In this paper, we investigate the evolution of an optical network architecture and discuss the future direction of research on optical network design and control. We review existing research on optical network design and control and present some open challenges. One of the important open challenges lies in multilayer resource optimization including IT and optical network resources. We propose an adaptive joint optimization method of IT resources and optical spectrum under time-varying traffic demand in optical networks while avoiding an increase in operation cost. We formulate the problem as mixed integer linear programming and then quantitatively evaluate the trade-off relationship between the optimality of reconfiguration and operation cost. We demonstrate that we can achieve sufficient network performance through the adaptive joint optimization while suppressing an increase in operation cost.

QoS of applications is essential for content providers, and it is required to improve the end-to-end communication quality from a content provider to users. Generally, a content provider's data center network is connected to multiple ASes and has multiple egress paths to reach the content user's network. However, on the Internet, the communication quality of network paths outside of the provider's administrative domain is a black box, so multiple egress paths cannot be quantitatively compared. In addition, it is impossible to determine a unique egress path within a network domain because the parameters that affect the QoS of the content are different for each network. We propose a “Performance Aware Egress Path Discovery” method to improve QoS for content providers. The proposed method uses two techniques: Egress Peer Engineering with Segment Routing over IPv6 and Passive End-to-End Measurement. The method is superior in that it allows various metrics depending on the type of content and can be used for measurements without affecting existing systems. To evaluate our method, we deployed the Performance Aware Egress Path Discovery System in an existing content provider network and conducted experiments to provide production services. Our findings from the experiment show that, in this network, 15.9% of users can expect a 30Mbps throughput improvement, and 13.7% of users can expect a 10ms RTT improvement.

This paper introduces a method to estimate latent traffic from its origin to destination from the link packet loss rate and traffic volume. In addition, we propose a method for the joint optimization of routing and link provisioning based on the estimated latent traffic. Observed traffic could deviate from the original traffic demand and become latent when the traffic passes through congested links because of changes in user behavioral and/or applications as a result of degraded quality of experience (QoE). The latent traffic is actualized by improving congested link capacity. When link provisioning is based on observed traffic, actual traffic might cause new congestion at other links. Thus, network providers need to estimate the origin-destination (OD) original traffic demand for network planning. Although the estimation of original traffic has been well studied, the estimation was only applicable for links. In this paper, we propose a method to estimate latent OD traffic by combining and expanding techniques. The method consists of three steps. The first step is to estimate the actual OD traffic and loss rate from the actual traffic and packet loss rate of the links. The second step is to estimate the latent traffic demand. Finally, using this estimated demand, the link capacity and routing matrix are optimized. We evaluate our method by simulation and confirm that congestion could be avoided by capacity provisioning based on estimated latent traffic, while provisioning based on observed traffic retains the congestion. The combined method can avoid congestion with an increment of 23% compared with capacity provisioning only. We also evaluated our method's adaptability, i.e., the ability to estimate the required parameter for the estimations using fewer given values, but values obtained in the environment.

Application-aware Traffic Engineering (TE) plays a crucial role in ensuring quality of services (QoS) for recently emerging applications such as AR, VR, cloud gaming, and connected vehicles. While a deterministic application-aware TE is required for these mission-critical applications, a negotiation procedure between applications and network operators needs to undergo major simplification to fulfill the scalability of the application based on emerging microservices and container-based architecture. In this paper, we propose a NetworkAPI framework which allows an application to indicate a desired TE behavior inside IP packets by leveraging Segment Routing over IPv6 (SRv6). In the NetworkAPI framework, the TE behavior provided by the network operator is expressed as an SRv6 Segment Identifier (SID) in the form of a 128-bit IPv6 address. Because the IPv6 address of an SRv6 SID is distributed using IP anycast, the application can utilize the unchanged SRv6 SID regardless of the application's location, as if the application controls an API on the transport network. We implement a prototype of the NetworkAPI framework on a Linux kernel. On the prototype implementation, a basic packet forwarding performance is evaluated to demonstrate the feasibility of our framework.

