The Order/Radix Problem (ORP) is an optimization problem that can be solved to find an optimal network topology in distributed memory systems. It is important to find the optimum number of switches in the ORP. In the case of a regular graph, a good estimation of the preferred number of switches has been proposed, and it has been shown that simulated annealing (SA) finds a good solution given a fixed number of switches. However, generally the optimal graph does not necessarily satisfy the regular condition, which greatly increases the computational costs required to find a good solution with a suitable number of switches for each case. This study improved the new method based on SA to find a suitable number of switches. By introducing neighborhood searches in which the number of switches is increased or decreased, our method can optimize a graph by changing the number of switches adaptively during the search. In numerical experiments, we verified that our method shows a good approximation for the best setting for the number of switches, and can simultaneously generate a graph with a small host-to-host average shortest path length, using instances presented by Graph Golf, an international ORP competition.

Random network topologies have been proposed as a low-latency network for parallel computers. Although multicast is a common collective-communication operation, multicast algorithms each of which consists of a large number of unicasts are not well optimized for random network topologies. In this study, we firstly apply a two-opt algorithm for building efficient multicast on random network topologies. The two-opt algorithm creates a skilled ordered list of visiting nodes to minimize the total path hops or the total possible contention counts of unicasts that form the target multicast. We secondly extend to apply the two-opt algorithm for the other collective-communication operations, e.g., allreduce and allgather. The SimGrid discrete-event simulation results show that the two-opt multicast outperforms that in typical MPI implementation by up to 22% of the execution time of an MPI program that repeats the MPI_Bcast function. The two-opt allreduce and the two-opt allgather operations also improve by up to 15% and 14% the execution time when compared to those used in typical MPI implementations, respectively.

In this article, we propose a method to identify the link layer home network topology, motivated by applications to cost reduction of support centers. If the topology of home networks can be identified automatically and efficiently, it is easier for operators of support centers to identify fault points. We use MAC address forwarding tables (AFTs) which can be collected from network devices. There are a couple of existing methods for identifying a network topology using AFTs, but they are insufficient for our purpose; they are not applicable to some specific network topologies that are typical in home networks. The advantage of our method is that it can handle such topologies. We also implemented these three methods and compared their running times. The result showed that, despite its wide applicability, our method is the fastest among the three.

High-performance computing (HPC) has penetrated into various research fields, yet the increase in computing power is limited by conventional electrical interconnections. The proposed architecture, NEST, exploits wavelength routing in arrayed waveguide grating routers (AWGRs) to achieve a scalable, low-latency, and high-throughput network. For the intra pod and inter pod communication, the symmetrical topology of NEST reduces the network diameter, which leads to an increase in latency performance. Moreover, the proposed architecture enables exponential growth of network size. Simulation results demonstrate that NEST shows 36% latency improvement and 30% throughput improvement over the dragonfly on an average.

Software-defined networking (SDN) has emerged as a promising approach to enable network innovation, which can provide network virtualization through a hypervisor plane to share the same cloud datacenter network among multiple virtual networks. While, this attractive approach may bring some new problem that leads to more susceptible to the failure of network component because of the separated control and forwarding planes. The centralized control and virtual network sharing the same physical network are becoming fragile and prone to failure if the topology of virtual network and the control path is not properly designed. Thus, how to map virtual network into physical datacenter network in virtualized SDN while guaranteeing the survivability against the failure of physical component is extremely important and should fully consider more influence factors on the survivability of virtual network. In this paper, combining VN with SDN, a topology-aware survivable virtual network embedding approach is proposed to improve the survivability of virtual network by an enhanced virtual controller embedding strategy to optimize the placement selection of virtual network without using any backup resources. The strategy explicitly takes account of the network delay and the number of disjoint path between virtual controller and virtual switch to minimize the expected percentage of control path loss with survivable factor. Extensive experimental evaluations have been conducted and the results verify that the proposed technology has improved the survivability and network delay while keeping the other within reasonable bounds.

End-to-end network latency has become an important issue for parallel application on large-scale high performance computing (HPC) systems. It has been reported that randomly-connected inter-switch networks can lower the end-to-end network latency. This latency reduction is established in exchange for a large amount of routing information. That is, minimal routing on irregular networks is achieved by using routing tables for all destinations in the networks. In this work, a novel distributed routing method called LOREN (Layout-Oriented Routing with Entries for Neighbors) to achieve low-latency with a small routing table is proposed for irregular networks whose link length is limited. The routing tables contain both physically and topologically nearby neighbor nodes to ensure livelock-freedom and a small number of hops between nodes. Experimental results show that LOREN reduces the average latencies by 5.8% and improves the network throughput by up to 62% compared with a conventional compact routing method. Moreover, the number of required routing table entries is reduced by up to 91%, which improves scalability and flexibility for implementation.

