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Efficient network management in optical backbone networks is crucial for handling continuous traffic growth. In this work, we address the challenges of managing dynamic traffic in C- and C+L-band optical backbone networks while exploring application flexibility, namely the compressibility and delayability metrics. We propose a strategy, named Delay-Aware and Compression-Aware (DACA) provisioning algorithm, which reduces blocking probability, thereby increasing information-carrying capacity of the network compared to baseline strategies.

We quantify and benchmark cost and power consumption of power profile monitoring (PPM) in opaque and transparent IPoWDM networks, comparing it to current optical time-domain reflectometer (OTDR) technology. Results show PPM is preferable even when PPM module adds 80% (50%) cost (power) to one transponder.

Many emerging Artificial Intelligence (AI) applications require on-demand provisioning of large-scale computing, which can only be enabled by leveraging distributed computing services interconnected through networking. To address such increasing demand for networking to serve AI tasks, we investigate new scheduling strategies to improve communication efficiency and test them on a programmable testbed. We also show relevant challenges and research directions.

We evaluate the resource efficiency of Mode Group Division Multiplexing (MGDM) with shared path protection (SPP) in optical networks. On our case studies, SPP with MGDM obtains significant savings in terms of both additional backup spectrum occupation and MIMO-computing resources compared to other few-mode-transmission scenarios.

We quantify and compare the power consumption of four IPoWDM transport network architectures employing ZR/ZR+ modules, considering different grooming, regeneration, and optical bypass capabilities. Results show that optical bypass is still the most power-efficient solution, reducing consumption by up to 30%.

Multi-band transmission is a promising technical direction for spectrum and capacity expansion of existing optical networks. Due to the increase in the number of usable wavelengths in multi-band optical networks, the complexity of resource allocation problems becomes a major concern. Moreover, the transmission performance, spectrum width, and cost constraint across optical bands may be heterogeneous. Assuming a worst-case transmission margin in U, L, and C-bands, this paper investigates the problem of throughput maximization in multi-band optical networks, including the optimization of route, wavelength, and band assignment. We propose a low-complexity decomposition approach based on Column Generation (CG) to address the scalability issue faced by traditional methodologies. We numerically compare the results obtained by our CG-based approach to an integer linear programming model, confirming the near-optimal network throughput. Our results also demonstrate the scalability of the CG-based approach when the number of wavelengths increases, with the computation time in the magnitude order of 10 s for cases varying from 75 to 1200 wavelength channels per link in a 14-node network. Code of this publication is available at github.com/cchen000/CG-Multi-Band., Comment: 6 pages, 4 figures, accepted by IEEE International Conference on Communications 2024 (ICC2024)

Flexible optical networks (FONs) are being adopted to accommodate the increasingly heterogeneous traffic in today's Internet. However, in presence of high traffic load, not all offered traffic can be satisfied at all time. As carried traffic load brings revenues to operators, traffic blocking due to limited spectrum resource leads to revenue losses. In this study, given a set of traffic requests to be provisioned, we consider the problem of maximizing operator's revenue, subject to limited spectrum resource and physical layer impairments (PLIs), namely amplified spontaneous emission noise (ASE), self-channel interference (SCI), cross-channel interference (XCI), and node crosstalk. In FONs, adaptive modulation, multiple FEC, and the tuning of power spectrum density (PSD) can be effectively employed to mitigate the impact of PLIs. Hence, in our study, we propose a universal bandwidth-related impairment evaluation model based on channel bandwidth, which allows a performance analysis for different PSD, FEC and modulations. Leveraging this PLI model and a piecewise linear fitting function, we succeed to formulate the revenue maximization problem as a mixed integer linear program. Then, to solve the problem on larger network instances, a fast two-phase heuristic algorithm is also proposed, which is shown to be near-optimal for revenue maximization. Through simulations, we demonstrate that using adaptive modulation enables to significantly increase revenues in the scenario of high signal-to-noise ratio (SNR), where the revenue can even be doubled for high traffic load, while using multiple FECs is more profitable for scenarios with low SNR.

Malicious jamming launched by smart jammer, which attacks legitimate transmissions has been regarded as one of the critical security challenges in wireless communications. Thus, this paper exploits intelligent reflecting surface (IRS) to enhance anti-jamming communication performance and mitigate jamming interference by adjusting the surface reflecting elements at the IRS. Aiming to enhance the communication performance against smart jammer, an optimization problem for jointly optimizing power allocation at the base station (BS) and reflecting beamforming at the IRS is formulated. As the jamming model and jamming behavior are dynamic and unknown, a win or learn fast policy hill-climbing (WoLF-PHC) learning approach is proposed to jointly optimize the anti-jamming power allocation and reflecting beamforming strategy without the knowledge of the jamming model. Simulation results demonstrate that the proposed anti-jamming based-learning approach can efficiently improve both the IRS-assisted system rate and transmission protection level compared with existing solutions., Comment: This paper appears in the Proceedings of IEEE Global Communications Conference (GLOBECOM) 2020. A full version appears in IEEE Transactions on Wireless Communications. arXiv:2004.12539

