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Currently, the multiprotocol label switching (MPLS) standard is extremely prevalent. By exploiting the features provided by MPLS technology, a range of services, including IP telephony, have enhanced their overall performance. However, due to the size of the packet header, the IP telephony service consumes a significant portion of the MPLS network's available bandwidth. For instance, in IP telephony over MPLS networks, the packet header might account for as much as 80% of lost time and bandwidth. Designers working on IP telephony are making substantial efforts to address this issue. This study contributes to current efforts by proposing a novel approach called Tel-MPLS, which involves IP telephony over MPLS. TelMPLS approach uses the superfluous fields in the IP telephony packet's header to retain the packet data, therefore lowering or zeroing the IP telephony packet's payload. Tel-MPLS is an approach that significantly reduces the bandwidth of IP telephony MPLS networks. According to the findings, the Tel-MPLS approach is capable of reducing the amount of bandwidth that is lost by 12% when using the G.729 codec. [ABSTRACT FROM AUTHOR]

An ensemble alignment subspace adaptation (EASA) method is proposed in this letter for the cross-scene classification. It can settle the problem of both foreign objects in the same spectrum and different spectra. The algorithm combines the idea of ensemble learning with the domain adaptive (DA) algorithm. Considering the sample imbalance problem of the original data (OD), the source data (SD) are obtained by multiple random sampling of OD according to certain rules and used as input. Then, geometric alignment and statistical alignment of SD and target data (TD) are performed to build a communal subspace, followed by the classification of TD. The classification labels are finally ensembled by counting the multiple classification results with retaining valid information. This technique can reduce the uncertainty and randomness of generating subspace projections. The experimental results on two real datasets show that the proposed algorithm has a terrific accuracy improvement compared with the traditional machine learning and DA methods. [ABSTRACT FROM AUTHOR]

The number of tunnels configured and state kept in IP/MPLS backbones depends on the number of flows and traffic engineering requirements. Segment routing automates tunnel configuration and reduces state in the network, based on the concept of segments: subpaths of the graph. A flow can be defined using only one segment if the route matches the shortest path computed by the IGP, while this number grows with the need for different subpaths. As a consequence, there is a trade-off between traffic engineering and the number of segments used, which translates to header overhead and state in routers. The challenge then is to have flows sharing as many segments as possible. We advance the state of the art with a two-step bi-objective optimization model to reduce the number of configured segments, considering two traffic engineering requirements, load balancing and latency. Our results show that, as we increase the number of flows in the network, the number of configured segments also increases, and then stabilizes regardless of the number of additional flows. Hence, using a real telecommunication network, we show that we can meet traffic engineering requirements with less than 22% of the total number of states as compared to the usual case of IP/MPLS backbones. [ABSTRACT FROM AUTHOR]

With the rapid growth of fifth-generation (5G), network operators have met with difficulties in ensuring user satisfaction due to diverse service-level agreement (SLA) requirements. Application-aware routing is able to potentially address this issue in implementing differentiated routing policies corresponding to various applications. To facilitate the application-aware routing, software-defined networking (SDN) and segment routing (SR) are potential solutions with programmability property to steer network traffic into appropriate routing paths. However, application-aware SR encounters two main problems, including application identification for encrypted traffic and path identification with human intervention. Therefore, this article proposes a new SDN-based SR mechanism that could help network operators overcome these problems. This approach implements reinforcement learning to adapt to dynamic networks in order to optimize the quality of experience (QoE) that network operators must guarantee. The proposal also considers the application class to implement corresponding routing policies for various applications to meet the stringent SLA requirements. Identifying the applications is sometimes complicated due to encrypted network traffic. Hence, our proposal implements a traffic classification approach to classify encrypted traffic into different kinds of applications. Obtained results under considered conditions illustrate that our proposal outperforms the standard SR mechanism related to QoE and decreases up to 64.39% of overhead compared to several benchmarks. [ABSTRACT FROM AUTHOR]

Today, more and more enterprises are embarking on a digital transformation where most of their applications are hosted in the Cloud. As a result, a reliable Wide Area Network (WAN) has become a primary need to interconnect their distributed branch offices and data centers that accommodate those applications. Software-Defined Wide Area Network (SD-WAN) represents the most promising technology solution for next-generation enterprise networks, being able to increase network agility and reduce costs. In this paper, we present an experimental SD-WAN solution capable of running and optimizing delay-sensitive high-priority services, such as real-time video streaming, while minimizing downtime caused by network failures. This solution comprises a monitoring and a traffic engineering system for SD-WAN. The first consists of a Transport-layer Passive Monitoring (TPM) system based on extended Berkeley Packet Filter (eBPF) technology with the goal of monitoring TCP flows; the second consists of an application, running inside the SD-WAN controller, with the goal of orchestrating the network traffic in consideration of the monitoring measurements by ensuring rapid recovery and resilience in case of unexpected congestion events. We validate our solution over two SD-WAN testbeds: the first is hosted in our laboratory at Politecnico di Milano, while the second is deployed in a municipal network of an Italian city. Results show that our SD-WAN solution can increase the overall service availability while meeting the stringent QoS requirements of delay-sensitive services.s [ABSTRACT FROM AUTHOR]