A number of battery-driven sensor nodes are deployed to operate a wireless sensor network, and many routing protocols have been proposed to reduce energy consumption for data communications in the networks. We have proposed a new routing policy which employs a nearest-neighbor forwarding based on hop progress. Our proposed routing method has a topology parameter named forwarding angle to determine which node to connect with as a next-hop, and is compared with other existing policies to clarify the best topology for energy efficiency. In this paper, we also formulate the energy budget for networks with the routing policy by means of stochastic-geometric analysis on hop-count distributions for random planar networks. The formulation enables us to tell how much energy is required for all nodes in the network to forward sensed data in a pre-deployment phase. Simulation results show that the optimal topology varies according to node density in the network. Direct communication to the sink is superior for a small-sized network, and the multihop routing is more effective as the network becomes sparser. Evaluation results also demonstrate that our energy formulation can well approximate the energy budget, especially for small networks with a small forwarding angle. Discussion on the error with a large forwarding angle is then made with a geographical metric. It is finally clarified that our analytical expressions can obtain the optimal forwarding angle which yields the best energy efficiency for the routing policy when the network is moderately dense.

Particle swarm optimizer network (PSON) is one of the multi-swarm PSOs. In PSON, a population is divided into multiple sub-PSOs, each of which searches a solution space independently. Although PSON has a good solving performance, it may be trapped into a local optimum solution. In this paper, we introduce into PSON a dynamic stochastic network topology called “PSON with stochastic connection” (PSON-SC). In PSON-SC, each sub-PSO can be connected to the global best (gbest) information memory and refer to gbest stochastically. We show clearly herein that the diversity of PSON-SC is higher than that of PSON, while confirming the effectiveness of PSON-SC by many numerical simulations.

In this paper, we give a formalization of the behavior of the Content-Centric Networking (CCN) protocol with parameterizing content managements. CCN is a communications architecture that is based on the names of contents, rather than on addresses. In the protocol used in CCN, each node sends packets to the nodes that are connected to it, which communicate with further nodes that are connected to them. This kind of behaviors prevents formalizing the CCN protocol as end-to-end communications. In our previous work, we formalized the CCN protocol using the proof assistant Coq. However, in this model, each node in the network can store any number of contents. The storage for each node is usually limited and the node may drop some of the contents due to its filled storage. The model proposed in this paper permits a node to have its own content management method, and still keeps the temporal properties that are also valid in the previous model. To demonstrate difference between these models, we give a specification that is valid in the previous model but invalid in the proposed model, called orthogonality. Since it is generally invalid in CCN, the proposed model is more precise than the previous one.

In the paradigm of network coding, when the network topology information cannot be utilized completely, random linear network coding (RLNC) is proposed as a feasible coding scheme. But since RLNC neither considers the global network topology nor coordinates codings between different nodes, it may not achieve the best possible performance of network coding. Hence, the performance analysis of RLNC is very important for both theoretical research and practical applications. Motivated by a fact that different network topology information can be available for different network communication problems, we study and obtain several upper and lower bounds on the failure probability at sink nodes depending on different network topology information in this paper, which is also the kernel to discuss some other types of network failure probabilities. In addition, we show that the obtained upper bounds are tight, the obtained lower bound is asymptotically tight, and we give the worst cases for different scenarios.

This paper presents a particle swarm optimization network (PSON) to improve the search capability of PSO. In PSON, multi-PSOs are connected for the purpose of communication. A variety of network topology can be realized by varying the number of connected PSOs of each PSO. The solving performance and convergence speed can be controlled by changing the network topology. Furthermore, high parallelism is can be realized by assigning PSO to single processor. The stability condition analysis and performance of PSON are shown.

The relationship between different brain areas is characterized by functional networks through correlations of time series obtained from neuroimaging experiments. Due to its high spatial resolution, functional MRI data is commonly used for generating functional networks of the entire brain. These networks are comprised of the measurement points/channels as nodes and links are established if there is a correlation in the measured time series of these nodes. However, since the evaluation of correlation becomes more accurate with the length of the underlying time series, we construct in this paper functional networks from MEG data, which has a much higher time resolution than fMRI. We study in particular how the network topologies change in an experiment on ambiguity of words, where the subject first receives a priming word before being presented with an ambiguous or unambiguous target word.

Network topology significantly affects network cost, path length, link load distribution, and reliability, so we need to consider multiple criteria with different units simultaneously when designing a network's topology. The analytic hierarchy process (AHP) is a technique of balancing multiple criteria in order to reach a rational decision. Using AHP, we can reflect the relative importance of each criterion on the evaluation result; therefore, we have applied it to network topology evaluation in past research. When evaluating network topologies using AHP, we need to construct the set of topology candidates prior to the evaluation. However, the time required to construct this set greatly increases as the network size grows. In this paper, we propose applying a binary partition approach for constructing a topology candidate set with dramatically reduced calculation time. To reduce the calculation time, we introduce an upper limit for the total link length. Although the results of AHP are affected by introducing the upper limit of the total link length, we show that desirable topologies are still selected in AHP.

