. It is shown that the computation time can be easily controlled below 1 ms for a network with 256 nodes, which means the total restoration time for a wide area network is dominated by the propagation delay. Simulations show that the restored network provides good delay performance for the signaling traffic.

Previous studies on routing and wavelength assignment algorithms assumed that the global link state information is obtained without delays. However, in distributed lightpath establishment, the probability of request blocking strongly depends on both the accuracy of the global link state information and the distributed protocol for wavelength reservation. In this paper, we evaluate how the frequency of link state information exchange affects the blocking probability in lightpath establishment. The evaluation is performed based on forward and backward reservation protocols in three network topologies. Simulation results show that while the forward reservation protocol is greatly affected by the frequency of link state information exchange and the amount of this information, the backward reservation protocol does not need as detailed information about the link state and as frequent link state exchange for routing as does the forward protocol.