The magnon propagation length (MPL) is one of the crucial factors to govern spin transport in a magnetic material. Here, we determine MPL ($\xi$) in compensated ferrimagnetic Gd3Fe5O12 (GdIG) thin films at room temperature by investigating longitudinal spin Seebeck effect (LSSE) in Gd3Ga5O12 (GGG)/GdIG($t$)/Pt (5 nm) heterostructures with different thicknesses ($t$) of the GdIG film. All films possess in-plane (IP) magnetic easy axis, and the compensation temperature shifts down to lower temperature with decreasing GdIG thickness. At room temperature, the LSSE signal is rather constant with thickness between $t = 221$ and 89 nm, but drops significantly below $t = 50$ nm. By fitting the thickness dependence of the LSSE voltage to a modified atomistic spin model considering the influence of saturation magnetization, we obtain $\xi $ = 45 nm ± 8 nm for GdIG thin films at room temperature. The MPL of GdIG is much shorter than that of Y3Fe5O12 films ($\xi $ ~90–140 nm), but higher than that of Fe3O4 films ($\xi $ ~20 nm). The obtained value of $\xi $ explains the drop in the LSSE voltage in GdIG films when the film thickness is reduced below 50 nm, knowledge of which is also essential to the design of spincaloritronic devices based on GdIG films. [ABSTRACT FROM AUTHOR]

Compositional Optimization (CompOpt) was recently proposed for optimization of discrete-event systems of systems. A modular optimization model allows CompOpt to divide the optimization into separate sub-problems, mitigating the state space explosion problem. This paper presents the Modular Optimization Learner (MOL), a method that interacts with a simulation of a system to automatically learn these modular optimization models. MOL uses a modular learning that takes as input a hypothesis structure of the system and uses the provided structural information to split the acquired learning into a set of modules, and to prune parts of the search space. Experiments show that modular learning reduces the state space by many orders of magnitude compared to a monolithic learning, which enables learning of much larger systems. Furthermore, an integrated greedy search heuristic allows MOL to remove many sub-optimal paths in the individual modules, speeding up the subsequent optimization. Note to Practitioners—Automation systems are becoming increasingly large and complex and the automation of more and more advanced tasks often requires the coordination of multiple subsystems. Optimization can have a great impact on the efficiency of these systems in terms of cost and operation speed. Finding optimal solutions is, however, a difficult task. As the number of tasks and subsystems of the automation system increases, the search space of the optimization problems tends to grow exponentially. Compositional Optimization (CompOpt) is a method specifically designed for the optimization of large-scale automation systems. A challenge with the application of CompOpt is that it takes as input a specific type of optimization model that divides the system into subsystems; like machines, vehicles, etc. Formulating these models requires a high level of expertise and system knowledge. This paper addresses this challenge with an algorithm that learns these models from a simulation of the system. The simulation can be implemented in any software as long as a suitable interface exists or can be constructed. To divide the learning into subsystems, the algorithm uses an initial plant structure hypothesis (PSH). This can be viewed as a meta-model that includes known structural information, such as the number of subsystems and which actions that affect each subsystem. The more structural information that is added to PSH, the more efficient the learning and subsequent optimization will be. The purpose of this is to reduce the level of expertise and system knowledge needed in the application of optimization, to simplify the transition to Industry 4.0. [ABSTRACT FROM AUTHOR]

Coverage performance is very important for operators when commercializing NR networks due to its direct impact on service quality as well as CAPEX and OPEX. Compared to LTE, NR systems may operate at higher frequency bands, resulting in larger path loss and worse coverage performance. In this article, potential coverage issues for NR networks are discussed. Through link budget analysis, the gaps between baseline and target performance for typical NR physical channels are described; then the bottleneck channels are identified, including PUSCH, PUCCH, and PRACH. This article mainly focuses on PUSCH coverage enhancement, and potential solutions are discussed in a comprehensive manner. On top of the potential solutions, four joint schemes, which take feasibility as well as specification impact into consideration, are proposed for VoIP and eMBB services to further improve the coverage performance. [ABSTRACT FROM AUTHOR]

This work experimentally demonstrates for the first time the practical feasibility of a freeform multipath lens (MPL) for optical wireless communications (OWC). The MPL is able to increase the eye safety laser power limit. Our transmitter is laser class 1M and features 190 mW optical output. We transmit 1.289 Gbit/s on-off keying (OOK) data and achieve a bit error ratio (BER) of 10−10 for an optical receiver power of −31.2 dBm. The link range is more than 5 m, depending on the targeted BER. We discuss the pros and cons of MPLs and compare them with diffusers. [ABSTRACT FROM AUTHOR]

Future communication networks are expected to be highly automated, disburdening human operators of their most complex tasks. While the first powerful and automated network analysis tools are emerging, existing tools provide only limited and inefficient support of reasoning about failure scenarios. We present P-REX, a fast what-if analysis tool, that allows us to test important reachability and policy-compliance properties even under an arbitrary number of failures and in polynomial-time, i.e., without enumerating all failure scenarios (the usual approach today, if supported at all). P-REX targets networks based on Multiprotocol Label Switching (MPLS) and its Segment Routing (SR) extension which feature fast rerouting mechanisms with label stacks. In particular, P-REX allows to reason about recursive backup tunnels, by supporting potentially infinite state spaces. As P-REX directly operates on the actual dataplane configuration, i.e., forwarding tables, it is well-suited for debugging. Our tool comes with an expressive query language based on regular expressions. We also report on an industrial case study and demonstrate that our tool can perform what-if reachability analyses on average in about 5 seconds for a 24-router network with over 250,000 MPLS forwarding rules. This is a significant improvement to an earlier prototype of our tool presented in the conference version of our paper where the verification took on average about 1 hour. [ABSTRACT FROM AUTHOR]

