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.

The ever-increasing diversity of devices and the complexity of communication content pose significant challenges for network traffic analysis. These limitations can hinder anomaly detection and network control, both essential for guaranteeing stable communication in next-generation networks. This paper proposes an approach leveraging Latent Dirichlet Allocation (LDA) topic modeling for network traffic analysis. LDA, a powerful machine learning technique, excels at identifying hidden thematic structures within data. When applied to network traffic, LDA can differentiate traffic types, such as web browsing, email, or file sharing. To optimize the effectiveness of traffic demand capture, we conducted experiments to determine the appropriate window size for individual connections and the ideal number of topics needed for accurate traffic analysis. Our proposed topic estimation algorithm, utilizing the bag-of-words representation, demonstrates its efficacy in identifying multiple topics within real-world network traffic datasets. This proposed approach has the potential to significantly improve network performance, security, and management.

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.

This study focuses on cooperative beamforming among base stations in wireless communication technology and proposes a new approach based on the Free Energy Principle (FEP). Traditionally, the trade-off between the accuracy of channel information acquisition and overhead has posed a challenge in beamforming. FEP addresses this trade-off by balancing the value from observation and the value from action to select the optimal behavior. This enables adaptive responses to dynamic environmental fluctuations through integrated search, reasoning, and learning. Additionally, by introducing a hierarchical coordination structure, information sharing among base stations and indirect state sharing among agents are achieved, enhancing the efficiency and stability of beamforming. Furthermore, this study utilizes Integrated Sensing and Communication (ISAC) to perform simultaneous communication and real-world sensing. By integrating feedback from terminals and terminal location information for channel state estimation, the overhead is reduced. Simulation results demonstrate that the proposed method effectively improves the Signal to Interference and Noise Ratio (SINR) and energy efficiency.

Deep learning (DL) models have rapidly evolved, and their scales have become larger. Pipeline parallelism is used to execute a large-scale DL model. In pipeline parallelism, DL models are partitioned and allocated graphics processing units (GPUs) to execute each partition. However, to execute numerous DL services in clusters with heterogeneous GPUs, a DL model partitioning that considers a specific type and number of GPUs available and the GPUs allocated for the service is required. We propose resource aware model partitioning and allocation (RAMPA) to execute more DL services while satisfying the performance requirements. RAMPA minimizes the allocation of important resources for future DL service execution to avoid inhibiting future DL service execution. We define the resource allocation cost based on resource importance. Furthermore, we formulate the impact of model partitioning and allocated resources on service performance. We define an optimization problem to minimize resource allocation costs while satisfying service performance requirements. We evaluated the effectiveness of RAMPA by simulating the execution of DL services in clusters with heterogeneous GPUs. The results demonstrate that more services can be executed while satisfying performance requirements compared to the conventional method. RAMPA enabled efficient GPU utilization to deliver many DL services.

To create a future society based on cyber-physical systems, we need real-time digital twins made with cameras and sensors. The challenge is making this energy-efficient. A model in [1] suggests dividing video analysis tasks among terminals, edge servers, and cloud servers to minimize power consumption. This paper addresses energy optimization in dynamic environments with a two-level approach: detecting and categorizing environmental changes (LEC and SEC) and using a two-level adaptive evolutionary algorithm (TAEA) to make corresponding adjustments. A case study with a differential evolution algorithm demonstrates its effectiveness in minimizing energy costs and improving processing accuracy and latency violations.

Network slicing technology is required to dynamically provide virtual networks in response to user requirements with a wide variety of services operating on the network. Generally, optimal allocation of virtual networks to resources on the real network is a combinatorial optimization problem, and it is difficult to find an exact solution in realistic time in the current large-scale and complex networks. In addition, user requirements change dynamically, and therefore, optimization methods that can cope with such temporal variations in the situation are required. In this paper, we propose a method to solve a virtual network embedding problem using quality-diversity (QD) algorithms, especially the MAP-Elites algorithm, and evaluate its effectiveness through computer simulations.

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.

Beamforming technology using massive MIMO in the millimeter wave (mmWave) band is attracting attention as a fundamental technology for next-generation wireless communication systems. Beamforming increases the signal-to-noise ratio (SNR) of signals received by terminals and enables high-speed communications. In beamforming, it is necessary to search for a beam with appropriate directivity from a predefined codebook and irradiate the beam toward the terminal. Although hierarchical codebooks can be used to reduce the search overhead, conventional beam training methods in hierarchical codebooks are not suitable under conditions where channel conditions change over time. This is because each time the beam is re-searched, a non-optimal beam is applied, and the SNR is repeatedly degraded temporarily and significantly. To solve this problem, this paper proposes a method to predict the optimal beam using active inference. This method avoids the problem of temporarily degrading SNR by predicting the optimal beam without searching for it. As a result, the method using active inference can increase the average SNR compared to the conventional beam training method in hierarchical codebooks.

Vehicular mobility and connectivity vary significantly over space and time when vehicular crowd sensing covers a city-wide area for a long time period, but it is important to achieve sufficiently uniform data coverage to satisfy the requirements of an environmental monitoring scenario. Our goal is thus to ensure uniform spatial-temporal coverage of sensed data over a city-wide area despite such vehicle dynamics. For a large area, trajectory-based approaches must deal with a great number and variety of participant mobility patterns. Hence, we propose a probabilistic control mechanism that adaptively adjusts the incentive to each participant, without using any prior information about participants. We provide a mathematical analysis that ensures stability of the number of participants with assigned tasks (called workers), and we evaluate the mechanism's robustness by using 24-hr vehicle trace data from a city-wide area. Our results demonstrate that, when the number of participants is up to 1500 times higher than the required number of workers, sensing actions result in a distribution with a mean of about 1 and an interquartile range of around 4 for a required sensing interval; moreover, the mean increases by 2% when 30% of communication messages are randomly lost.

In a disaggregated micro data center$(\mu \mathbf{DDC})$, network resources have a large impact on the performance of applications. Thus, we propose a resource allocation method for$\mu \mathbf{DDC}$that allows it to efficiently utilize network resources. In this method, we model the impact of the allocated resources on the performance of the application. Then, we allow multiple applications to share links if all of the processes are expected to be completed within the acceptable time. To accommodate more applications, if multiple candidate resources exist, this method avoids allocating those that will be requested by applications in the future based on their importance. In this paper, we evaluate our method by simulating the allocation of continually generated resource requests and demonstrate that it can accommodate more applications without affecting the required performance.

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 5G, flexible resource management, mainly by base stations, will enable support for a variety of use cases. However, in a situation where a large number of devices exist, such as in mMTC, devices need to allocate resources appropriately in an autonomous decentralized manner. In this paper, autonomous decentralized timeslot allocation is achieved by using a decision model for each device. As a decision model, we propose an extension of the Bayesian Attractor Model (BAM) using Bayesian estimation. The proposed model incorporates a feature of human decision-making called magnitude sensitivity, where the time to decision varies with the sum of the values of all alternatives. This allows the natural introduction of the behavior of making a decision quickly when a time slot is available and waiting otherwise. Simulation-based evaluations show that the proposed method can avoid time slot conflicts during congestion more effectively than conventional Q-learning based time slot selection.

For the future cyber-physical system (CPS) society, it is necessary to construct digital twins (DTs) of a real world in real time using a lot of cameras and sensors. Hence, the energy efficiency of both networks and computers for largescale distributed video analysis is a major challenge for the full-scale spread of CPSs and DTs. Toward this goal, we first propose a model to arbitrarily split and distribute the video analysis task to terminals, edge servers, and cloud servers and dynamically assign appropriate CNN models to them. System-wide optimization of such distributed processing can reduce overall system power consumption by reducing network bandwidth and efficiently utilizing distributed CPU/GPU resources. To realize this optimization in a real system, we also propose a model to estimate the GPU load, processing time, and power consumption of these devices based on massive experimental measurements. Since such a large-scale optimization is difficult because of the dynamic and multi-objective nature of the problem, we propose a new optimization algorithm composed of Genetic Algorithm and Bayesian Attractor Model. Finally, simulation evaluations are performed to demonstrate that the proposed method can minimize system power consumption and satisfy latency and recognition accuracy requirements of each video analysis, even under changing environmental conditions.

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.

To realize real-time mobile augmented reality applications, various objects in the real world need to be instantly identified, located, and represented as a digital twin through sensor devices and edge IoT systems. However, it is challenging to make a fast and accurate decision on what the object is from real-time noisy streaming information. Multimodal decision making has been expected to mitigate such incomplete information and improve the accuracy of simplified recognition algorithms tuned for edge devices. In this paper, we propose an object estimation method inspired from the multimodal information processing mechanism of the brain, which makes decisions based on multiple types of uncertain observed information. Through computer simulations, we show that our proposed method identifies an object accurately and quickly from uncertain observed information.

LoRaWAN has become a major research target in low-power and wide-area (LPWA) communication because of its ease of development and the possibility of building self-managed networks. In LoRaWAN, the data rate of a node can be dynamically controlled by the gateway through changing the spreading factor of nodes, and this control can be performed according to the network conditions. However, it is difficult to immediately grasp the individual states of a large number of nodes, calculate the optimal data rate, and assign the appropriate spreading factor to the nodes due to the low communication speed of LoRaWAN compared to conventional wireless networks. In this paper, we propose a spreading factor allocation method for LoRaWAN that simultaneously improves the data arrival rate and network lifetime by using the thermodynamical genetic algorithm. Through computer simulation, we show that our proposed method assigns an appropriate spreading factor to each node, which achieves a higher data arrival rate and prolongs the network lifetime.

Recently, digital twins have been paid much attention as a major application towards Beyond 5G/6G network, and real-time object recognition methods are key technology to digitize the real world as a digital twin. However, it is challenging to make a fast and accurate decision on what the object is from real-time streaming information such as video because accurate object recognition algorithms require a huge computation. To satisfy delay requirement of digital twin applications, such computations have to be moved from cloud to edges or even small terminal devices, where computing capacity is very limited. Thus, recognition mechanisms have to be simplified for small devices but they would result in degraded accuracy. In this paper, we focus on the multimodal information processing mechanism of the brain, which makes decisions based on multiple types of uncertain observed information, to improve accuracy of simplified recognition mechanisms. We first propose a unimodal object recognition mechanism based on the Bayesian attractor model, which continuously recognizes objects from noisy streaming media data. Then, we extend the mechanism with Bayesian causal inference to fuse the results of unimodal media recognition. Through computer simulations, we show that our proposed method identifies an object accurately and quickly from uncertain observed information.

In recent years, with the spread of video streaming services and remote web conferencing systems, the increase in the number of end hosts connected to the Internet, the improvement of end host performance, and the diversification and sophistication of applications, the demand for communication quality of service (QoS) from users and service providers has become even higher. However, the amount of traffic flowing through the network is increasing year by year, and it has become difficult to operate a system that guarantees a certain level of QoS for users. In other words, best-effort type network systems have become the mainstream. Not only QoS, but also the user's own quality of experience (QoE) has become important, and application level control to improve user QoE within limited communication resources has become very important for both users and service providers. Since QoE is a subjective measure of a user's perception of a service, it is considered to be affected by cognitive biases that are observed in human subjective decision-making. In this paper, we conduct an experiment focusing on the choice-supportive bias, which is one of the cognitive biases, and clarify the effect of this cognitive bias on users during video viewing.

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.

In this paper, we propose a method for detecting malware-infected hosts with a high rate of detection and a low rate of false positives without using any data on benign communication. Based on the fact that many malware-infected hosts generate multiple HTTP requests, we propose a method using the templates of sets of those HTTP requests. For each malware, this method generates a template that comprises the set of templates of the HTTP requests that the malware generates. We call the set of templates group template. It then detects malware-infected hosts by comparing the set of monitored HTTP requests with the group templates.