The outbreak of coronavirus disease 2019 (covid-19) is imposing a severe worldwide lock-down. Contact tracing based on smartphones' applications (apps) has emerged as a possible solution to trace contagions and enforce a more sustainable selective quarantine. However, a massive adoption of these apps is required to reach the critical mass needed for effective contact tracing. As an alternative, geo-location technologies in next generation networks (e.g., 5G) can enable Mobile Operators (MOs) to perform passive tracing of users' mobility and contacts with a promised accuracy of down to one meter. To effectively detect contagions, the identities of positive individuals, which are known only by a Governmental Authority (GA), are also required. Note that, besides being extremely sensitive, these data might also be critical from a business perspective. Hence, MOs and the GA need to exchange and process users' geo-locations and infection status data in a privacy-preserving manner. In this work, we propose a privacy-preserving protocol that enables multiple MOs and the GA to share and process users' data to make only the final users discover the number of their contacts with positive individuals. The protocol is based on existing privacy-enhancing strategies that guarantee that users' mobility and infection status are only known to their MOs and to the GA, respectively. From extensive simulations, we observe that the cost to guarantee total privacy (evaluated in terms of data overhead introduced by the protocol) is acceptable, and can also be significantly reduced if we accept a negligible compromise in users' privacy.

Malicious jamming launched by smart jammers can attack legitimate transmissions, which has been regarded as one of the critical security challenges in wireless communications. With this focus, this paper considers the use of an intelligent reflecting surface (IRS) to enhance anti-jamming communication performance and mitigate jamming interference by adjusting the surface reflecting elements at the IRS. Aiming to enhance the communication performance against a smart jammer, an optimization problem for jointly optimizing power allocation at the base station (BS), and reflecting beamforming at the IRS is formulated while considering quality of service (QoS) requirements of legitimate users. As the jamming model and jamming behavior are dynamic and unknown, a fuzzy win or learn fast-policy hill-climbing (WoLFPHC) learning approach is proposed to jointly optimize the anti-jamming power allocation and reflecting beamforming strategy, where WoLFPHC is capable of quickly achieving the optimal policy without the knowledge of the jamming model, and fuzzy state aggregation can represent the uncertain environment states as aggregate states. Simulation results demonstrate that the proposed anti-jamming learning-based approach can efficiently improve both the IRS-assisted system rate and transmission protection level compared with existing solutions.

In 5G networks, slicing allows partitioning of network resources to meet stringent end-to-end service requirements across multiple network segments, from access to transport. These requirements are shaping technical evolution in each of these segments. In particular, the transport segment is currently evolving in the direction of the so-called elastic optical networks (EONs), a new generation of optical networks supporting a flexible optical-spectrum grid and novel elastic transponder capabilities. In this paper, we focus on the reliability of 5G transport-network slices in EON. Specifically, we consider the problem of slicing 5G transport networks, i.e., establishing virtual networks on 5G transport, while providing dedicated protection. As dedicated protection requires large amount of backup resources, our proposed solution incorporates two techniques to reduce backup resources: (i) bandwidth squeezing, i.e., providing a reduced protection bandwidth with respect to the original request; and (ii) survivable multi-path provisioning. We leverage the capability of EONs to fine tune spectrum allocation and adapt modulation format and Forward Error Correction (FEC) for allocating rightsize spectrum resources to network slices. Our numerical evaluation over realistic case-study network topologies quantifies the spectrum savings achieved by employing EON over traditional fixed-grid optical networks, and provides new insights on the impact of bandwidth squeezing and multi-path provisioning on spectrum utilization.

Virtualization of network functions (as virtual routers, virtual firewalls, etc.) enables network owners to efficiently respond to the increasing dynamicity of network services. Virtual Network Functions (VNFs) are easy to deploy, update, monitor, and manage. The number of VNF instances, similar to generic computing resources in cloud, can be easily scaled based on load. Hence, auto-scaling (of resources without human intervention) has been receiving attention. Prior studies on auto-scaling use measured network traffic load to dynamically react to traffic changes. In this study, we propose a proactive Machine Learning (ML) based approach to perform auto-scaling of VNFs in response to dynamic traffic changes. Our proposed ML classifier learns from past VNF scaling decisions and seasonal/spatial behavior of network traffic load to generate scaling decisions ahead of time. Compared to existing approaches for ML-based auto-scaling, our study explores how the properties (e.g., start-up time) of underlying virtualization technology impacts Quality of Service (QoS) and cost savings. We consider four different virtualization technologies: Xen and KVM, based on hypervisor virtualization, and Docker and LXC, based on container virtualization. Our results show promising accuracy of the ML classifier using real data collected from a private ISP. We report in-depth analysis of the learning process (learning-curve analysis), feature ranking (feature selection, Principal Component Analysis (PCA), etc.), impact of different sets of features, training time, and testing time. Our results show how the proposed methods improve QoS and reduce operational cost for network owners. We also demonstrate a practical use-case example (Software-Defined Wide Area Network (SD-WAN) with VNFs and backbone network) to show that our ML methods save significant cost for network service leasers.