Many organizations nowadays have multiple sites at different geographic locations. Typically, transmitting massive data among these sites relies on the interconnection service offered by ISPs. Segment Routing over IPv6 (SRv6) is a new simple and flexible source routing solution which could be leveraged to enhance interconnection services. Compared to traditional technologies, e.g., physical leased lines and MPLS-VPN, SRv6 can easily enable quick-launched interconnection services and significantly benefit from traffic engineering with SRv6-TE. To parse the SRv6 packet headers, however, hardware support and upgrade are needed for the conventional routers of ISP. In this paper, we study the problem of SRv6 incremental deployment to provide a more balanced interconnection service from a traffic engineering view. We formally formulate the problem as an SRID problem with integer programming. After transforming the SRID problem into a graph model, we propose two greedy methods considering short-term and long-term impacts with reinforcement learning, namely GSI and GLI. The experiment results using a public dataset demonstrate that both GSI and GLI can significantly reduce the maximum link utilization, where GLI achieves a saving of 59.1% against the default method. [ABSTRACT FROM AUTHOR]

A technique to suppress both the second and third order nonlinear distortions in a long-haul microwave photonic link (MPL) is presented. The MPL consists of a dual-polarization dual-parallel Mach-Zehnder modulator (DP-DPMZM), a polarizer, a length of fiber, an optical bandpass filter (OBPF), and a balanced photodetector (BPD). The DP-DPMZM has two sub-DPMZMs. For a two-tone signal at f1 and f2, the second order harmonic distortions (SHD) and the second order intermodulation distortions (IMD2) at f2+f1 are suppressed by biasing one sub-DPMZM to operate with single-sideband suppressed carrier (SSB-SC) modulation. The IMD2 at f2–f1 is suppressed by balanced detection at the BPD. The third order intermodulation distortions (IMD3) are suppressed by adjusting the state of polarization of the light wave into the polarizer. The spurious free dynamic range (SFDR) in a multi-octave band is free from fiber dispersion effect due to the bias setting of the DP-DPMZM. As a result, a high multi-octave SFDR can be obtained irrespective of the transmission distance. The proposed MPL is analyzed theoretically and is verified experimentally. The measured multi-octave SFDR is 95.9 dB·Hz1/2 and 92.1 dB·Hz1/2 when a long fiber link at 10 km and 20 km is used for signal transmission, respectively, which is 16.1 dB and 17.3 dB higher compared to a conventional Mach-Zehnder modulator (MZM) based MPL operating under the same condition. [ABSTRACT FROM AUTHOR]

Network operators cannot change their foot-print to upgrade the entire network to support SDN from scratch. Hence, network operators adapted the original SDN concept into a hybrid SDN approach to have a pragmatic, evolutionary, and economically viable solution. This article tests an SDN architecture based on a hierarchical structure of a Software-Defined Transport Networking (SDTN) controller, which deals with the end-to-end service provision and topology collection on top of a set of SDN controllers for each technological domain. The authors demonstrate L3/L2 VPN service deployment using an underlay multi-domain IP/Optical network in a field trial. The approach allows a service provider to migrate brownfield scenarios into SDN domains using programmatic interfaces. [ABSTRACT FROM AUTHOR]

Multi-photon lithography -a powerful laser nanoscale additive-manufacturing method- is employed for structuring micro-ring traveling-wave resonators onto micrometric diameter, optical fiber tapers. These weakly guided, micro-ring resonating structures achieve light circulation with Q-factors of the order of ∼2.0 × 103, for typical diameters of tens of micrometers, in the spectral band of 1550 nm. The parametrization of the fabrication process, the characterization of these structures in TE and TM polarization, and the numerical simulation of their spectral performance is presented and analyzed. Moreover, these micro-ring resonators are exemplified into the demonstration of an ethanol vapor sensor, readily achieving detectivities of 0.5 ppm, based on reversible physisorption effects. Our demonstration aims at developing a new type of photonic platforms, based on a versatile, laser based prototyping approach onto micrometric size, optical fiber tapers, while exhibiting unique guiding and modal interaction characteristics, for departing the laboratory bench, while being implemented into diverse types of sensing and actuating devices. [ABSTRACT FROM AUTHOR]

This article presents an active ball joint mechanism (ABENICS) enhanced by interactions of spherical gears. The gear-based joint drives three rotational degrees of freedom (RDoF) without slippage. The capabilities were inspired by the unique interactions between two different innovative gears [the cross spherical gear (CS-gear) and monopole gear (MP-gear)] and the superimposition of those interactions by the CS-gear's quadrature spherical tooth structure. One MP-gear constrains two of the three RDoF of the CS-gear. The driving module which drives the MP-gear converts this “constraint” into a “drive” and drives the CS-gear with two RDoF. The CS-gear orthogonally superimposes the interactions caused by two MP-gears to achieve three RDoF driving forces. The principle was revealed by analyzing an equivalent linkage modeled on the mechanism of ABENICS. The linkage also led to the kinematics and torque equations. The theory and physical characteristics of ABENICS were verified in comprehensive and continuous positioning experiments on manufactured prototypes. The flexibility of the actuator placement was also verified in different configurations of the driving modules. The active ball joint, ABENICS can transmit high torque and reliable positioning in three RDoF without an orientation sensor, which applies to robot joints and orientation control mechanisms. [ABSTRACT FROM AUTHOR]