The “Internet of things” has become a common term, and low-power wide-area (LPWA) technology is attracting much attention as one of its elemental technologies. LPWA achieves wide-area communication without consuming much energy, allowing various data sensing and gathering applications. LoRa is an LPWA communication technology that uses unlicensed bands. Because it is possible to build a self-managed network with LoRa, many LoRa-based services will be scattered in the same area without an overall administrator. As a result, the communication performance of LoRa may degrade due to unintended radio interference. Unfortunately, many LPWA techniques, including LoRa, have low data rates, making it difficult to gather sufficient control information to avoid such degradation of communication performance. In this paper, we propose a method for estimating network congestion states through successive estimation using Bayesian updates of prior distributions. Computer simulations show the network state can be estimated by our proposed method with accumulating a little control information.

One of the major shortcomings of Centralized Radio Access Networks (C-RAN) is that the large capacity is required for fronthaul network between Remote Radio Heads (RRHs) and central office with baseband unit (BBU) pool. Possible solutions are to introduce lower-cost networking technology for fronthaul network, such as Time and Wavelength Division Multiplexing Passive Optical Network (TWDM-PON), and to introduce functional split, that moves some baseband processing functions to cell site to decrease the utilization of the fronthaul network. In this paper, we construct the mathematical model for selecting function split options of baseband processing to minimize the power consumption of TWDM-PON based fronthaul network. In detail, we formulate the optimization problem for minimizing the total power consumption of fronthaul network in terms of the capacity of TWDM-PON, the number of RRHs in each cell site, server resources, latency constraints, the amount of traffic from each RRH, physical/virtual server power consumption characteristics. Numerical examples are shown for confirming the correctness of the proposed model and for presenting the effect of resource enhancement methods on the capacity and energy efficiency of the system.

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].

Information-centric networking (ICN), a new network architecture for efficiently delivering content, has been widely investigated recently. In ICN, cache memory is implemented at each router, and content items are routed in the network by using content name as the locator determining the destination. The caching strategy that determines the content to be cached at each router strongly affects the cache hit ratio and flow hop length, and it is important to efficiently utilize limited cache resources by avoiding duplicated caching of the same content among routers located closely. In this paper, we propose a spatially dispersed caching (SDC), which is a caching strategy dispersing content by assigning a binary ID to each router and limiting the cache targets at each router to content with names whose hash value coincides with the router ID. Through computer simulations using backbone networks of actual ISPs in the United States, we show that the SDC improves the cache hit ratio by about 30%-170% compared with the case when caching content at all routers on the default path, and the SDC reduces the average hop length at cache hit by about 50%-90% compared with the existing caching strategies. Moreover, we show that the SDC improves the sustainable ratio of content acquisition in large-scale failures of routers by about 25%-200% compared with the existing caching strategies.

To provide video streaming of user-generated contents (UGCs) with high quality and at low cost by maximizing the effect of content delivery network (CDN), CDN providers are required to adequately design CDN cache servers by accurately estimating the UGC view-count distribution. To achieve this goal in a practical time frame, we need to construct a simple time-series model that captures the transition of UGC popularity. Therefore, in this paper, we first analyze the daily view count (DVC) of YouTube videos over nine months and find that the DVC of YouTube videos obeys a lognormal distribution. As a simple time-series model of the DVC of each YouTube video, we propose the grouped MPP (gMPP), extending the multiplicative process (MPP) which is widely known as a simple time-series model generating a lognormal distribution. We also propose reproducing the DVC distribution of YouTube videos by using a superposed gMPP (SgMPP) that aggregates multiple gMPPs. The SgMPP can accurately reproduce the DVC distribution of YouTube videos with a low computational overhead, so we can expect to use the SgMPP as the input for computer simulations for designing various network components that require the popularity distribution of UGC, e.g., cache capacities. Through numerical evaluation, we confirm that we can adequately design the storage capacity of a cache server with the average error rate of several percent against the target cache hit ratio.

In network functions virtualization (NFV), network functions are operated in software as virtual network functions (VNFs) instead of dedicated hardware. The most important issues that need to be addressed in NFV are where the VNFs should be placed in the network, as well as what amount of resources should be assigned to each VNF. Evolvable VNF placement (EvoVNFP) is a meta-algorithm that we previously proposed for controlling an underlying iterative VNF placement method. EvoVNFP realizes better adaptability to regular demand changes by mimicking biological evolution under time-varying environments leading to faster generation of placements. We provide detailed evaluation studies about the mechanism of EvoVNFP and show that iterative placement methods combined with EvoVNFP can generate placements that adapt better to varying goals because of triggers. Numerical results verify that EvoVNFP is able to reduce the required number of calculation steps by up to 48%.

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.

Information-centric Networking (ICN) is an approach to network architectures of the future. We consider physical movement control over ICN relay nodes installed on a drone. Following this, we propose and design Router-movable Information-centric Networking (RMICN) to implement communication between disjoint networks using Flying Routers (FRs), where communication tends to be hindered because of large distances.

Self-organizing systems are focused on to realize novel network control systems that are highly scalable, adaptable, and robust. However, the uncertainty of information makes it difficult for self-organizing systems to work appropriately, thereby degrading their performance. It is necessary to improve the performance of self-organizing systems while retaining their advantages. Therefore, we apply the concept of flexible leadership of the collective decision-making in human groups to self-organizing control mechanisms. By incorporating this concept, agents dynamically and flexibly change their role (leader or follower) according to the confidence of their own information, which increases decision accuracy under information uncertainty. We propose a channel-selection mechanism based on collective decision-making in accordance with information confidence. Simulation experiments show that the proposed mechanism improves the performance of network systems while retaining the high adaptability.

Information-centric networking (ICN), a new network architecture for efficiently delivering content, has been widely investigated recently. In ICN, cache memory is implemented at each router, and content items are routed in the network by using the content name as the locator determining the destination. The caching strategy that determines the content to be cached at each router strongly affects the cache hit ratio and flow hop length, and it is important to efficiently utilize limited cache resources by avoiding duplicated caching of the same content among routers located closely. However, no caching strategy aiming at dispersing content over networks has been investigated. In this paper, we propose spatially dispersed caching (SDC), which is a caching strategy dispersing content by assigning a binary ID to each router and limiting the cache targets at each router to content with names whose hash value coincides with the router ID. Through computer simulations using backbone networks of actual ISPs in the USA, we show that SDC reduced the average hop length by about 5% to 20% compared with the existing caching strategies.

Explosive dynamics in on-line social networks including flaming phenomena give serious impacts not only on stable operation of information networks but also social activities on the real world. The oscillation model in directed networks can describe generation the flaming phenomena in on-line social networks. In this paper, by introducing perturbation analysis, we describe the effect of directed link in the network to generation of the flaming phenomena, based om the oscillation model.

Self-organization has the potential for high scalability, adaptability, flexibility, and robustness, which are vital features for realizing future networks. The convergence of self-organizing control, however, is slow in some practical applications in comparison with control by conventional deterministic systems using global information. It is therefore important to facilitate the convergence of self-organizing controls. Incontrolled self-organization, which introduces an external controller into self-organizing systems, the network is controlled to guide systems to a desired state. Although existing controlled self-organization schemes could achieve the same state, it is difficult for an external controller to collect information about the network and to provide control inputs to the network, especially when the network size is large. This is because the computational cost for designing the external controller and for calculating the control inputs increases rapidly as the number of nodes in the network becomes large. Therefore, we partition a network into several sub-networks and introduce two types of controllers, a central controller and several sub-controllers that control the network in a hierarchical manner. In this study, we propose a hierarchical optimal feedback mechanism for self-organizing systems and apply this mechanism to potential-based self-organizing routing. Simulation results show that the proposed mechanism improves the convergence speed of potential-field construction (i.e., route construction) up to 10.6-fold with low computational and communication costs.

Because of the rapid growth in the scale and complexity of information networks, self-organizing systems are increasingly being used to realize novel network control systems that are highly scalable, adaptable, and robust. However, the uncertainty of information (with regard to incompleteness, vagueness, and dynamics) in self-organizing systems makes it difficult for them to work appropriately in accordance with the network state. In this study, we apply a model of the collective decision-making of animal groups to enable self-organizing control mechanisms to adapt to information uncertainty. Specifically, we apply a mathematical model of collective decision-making that is known as the effective leadership model (ELM). In the ELM, informed individuals (those who are experienced or well-informed) take the role of leading the others. In contrast, uninformed individuals (those who perceive only local information) follow neighboring individuals. As a result of the collective behavior of informed/uninformed individuals, the animal group achieves consensus. We consider a self-organizing control mechanism using potential-based routing with an optimal control, and propose a mechanism for determining a data-packet forwarding scheme based on the ELM. Through evaluation by simulation, we show that, in a situation in which the perceived information is incomplete and dynamic, nodes can forward data packets in accordance with the network state by applying the ELM.

In response to the growing demand for cellular networks, it is essential to improve the capacity of mobile core networks. Especially, in terms of accommodating machine-to-machine/Internet-of-Things (M2M/IoT) terminals into cellular networks, the load on the control and the user planes of the mobile core network increases massively. To deal with this problem, it is possible to apply virtualization technologies, such as software-defined network and network function virtualization. However, few existing studies evaluate such solutions for mobile core networks numerically and in detail. In this paper, we first evaluate mobile core network architectures with virtualization technologies and control/user (C/U) plane separation using the mathematical analysis. We also propose a novel bearer aggregation method to reduce the control plane load to accommodate massive M2M/IoT terminals. The result of numerical evaluation shows that the capacity of the mobile core network can be increased by up to 32.8% with node virtualization and C/U plane separation, and further by 201.4% by using bearer aggregation. Moreover, to maintain the performance of the mobile core network, we should carefully determine where the bearer aggregation is applied and when the shared bearer for each terminal is determined based on application characteristics and the number of accommodated M2M/IoT terminals.

A flash crowd is a situation in which a large number of users access a Web service simultaneously. Conventional methods for managing flash crowds include dropping of excessive requests, preparing of redundant servers, and dynamic provisioning of server resources. It is, however, difficult to use these methods to satisfy all the related requirements such as prevention of system down, avoidance of user abandonment, and efficient use of resources. With a focus on the psychological factor of users, we propose a method for flash crowd mitigation based on the presentation of feedback information. The proposed method was confirmed by experiment to be effective for influencing user behaviors such as abandonment and retransmission of requests. Consequently, we developed a user behavior model and used it to conduct website stress tests using a simulated website. The results further revealed that feedback information could be used to reduce the server load and user abandonment ratio. This paper also suggests the type of Web services for which feedback information-based flash crowd mitigation is most suitable.

This paper proposes an oscillation model for describing the flaming phenomena in on-line social networks, and discusses countermeasures to flaming based on the proposed oscillation model. The most significant feature of the proposed model is that the cause of flaming can be explained by the structure of the network. Based on the proposed model, we can suppress the generation of flaming by controlling link weights of a part of the network. This passive solution to flaming is important for the stable operation of social media networks.

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 %.

Attackers have increased the number of infected hosts by redirecting users of compromised popular websites toward websites that exploit vulnerabilities of a browser and its plugins. To prevent damage, detecting infected hosts based on proxy logs, which are generally recorded on enterprise networks, is gaining attention rather than blacklist-based filtering because creating blacklists has become difficult due to the short lifetime of malicious domains and concealment of exploit code. Since information extracted from one URL is limited, we focus on a sequence of URLs that includes artifacts of malicious redirections. We propose a system for detecting malicious URL sequences from proxy logs with a low false positive rate. To elucidate an effective approach of malicious URL sequence detection, we compared three approaches: individual-based approach, convolutional neural network (CNN), and our newly developed event de-noising CNN (EDCNN). Our EDCNN is a new CNN to reduce the negative effect of benign URLs redirected from compromised websites included in malicious URL sequences. Our evaluation shows that the EDCNN lowers the operation cost of malware infection by reducing 47% of false alerts compared with a CNN when users access compromised websites but do not obtain exploit code due to browser fingerprinting.