Today a large portion of Internet traffic is video. Over The Top (OTT) service providers offer video streaming services by creating a large distributed cloud network on top of a physical infrastructure owned by multiple entities. Our study explores insights from video streaming activity by analyzing data collected from Korea's largest OTT service provider. Our analysis of nationwide data shows interesting characteristics of video streaming such as correlation between user profile information (e.g., age, sex) and viewing habits, viewing habits of users (when do the users watch? using which devices?), viewing patterns (early leaving viewer vs. steady viewer), etc. Video on Demand (VoD) streaming involves costly (and often limited) compute, storage, and network resources. Findings from our study will be beneficial for OTTs, Content Delivery Networks (CDNs), Internet Service Providers (ISPs), and Carrier Network Operators, to improve their resource allocation and management techniques., Comment: This is a preprint electronic version of the article accepted to IEEE CloudNet 2018

Common Public Radio Interface (CPRI) is a successful industry cooperation defining the publicly available specification for the key internal interface of radio base stations between the radio equipment control (REC) and the radio equipment (RE) in the fronthaul of mobile networks. However, CPRI is expensive to deploy, consumes large bandwidth, and currently is statically configured. On the other hand, an Ethernet-based mobile fronthaul will be cost-efficient and more easily reconfigurable. Encapsulating CPRI over Ethernet (CoE) is an attractive solution, but stringent CPRI requirements such as delay and jitter are major challenges that need to be met to make CoE a reality. This study investigates whether CoE can meet delay and jitter requirements by performing FPGA-based Verilog experiments and simulations. Verilog experiments show that CoE encapsulation with fixed Ethernet frame size requires about tens of microseconds. Numerical experiments show that the proposed scheduling policy of CoE flows on Ethernet can reduce jitter when redundant Ethernet capacity is provided. The reduction in jitter can be as large as 1 {\mu}s, hence making Ethernet-based mobile fronthaul a credible technology., Comment: IEEE/OSA Journal of Optical Communications and Networking 2018

Bandwidth requirements of both wireless and wired clients in access networks continue to increase rapidly, primarily due to the growth of video traffic. Application awareness can be utilized in access networks to optimize quality of experience (QoE) of end clients. In this study, we utilize information at the client-side application (e.g., video resolution) to achieve superior resource allocation that improves user QoE. We emphasize optimizing QoE of the system rather than quality of service (QoS), as user satisfaction directly relies on QoE and optimizing QoS does not necessarily optimize QoE, as shown in this study. We propose application-aware resource-allocation schemes on an Ethernet passive optical network (EPON), which supports wireless (utilizing orthogonal frequency division multiple access) and wired clients running video-conference applications. Numerical results show that the application-aware resource-allocation schemes improve QoE for video-conference applications for wired and wireless clients., Comment: Journal of Optical Communications and Networking, 2018

Today's telecommunication networks have become sources of enormous amounts of widely heterogeneous data. This information can be retrieved from network traffic traces, network alarms, signal quality indicators, users' behavioral data, etc. Advanced mathematical tools are required to extract meaningful information from these data and take decisions pertaining to the proper functioning of the networks from the network-generated data. Among these mathematical tools, Machine Learning (ML) is regarded as one of the most promising methodological approaches to perform network-data analysis and enable automated network self-configuration and fault management. The adoption of ML techniques in the field of optical communication networks is motivated by the unprecedented growth of network complexity faced by optical networks in the last few years. Such complexity increase is due to the introduction of a huge number of adjustable and interdependent system parameters (e.g., routing configurations, modulation format, symbol rate, coding schemes, etc.) that are enabled by the usage of coherent transmission/reception technologies, advanced digital signal processing and compensation of nonlinear effects in optical fiber propagation. In this paper we provide an overview of the application of ML to optical communications and networking. We classify and survey relevant literature dealing with the topic, and we also provide an introductory tutorial on ML for researchers and practitioners interested in this field. Although a good number of research papers have recently appeared, the application of ML to optical networks is still in its infancy: to stimulate further work in this area, we conclude the paper proposing new possible research directions.