The hardcore model on a graph $G$ with parameter $\lambda > 0$ is a probability measure on the collection of all independent sets of $G$ , that assigns to each independent set $I$ a probability proportional to $\lambda ^{|I|}$. In this paper we consider the problem of estimating the parameter $\lambda $ given a single sample from the hardcore model on a graph $G$. To bypass the computational intractability of the maximum likelihood method, we use the maximum pseudo-likelihood (MPL) estimator, which for the hardcore model has a surprisingly simple closed form expression. We show that for any sequence of graphs $\{G_{N}\}_{N \geq 1}$ , where $G_{N}$ is a graph on $N$ vertices, the MPL estimate of $\lambda $ is $\sqrt {N}$ -consistent (that is, it converges to the true parameter at rate $1/\sqrt {N}$), whenever the graph sequence has uniformly bounded average degree. We then extend our methods to obtain estimates for the vector of activity parameters in general $H$ -coloring models, in which restrictions between adjacent colors are encoded by a constraint graph $H$. These constitute an important class of Markov random fields that includes all hard-constraint models, which arise in a broad array of fields including combinatorics, statistical physics, and communication networks. Given a single sample from an $H$ -coloring model, we derive sufficient conditions under which the MPL estimate is $\sqrt {N}$ -consistent. Moreover, we verify the sufficient conditions for $H$ -coloring models for which there is at least one ‘unconstrained’ color (that is, there exists at least one vertex in the constraint graph $H$ that is connected to all vertices), as long as the graph sequence has uniformly bounded average degree. This applies to many $H$ -coloring examples such as the Widom-Rowlinson and multi-state hard-core models. On the other hand, for the $q$ -coloring model, which falls outside this class, we show that the condition can fail and consistent estimation may be impossible even for graphs with bounded average degree. Nevertheless, we show that the MPL estimate is $\sqrt {N}$ -consistent in the $q$ -coloring model when $\{G_{N}\}_{N \geq 1}$ has bounded average double neighborhood. The presence of hard constraints, as opposed to soft constraints, leads to new challenges, and our proofs entail applications of the method of exchangeable pairs as well as combinatorial arguments that employ the probabilistic method. [ABSTRACT FROM AUTHOR]

Optical multilayer optimization periodically reorganizes layer 0-1-2 network elements to handle both existing and dynamic traffic requirements in the most efficient manner. This delays the need for adding new resources in order to cope with the evolution of the traffic, thus saving CAPEX. The focus of this paper is on Layer 2, i.e., on capacity reoptimization at the optical transport network (OTN) layer when routes (e.g., LSPs in MPLS networks) are making unnecessarily long detours to evade congestion. Reconfiguration into optimized routes can be achieved by re-defining the routes, one at a time, so that they use the vacant resources generated by the disappearance of services using part of a path that transits the congested section. To maintain the Quality of Service, it is desirable to operate under a Make-Before-Break (MBB) paradigm, with the minimum number of reroutings. The challenge is to determine the best rerouting order while minimizing the bandwidth requirement. We propose an exact and scalable optimization model for computing a minimum bandwidth rerouting scheme subject to MBB in the OTN layer of an optical network. Numerical results show that we can successfully apply it on networks with up to 30 nodes, a very significant improvement with respect to the state of the art. We also provide some reoptimization analysis in terms of the bandwidth requirement vs. the number of reroutings. [ABSTRACT FROM AUTHOR]

The demand for reliable and efficient Wide Area Networks (WANs) from business customers is continuously increasing. Companies and enterprises use WANs to exchange critical data between headquarters, far-off business branches and cloud data centers. Many WANs solutions have been proposed over the years, such as: leased lines, Frame Relay, Multi-Protocol Label Switching (MPLS), Virtual Private Networks (VPN). Each solution positions differently in the trade-off between reliability, Quality of Service (QoS) and cost. Today, the emerging technology for WAN is Software-Defined Wide Area Networking (SD-WAN) that introduces the Software-Defined Networking (SDN) paradigm into the enterprise-network market. SD-WAN can support differentiated services over public WAN by dynamically reconfiguring in real-time network devices at the edge of the network according to network measurements and service requirements. On the one hand, SD-WAN reduces the high costs of guaranteed QoS WAN solutions (as MPLS), without giving away reliability in practical scenarios. On the other, it brings numerous technical challenges, such as the implementation of Traffic Engineering (TE) methods. TE is critically important for enterprises not only to efficiently orchestrate network traffic among the edge devices, but also to keep their services always available. In this work, we develop different kind of TE algorithms with the aim of improving the performance of an SD-WAN based network in terms of service availability. We first evaluate the performance of baseline TE algorithms. Then, we implement different deep Reinforcement Learning (deep-RL) algorithms to overcome the limitations of the baseline approaches. Specifically, we implement three kinds of deep-RL algorithms, which are: policy gradient, TD- $\lambda $ and deep Q-learning. Results show that a deep-RL algorithm with a well-designed reward function is capable of increasing the overall network availability and guaranteeing network protection and restoration in SD-WAN. [ABSTRACT FROM AUTHOR]

We consider protection switching in an IP over optical network. There is IP Fast Reroute Loop-free Alternates (IP FRR LFA) at the IP layer, and protection switching at the optical layer. Our network model assumes a completely connected IP network over an optical ring network that is required to protect a predetermined fraction of network bandwidth. This model leads to analytical formulas on network protection costs which we show are accurate. We also show that network cost is at or near zero when the network is required to protect at most 33% of traffic. A cost comparison to networks using MPLS Fast Reroute (MPLS FRR) further indicates that LFA is a feasible protection mechanism. [ABSTRACT FROM AUTHOR]

Video prediction is challenging, due to the pixel-level precision requirement and the difficulty in capturing scene dynamics. Most approaches tackle the problems by pixel-level reconstruction objectives and two decomposed branches, which still suffer from blurry generations or dramatic degradations in long-term prediction. In this paper, we propose a Motion-Aware Feature Enhancement (MAFE) network for video prediction to produce realistic future frames and achieve relatively long-term predictions. First, a Channel-wise and Spatial Attention (CSA) module is designed to extract motion-aware features, which enhances the contribution of important motion details during encoding, and subsequently improves the discriminability of attention map for the frame refinement. Second, a Motion Perceptual Loss (MPL) is proposed to guide the learning of temporal cues, which benefits to robust long-term video prediction. Extensive experiments on three human activity video datasets: KTH, Human3.6M, and PennAction demonstrate the effectiveness of the proposed video prediction model compared with the state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