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.

The Internet is rapidly developing toward the next generation of the Internet of Things (IoT), which accelerates the emergence of interconnected network architectures even further. However, the way to design interconnected networks that can meet various changes in environment and service demands remains an important issue that has not been appropriately addressed yet. These interconnected networks should be robust to suppress or prevent diffusion of malicious information, whereas they should also be efficient to enhance diffusion of urgent information throughout the entire network. In this study, we propose a Network of Networks (NoN) model inspired by the modularly interconnected networks in the brain, and show that the model has two prominent characteristics. First, unintentional information diffusion from one subnetwork to another can be prevented. Second, speed of information diffusion can be controlled even when the diffusion starts from interconnecting links across two subnetworks. For the second point, we further investigate a strategy for influencing the information diffusion speed by appropriately configuring the connectivity within and between subnetworks. Simulation experiments confirm that by adapting the brain-inspired activation rules on the interconnecting links, our model is able to switch from the unhindered and fast information diffusion between subnetworks to the slow case where all interconnecting links are inoperative.

Due to the rapid growth in scale and complexity of information networks, self-organizing systems have been focused on for realizing new network control architectures that have high scalability, adaptability, and robustness. However, in self-organizing systems, the uncertainty (incompleteness, ambiguity, and dynamicity) of information observable for components in the system can lead to the slow adaptation to environmental changes and the lack of a global optimality, which complicates a practical use of self-organizing systems in industrial and business fields. In this study, we adopt the principle of collective decision making, in which a coordinated decision in a group is achieved through local interactions of components, in order to realize a network control mechanism adaptable to such information uncertainty. Specifically, we apply Effective Leadership model, which is a mathematical model of collective decision making, to a self-organizing control mechanism. In Effective Leadership model, there are two types of individuals, informed and non-informed ones, and collective decision is achieved through local interaction of them. Through simulation experiments, we reveal the advantages and characteristics of the network control mechanism based on Effective Leadership model.

Modern webpages consist of many rich objects dynamically produced by servers and client terminals at diverse locations, so we face an increase in web response time. To reduce the time, edge computing, in which dynamic objects are generated and delivered from edge nodes, is effective. For ISPs and CDN providers, it is desirable to estimate the effect of reducing the web response time when introducing edge computing. Therefore, in this paper, we derive a simple formula that estimates the lower bound of the reduction of the response time by modeling flows obtaining objects of webpages. We investigate the effect of edge computing in each webpage category, e.g., News and Sports, using data measured by browsing about 1,000 popular webpages from 12 locations in the world on PlanetLab.

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.

To provide cellular communications for a large number of internet of things (IoT) devices, the mobile core system, called as evolved packet core (EPC) in the 4G system case, emerges to equip a large amount of computational resources. This is because the EPC needs to maintain state information on the communications of the IoT devices, even if they rarely send data. This paper proposes a method for reducing the state information. The proposed method assigns the same international mobile subscriber identity (IMSI) to multiple IoT devices, which upload data with the same cycle, and manages devices uploading data at different timings. This paper shows through the simulation-based verifications that the proposal can reduce the state information to less than one-hundredth.

Information-Centric Networking (ICN) enables retrieval of content with various controls using flexibility of a content name, which is treated as a routing information. In existing works, however, data processings on end nodes or routers are mainly considered. Therefore, controls of physical behaviors, especially actual movement of devices, are not considered so much. we consider to incorporate physical movement of routers into routing control in ICN, and we propose a novel architecture of ICN that makes it possible to retrieve unretrievable contents between physically disjoint networks, by only specifying its name, which is realized by a Content-Centric Networking (CCN) router mounting on an Unmanned Air Vehicle (UAV). Moreover, we design and develop a Flying Router (FR), which combines a CCN router and a UAV, and we propose a detail design of communication architecture in which multiple FRs are able to process routing cooperatively among disjoint networks. In addition, we examine feasibility of the proposed architecture by carrying out a basic experiment using a prototype of an FR.

For Internet service providers to efficiently use network resources, they need to conduct traffic engineering to dynamically control traffic routes to accommodate traffic with limited network resources. The performance of traffic engineering depends on the accuracy of traffic prediction. However, the volume of network traffic has been changing drastically in recent years due to the growth of various types of network services, making traffic prediction increasingly difficult. Our simple ideas to overcome this challenge are to separate traffic into predictable and unpredictable parts and to apply different control policies to predictable and unpredictable traffic. To promote these ideas, we use software-defined networking technology, particularly Open-Flow, that can control macroflows defined by any combination of L2-L4 packet header information such as 5-tuple. In this paper, we therefore propose the macroflow-generating method for separating traffic into predictable macroflows that have little traffic variation and unpredictable macroflows that have large traffic variation within a limited flow table size. We also propose a macroflow-based traffic engineering scheme that uses different routing policies in accordance with traffic predictability. Simulation evaluation results suggest that our proposed scheme can reduce the maximum link load in a network at the most congested time by 34% and the average link load in a network on average by 11% compared with the current traffic engineering schemes.

Recently, communication network services have become increasingly diverse and dynamic. Network Function Virtualization (NFV) is an effective technique to deal with these dynamic situations. Most related work on the VNF placement problem does not consider the dynamics of requests, but only static scenarios. The important goals of the dynamic VNF placement problem include accommodating new requests following the traffic dynamics and reducing the time to calculate solutions. To tackle this problem, we utilize the concept of Modularly Varying Goals (MVG), which is based on a genetic algorithm (GA) and generates solutions that can easily adapt to time-varying goals in short time. In this paper, we propose Evolvable VNF Placement (EvoVNFP) that applies the concept of MVG to the dynamic VNF placement problem to reduce the time to obtain solutions. Results from numerical evaluations show that our method is able to better follow the dynamics of VNF requests and also reduce time until adapting to successive objectives.

Dynamic placement of the virtual network functions (VNFs) is one of the promising approaches to handling time-varying demands; when demands are small, the energy consumption can be reduced by placing the VNFs to a small number of physical nodes and shutting down unused nodes. If the demands becomes large, the VNFs are migrated to allocate the sufficient resources. In the dynamic placement of the VNFs, it is important to avoid a large number of migrations at each time because the migration requires a large amount of bandwidth. In this paper, we propose a new method to dynamically place the VNFs to follow the traffic variation without migrating a large number of VNFs. Our method is based on the model predictive control (MPC). By applying the MPC to the dynamic placement of the VNFs, our method starts migration in advance by considering the predicted future demands. As a result, our method allocates sufficient resources to the VNFs without migrating a large number of VNFs at the same time even when traffic variation occurs. Through simulation, we demonstrate that our method handles the time variation of the demands without requiring a large number of migration at any time slot.

Attacks against websites are increasing rapidly with the expansion of web services. An increasing number of diversified web services make it difficult to prevent such attacks due to many known vulnerabilities in websites. To overcome this problem, it is necessary to collect the most recent attacks using decoy web honeypots and to implement countermeasures against malicious threats. Web honeypots collect not only malicious accesses by attackers but also benign accesses such as those by web search crawlers. Thus, it is essential to develop a means of automatically identifying malicious accesses from mixed collected data including both malicious and benign accesses. Specifically, detecting vulnerability scanning, which is a preliminary process, is important for preventing attacks. In this study, we focused on classification of accesses for web crawling and vulnerability scanning since these accesses are too similar to be identified. We propose a feature vector including features of collective accesses, e.g., intervals of request arrivals and the dispersion of source port numbers, obtained with multiple honeypots deployed in different networks for classification. Through evaluation using data collected from 37 honeypots in a real network, we show that features of collective accesses are advantageous for vulnerability scanning and crawler classification.

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.

To provide cellular communications for a large number of Internet of Things (IoT) devices, the mobile core system, called evolved packet core (EPC) in the case of a 4G system, requires a large amount of computational resources. This is because the EPC needs to maintain communication state information for IoT devices, even if they rarely send data. This paper proposes a method for reducing state information. The proposed method assigns the same international mobile subscriber identity to multiple IoT devices, which upload data with the same cycle, and manages devices uploading data at different timings. This paper investigates the impact of factors affecting the performance of the proposed method through several simulation-based verifications and shows that the proposal can reduce the required state information to less than 0.5% compared to the current EPC method.

Self-organization has potential for high scalability, adaptability, flexibility, and robustness, which are vital features for realizing future networks. Convergence of self-organizing control, however, is slow in some practical applications compared to control with conventional deterministic systems using global information. It is therefore important to facilitate convergence of self-organizing controls. In controlled self-organization, which introduces an external controller into self-organizing systems, the network is controlled to guide systems to a desired state. Although existing controlled self-organization schemes could achieve this feature, convergence speed for reaching an optimal or semioptimal solution is still a challenging task. We perform potential-based self-organizing routing and propose an optimal feedback method using a reduced-order model for faster convergence at low cost. Simulation results show that the proposed mechanism improves the convergence speed of potential-field construction (i.e., route construction) by at most 22.6 times with low computational and communication cost.

Web performance, the time from clicking a link on a web page to finishing displaying the web page of the link, is becoming increasingly important. Low web performance of web pages tends to result in the loss of customers. In our research, we measured the time for downloading files on popular web pages by running web browsers on four hosts worldwide using PlanetLab and detected the longest portion in download time. We found the longest portion in download time to be Blocked time, which is the waiting time for the start of downloading in web browsers. In this paper, we propose a method for accelerating web performance by reducing such Blocked time with a cache of rendering results. The proposed method uses an in-network rendering function which renders web pages instead of web browsers. The in-network rendering function also stores the rendering results in its cache and reuses them for other web browsers to reduce the Blocked time. To evaluate the proposed method, we calculated the web performance of web pages whose render results are cached by analyzing the measured download time of actual web pages. We found that the proposed method accelerates web performance of long round trip time (RTT) web pages or long RTT clients if the web pages' dynamic file percentages are within 80%.

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.

In this paper, we propose a method for increasing the capacity of Machine-To-Machine (M2M) communication in mobile core networks. The proposed method combines two approaches: bearer aggregation inside mobile core networks for decreasing the load of Evolved Packet Core (EPC) nodes, and applying a Software Defined Networking (SDN) architecture to separate the control and data planes and aggregate control plane nodes in a cloud network environment for resource sharing. The combination of these two approaches is meaningful because they have a complementary relationship. We give a mathematical analysis and numerical results of a performance evaluation of the proposed method. The evaluation results show that we can increase the capacity of a mobile core network for M2M communication by around 30% when one of the two approaches is applied, while the performance gain increases up to 124% when both approaches are combined.

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.

Self-organization has potential for high scalability, adaptability, flexibility, and robustness, which are vital features for realizing future networks. Convergence of self-organizing control, however, is comparatively slow in some practical applications. It is therefore important to enhance convergence of self-organizing controls without sacrificing the above advantages. Controlled self-organization is one key idea for that, which introduces an external controller into self-organizing systems to guide them to a desired state. We previously designed an external controller that provided the optimal input for fast convergence, however, it suffered from scalability issues. In this paper, we propose a hierarchical control system where a network is partitioned into some sub-networks, sub-controllers manage respective sub-networks, and the top-level deals with the global network stability. The proposed system achieves fast convergence speed with low computational and communication cost.

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.

Modern websites consist of many rich objects dynamically produced by servers and client terminals at diverse locations. Consequently, we face complications in understanding the communication structure generated when accessing websites. To reduce the response time at browsed websites, many website objects are delivered using content delivery networks (CDNs), in which data objects are delivered from cache servers located close to user terminals. Although the use of CDNs have been assumed to reduce web response time, the actual effect of CDNs on this reduction has not been clarified. To answer this fundamental question, we measured the communication structure of traffic generated when accessing the 1,000 most popular websites from 12 locations worldwide. We found, for example, that it will be desirable to give high priority to entertainment websites at night and to business-related websites during the day.