Traffic engineering refers to techniques to accommodate traffic efficiently by dynamically configuring traffic routes so as to adjust to changes in traffic. If traffic changes frequently and drastically, the interval of route reconfiguration should be short. However, with shorter intervals, obtaining traffic information is problematic. To calculate a suitable route, accurate traffic information of the whole network must be gathered. This is difficult in short intervals, owing to the overhead incurred to monitor and collect traffic information. In this paper, we propose a framework for traffic engineering in cases where only partial traffic information can be obtained in each time slot. The proposed framework is inspired by the human brain, and uses conditional probability to make decisions. In this framework, a controller is deployed to (1) obtain a limited amount of traffic information, (2) estimate and predict the probability distribution of the traffic, (3) configure routes considering the probability distribution of future predicted traffic, and (4) select traffic that should be monitored during the next period considering the system performance yielded by route reconfiguration. We evaluate our framework with a simulation. The results demonstrate that our framework improves the efficiency of traffic accommodation even when only partial traffic information is monitored during each time slot.

To efficiently use network resources, internet service providers need to conduct traffic engineering that dynamically controls traffic routes to accommodate traffic change with limited network resources. The performance of traffic engineering (TE) depends on the accuracy of traffic prediction. However, the size of traffic change has been drastically increasing in recent years due to the growth in various types of network services, which has made traffic prediction difficult. Our approach to tackle this issue is to separate traffic into predictable and unpredictable parts and to apply different control policies. However, there are two challenges to achieving this: dynamically separating traffic according to predictability and dynamically controlling routes for each separated traffic part. In this paper, we propose a macroflow-based TE scheme that uses different routing policies in accordance with traffic predictability. We also propose a traffic-separation algorithm based on real-time traffic analysis and a framework for controlling separated traffic with software-defined networking technology, particularly OpenFlow. An evaluation of actual traffic measured in an Internet2 network shows that compared with current TE schemes the proposed scheme can reduce the maximum link load by 34% (at the most congested time) and the average link load by an average of 11%.

This paper presents past and recent trends of optical networks and addresses the future directions. First, we describe path networks with the historical backgrounds and trends. path networks have advanced by using various multiplexing technologies. They include time-division multiplexing (TDM), asynchronous transfer mode (ATM), and wavelength-division multiplexing (WDM). ATM was later succeeded to multi-protocol label switching (MPLS). Second, we present generalized MPLS technologies (GMPLS). In GMPLS, the label concept of MPLS is extended to other labels used in TDM, WDM, and fiber networks. GMPLS enables network operators to serve networks deployed by different technologies with a common protocol suite of GMPLS. Third, we describe multi-layer traffic engineering and a path computation element (PCE). Multi-layer traffic engineering designs and controls networks considering resource usages of more than one layer. This leads to use network resources more efficiently than the single-layer traffic engineering adopted independently for each layer. PCE is defined as a network element that computes paths, which are used for traffic engineering. Then, we address software-defined networks, which put the designed network functions into the programmable data plane by way of the management plane. We describe the evaluation from GMPLS to software defined networking (SDN) and transport SDN. Fifth, we describe the advanced devices and switches for optical networks. Finally, we address advances in networking technologies and future directions on optical networking.

This article describes an approximate model of a group of cells in the wireless 4G network with implemented load balancing mechanism. An appropriately modified model of Erlang's Ideal Grading is used to model this group of cells. The model makes it possible to take into account limited availability of resources of individual cells to multi-rate elastic and adaptive traffic streams generated by Erlang and Engset sources. The developed solution allows the basic traffic characteristics in the considered system to be determined, i.e. the occupancy distribution and the blocking probability. Because of the approximate nature of the proposed model, the results obtained based on the model were compared with the results of a digital simulation. The present study validates the adopted assumptions of the proposed model.