Multiprotocol Label Switching (MPLS) is a technology developed to address the bottleneck of Internet Protocol over Asynchronous Transfer Mode network. It is a packet forwarding technology using labels to make data forwarding decisions. The use of label forwarding instead of IP address consumed a lot less resources in the old days as it was faster but nowadays the speed improvement is negligible due to new lookup table algorithm. However, it is one of the most utilized technologies among Internet Service Providers due to its Traffic Engineering (TE) capability. In this research, a deductive methodology has been used to evaluate the benefit of MPLS Traffic Engineering in the presence of traffic collision and we investigate how the implementation of Fast Reroute (FRR) helps to reduce the convergence time in case of link/node failure. Results show that TE helps to improve the quality of service in terms of latency in the presence of background traffic and FRR helps to reduce convergence time to less than 100 ms. [ABSTRACT FROM AUTHOR]

Traffic engineering is an important concept that allows Internet Service Providers (ISPs) to utilize their existing routing hardware more efficiently. One technology that can be used is Segment Routing (SR). In this paper, we address the use of SR to increase the resilience against failure scenarios. In addition, we develop solutions that are manageable and, thus, deployable in a tier 1 ISP network. We propose a post-convergence aware SR based optimization model. With it, we can proactively find a single SR configuration that is beneficial in all predefined failure scenarios, including single link failures, shared risk link group failures, and node failures. In addition to this use-case, we also extend the optimization model to include other important practical requirements such as keeping the number of SR tunnels to a minimum, avoiding arbitrary traffic splitting, or meeting latency bounds. We evaluate our approaches with recently measured data from a tier 1 ISP and show that we can improve over state of the art routing approaches. [ABSTRACT FROM AUTHOR]

Segment routing is an emerging networking technology, where an arbitrary forwarding path can be identified using a list of segments. In this article, we study network monitoring in segment routing based on monitoring trails. Unlike the existing monitoring cycle, a trail is more flexible because it can be either open or closed (i.e., a cycle). Consider a network with a given set of ${k}$ monitors, which are devices responsible for network monitoring. A monitoring trail must start and end at a monitor. The ${k}$ -monitor cover problem is to cover every link in the network using trails such that each trail has no more than ${K}$ segments and the total length of all trails is minimized. In this article, we prove that the ${k}$ -monitor cover problem is NP-hard. To solve it, the first ILP is formulated and an efficient heuristic algorithm (${k}$ -MCA) is designed. [ABSTRACT FROM AUTHOR]

Mobile Ad Hoc Network (MANET) is a wireless network that does not have any fixed structure. MANET is a collection of independent mobile nodes that can communicate with each other through radio waves. In conventional Internet Protocol (IP) forwarding, each router creates forwarding decision on the basis of IP header information. The router has to analyse the packet and routing table at each hop to take the decision for data transmission. Since, inappropriate decisions in data transmission causes delay in packet delivery. This can be overcome with the help of a special feature of Multiprotocol Label Switching (MPLS) called Label switching. MPLS is a switching mechanism that plays a good role in routing, switching and forwarding using small labels. The MPLS is integrated into MANET to improve network performance. In this paper, an effective routing strategy is introduced to reduce the transmission time between routers during the data transmission. Here the Ad Hoc On-Demand Distance Vector (AODV) with Biogeography Based Optimization (BBO) based routing method is used to identify the optimal route among the routers of the MPLS based MANET. The proposed methodology is named as AODV-BBO. Three different objective functions are considered in this routing strategy viz., residual energy, distance and number of hops. Moreover, the rerouting is performed when the node or link failure occurs in the network. This rerouting is used to overcome the packet drop through the MPLS based MANET. The performance of AODV-BBO methodology is analysed in terms of percentage of alive nodes, dead nodes, energy consumption, end to end delay and bandwidth. The performance of the AODV-BBO methodology is compared with two existing methods P2R2 and PS-ROGR. Result showed that, the energy consumption of the AODV-BBO methodology with 400 nodes is 536.52 J, which is less compared to the PS-ROGR method. [ABSTRACT FROM AUTHOR]

In segment routing, a packet is forwarded along a path identified by a segment list. A segment list consists of segment identifiers (SIDs). A node-SID identifies a shortest-path segment, and an adjacency-SID identifies a link segment. A ${K}$ -segment path is a path with no more than ${K}$ segments. In this paper, we study the problem of finding a set of K-segment paths to carry all the flows in a given traffic matrix such that the maximum link utilization in the network is minimized. We first show that the solutions found by ${K}$ -LP, an existing linear programming (LP) approach, are not optimal because ${K}$ -LP does not support adjacency-SIDs. Focusing on 2-segment paths, a new LP formulation, denoted by e2-LP, is designed to support adjacency-SIDs in part. To fully support adjacency-SIDs, a mixed integer linear programming (MILP), denoted by ${K}$ -MILP, is designed. Since solving ${K}$ -MILP is time-consuming, a simplified version (${K}$ -sMILP) is also proposed. Finally, ${K}$ -sMILP is extended to prevent excessive flow splitting or using paths that are too long. [ABSTRACT FROM AUTHOR]

Segment Routing (SR) is a new fertile ground for Traffic Engineering (TE). By decomposing forwarding paths into segments, which specify a list of intermediate delivery points that a packet must visit on its way to the final destination, SR improves TE tasks and enables new solutions for the optimization of network resource utilization. This work proposes an Evolutionary Computation approach that enables Path Computation Element (PCE), or Software-defined Network (SDN) controllers, to optimize SR configurations for improved traffic distribution. Furthermore, we present a robust semi-oblivious method to address the variability of traffic requirements as well as alternative approaches to ensure a good network performance after link failures. In all cases, the optimization of network resource utilization is achieved using at the most three segments to configure each SR path. Moreover, all proposed optimization methods are made publicly available in a optimization framework developed by the authors. [ABSTRACT FROM AUTHOR]