Traffic engineering (TE) plays an essential role in deciding routes that effectively use network resources. This is particularly important when one considers the increasing time variation of Internet traffic such as streaming and cloud services. Traffic engineering with traffic prediction is one approach to stably accommodating time-varying traffic. This approach calculates routes from predicted traffic to avoid congestion, but predictions may include errors that instead cause congestion. We propose a prediction-based traffic engineering method that is robust to prediction errors by considering the probability distribution of predicted traffic. Our approach is based on a control-theoretic approach called stochastic model predictive control. Routes are calculated using a probability distribution of prediction errors so that the occurrence probability of congestion is lower than an operator-specified level. By considering the multi-step future dynamics of traffic, the routes are changed gradually to avoid route oscillation. We also show a relaxation method for unreliable far-future probabilistic constraints to avoid overly conservative route changes. Through simulations using backbone network traffic traces, we demonstrate that our method can accommodate most traffic variations under a given target link capacity without sudden large routes changes.

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.

With the increasing diversity of network services and applications, caching technologies for content delivery networks (CDNs) and information-centric networking (ICN) have become beneficial to improve the service quality such as communication latency by storing content data on network nodes near to users. Cache performance depends on the memory size at each node as well as the request distribution of content, and it becomes an important issue to analyze the performance of current caching mechanisms for designing efficient cache systems. In general, the request distribution in content dissemination networks is heavy-tailed, containing many objects called one-timers, which are accessed only once and are not hit while in the cache. Moreover, one-timers may diminish the whole cache performance by causing inefficient cache replacement of more popular content. To solve this problem, effective caching mechanisms having separate queues for one-timers, such as 2Q and adaptive replacement caching (ARC), have been proposed. In this paper, we focus on analyzing the 2Q and ARC mechanisms and propose approximation models that can statistically analyze the influence of memory size and request distribution on the cache performance. Furthermore, we evaluate the accuracy of both approximation models.

Existing techniques for measuring available bandwidth measure the available bandwidth at bottlenecks along the path, and most of them do not specify the bottleneck location. In this paper, we propose an end-to-end measurement method for the hop-by-hop available bandwidth along a network path. Such a technique can facilitate advanced traffic control, especially in heterogeneous network environments. The proposed method assumes a situation where intermediate routers can record the arrival and departure times of incoming packets as timestamps in the packets themselves. The end host sends probe packets at various rates and estimates the available bandwidth at each network section using the incoming and outgoing rates of packets calculated from intermediate timestamps, based on statistical processing under a fluid traffic model. We present extensive simulation results for the proposed method and confirm that it can accurately measure the available bandwidth of each section along the network path even when the available bandwidth of the sender-side network is smaller than that of the receiver-side network.

In a multitenant data center, nodes and links of tenants' virtual networks (VNs) share a single component of the physical substrate network (SN). A failure of the single SN component can thereby cause simultaneous failures of multiple nodes and links in a VN, this complex of failures must significantly disrupt the services offered on the VN. In the present paper, we clarify how the fault tolerance of a VN is affected by a SN failure, especially from the perspective of VN allocation in the SN. We propose a VN allocation model for multitenant data centers and formulate a problem that deals with the bandwidth loss in the VN due the SN failure. We conduct numerical simulations with the setting that has 1.7 × 108bit/s bandwidth demand on each VN. The results show that the bandwidth loss can be reduced to 5.3 × 102bit/s per VN, but the required bandwidth between physical servers in the SN increases to 1.0 × 109bit/s per VN when each node in the VN is mapped to an individual physical server. The balance between the bandwidth loss and the required bandwidth between physical servers can be optimized by assigning every four nodes of the VN to each physical server, meaning that we minimize the bandwidth loss without providing too sufficient bandwidth in the core area of the SN.

As key technologies for future information networks, many researchers have focused on self-organized network controls. In the process of their ordering, their robustness against environmental changes decreases while their performance increases. Therefore, their behavior in dynamic environment should retain appropriate amount of disorder. In this paper, we conduct simulation experiments and show that higher entropy leads to higher robustness against node failures.

Although the use of software-defined networking (SDN) enables routes of packets to be controlled with finer granularity (down to the individual flow level) by using traffic engineering (TE) and thereby enables better balancing of the link loads, the corresponding increase in the number of states that need to be managed at routers and controller is problematic in large-scale networks. Aggregating flows into macro flows and assigning routes by macro flow should be an effective approach to solving this problem. However, when macro flows are constructed as TE targets, variations of traffic rates in each macro flow should be minimized to improve route stability. We propose two methods for generating macro flows: one is based on a greedy algorithm that minimizes the variation in rates, and the other clusters micro flows with similar traffic variation patterns into groups and optimizes the traffic ratio of extracted from each cluster to aggregate into each macro flow. Evaluation using traffic demand matrixes for 48 hours of Internet2 traffic demonstrated that the proposed methods can reduce the number of TE targets to about 1/50 ~ 1/400 without degrading the link-load balancing effect of TE.

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.

Energy efficiency of wireless clients is an important issue in wireless communications. When multiple network applications are running concurrently on a single wireless client, packets belonging to each application are sent and received independently, but these packets are multiplexed at the MAC level. This uncoordinated behavior makes it difficult to control sleep timing. In addition, transitioning between active and sleep modes frequently will consume a non-negligible amount of energy. To address these difficulties, we have proposed SCTP tunneling: a transport-layer approach that resolves the coordination problem and so reduces the energy consumed by multiple TCP flows on a wireless LAN (WLAN) client. In this study, we perform experiments with off-the-shelf WLAN devices to assess the energy efficiency and the transfer time of SCTP tunneling. We show experimentally that SCTP tunneling with unscheduled automatic power save delivery (U-APSD) can save energy when compared to U-APSD alone, while still increasing file transfer speed.

To tackle with problems emerging with rapid growth of information networks in scale and complexity, selforganization is one of promising design principles for future networks. Convergence of self-organizing controls, however, is pointed out to be comparatively slow in some practical applications. Therefore, it is important to reveal and enhance convergence of self-organizing controls. In controlled self-organization, which introduces an external observer/controller into self-organizing systems, systems are controlled in order to guide them to the desired state. Although previous controlled self-organization schemes could achieve this feature, convergence speed for reaching an optimal or a semi-optimal solution is still a challenging task. In this paper, we take potential-based self-organizing routing and provide an optimal feedback for faster convergence using the future state of the system. Simulation results show that the convergence speed of potentials is improved by 7.3 times with a proposed mechanism.

Traffic engineering with traffic prediction is one approach to accommodate time-varying traffic without frequent route changes. In this approach, the routes are calculated so as to avoid congestion based on the predicted traffic. The accuracy of the traffic prediction however has large impacts on this approach. Especially, if the predicted traffic amount is significantly less than the actual traffic, the congestion may occur. In this paper, we propose the traffic prediction methods suitable to the traffic engineering. In our method, we perform preprocessing before the prediction in order to predict the periodical variation accurately. Moreover, we consider the confidence interval for the prediction error and the variation excluded by the preprocessing to avoid the congestion caused by the temporal traffic variation. In this paper, we discuss three preprocessing approaches; the trend component, the lowpass filter, and the envelope. Through simulation, we clarify that the preprocessing by the trend component or the lowpass filter increases the accuracy of the prediction. In addition, considering the confidence interval achieves the lower link utilization within a fixed control period.

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.

Improving the scalability and robustness of wireless sensor networks is an important task, and much research on self-organization has been conducted toward this end. However, desired behavior is not yet guaranteed in much larger networks based on pure self-organization. In this article, we propose a controlled potential-based routing protocol implementing a novel controlled self-organization scheme that also allows for external control. The scheme obtains close-to-optimal network behavior by this external control which controls a part of nodes in the network. We show that global traffic flow can be controlled through simulation experiments with a multi-sink sensor network. For example, traffic loads can be equalized among heterogeneously distributed sink nodes, and load balancing among the relay nodes based on remaining energy can bring an approximate four times extension of network lifetime. The proposed method is furthermore robust to message loss and resilient to failure of the sink node.

As various multimedia services are being provided on networks, broadband traffic is growing as well. Reducing traffic is important because power consumption in networks has been increasing year by year. Meanwhile, content caching is expected to reduce data traffic by storing content replicas on the network nodes and it is beneficial in view of energy efficiency. However, in order to realize an energy efficient network, it is necessary to allocate content replicas effectively in consideration of both power consumption of content caching and transmission of traffic. In this paper, we propose a design method which derives the energy efficient cache locations for content dissemination. Furthermore, we demonstrate the effectiveness of our proposed method.

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.

Asynchronous, receiver-driven communication methods are suited to wireless mesh networks with high node density and low data generation frequency. In such networks, however, control packet collisions between hidden terminals can degrade performance. We propose collision avoidance techniques that improve packet collection rates and delay, and that furthermore decrease power consumption.

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.

We introduce activities on research and development for new-generation networks (or future Internet) in Japan.

A new generation network is expected to keep operating and providing users and applications with means of communication while being exposed to dynamic and substantial change in the operational environment such as network topology, traffic, and QoS requirement. To establish a highly adaptive and reliable network, we take an approach to be inspired by biological systems, which adapt themselves to dynamically changing and even unexpected environment. In this paper we show examples of application of bio-inspired models, more specifically the attractor selection/composition models built on adaptive behavior of biological systems, to autonomous and adaptive networking in wireless communication systems. The first application is MANET routing, where a path connecting a source-destination pair must be maintained under dynamically changing environment. The second application is resource allocation among nodes and applications competing for wireless networks with heterogeneous characteristics. We further discuss future direction of bio-inspired adaptive networking.

Improving the scalability of wireless sensor networks is an important task, and toward this end, much research on self-organization has been conducted. However, the problem remains that much larger networks based on pure self-organization cannot be guaranteed to behave as desired. In this paper, we propose a controlled potential-based routing protocol. This protocol is based on a novel concept: a "controlled self-organization scheme", which is a self-organization scheme accompanied by control from outside the system. This scheme ensures desired network behavior by controlling a portion of nodes operated in self-organization. Through simulation experiments with a multi-sink network, we show that traffic loads can be equalized among heterogeneously distributed sink nodes, and moreover, that load balancing among the relay nodes can bring about a 138% extension of network lifetime.

In this paper we propose a clustering and data gathering scheme for wireless sensor networks, based on a biologically-inspired approach that uses the concept of adaptive response by attractor selection as observed in a gene network. The approach uses a layered concept of clustering and routing that work independently, yet, resulting in an interdependent interaction via a common activity factor. This approach is aimed at establishing resilience and robustness in a two-layered protocol, where the network would perform a distributed clustering and routing of data to a sink. In this scheme, cluster heads are chosen according to their relative residual energy and routing is performed upon the relative cached data sizes and energy recharging rates of the next hop gateway nodes.

Technology is taking us to a world where myriads of heavily networked devices interact with the physical world in multiple ways, and at multiple scales, from the global Internet scale down to micro- and nano-devices. Many of these devices are highly mobile and autonomous, and must adapt to the surrounding environment in a totally distributed and unsupervised way. fundamental research challenge is the design of robust decentralized computing systems, which are capable of operating under changing environments, and yet exhibit the desired behavior and response time, under unpredictable operating constraints, such as traffic demand, energy consumption, size, and processing power. These systems should be able to adapt and learn how to react to unforeseen scenarios, as well as to display desired emergent properties.