Network structures can be found in almost any kind of natural or artificial systems as transport medium for communication between the respective nodes. In this paper we study certain key topological features of brain functional networks obtained from functional magnetic resonance imaging (fMRI) measurements. We compare complex network measures of the extracted topologies with those from Internet service providers (ISPs). Our goal is to identify important features which will be helpful in designing more robust and adaptive future information network architectures.

Bittorrent is one of the most popular and successful applications in the current Internet. However, we still have little knowledge about the topology of real Bittorrent swarms, how dynamic the topology is, and how it affects overall behavior. This paper describes an experimental study of the overlay topologies of real-world Bittorrent networks, focusing on the activity of the nodes of its P2P topology and especially their dynamic relationships. Peer Exchange Protocol (PEX) messages are analyzed to infer topologies and their properties, capturing the variations of their behavior. Our measurements, verified using the Kolmogorov-Smirnov goodness of fit test and the likelihood ratio test and confirmed via simulation, show that a power-law with exponential cutoff is a more plausible model than a pure power-law distribution. We also found that the average clustering coefficient is very low, supporting this observation. Bittorrent swarms are far more dynamic than has been recognized previously, potentially impacting attempts to optimize the performance of the system as well as the accuracy of simulations and analyses.

The emergence of new services in the cloud computing era has made smooth service migration an important issue in access networks. However, different types of equipment are typically used for the different services due to differences in service requirements. This leads to an increase in not only capital expenditures but also operational expenditures. Here we propose using a service adaptive approach as a solution to this problem. We analyze the requirements of a future access network in terms of service, network, and node. We discuss available access network technologies including the passive optical network, single star network. Finally, we present a future service adaptive access/aggregation network and its architecture along with a programmable optical line terminal and optical network unit, discuss its benefit, and describe example services that it would support.

This paper presents a new particle swarm optimizer characterized by growing tree topology. If a particle is stagnated then a new particle is born and is located away from the trap. Depending on the property of objective problems, particles are born successively and the growing swarm constitutes a tree-topology. Performing numerical experiments for typical benchmarks, the algorithm efficiency is evaluated in several key measures such as success rate, the number of iterations and the number of particles. As compared with other basic PSOs, we can suggest that the proposed algorithm has efficient performance in optimization with low-cost computation.

This paper describes a general quantum lower bounding technique for the communication complexity of a function that depends on the inputs given to two parties connected via paths, which may be shared with other parties, on a network of any topology. The technique can also be employed to obtain a lower-bound of the quantum communication complexity of some functions that depend on the inputs distributed over all parties on the network. As a typical application, we apply our technique to the distinctness problem of deciding whether there are a pair of parties with identical inputs, on a k-party ring; almost matching upper bounds are also given.

This paper proposes a novel hybrid NoC structure and a dynamic job distribution algorithm which can reduce system area and power consumption by reducing packet drop rate for various multimedia applications. The proposed NoC adopts different network structures between sub-clusters. Network structure is determined by profiling application program so that packet drop rate can be minimized. The proposed job distribution algorithm assigns every job to the sub-cluster where packet drop rate can be minimized for each multimedia application program. The proposed scheme targets multimedia applications frequently used in modern embedded systems, such as MPEG4 and MP3 decoders, GPS positioning systems, and OFDM demodulators. Experimental results show that packet drop rate was reduced by 31.6% on the average, when compared to complex network structure topologies consisting of sub-clusters of same topology. Chip area and power consumption were reduced by 16.0% and 34.0%, respectively.

Network topology has no direct effect on the correctness of network protocols, however, it influences the performance of networks and their survivability when they are under attack. Recent studies have analyzed the robustness of the Internet in the face of faults or attacks which may cause node failures. However, the effect of link failure or a series of link failures has not been extensively examined, even though such a situation is more likely to occur in the current Internet environment. In this paper, we propose an attack-and-failure graph model and practical techniques for attacking strategies against nodes, edges or paths in order to reflect real-life attack scenarios. The resiliency of Internet topologies is examined under the attacking strategies, with various metrics including path-failure ratio and "attack power," which is defined as the ratio of the failure to attack. The experiments reveal that "path-based" attacks can result in greater damage to the connectivity of a network than the other types of attack. Nonetheless, the effectiveness of an attack depends on the objective that the attacker wants to achieve through the attack. The proposed simple but formalized approach can be a springboard for developing more resilient Internet topologies in a variety of aspects.