Considerable research and standardization efforts are being made to support time-sensitive traffic, e.g., generated by applications like Industry 4.0 and 5G fronthaul, on packet networks. This letter focuses on analyzing the impact of conveying time-sensitive traffic in operators’ networks when such traffic is mixed with best-effort traffic. Extensions to a continuous $G/G/1/k$ queue model are proposed to evaluate two different Ethernet technologies, synchronous and asynchronous, supporting time-sensitive flows in terms of their influence on the performance of best-effort traffic. Results highlight pros and cons of those technologies to protect best-effort performance. [ABSTRACT FROM AUTHOR]

Segment Routing (SR) is a source routing paradigm which is widely used in Traffic Engineering (TE). By using SR, a node steers a packet through an ordered list of instructions called segments. By some extensions of interior gateway protocol, SR can be applied to IP/MPLS or IPv6 network without signal protocol. SR over IPv6 (SRv6) is attracting wide attention because of its interoperation ability with IPv6. However, upgrading the existing IPv6 network directly to a full SRv6 one can be difficult, because large-scale equipment replacement or software upgrade may cause economic and technical problems. TE in partially deployed SR network is becoming a hot research topic. In this paper, we propose the TE algorithm Weight Adjustment-SRTE (WA-SRTE) in partially deployed SRv6 network, in which SRv6 capable nodes are dispersedly deployed. Our objective is to minimize the network’s maximum link utilization. WA-SRTE converts the TE problem into a Deep Reinforcement Learning problem and optimizes the OSPF weight, SRv6 node deployment and traffic paths simultaneously. Besides, traffic variation is also considered and we use a representative Traffic Matrix (TM) to epitomize the traffic characteristics over a period of time. Experiments demonstrate that with 20% to 40% of the SRv6 nodes deployed, we can achieve TE performance as good as in a full SR network for the experiment topologies. The results with WA remarkably outperform the results without it. Our algorithm also gets near-optimal results with changing traffic. [ABSTRACT FROM AUTHOR]

Rectangular grid graph (RGG) and diagonal grid graph (DGG) are induced subgraphs of a rectangular or diagonal grid, respectively. Their $k$ -coloring problem has direct applications in printing contact/via layouts by multipatterning lithography (MPL). However, the problem in general is computationally difficult for $k>2$ , while it remains an open question on grid graphs due to their regularity and sparsity. On the other hand, directed self-assembly (DSA) technique can work with MPL to optimize the graph by grouping neighboring vertices such that $k$ can be reduced, but the problem of deploying the grouping for coloring is even more intractable. In this paper, we study both of the $k$ -coloring problems, with and without DSA grouping, on RGG and DGG. Without grouping, a complete $k$ -coloring analysis is conducted and particularly the NP-completeness of 3-coloring on a diagonal grid is proved. When considering grouping, we present a 3-coloring solution and prove the NP-completeness to solve the problem of $k=2$. [ABSTRACT FROM AUTHOR]

Internet topology discovery aims at analyzing one of the most complex distributed systems currently deployed. Usually, it relies on measurement campaigns using hop-limited probes sent with traceroute. However, this probing tool comes with several limits. In particular, some MPLS clouds might obfuscate collected traces. The resulting Internet maps, their inferred properties, and the graph models are thus incomplete and inaccurate. In this paper, we introduce TNT (Trace the Naughty Tunnels), an extension to Paris traceroute for revealing, or at least detect, all MPLS tunnels along a path. First, along with traceroute and ping probes, TNT looks for hints indicating the presence of hidden tunnels. Those hints are peculiar patterns in the resulting output, e.g., significant TTL shifts or duplicate IP addresses. Second, if those hints trigger alarms, TNT launches additional dedicated probing for possibly revealing hidden tunnels. We use GNS3 to reproduce, verify, and understand the limits and capabilities of TNT in a controlled environment. We also calibrate the thresholds at which alarms are triggered through a dedicated measurement campaign. Finally, we deploy TNT on the Archipelago platform and provide a quantified classification of MPLS configurations. All our results, including the data, the code, and the GNS3 experiments, are fully and publicly available. [ABSTRACT FROM AUTHOR]

Featured with simple topology and easy realization of zero-voltage-switching, the Class E dc–dc converter is preferable for very high-frequency application. In this article, a multipower-level (MPL) hysteresis control is proposed for the Class E dc–dc converter, in which, multiple switching frequencies are selected for obtaining multiple input power levels, and the two adjacent input powers closest to the load power are chosen to synthesize the load power. Compared with the on–off control, which has only two input power levels, i.e., the input power in on mode and zero power in off mode, the MPL hysteresis control reduces the modulation frequency, and thus reducing the mode transition losses. Meanwhile, the conduction losses are also reduced. As a result, the efficiency of the converter is greatly improved. The parameters design and the switching frequencies selection for the Class E dc–dc converter with MPL hysteresis control are presented. Finally, a prototype of 10-W Class E dc–dc converter with 9–18 V input is fabricated and tested in the lab, and the experimental results are provided to verify the effectiveness of the proposed MPL hysteresis control scheme. [ABSTRACT FROM AUTHOR]

Device-free localization (DFL) methods use measured changes in the received signal strength (RSS) between many pairs of RF nodes to provide location estimates of a person inside the wireless network. Fundamental challenges for RSS DFL methods include having a model of RSS measurements as a function of a person's location, and maintaining an accurate model as the environment changes over time. Current methods rely on either labeled empty-area calibration or labeled fingerprints with a person at each location. Both need to be frequently recalibrated or retrained to stay current with changing environments. Other DFL methods only localize people in motion. In this paper, we address these challenges by, first, introducing a new mixture model for link RSS as a function of a person's location, and second, providing the framework to update model parameters without ever being provided labeled data from either empty-area or known-location classes. We develop two new Bayesian localization methods based on our mixture model and experimentally validate our system at three test sites with seven days of measurements. We demonstrate that our methods localize a person with non-degrading performance in changing environments, and, in addition, reduce localization error by $\mathbf {11-51}$ 11 - 51 percent compared to other DFL methods. [ABSTRACT FROM AUTHOR]