This paper proposes a novel recovery mechanism from large-scale network failures caused by earthquakes, terrorist attacks, large-scale power outages and software bugs. Our method, which takes advantage of overlay networking technologies, pre-calculates multiple routing configurations to prevent possible simultaneous network failures and selects one configuration immediately after detecting the failures. Through numerical calculation results using actual AS-level topology, we show that our proactive method improves network reachability from 89% to 99%, while keeping the path length sufficiently short, when up to 8% of the nodes in a network are down simultaneously.

In this paper we propose a transmission range reduction scheme for a clustered wireless sensor network (WSN), in order to reduce power consumption while maintaining the network connectivity, particularly in scenarios where consecutive reporting of sensing values is inherent in the network. The platform WSN is based on a multi-hop clustering algorithm, where clusterheads send information to the sink via either gateway nodes or other clusterheads of intermediate clusters towards the sink. The main idea of this scheme is that the sink initially gathers the number of nodes or clusters in the network and instructs the reduction in the transmission range of the sensors until this number, hence connectivity is compromised. The proposed scheme does not require geographical information of nodes, and is independent of propagation model and environmental conditions that may cause non-uniform attenuation to radio signals. Furthermore, the scheme is independent of network topology, and can be applied to both uniform and non-uniform distribution of nodes. Simulation results show the effectiveness of the approach in two different clustering schemes, in regards to reducing energy consumption in the network.

Abstract In this paper we propose an energy‐efficient self‐organizing global extremity reporting scheme for wireless sensor networks. The proposed scheme assists applications of periodic reporting of extreme values (such as maximum or minimum temperature/pressure) across a wireless sensor field, back to the sink. Furthermore, an event‐driven counterpart is supplied for individual sensor nodes to supply their instantaneous sensed values back to the sink, once queried. The targeted sensors initially establish their relative distances to the sink in regards to number of hops, whilst the highest hopcount nodes (HHNs) from the sink identify themselves. The broadcast initiation of the HHNs have the ability to penetrate all nodes within the network towards the sink, and hence obtain the extreme value of the entire network in an efficient manner. This is due to the relative position of these special nodes within the network. Furthermore, the scheme does not require nodes to possess location information of themselves or other nodes, avoiding the need for the global positioning system (GPS) or other location‐aware methods. Simulation results show the effectiveness of the proposed protocol in its target application. In particular, the advantage of HHN‐initiated broadcasting can be seen in both uniformly and randomly distributed topology networks. Copyright © 2008 John Wiley & Sons, Ltd.

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.

In this paper we propose an architecture which integrates the notion of self-adaptability and self-organization in the pervasive computing architecture. Furthermore we describe how a biologically-inspired approach may be a good candidate for this purpose in order to provide a resilient, self-adaptive system similar to living biological systems. Additionally the emergent intelligence that would ultimately encompass the human being and its environment would inevitably assist us where our consciousness is not present.

In ambient information society, a variety of networks consisting of sensory and computational devices are embedded in the environment and individuals. They are autonomously connected, merged, and divided depending on the context and provide a network service appropriate for time, place, occasion, and individuals. In this paper, we investigate how such dynamic network formation is performed focusing on synchronization of operational frequency. Assuming that a pulse-coupled oscillator model is adopted for autonomous and self-organizing synchronization, we show how networks operating on different operational frequency are connected, merged, and divided.

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.

Grid technology has been studied and developed by numerous researchers in recent years. Data in conventional grid environments are changed by using TCP/IP. However, as long as the architecture is based on packet switching, highly efficient computing is difficult to achieve. We thus propose a new architecture, the λ computing environment, where network switches and computing nodes are connected to one another with optical fibers, thereby offering high-performance computing by establishing an optical wavelength path between shared memories on computing nodes. We established the lambda computing environment using the AWG-STAR system, and designed a data-sharing structure for the OpenMP library, which is a parallel-computing programming language utilizing shared memory. Moreover, we evaluated its performance against existing parallel computing in a PC-cluster environment by executing OpenMP applications.

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.

It has been recognized that in the current Internet it is important to control not only congestion caused by overload of the data plane but also congestion caused by overload of the control plane. In particular, it is important to avoid congestion from retrial traffic. This paper focuses on RSVP-based communication services. In general, users reattempt to set up connections not only when transmission systems are overloaded but also when processing systems in the network are overloaded. The latter is caused by user psychology: an increase in the waiting time for the processing to be completed tends to increase his or her reattempts. Thus, it is important to manage both transmission and processing resources properly. In this paper, we introduce a model that takes account of both transmission and processing systems, and we examine the behavior of retrial traffic. In addition, we compare the system stability achieved by two different resource management methods, the hard-state method and the soft-state method.

In our previous studies, an optical-code-division multiplexing (OCDM)-based network architecture has been proposed to improve the wavelength utilization and to provide finer bandwidth granularities to users. However, since up-to-date all-optical regeneration technologies are still immature, signal quality degradation at intermediate nodes is considered as a crucial problem in transparent optical networks. Because the remaining noises may propagate, and be accumulated along the light-path, which eventually result in unacceptable signal quality. In this paper, we first define a cycle attack problem due to multi-access interference (MAI) propagation in transparent OCDM-based networks, which may cause severe service disruption. A depth-first-search (DFS)-based algorithm is proposed to diagnose such cycle attacks and a heuristic wavelength assignment called advanced first-fit (AFF) scheme taking cycle attacks into account is found to be a simple but effective approach mitigating the cycle attack impacts by computer simulations.

In this paper, we discuss robustness issues of a biologically inspired routing protocol for mobile ad hoc networks and the influence it has on the quality of service (QoS) in the system. Contrary to fixed network structures, ad hoc networks are susceptible to frequent topology changes due to the mobility and churn of the participating nodes. Our goal is therefore to provide fast recovery from connectivity failures, as well as a fast reaction to path changes due to node mobility or churn.

In this paper, we propose a new mechanism which detects tampered-TCP connections at edge routers and protects well-behaved TCP connections from the tampered-TCP connections, resulting in maintaining the fairness amongst TCP connections. The proposed mechanism monitors the TCP packets at an edge router and estimates the window size or the throughput for each TCP connection. By using estimation results, the proposed mechanism assesses whether each TCP connection is tampered or not and drops packets intentionally if necessary to improve the fairness amongst TCP connections. From the results of simulation experiments, we exhibit that the proposed mechanism can accurately identify tampered-TCP connections. We also show that the proposed mechanism can regulate throughput ratio between tampered-TCP connections and competing TCP Reno connections to about 1.

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.

The wireless sensor network (WSN) is one of the most promising technologies which helps making our society safe, secure, and comfortable. A WSN as a social infrastructure must transmit critical information faster and more reliable than other information. In this paper, we propose an autonomous and distributed mechanism, called an "assured corridor" mechanism (ACM), for fast and reliable transmission for urgent information in WSNs. In ACM, a self-organizing corridor consists of nodes surrounding the path from the source node to the base station and nodes in the path. The former refrains from transmitting non-urgent information to avoid collisions with emergency packets, and the latter suspends their sleep schedule and keeps awake to avoid delay caused by sleeping. We conducted simulation experiments with a tree-based and broadcast-based network. It was shown that ACM improved the delivery ratio and the delay of emergency packets.

In this paper, we propose a self-organizing communication mechanism for a wireless sensor network where a large number of sensor nodes are deployed. To accomplish application-oriented periodic communication without any centralized controls, we adopt traveling wave phenomena of a pulse-coupled oscillator model by regarding sensor nodes as oscillators and the emission of radio signals as firing. We first investigat conditions of a phase-response curve to attain wave-formed firing patterns regardless of the initial phase of oscillators. We adopte the derived phase-response curve to accomplish the desired form of message propagation through local and mutual interactions among neighboring sensor nodes. Through simulation experiments, we confirm that our mechanism can gather or diffuse information effectively in accordance with the application's requirements.

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 a P2P file-sharing system, a node finds and retrieves its desired file. If multiple nodes cache the same file to provide others, we can achieve a file-sharing system with low latency and high file availability. However, a node has to spend costs, e.g., processing load or storage capacity, on caching of a file. Consequently, a node may selfishly behave and hesitate to cache a file. In such a case, there is a possibility that unpopular files disappear from the system. In this paper, we aim to accomplish effective caching in the whole system that emerges from autonomous and selfish node behavior. We first discuss relationship between selfish node behavior and system dynamics according to evolutionary game theory. As a result, we show that a file-sharing system can be robust to file disappearance depending on a cost and demand model for caching even if nodes behave selfishly.

Wireless sensor networks used as a social infrastructure must be capable of differentiating and prioritizing transmission of urgent sensor information over other non-urgent information. In this paper, we developed a novel and simple network architecture, in which sensor information is classified into three traffic classes and each node activates one or more of several simple, self-organizing, and fully-distributed mechanisms in accordance with the scale of an emergency for fast and reliable transmission of urgent sensor information. In the demonstration, we show the operation of preferential transmission of urgent information under this architecture in a wireless sensor network.

We have proposed a simple and energy-efficient communication mechanism which can organize a variety of communication depending on dynamically changing application requirements. In this demonstration, we show that our mechanism can gather or diffuse information in accordance with application requirements in a dynamic wireless sensor network.

Providing end-to-end reliability for data transmission is a major challenge for energy-constrained and bandwidth-limited wireless sensor networks. To meet this challenge, attempts have been made using a multi-path transmission mechanism that encodes an information bitstream using forward error correction (FEC), and sends the obtained packets over a number of paths to the destination. Algorithms that, given the paths statistics (e.g., failure probability and energy cost of data transmission over the paths), find the number of channel packets and their transmission paths that optimize the reliability-energy cost tradeoff have been proposed. Once a transmission strategy is obtained, all packets are transmitted regardless of the actual state of the wireless links, which may result in energy and bandwidth losses due to unnecessary transmissions. This can be more severe when arbitrary transmission strategies have to be used due, for example, to the unavailability of paths statistics at the source node, which is the case in many practical applications. In this paper, we propose a hybrid FEC-feedback mechanism. The feedback scheme is used to send ACK packets from the sink to the source node when the information bitstream is actually recovered. We then derive a measure of the expected energy cost of data transmission for the hybrid mechanism. Simulation results show that, on average, the hybrid mechanism consumes up to about 44% less energy than the FEC-based mechanism for the transmission of a bitstream with the same reliability constraint. The energy savings of the hybrid mechanism are even higher when arbitrary transmission strategies have to be used.

Most sensor nodes usually have the ability to vary their own wireless transmission power. Hence they have the potential to control the network topology, which is the pattern of links connecting pairs of nodes in a network. In this paper, we propose a connectivity ascertainment based distributed topology control algorithm. The algorithm draws out the potential from nodes for constructing an energy-efficient network topology. Each node performs the algorithm and makes local decisions about its transmission power; these local decisions minimize the total transmission power of the network, while maintaining network connectivity. Specifically, each node ascertains the existence of an actual path that is a substitute for a wireless link to an own edge node, which requires the maximum transmission power among its own neighbors. To contribute to path ascertaining, the other nodes use locally-collected information. If a node can ascertain the existence, it decides to remove the wireless link to the edge node by reducing the transmission power. The node targets a new edge node from among its own current neighbors and performs the same process until there are no substitute paths to the target. We show that these local decisions collectively guarantee global connectivity and minimize the total transmission power of the network. We also evaluated the performance of our algorithm on terms of network connectivity and transmission power reduction using simulation studies.

Taking into account requirements of sensor networks, we need fully-distributed and self-organizing control mechanisms which are scalable to the size of a network, robust to failures of sensor nodes, and adaptive to different and dynamically changing topology and changes in wireless communication environment. To accomplish this goal, our research group focuses on behavior of biological systems, which inherently are scalable, adaptive, and robust. In this paper, we first verify the practicality of control mechanisms adopting a reaction diffusion equation, which explains emergence of patterns on the surface of body of fishes and mammals, and then propose two methods for faster pattern generation to save energy consumption. From simulation and practical experiments on a prototype, it was shown that a stable pattern could be generated in a wireless sensor network in several minutes, even when packets were lost for collisions in wireless communication.