Stability-featured dynamic multi-path routing (SDMR) based on the existing Traffic engineering eXplicit Control Protocol (TeXCP) is proposed and evaluated for traffic engineering in terrestrial networks. SDMR abandons the sophisticated stability maintenance mechanisms of TeXCP, whose load balancing scheme is also modified in the proposed mechanism. SDMR is proved to be able to converge to a unique equilibria state, which has been corroborated by the simulations.

Designing a backbone IP network, especially to support both unicast and multicast traffic under delay constraints, is a difficult problem. Real network design must consider cost, performance and reliability. Therefore, a simulator can help a network designer to test the functionality of the network before the implementation. This paper proposes a heuristic design algorithm called D-MENTOR, and the algorithm was developed by programming based on Mesh Network Topological Optimization and Routing Version 2 (MENTOR-II) to integrate as a new module of DElite tool. The simulation results show that, in almost all test cases, the proposed algorithm yields lower installation cost.

The hop-limited adaptive routing (HLAR) mechanism and its enhancement (EHLAR), both tailored for the packet-switched non-geostationary (NGEO) satellite networks, are proposed and evaluated. The proposed routing mechanisms exploit both the predictable topology and inherent multi-path property of the NGEO satellite networks to adaptively distribute the traffic via all feasible neighboring satellites. Specifically, both mechanisms assume that a satellite can send the packets to their destinations via any feasible neighboring satellites, thus the link via the neighboring satellite to the destination satellite is assigned a probability that is proportional to the effective transmission to the destination satellites of the link. The satellite adjusts the link probability based on the packet sending information observed locally for the HLAR mechanism or exchanged between neighboring satellites for the EHLAR mechanism. Besides, the path of the packets are bounded by the maximum hop number, thus avoiding the unnecessary over-detoured packets in the satellite networks. The simulation results corroborate the improved performance of the proposed mechanisms compared with the existing in the literature.

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.

In recent years, the time variation of Internet traffic has increased due to the growth of streaming and cloud services. Backbone networks must accommodate such traffic without congestion. Traffic engineering with traffic prediction is one approach to stably accommodating time-varying traffic. In this approach, routes are calculated from predicted traffic to avoid congestion, but predictions may include errors that cause congestion. We propose prediction-based traffic engineering that is robust against prediction errors. To achieve robust control, our method uses model predictive control, a process control method based on prediction of system dynamics. Routes are calculated so that future congestion is avoided without sudden route changes. We apply calculated routes for the next time slot, and observe traffic. Using the newly observed traffic, we again predict traffic and re-calculate the routes. Repeating these steps mitigates the impact of prediction errors, because traffic predictions are corrected in each time slot. Through simulations using backbone network traffic traces, we demonstrate that our method can avoid the congestion that the other methods cannot.

Large-scale content transfer, especially video transfer, is now a dominant traffic component in the Internet. Originally, content transfer had a content-oriented feature, i.e., “Users do not care where content is retrieved. Users only take care of what content they obtain.” Conventional traffic engineering (TE) aims to obtain optimal routes for traffic between ingress and egress router pairs, i.e., TE has focused on a location-oriented approach that takes care of where to connect. With increased demand for content-oriented features for content traffic, TE needs to focus on content-oriented routing design. In this study, we therefore propose a novel approach to content-oriented TE, called content aware routing (CAR). In CAR, routes are designed for content and egress router pairs, i.e., content traffic toward a receiver-side router. Content demand can be flexibly distributed to multiple servers (i.e., repositories) providing the same content, meaning that content can be obtained from anywhere. CAR solves the optimization problem of minimizing maximum link utilization. If there are multiple optimal solutions, CAR selects a solution in which resource usage is minimized. Using numerical examples formulated by the linear programming problem, we evaluated CAR by comparing it with combinations of conventional content delivery networks and TE, i.e., location-oriented designs. Our numerical results showed that CAR improved maximum link utilization by up to 15%, with only a 5% increase of network resource usage.