This paper proposes a scheme to reduce the number of flow entries permanently stored in an OpenFlow switch and the number of configuration messages from a controller in a software-defined network (SDN). In an SDN, a flow table in an OpenFlow switch is used to instruct packets. The flow table consists of flow entries decided by the controller. A flow request is sent from the OpenFlow switch to the controller if the incoming packet does not match any flow entry in the flow table. The controller's central processing unit may be overloaded to handle user requests, since the user requests for different data types have been rapidly increasing. As a result, flow configuration in switches is delayed. Moreover, the control plane may be flooded by configuration messages of those requests. A scheme to permanently keep the flow entries in the switch can reduce the number of requests. However, a large number of permanent flow entries is required. Other switch features may be degraded, since there is not enough memory in the flow table to implement those features. In the proposed scheme, switches in the network are divided into multiple regions. In order to guide packets from sources to destinations, the flow table incorporating the concept of two multiprotocol label switching tags is re-designed. One tag directs a packet from a source switch to an edge switch in the destination region. The other tag directs the packet from that edge switch to another switch in the same region. A mathematical model for the proposed scheme is formulated as an integer linear programming to determine a set of switches in each region so that the total number of permanent flow entries in the network can be minimized. The performance of the proposed scheme is analyzed. Moreover, the proposed scheme is implemented and demonstrated via Japanese Science Information Network 5.

Multiprotocol label switching is the latest and developing technology in the world of the internet. It speeds up the network by using the technique of label instead of an IP address. It provides the reliable transmission of data with high speed and low delay. For efficient use of network utilization MPLS has a key feature of QoS. Due to the effective utilization of network resources, minimum delay and predictable performance MPLS technology make it more appropriate for implementing multimedia type applications. In this research, the performance of MPLS technology is compared with the traditional IP network for multimedia traffic in node scalable networks. For simulating and comparing the performance of both technologies OPNET modular 14.5 is used. This comparison is done on the basis of network performance parameters such as packet loss/ traffic drop, end-to-end delay, and throughput. Finally, the results have been evaluated which show that MPLS technology provides better performance as compared to IP in node scalable environment.

In this paper, we want to promote the influence of randomized arithmetic on the leaks during a code execution. When somebody wants to extract some specific information from these leaks, one can observe different emanations of the device like power consumption. These leaks mostly come from the variations of the Hamming distances of the successive states of the system. This phenomenon is particularly critical for cryptographic devices. Our work evaluates the resilience of randomized moduli in Residue Number System (RNS) against Correlation Power Analysis (CPA), Differential Power Analysis (DPA). Our analysis is illustrated through the evaluation of scalar multiplication on an elliptic curve using the Montgomery Powering Ladder (MPL) algorithm which protects from Simple Power Analysis (SPA). We also propose an evaluation based on the Maximum Likelihood Estimator (MLE), which crosses the information of the whole state vector, instead of analysing only the current state like with CPA or DPA. Furthermore, MLE gives better performance and smooths the results allowing a better evaluation of the behaviour of the leakage. Our experimental evaluation suggests that the number of observations, needed to perform exploitable information leakage, is proportional to the number of possible RNS bases. [ABSTRACT FROM AUTHOR]

Many-objective optimization problems (MaOPs) contain four or more conflicting objectives to be optimized. A number of efficient decomposition-based evolutionary algorithms have been developed in the recent years to solve them. However, computationally expensive MaOPs have been scarcely investigated. Typically, surrogate-assisted methods have been used in the literature to tackle computationally expensive problems, but such studies have largely focused on problems with 1–3 objectives. In this paper, we present an approach called hybrid surrogate-assisted many-objective evolutionary algorithm to solve computationally expensive MaOPs. The key features of the approach include: 1) the use of multiple surrogates to effectively approximate a wide range of objective functions; 2) use of two sets of reference vectors for improved performance on irregular Pareto fronts (PFs); 3) effective use of archive solutions during offspring generation; and 4) a local improvement scheme for generating high quality infill solutions. Furthermore, the approach includes constraint handling which is often overlooked in contemporary algorithms. The performance of the approach is benchmarked extensively on a set of unconstrained and constrained problems with regular and irregular PFs. A statistical comparison with the existing techniques highlights the efficacy and potential of the approach. [ABSTRACT FROM AUTHOR]

Failure is common instead of exception in large-scale networks. To provide high service quality to upper-layer applications, it is desired that a converged backup path can be rapidly launched when failure occurs. In this paper, we design an IP based Fast ReRouting (FRR) scheme called Metro, which can solve the traffic rerouting convergence problem after arbitrary single link/node failure with low stretch for the backup path. When failure occurs in the network, Metro first indicates all the network areas that would be affected by the failure, and then finds out a few bridge links to drain the traffic in the affected network area to the network area that is not affected by the failure. In this way, Metro does not configure tunnels, encapsulate or modify data packets, and hence it is easy to be deployed in current networks. Extensive simulations show that Metro can solve arbitrary single link/node failure with backup paths shorter than the state-of-the-art solutions, and about 98% of the backup path stretch in Metro are the same as the optimal tunnel scheme. [ABSTRACT FROM AUTHOR]