In this paper, we propose and evaluate a self-organizing communication mechanism for wireless sensor networks where a large number of sensor nodes are deployed. To accomplish application-oriented periodic communication without any centralized controls, we adopt traveling wave phenomena of a pulse-coupled oscillator model by regarding sensor nodes as oscillators and emission of radio signals as firing. Through simulation experiments, we confirm that our mechanism delivers sensor information to / from a designated node in a more energy-efficient manner than other method, although it takes time to generate a traveling wave. In addition, we implement our mechanism using MOTE MICAz and verify its practicality.

In wireless sensor networks, the power of energy- constrained sensor nodes is largely drained by data communication tasks. Designing energy-efficient data communication mechanisms is, therefore, a major key to maximizing the life-time of wireless sensor networks. This challenge is magnified for visual sensor networks, where the collected and transmitted data is often very large and composed of multiple signals (Infrared signals, audio, video, ...) which have different and varying quality of service requirements (QoS). Motivated by this challenge, we investigate a forward error correction recovery mechanism for multi-path data transmission in wireless sensor networks. Based on this mechanism, we propose a fast algorithm for the trade-off between the end-to-end energy cost and reliability requirement of multi-path data transmission. Under the practical considerations of a fixed transmission power and a realistic modulation scheme, we derive the reliability and expected energy cost metrics of transmission paths. We then demonstrate the efficiency of our algorithm through simulations and discuss future work.

In this paper, an OCDM-based multi- granularity optical cross-connect (MG-OXC) is proposed to enable the optical path switching in bandwidth granularity finer than the wavelength-based path. Such an optical path switched by OCDM-based MG-OXC is called OCDM label-switched path (OCDM-LSP), which is capable of hybrid optical code (OC)/wavelength conversion along the path from a source to destination. With distinct OCs, OCDM-LSPs can share a single wavelength without any synchronization mechanism and be discriminated by ultra- fast optical correlation. The asynchronous ability and OC conversion distinguish the OCDM-LSP from Packet-LSP or TDM-LSP in resolving granularity problem in wavelength- based path. Based upon our proposal, all-optical optical path provisioning including F-LSP, WB-LSP, L-LSP and OCDM- LSP can be realized in the transport layer. Simulation is conducted and the results show the proposed OCDM-switching has gained a 2 order of blocking probability reduction even though only 5 OCs are employed. Furthermore, OC conversion in OCDM-LSP is proved to be another benefit for high flexibility in optical path provisioning and blocking performance improvement comparing to TDM-LSP.

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.

Since a peer searches for its desired file in a P2P file sharing system, the structure of an overlay network determines the effectiveness of search. In this paper, based on the Barabasi-Albert (BA) model, we propose a novel scheme (LLR) to construct a low-diameter and location-aware overlay network where peers can easily find physically-close file holders. LLR has a rewiring method to improve the structure of an overlay network and a recovery method to cope with disappearance of peers. Through several simulation experiments using real physical topologies, we found that LLR could construct an overlay network that had the higher reachability than BA and the higher correlation between physical and logical distances

In this paper we propose MARAS, a biologically-inspired method for routing in a mobile ad-hoc/sensor network environment. We assume that all nodes have no explicit knowledge of the network topology, except for their coordinates and the neighboring nodes within an RF transmission range. MARAS then selects the next hop for forwarding a packet towards a destination node which is best suited depending on some measured metric values. The benefit of our proposed method is its ability to operate entirely in a self-adaptive manner and that it can easily compensate for sudden changes in the topology of the network.

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.

We study optical fiber-delay-line buffer management schemes for supporting service differentiation with less complexity, with the objective of achieving ultra-high-speed packet forwarding in a photonic packet switch. We propose the vPBSO (Variable-length-packet-capable Partial Buffer Sharing with Overwriting) method, which is a prioritized buffer management scheme for providing DiffServ Assured Forwarding. This method can handle asynchronously arriving variable-length packets. vPBSO is based on a single queue, and its complexity is Oscr(p), where p is the number of priority classes. We show that vPBSO is effective when the arrival rate of higher priority packets is lower than that of lower priority packets, which is a likely situation in a prioritized system. vPBSO provides better performance than vPBS (an extension of partial buffer sharing) for high-priority packets, and the performance for low-priority packets is also better or at least almost the same. vPBSO provides service differentiation in the case of two or more priority classes in which the difference of packet loss probability in each class is made larger than one order of magnitude by appropriate tuning of thresholds

Anycast is a new IPv6 feature that supports service-oriented address assignments in IPv6 networks. Because there are no protocol standards or even consensus on routing protocols, intersegment anycast communications are not yet available. In this paper, we propose two routing protocols for intersegment anycast to support anycast-oriented communication. Our proposed architecture (1) achieves the advantages of anycast communications, (2) takes the deployment scenario into the existing unicast network into consideration, and (3) maintains scalability. We also implement the proposed routing protocols in an experimental environment and verify that they work correctly.

Today, the use of anycast address is quite limited. One of the reasons is because there is no routing protocol providing a global anycasting service. In this paper we design and implement a new anycast routing protocol called PIA-SM (protocol independent anycast-sparse mode). We focus on PIM-SM (protocol independent multicast-sparse mode), which is one of multicast routing protocols available now, to develop an anycast routing protocol because anycast and multicast have many similar properties. We modified PIM-SM based on differences between multicast and anycast. We next describe technical issues to be solved on the implementation of PIA-SM. We also show some experimental results to demonstrate PIA-SM, and verify that PIA-SM enables routers to forward an anycast packet to an appropriate node of multiple candidate nodes.

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.

In this paper we discuss the effects of symbiosis when using an attractor selection model for multi-path routing in an overlay network. Attractor selection is a biologically inspired approach which is found in E. coli cells to self-adaptively react to changes of a nutrient in the environment. It is driven by noise and we present its application to selecting the paths in an overlay network for the transmission of a packet. This selection is performed with randomization to reduce the selfishness of each flow and to improve the overall performance of the network. Our main focus in this paper lies on showing the symbiotic behavior in the interaction of competing flows

One can obtain information about a region by deploying a network of sensor nodes there. Since remotely deployed nodes are usually powered by batteries, an energy-efficient data gathering scheme is needed to prolong the lifetime of the sensor network. We proposed a novel scheme for periodic data gathering but evaluated it only in simulation experiments assuming ideal environments. In this paper, we evaluated the scheme experimentally in small networks consisting of commercial, off-the-shelf wireless sensor units. We also developed mechanisms to solve problems due to the instability of radio communications and demonstrated the effectiveness of these mechanisms experimentally. We confirmed that energy-efficient data gathering can be implemented by using our proposed scheme with several improvements and that synchronization can be established and maintained under unstable and changing conditions.

TCP overlay networks that control data transmission quality at the transport layer are being paid a lot of attention as users' demands for diversified Internet services increase. They are expected to enhance the end-to-end throughput of the TCP connection essentially because the round trip times and the packet loss ratios of each split TCP connection are reduced. However, performance degradation may occur due to undesired interactions among the split TCP connections. We introduce an analysis approach to estimate end-to-end throughput of data transmission with a TCP proxy mechanism considering performance degradation. Our analysis results reveal that we confirm the effect of the TCP proxy mechanism. We also find that we cannot ignore performance degradations due to interactions among split TCP connections, especially when the congestion level of the network they traverse is small. Further, we clarify that we should take into account the packet loss ratios, performance degradations and propagation delays of the network when we consider issues relating to the design of TCP overlay networks.

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.

Distributed denial-of-service attacks on public servers have recently become more serious. To assure that network services will not be interrupted, we need faster and more accurate defense mechanisms against malicious traffic, especially SYN floods. But single point defense (ex. firewalls) lacks a scalability to catch up the increase of the attack traffic. In this paper, we introduce a distributed defense mechanism using overlay networks. This mechanism detects attacks near the victim servers and alert messages are sent via the overlay networks. Then defense nodes identify legitimate traffic and block malicious ones. The legitimate traffic is protected via the overlay networks. We simulate and verify our proposed method can effectively block malicious traffic and protect legitimate traffic. We also describe the deployment scenario of our defense mechanism.

Simultaneous overlay networks compete for network resources and disrupt each other. If they cooperate with each other, the collective performance can be improved and they can coexist comfortably. Taking inspiration from biology, in this paper the authors presented a model of symbiotic overlay networks. Coexisting overlay networks dynamically evolve, interact with each other, and change their internal structures. Overlay networks in a symbiotic condition eventually establish the strong relationship and finally merge into one. The effect of interconnection of two overlay networks from the viewpoint of the robustness and the rate of message dissemination was also evaluated.

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.

Distributed denial-of-service attacks on public servers have recently become more serious. More are SYN flood attacks, since the malicious attackers can easily exploit the TCP specification to generate traffic making public servers unavailable. To assure that network services will not be interrupted, we need faster and more accurate defense mechanisms against malicious traffic, especially SYN floods. One of the problems in detecting SYN flood traffic is that server nodes or firewalls cannot distinguish the SYN packets of normal TCP connections from those of SYN flood attack. Moreover, since the rate of normal network traffic may vary, we cannot use an explicit threshold of SYN arrival rates to detect SYN flood traffic. In this paper we introduce a mechanism for detecting SYN flood traffic more accurately by taking into consideration the the time variation of arrival traffic. We first investigate the statistics of the arrival rates of both normal TCP SYN packets and SYN flood attack packets. We then describe our new detection mechanism based on the statistics of SYN arrival rates. Our analytical results show that the arrival rate of normal TCP SYN packets can be modeled by a normal distribution and that our proposed mechanism can detect SYN flood traffic quickly and accurately regardless of time variance of the traffic.

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.

With the development of low-cost microsensor equipment having the capability of wireless communications, sensor network technology has attracted the attention of many researchers and developers. By deploying a large number of sensors in a monitored region and composing a sensor network of them, one can remotely obtain information on behavior, condition, and position of elements in the region.

Because of the development of recent broadband access technologies, fair services among users are becoming more important criteria. The most promising scheme of router mechanisms for providing fair service is per-flow traffic management. However, it is difficult to be implemented in high speed core routers because per-flow state management is prohibitive; thus, a large number of flows are aggregated into a small number of queues. This is not a preferable situation because the more number of flows aggregated into a queue increases, the worse fairness tends to become. In this paper, we propose a new traffic management scheme called hierarchically aggregated fair queueing (HAFQ) to provide per-flow fair service. Our proposed scheme can adjust flow aggregation levels according to the queue handling capability of various routers. That means the proposed scheme is scalably used in high-speed networks. HAFQ improves the fairness among aggregated flows by estimating the number of flows aggregated in a queue and allocating bandwidth to the queue proportionally. In addition, since HAFQ can identify flows having higher arrival rates simultaneously in estimating the number of flows, it enhances the fairness by preferentially dropping their packets. We show that our proposed scheme can provide per-flow fair service through extensive simulation and experimental studies using a network processor. Since the currently available network processors (Intel IXP1200 in our case) are not high capacity, we also give extensive discussions on the applicability of our scheme to the high-speed core routers.

With the growth of computing power and the proliferation of broadband access to the Internet, media streaming has widely diffused. Although the proxy caching technique is one method to accomplish effective media streaming, it cannot adapt to the variations of user locations and diverse user demands. By using the P2P communication architecture, media streaming can be expected to smoothly react to network conditions and changes in user demands for media-streams. We propose efficient methods to achieve continuous and scalable media streaming system. In our mechanisms, a media stream is divided into blocks for efficient use of network bandwidth and storage space. We propose two scalable search methods and two algorithms to determine an optimum provider peer from search results. Through several simulation experiments, we show that the FLS method can perform continuous media play-out while reducing the amount of search traffic to 1/6 compared with full flooding.