In recent years, technologies and markets related to data centers have been rapidly changing and growing. Data centers are playing an important role in ICT infrastructure deployment and promise to become common platforms for almost all social infrastructures. Even though research has focused on networking technologies, various technologies are needed to develop high-performance, cost-efficient, and flexible large-scale data centers. To understand those technologies better, this paper surveys recent research and development efforts and results in accordance with a data center network taxonomy that the authors defined.

This paper describes a novel channel allocation and DBA (Dynamic Bandwidth Allocation) mechanism for ECDM-PON (Electric Code Division Multiplex -- Passive Optical Network) systems. In the current ECDM-PON systems, each ONU (Optical Network Unit) is limited to 2 or 3 CDM channels. This is because (fixed channel) CDM transmitters are expensive, and tunable CDM transmitters even more expensive. With a small number of CDM channels, the bandwidth utilization ratio is restricted by channel blocking. Our proposed mechanisms can reduce the channel blocking ratio without increasing the number of CDM transmitters or using tunable CDM transmitters. To clarify the advantages of the proposed system performance, we have evaluated the channel non-blocking ratio (Rn) and wasted resource ratio (Rw) when some users request bandwidth more than 100%. Evaluation of the non-blocking ratio, Rn shows that the proposed mechanisms approach the performance of a system with tunable CDM transmitters when the number of ONUs with over 100% traffic load is small. We have also simulated throughput for uniform traffic. In addition to these evaluations, we implemented our proposed mechanism on an FPGA (Field Programming Gate Array) and evaluated the calculation speed to allocate timeslots on CDM channels and a timeline.

This paper presents an autonomous traffic engineering framework, named ATE, a highly efficient data dissemination mechanism for multipath data forwarding in Wireless Sensor Networks (WSNs). The proposed ATE has several salient features. First, ATE utilizes three coordinating schemes: an incipient congestion inference scheme, an accurate link quality estimation scheme and a dynamic traffic diversion scheme. It significantly minimizes packet drops due to congestion by dynamically and adaptively controlling the data traffic over congested nodes and/or poorer quality links, and by opportunistically exploiting under-utilized nodes for traffic diversion, while minimizing the estimation and measurement overhead. Second, ATE can provide with high application fidelity of the network even for increasing values of bit error rates and node failures. The proposed link quality estimation and congestion inference schemes are light weight and distributed, improving the energy efficiency of the network. Autonomous Traffic Engineering has been evaluated extensively via NS-2 simulations, and the results have shown that ATE provides a better performance with minimum overhead than those of existing approaches.

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 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 that uses 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 at previous 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 fail to match the current traffic, our method detects mismatch. Then, so as to capture the current traffic variations, the method re-estimates the long-term variations after removing monitored data corresponding to the end-to-end traffic causing the mismatches. We evaluate 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.

Traffic Engineering (TE) is important for improving QoS in forwarding paths by efficient use of network resources. In fact, MPLS allows several detour paths to be (pre-)established for some source-destination pair as well as its primary path of minimum hops. Thus, we focus on a two-phase path management scheme using these two kinds of paths. In the first phase, each primary path is allocated to a flow on a specific source-destination pair if the path is not congested, i.e., if its utilization is less than some predetermined threshold; otherwise, as the second phase, one of the detour paths is allocated randomly if the path is available. Therefore, in this paper, we analytically evaluate this path management scheme by extending the M/M/c/c queueing system, and through some numerical results we investigate the impact of a threshold on the flow-blocking probability. Through some numerical results, we discuss the adequacy of the path management scheme for MPLS-TE.

This letter proposes a novel method of large-scale IP traffic matrix (TM) estimation, called algebraic reconstruction technique inference (ARTI), which is based on the partial flow measurement and Fratar model. In contrast to previous methods, ARTI can accurately capture the spatio-temporal correlations of TM. Moreover, ARTI is computationally simple since it uses the algebraic reconstruction technique. We use the real data from the Abilene network to validate ARTI. Simulation results show that ARTI can accurately estimate large-scale IP TM and track its dynamics.