In this paper, we propose a control framework for virtual-link provisioning in software defined networking (SDN)-based Internet service provider (ISP) networks. Within the problem’s scope, we have analyzed the traffic engineering (TE), quality of service (QoS) provisioning, network stability, and failure recovery issues. The proposed solution aims to optimize the network throughput subject to multiple QoS constraints and link-failure robustness constraints that are defined in service level agreements (SLAs), while minimizing the time-average rate of routing updates according to ISP preferences. In contrast to the conventional TE mechanisms which periodically optimize all the routing rules, our solution is based on a bi-objective optimization model which jointly optimizes the routing and the number of flow-table updates. In this way, the reconfiguration overhead of TE is reduced, thus the SDN controller could perform TE more frequently, in order to quickly and efficiently respond to traffic and topology changes. To generate a set of Pareto optimal solutions for the proposed optimization model we use the augmented $\epsilon $ -constrained method. The most efficient trade-off solution from the Pareto set is chosen by the Lypunov drift-plus-penalty algorithm. In order to make the proposed approach scalable enough for large network topologies, we also propose a heuristic TE algorithm that runs in polynomial time. The proposed solution is validated via simulations and experimentally, and it is shown that it outperforms conventional periodic TE techniques. The advantages of our proposal over a state-of-the-art TE technique in terms of the capability to adjust the reconfiguration rate based on the threshold set by ISP is demonstrated. In the analysis, a special attention is paid to the impact of proactive failure protection mechanisms on the network service capacity. [ABSTRACT FROM AUTHOR]

With an increasing demand for resilience in software-defined networks (SDN), it becomes critical to minimize service recovery delay upon route failures. Fast reroute (FRR) mechanisms are widely used in IP and MPLS networks by computing the recovery path before a failure occurs. The centralized control plane in SDN can potentially enhance path computation, so that FRR path computation can better scale in SDN than in traditional networks. However, the traditional FRR path computation algorithms could lead to a poor performance in large-scale SDN. The problem can become more severe for a highly dynamic network, which often sees dozens of failures or configuration changes in any single day. We propose a new algorithm that exploits pruned searching to quickly compute recovery paths for all-pair switches/hosts upon a link failure. For applications requiring stringent path robustness levels, we also extend this algorithm to quickly find the shortest guaranteed-cost path, which ensures that the recovery path used upon on-path link failures has the minimum cost. Compared with traditional solutions, our evaluations show that our algorithm is about 8 ~ 81 times faster than the practical implementation, 1.93 ~ 3.11 times faster than the state-of-the-art solution. Our results also show that the shortest guaranteed-cost path can reduce the cost of the recovery path significantly. Moreover, we design a prototype to show how to deploy our algorithm in an OpenFlow network. [ABSTRACT FROM AUTHOR]

We show nonparametric identification of the parameters in the dynamic stochastic block model as recently introduced by Matias and Miele (2017) in the case of binary, finitely weighted, and general edge states. We formulate conditions on the true parameters, which guarantee actual point identification instead of mere generic identification, and which also lead to novel conclusions in the static case. In particular, our results justify in terms of the identification of the applications by Matias and Miele to finitely weighted edges with three edge states. We also give numerical illustrations via the variational EM algorithm in simulation settings covered by our identification analysis. [ABSTRACT FROM AUTHOR]

A microwave photonic link with flexible suppression of the even-order and third-order distortions is proposed by using a single integrated polarization-multiplexing dual parallel Mach-Zehnder modulator (PM-DPMZM). A 180° electric hybrid is applied to introduce a phase shift of 180° between the even and odder modulation sidebands of the radio frequency (RF) signal. One DC bias voltage of the PM-DPMZM is adjusted to implement not only the optimal suppression of the even-order or third-order distortions but also a simultaneous suppression of these two distortions. The experimental system is carried out. In the best suppression points for the second or third intermodulation distortions (IMD2 or IMD3), the spurious-free dynamic range (SFDR) of IMD2 and IMD3 is improved by 17.1 and 17.7 dB, comparing with the conventional MZM based MPL. In the case of the simultaneous suppression, the SFDR values of IMD2 and IMD3 are enhanced by 10.6 and 6.4 dB. The proposed link has great flexibility, simple structure, and convenient operation, and it can be applied in both the single-octave and multi-octave communication systems. [ABSTRACT FROM AUTHOR]

Software-defined networking provides a promising solution for traffic engineering (TE) by utilizing a centralized controller to remotely configure programmable network switches. In order to achieve optimal traffic distribution and meet the Quality of Service (QoS) requirements, the SDN controller must frequently solve complex optimization models. Applying an optimization model in ISP (Internet Service Provider) networks implies abundant routing reconfigurations, which adversely impact the network stability and QoS parameters. Namely, since routing updates usually cannot be applied on all switches at the same time, transient congestion and loops often happen as a result. Here, in this paper, we propose a new control framework that strives to maximize the network throughput and provide QoS with minimal reconfiguration costs. In contrast to the conventional TE approaches, which perform the network optimizations periodically and control the side effects of reconfigurations by carefully choosing the period length between the optimization cycles, we propose a multi-objective optimization model which jointly minimizes the routing cost function and the reconfiguration overhead. The Pareto frontier of the optimization model is generated by the augmented $\epsilon $ -constrained method, whereas a Lyapunov drift-plus-penalty function is used to select the best compromise solution from the Pareto set. Since the reconfiguration overhead is reduced, the network controller could be allowed to optimize resource allocation more frequently, in order to respond quickly and efficiently to the network changes. Considering that the proposed optimization model is computationally intractable in large-scale networks, we also propose a heuristic algorithm to efficiently solve large instances of the problem. In simulations and Mininet experiments, we show that our solution brings performance improvement over the conventional periodic TE techniques. Moreover, the proposed heuristic achieves better QoS request acceptance ratio than the state-of-the-art multi-objective optimization solution, despite the significantly reduced computational complexity. [ABSTRACT FROM AUTHOR]