End-to-end packet delay is an important performance parameter on the Internet, because it heavily affects the quality of real-time applications. Currently, however, because the packet transmission quality (e.g., transmission delay, jitter, packet loss) may vary dynamically, it is not easy to handle real-time traffic. For UDP-based real-time applications, a smoothing buffer (playout buffer) is typically used at the client to compensate for variable delays. The issue of playout control has been studied previously, and several algorithms for controlling the playout buffer have been proposed. These studies considered the network parameters (e.g., packet loss ratio and playout delay), but not the quality perceived by end users. We first clarify the relations between mean opinion score (MOS) of played audio and the network parameters (e.g., packet loss, packet transmission delay, and transmission rate). Then, utilizing the MOS function, we propose a new playout buffer algorithm that considers the user's perceived quality for real-time applications. Our simulation and implementation tests show that the algorithm can enhance the perceived quality more effectively than existing algorithms.

To provide distributed multimedia applications with end-to-end QoS (quality of service) guarantees, resource reservation-based control mechanisms should be employed in both networks and end systems. In this paper, we propose a resource allocation scheme for real-time video multicasting described as a utility maximization problem. In this scheme, clients are first divided into multicast groups by means of a clustering technique. Then system resources are allocated to each group so that the total utility is maximized. We have confirmed that our proposed scheme can achieve effective use of resources while providing high-quality video to users.

Many address lookup methods on IP routers have been recently proposed to improve the packet forwarding capability; nevertheless, their performance prediction is very limited because of lack of consideration of actual traffic characteristics in their evaluations. It is necessary to consider actual traffic to predict more realistic performance in routers, especially in the case of layer 3 and 4 switches whose performance is more influenced by flow characteristics. In this paper, we propose new methods for predicting the router's performance based on the statistical analysis of the Internet traffic. We also present an example of its application to the existing table lookup algorithm, and show that simulation results based on our method can provide accurate performance prediction.

The assured forwarding per hop behavior standardized by the IETF Differentiated Services working group provides four class-based differentiated IP services. In this service, however, unexpected service degradation may occur and differentiation among classes may be disordered if the network is designed to minimize over-provisioning or is under-provisioned. We therefore developed a packet scheduling scheme that dynamically allocates bandwidth to each class queue to guarantee the differentiation among classes under any traffic conditions. The scheme estimates the sum of CIRs (committed information rates), i.e. rate of the packets having lowest drop preference, of active flows in each class and initially allocates the link bandwidth according to the sum of CIRs. It allocates the excess bandwidth by using a combination of CIR-proportional allocation and equal-share allocation. The equal share part enables that the flows in best effort class or the flows having zero CIRs can utilize minimum share of the bandwidth. Our scheme also introduces a scalable scheduling technique to improve fairness among flows in the same class. We evaluate the proposed scheme and show that it makes DiffServ operations fairer under any traffic conditions.

This paper describes new optical switching architectures supporting asynchronous variable-length packets. Output line contention is resolved by optical delay line buffers. By introducing a WDM technology, a parallel buffer can be equipped with multiple wavelengths on the optical delay line buffer. Using an ultrafast photonic label processing technique, an implementation of our architecture would be fast enough for packet scheduling that selects the appropriate output port, wavelength, and delay line buffer. To evaluate the switch performance, we model an output port of our switch as a multi-server and multi-queue system where each server corresponds to a wavelength and where each arriving packet joins the shortest queue. We use an approximate analytic approach to evaluate the switch performance. The results of the analysis and of simulation experiments show that the use of the WDM technique can greatly improve the switch performance in terms of packet loss probabilities.

We propose a new adaptive FEC scheme combined with ELN (explicit loss notification) that was proposed for improving TCP performance in wireless cellular networks. In our method, transmission errors on the wireless link are measured at the packet level and the error status is notified the TCP sender with ELN. According to this information, an appropriate FEC code is determined in order to maximize the TCP performance. We first compare the TCP performance using the Snoop protocol, ELN and the fixed FEC, through which we find the appropriate FEC code against given BER (bit error ratio). We then show how the adaptive FEC can be realized using our solution, and also examine the appropriate observation period of measuring BER enough for the fading speed on the noisy wireless link. We finally demonstrate that our method can achieve better performance than the conventional fixed FEC by using the Gilbert model as a wireless error model.

Appropriate control parameters are important for the successful deployment of RED (Random Early Detection) routers, especially when many TCP connections share the bottleneck link. In this paper, we first describe a new simple analysis method for determining the window size distribution of many TCP connections sharing a bottleneck router. We consider two kinds of buffering disciplines: TD (Tail Drop) and RED. We model the window size evolution of TCP connections by using a Markov process whose state is represented by a set of the current window size and the ssthreth value. The state transition matrix is then calculated by considering the characteristics of TD and RED routers. We show numerical results demonstrating the accuracy of our analysis and we discuss the fairness of TD and RED. We confirm that RED does not help improve the router's throughput even when appropriate control parameters are chosen but that it is still useful to provide the fairness among many competing TCP connections.

Capacity dimensioning in the Internet is becoming more important to provide stable and predictable quality of service (QoS) to the customers. However, capacity dimensioning is not an easy task because the network state changes frequently. We first propose a method to identify the bottleneck for improving the performance of the end users. We then focus on the measurement method for utilization of the bottleneck link between end-to-end hosts. To measure the utilization of the bottleneck link accurately, we propose a new measurement method that can eliminate several kinds of measurement errors. We collected measurement results from the actual operating Internet, and provide evidence to support the appropriateness of our approach. We finally propose a design framework in order to determine the adequate link capacity for offering a desired QoS level to end users.

The policy-based service is becoming more important for the Internet shared by various applications. To realize the policy-based service, the router is required to forward packets according to the requirements of the traffic flow that the packet belongs to. However, the packet (and flow) classification easily becomes a bottleneck because the router is necessary to handle multiple fields within the packet. We propose a new packet classification algorithm capable of following the packet forwarding rate of the high-speed routers with the commercially available CPU, RAM, and cache memories. Through our experiments, we show the effects of parameters of the cache structure on the processing rate of the packet classification.

A packet transmission delay is an important quality characteristic for various applications including real-time and data applications. In particular, it is necessary to investigate not only the whole distribution of the packet transmission delay, but also the tail part of the distribution, in order to detect the packet loss. In this paper, we analyze the characteristics of the tail part of packet delay distributions by the statistical analytic approach. Our analytic results show that the Pareto distribution is most appropriate in 95-99.9% region of the cumulative distribution of packet transmission delays. Based on our statistical analysis, we next propose an adaptive playout control algorithm, which is suitable to real-time applications. Numerical examples show that our algorithm provides a stable packet loss ratio independently of traffic fluctuations.

As the use of real-time multimedia applications increases, the bandwidth available to TCP connections is oppressed by "greedy" UDP traffic and their performance deteriorates extremely. In order that both TCP and UDP sessions fairly co-exist in the Internet, UDP sessions should properly react against congestion as TCP. We implement a "TCP-friendly" rate control mechanism suitable to video applications and investigate its applicability to a real system through observation of the video quality at the receiver. It is shown through our experimental system that we can achieve high-quality and stable video transfer while fairly sharing the network bandwidth with TCP by applying our rate control at a control interval of 16 or 32 times as long as the round trip time (RTT).

Another version of TCP called TCP Vegas has been proposed and studied in the literature. It can achieve better performance than the current TCP Reno. In our previous studies, steady-state behavior of a window-based flow control mechanism based on TCP Vegas has been analyzed for a simple network topology. In this paper, we extend our analysis to a generic network topology where multiple bottleneck links exist. We first derive equilibrium values of a window size of a TCP connection and the number of packets waiting in a router's buffer in steady state. We also derive throughput of each TCP connection in steady state, and investigate the effect of control parameters of TCP Vegas on fairness among TCP connections. We then present several numerical examples, showing how control parameters of TCP Vegas should be configured for achieving both stability and better transient performance.

Although many research efforts have been devoted to network congestion in the face of an increase in the Internet traffic, there is little discussion on performance improvements for endhosts. We propose a new architecture, called scalable socket buffer tuning (SSBT), to provide high-performance and fair service for many TCP connections at Internet endhosts. SSBT has two major features. One is to reduce the number of memory accesses at the sender host by using some new system calls, called simple memory-copy reduction (SMR) scheme. The other is equation-based automatic TCP buffer tuning (E-ATBT), where the sender host estimates 'expected' throughput of the TCP connections through a simple mathematical equation, and assigns a send socket buffer to them according to the estimated throughput. If the socket buffer is short, the max-min fairness policy is used. We confirm the effectiveness of our proposed algorithm through both a simulation technique and an experimental system. From the experimental results, we have found that our SSBT can achieve up to a 30% gain for Web server throughput, and a fair and effective usage of the sender socket buffer can be achieved.

According to past research, a TCP Vegas version is able to achieve higher throughput than TCP Tahoe and Reno versions, which are widely used in the current Internet. However we need to consider a migration path for TCP Vegas to be deployed in the Internet. In this paper, by focusing on the situation where TCP Reno and Vegas connections share the bottleneck link, we investigate the fairness between two versions. From the analysis and the simulation results, we find that the performance of TCP Vegas is much smaller than that of TCP Reno as opposed to an expectation on TCP Vegas. The RED algorithm improves the fairness to some degree, but there may still be an inevitable trade-off between fairness and throughput. Accordingly, we consider two approaches to improve the fairness. The first one is to modify the congestion control algorithm of TCP Vegas, and the other is to modify the RED algorithm to detect misbehaved connections and drop more packets from those connections. We use both of analysis and simulation experiment for evaluating the fairness, and validate the effectiveness of the proposed mechanisms.

We investigate the performance of TCP under three representatives of packet scheduling algorithms at the router. Our main focus is to investigate how fair service can be provided for elastic applications sharing the link. Packet scheduling algorithms that we consider are FIFO (first in first out), RED (random early detection), and DRR (deficit round robin). Through simulation and analysis results, we discuss the degree of achieved fairness in those scheduling algorithms. Furthermore, we propose a new algorithm which combines RED and DRR algorithms in order to prevent the unfairness property of the original DRR algorithm, which appears in some circumstances where we want to resolve the scalability problem of the DRR algorithm. In addition to the TCP Reno version, we consider TCP Vegas to investigate its capability of providing fairness. The results show that the principle of TCP Vegas conforms to DRR, but it cannot help improving the fairness among connections in FIFO and RED cases, which seems to be a substantial obstacle for the deployment of TCP Vegas.

We focus on fairness and stability of the congestion control mechanisms adopted in several versions of TCP by investigating their time-transient behavior through an analytic approach. In addition to TCP Tahoe, TCP Reno, and TCP Vegas, we consider enhanced TCP Vegas which is proposed in this paper for fairness enhancements. We consider the homogeneous case, where two connections have the equivalent propagation delays, and the heterogeneous case, where each connection has different propagation delay. We show that TCP Tahoe and TCP Reno can achieve fairness among connections in the homogeneous case, but cannot in heterogeneous case. We also show that TCP Vegas can provide almost fair service among connection, but there is some unfairness caused by the essential nature of TCP Vegas. Finally, we explain the effectiveness of our enhanced TCP Vegas in terms of fairness and throughput.

Previous analytic approaches for all-optical networks have only allowed a random wavelength assignment policy in spite of the fact that network performance can be improved by other wavelength assignment policies such as first-fit wavelength assignment. We develop an approximate analytic method to allow flexible wavelength assignment policies by virtue of a layered-graph approach. Our analysis is also applicable to the networks that wavelength conversion is limitedly provided. By comparing with simulation results, we show that our analytic approach has good accuracies when the number of wavelengths is not large. We also show that our analysis is applicable to general network topologies.

Previous works have shown that wavelength conversion can considerably improve the performance in all-optical networks. However, it is an unrealistic assumption under the current technology and wavelength conversion with a limited range, not a full range, is more promising. In this paper, we propose a new analytic approach for such networks by virtue of the layered-graph where each wavelength is virtually assigned a separate link. Our analytic approach can allow a flexible routing method. For example, wavelength assignment and routing selection can be considered at the same time, which has never been treated in the past literature.

A rate-based congestion control algorithm regulates cell emission rate of source end systems based on feedback information from the network. It was standardized by the ATM Forum for application to an ABR (available bit rate) service class. In the standard, two types of congestion notification methods of the switch are specified: EFCI marking and explicit-rate marking. In this paper, we focus on the explicit-rate marking switch. We propose our enhancements on a recently proposed switch algorithm known as the max-min scheme. The main objective of our enhancements is to control the queue length of the switch for preventing cell loss and achieving full link-utilization. We show the effectiveness of our switch algorithm by simulation experiments.

We study routing methods in all-optical switching networks. In all-optical switching networks, the connection with more hops encounters more call blocking, and it is especially true in optical networks with no wavelength conversions. We therefore consider an alternate routing method with limited trunk reservation in which connections with more hops are prepared more alternate routes. Through developing an approximate analytic approach, we show that our method keeps good performance when compared with the existing alternate routing methods, and also that the fairness among connections can be improved. Further performance improvement is investigated by introducing a wavelength assignment policy and a dynamic routing method. An effectiveness of the proposed method is investigated through simulation.

The rate control method of MPEG-2 over the ATM DBR service class is proposed. It is intended to guarantee the quality of service required by video applications. The disadvantages of MPEG-2 Test Model 5 are resolved by introducing the bandwidth re-negotiation with the network. And user-oriented high quality video transfer can be guaranteed with the method.

An ATM switch with both input and output buffers provided with a back-pressure function has been proposed as a cost-effective switch architecture. The back-pressure function prohibits cell transmission from the input buffer to the corresponding output buffer to avoid cell loss at the output buffer due to a temporary congestion. Especially when this switch is applied to ATM LANs for data transfer services, its performance should be evaluated by taking into account bursty traffic. In this paper, we show the maximum throughput, the packet delay distribution, and the approximate packet loss probability of such an ATM switch for bursty traffic through an analytic method. In addition to a balanced traffic condition, an unbalanced traffic and a mixture of bursty and stream traffic are also analyzed. Through several numerical examples, we quantitatively show the effects of the average packet length and the output buffer size on its performance. Key words: ATM LAN, Input/Output Buffered Type S...

The FRP (fast reservation protocol) utilizes a unique feature of ATM (asynchronous transfer mode) technology. The FRP is supposed mainly to be applied to LAN interconnection. We provide an exact analysis for a class of the FRP to obtain the burst (a protocol data unit of FRP) level performance of the FRP. One of the main features of the ATM is that the transmission rate can be adjusted according to the network congestion states. In our modeling, this feature is incorporated in such a way that the bandwidth a source requests is reduced if an attempt to reserve the bandwidth is rejected by the network. A rationale behind this is that rejection of the bandwidth request indicates network congestion. Therefore, the request with the smaller bandwidth after the reservation failure enables better sharing of network resources, which results in a performance improvement. To solve our model, which contains a very huge number of system states, we introduce a new numerical approach which is an extension of Sumita and Rieders's (1991) replacement process approach. Through numerical examples, the appropriate transmission rate control method is examined, and considerations on backoff time and overhead of RM cells are also presented.

In ATM networks, preventive congestion control is widely recognized for efficiently avoiding congestion, and it is implemented by a conjunction of connection admission control and usage parameter control. However, congestion may still occur because of unpredictable statistical fluctuation of traffic sources even when preventive control is performed in the network. The authors study another kind of congestion control, i.e., reactive congestion control, in which each source changes its cell emitting rate adaptively to the traffic load at the switching node (or at the multiplexer). The intention is that, by incorporating such a congestion control method in ATM networks, more efficient congestion control is established. They develop an analytical model, and carry out an approximate analysis of reactive congestion control algorithm. Numerical results show that the reactive congestion control algorithms are very effective in avoiding congestion and in achieving the statistical gain. Furthermore, the binary congestion control algorithm with push-out mechanism is shown to provide the best performance among the reactive congestion control algorithms treated.>

The authors consider a three-stage switching configuration with no internal buffers, i.e., bufferless switches are used at the first and second stages, and output buffered switches at the third stage. Short-term cell loss probability is analyzed in order to examine the influence of bursty traffic on the performance of the bufferless switches used at the first two stages. A four-stage switching configuration with traffic distributors added at the first stage is proposed. This switch provides more paths between a pair of input and output ports than the three-stage configuration. Some schemes for distributing cells are compared. It is shown that the distributor successfully reduces cell loss probability due to bursty traffic by splitting incoming cells into several switching modules.>

The authors analyze the flow enforcement algorithm suitable for regulating the flow of the bursty traffic in asynchronous transfer mode (ATM) networks (for broadband packet switching). The flow enforcement algorithm has two objectives: to force the input traffic to conform to the traffic parameters which are specified by the source itself at its connection time; and to obtain greater performance gain at the cell multiplexer by regulating the flow to some extent. The authors reveal the tradeoff relationship between the regulation level of the cell flow and the improvement of the link performance. The authors first analyze the flow enforcement mechanism to derive the delay distribution and the interdeparture time distribution for cells at the flow enforcer. For the input traffic, they allow the general independent arrivals of cells to represent the traffic burstiness larger than a Poisson distribution. The obtained interdeparture time distributions from the flow enforcer are then used to approximately analyze the performance of the cell multiplexer. For this purpose, a Markov-modulated Poisson process (MMPP) approximation method is extended to investigate the performance of individual traffic stream in the superposed arriving traffic streams.>

The performance of nonblocking packet switches such as the knockout switch and Batcher banyan switch for high-speed communication networks can be improved as the switching capacity L per output increases; the switching capacity per output refers to the maximum number of packets transferred to an output during a slot. The N*N switch with L=N was shown to attain the best possible performance by M.J. Karol et al. (1987). Here a N*N nonblocking packet switch with input and output buffers is analyzed for an arbitrary number of L such that 1>

Admission control in asynchronous transfer mode (ATM) networks is considered. An ATM switch is modeled as a discrete-time single-server queue at which the following three different kinds of arrival processes are allowed to join together: arrivals of cells with a general interarrival time distribution; Bernoulli arrivals of cells in batches; and interrupted Poisson processes. An exact analysis is given to derive the waiting-time distributions and interdeparture-time distributions for arriving cells subject to admission control in ATM networks. The model is extended to approximately obtain the end-to-end delay distributions for the designated traffic stream. Such an analysis is important for voice or coded video because they require a playout mechanism at the destination. Since the analysis is approximate, the simulation results needed to assess its accuracy are provided. Numerical examples show how the traffic characteristics of the designated traffic stream are affected by the total traffic load at the switching node, the number of other bursty traffic sources, and the degree of burstiness of the traffic source itself.>

The major ATM switch architectures and their improvement techniques are categorized and discussed, focusing on performance issues. The performance measures of interest are the maximum throughput, the delay time, and the cell loss probability. The major assumptions and notations used are summarized. A survey is presented of nonblocking switches and their improvement techniques. The performances of a variety of nonblocking switches are compared.>

An exact analysis is presented for the 2*2 switching element with input port buffers that use the cell bypass queueing discipline. An approximate analysis is developed for the Banyan network composed of 2*2 switching elements with buffers that use the cell bypass queuing discipline. An investigation is made of the effect of this discipline on performance through numerical results obtained by analyses as well as simulation results. The authors also examine the nonuniform input traffic models. The analysis shows that the cell bypass queuing discipline considerably improves the performance of the ATM (asynchronous transfer mode) switches based on Banyan networks with respect to both throughput and cell blocking probability.>

The performance of an asynchronous transfer mode (ATM) network subject to admission control and traffic smoothing is analyzed. Basically, an ATM switch is modeled as a discrete-time single-server queuing system in which a new call joins existing calls. Cell arrivals from a new call are assumed to follow a general distribution. It is also assumed that aggregated arrivals of cells from existing calls form batch arrivals with a general batch size distribution and a geometric distribution of the interarrival times between batches. Both finite- and infinite-buffer cases are considered. An exact analysis yields the waiting time distribution and cell loss probability for a new call and for existing calls. Numerical examples are given to show how the network performance depends on the statistics of a new call (burstiness, time that a call stays in an active or inactive state, etc.) and to demonstrate the effectiveness of admission control and traffic smoothing.>

An analytical method is developed for a token ring network with single-buffer terminals and an infinite-capacity bridge in which the bridge has priority over the terminals is developed. The priority-mode operation realized by the reservation discipline of IEEE standard 802-5 is considered. An approximate analysis is considered for both terminals and the bridge in two reservation disciplines: the T-reservation discipline in which the reservation field resides in the message trailer, and the H-reservation discipline in which the reservation field resides in the message header. System performance measures (mean waiting times and throughput) derived for these disciplines are compared to those for a mixed discipline which is an alternative method for priority-mode operation in token ring networks. The accuracy of the approximate analytical results is assessed for both terminals and the bridge by comparing them to the simulation results. For almost all tried cases, it is shown to be within 5-20% relative error in the mean message waiting time.>

A token ring network is considered, with priority-mode operation by a reservation discipline which is adopted by an IEEE standard 802.5. In the reservation discipline, a high-priority station can reserve the next transmission right in the reservation field of the message transmitted by the low-priority station when the reservation field passes through the high-priority station. Then the free token at the high-priority level is passed to the high-priority station. After all messages from the high-priority stations are transmitted, the free token is returned to the station in the downstream of the previous low-priority station. To apply this reservation discipline to an interconnected token ring network system, an analytical model is constructed which consists of low-priority stations with single buffer and a single high-priority station with infinite buffer, which is intended to represent a bridge. An exact analysis for the low-priority stations and an approximate analysis for the high-priority station in the reservation discipline is presented. System performance measures (mean waiting times and throughput) derived from the present analysis are compared with a mixed discipline which is an alternative method for priority-mode operation in token ring networks.>

A two-layer performance model of a local-area network (LAN), consisting of a media-access control (MAC) layer submodel and transport layer submodels, is presented. A multiple-queue cyclic-service model of the token-passing MAC layer is used along with a closed-queueing network model for the transport layer. To deal with acknowledgement traffic with priority over data messages, a mean-value analysis (MVA) priority approximation is devised for the case where the priority can change at each queue. An iterative solution algorithm is proposed for this two-layer performance model. The method is applied to five models of communication systems: a symmetric-load, piggybacked acknowledgement model: a symmetric-load, explicit-acknowledgement model; full-duplex communication models (without and with priorities); and a client/server model. System performances measures such as throughput and mean-message delay, computed from the analysis, are compared to the simulation results over a wide range of parameters. In most numerical examples, the approximate analysis is shown to be within 90% confidence intervals in the mean-message delay, and within a few percent error in the throughput.>

Analysis is given to a nonpreemptive priority queueing system with P classes of messages where the class of message to be served next is the highest priority class waiting at the time of service start. (If this were the highest priority class waiting at the service completion epoch, we would have a classical nonpreemptive head-of-line priority queueing system.) We assume that the message service time distribution is identical for all classes. The mean message waiting time is obtained explicitly for each class, and numerically compared to the values in the corresponding head-of-line system. We have also proposed and evaluated a fairness measure to demonstrate the degree of discrimination. This model can be applied to the performance analysis of the prioritized token-ring scheme in local area computer networks when the propagation delay and bit latency are negligible compared to the frame transmission time.