Traditional highly-centralized mobile core networks (e.g., Evolved Packet Core (EPC)) need to be constantly upgraded both in their network functions and backhaul links, to meet increasing traffic demands. Network Function Virtualization (NFV) is being investigated as a potential cost-effective solution for this upgrade. A virtual mobile core (here, virtual EPC, vEPC) provides deployment flexibility and scalability while reducing costs, network-resource consumption and application delay. Moreover, a distributed deployment of vEPC is essential for emerging paradigms like Multi-Access Edge Computing (MEC). In this work, we show that significant reduction in networkresource consumption can be achieved as a result of optimal placement of vEPC functions in metro area. Further, we show that not all vEPC functions need to be distributed. In our study, for the first time, we account for vEPC interactions in both data and control planes (Non-Access Stratum (NAS) signaling procedure Service Chains (SCs) with application latency requirements) using a detailed mathematical model.

Network Function Virtualization (NFV) provides higher flexibility for network operators and reduces the complexity in network service deployment. Using NFV, Virtual Network Functions (VNF) can be located in various network nodes and chained together in a Service Function Chain (SFC) to provide a specific service. Consolidating multiple VNFs in a smaller number of locations would allow decreasing capital expenditures. However, excessive consolidation of VNFs might cause additional latency penalties due to processing-resource sharing, and this is undesirable, as SFCs are bounded by service-specific latency requirements. In this paper, we identify two different types of penalties (referred as "costs") related to the processingresource sharing among multiple VNFs: the context switching costs and the upscaling costs. Context switching costs arise when multiple CPU processes (e.g., supporting different VNFs) share the same CPU and thus repeated loading/saving of their context is required. Upscaling costs are incurred by VNFs requiring multi-core implementations, since they suffer a penalty due to the load-balancing needs among CPU cores. These costs affect how the chained VNFs are placed in the network to meet the performance requirement of the SFCs. We evaluate their impact while considering SFCs with different bandwidth and latency requirements in a scenario of VNF consolidation., Comment: Accepted for publication in IEEE Transactions on Cloud Computing

Traditionally, networks operate at a small fraction of their capacities; however, recent technologies, such as Software-Defined Networking, may let operators run their networks harder (i.e., at higher utilization levels). Higher utilization can increase the network operator's revenue, but this gain comes at a cost: daily traffic fluctuations and failures might occasionally overload the network. We call such situations Resource Crunch. Dealing with Resource Crunch requires certain types of flexibility in the system. We focus on scenarios with flexible bandwidth requirements, e.g., some connections can tolerate lower bandwidth allocation. This may free capacity to provision new requests that would otherwise be blocked. For that, the network operator needs to make an informed decision, since reducing the bandwidth of a high-paying connection to allocate a low-value connection is not sensible. We propose a strategy to decide whether or not to provision a request (and which other connections to degrade) focusing on maximizing profits during Resource Crunch. To address this problem, we use an abstraction of the network state, called a Connection Adjacency Graph (CAG). We propose PROVISIONER, which integrates our CAG solution with an efficient Linear Program (LP). We compare our method to existing greedy approaches and to LP-only solutions, and show that our method outperforms them during Resource Crunch., Comment: 13 pages, 10 figures

Network Function Virtualization (NFV) aims to simplify deployment of network services by running Virtual Network Functions (VNFs) on commercial off-the-shelf servers. Service deployment involves placement of VNFs and in-sequence routing of traffic flows through VNFs comprising a Service Chain (SC). The joint VNF placement and traffic routing is called SC mapping. In a Wide-Area Network (WAN), a situation may arise where several traffic flows, generated by many distributed node pairs, require the same SC; then, a single instance (or occurrence) of that SC might not be enough. SC mapping with multiple SC instances for the same SC turns out to be a very complex problem, since the sequential traversal of VNFs has to be maintained while accounting for traffic flows in various directions. Our study is the first to deal with the problem of SC mapping with multiple SC instances to minimize network resource consumption. We first propose an Integer Linear Program (ILP) to solve this problem. Since ILP does not scale to large networks, we develop a column-generation-based ILP (CG-ILP) model. However, we find that exact mathematical modeling of the problem results in quadratic constraints in our CG-ILP. The quadratic constraints are made linear but even the scalability of CG-ILP is limited. Hence, we also propose a two-phase column-generation-based approach to get results over large network topologies within reasonable computational times. Using such an approach, we observe that an appropriate choice of only a small set of SC instances can lead to a solution very close to the minimum bandwidth consumption. Further, this approach also helps us to analyze the effects of number of VNF replicas and number of NFV nodes on bandwidth consumption when deploying these minimum number of SC instances., Comment: arXiv admin note: substantial text overlap with arXiv:1704.06716

Network Function Virtualization (NFV) aims to simplify deployment of network services by running Virtual Network Functions (VNFs) on commercial off-the-shelf servers. Service deployment involves placement of VNFs and in-sequence routing of traffic flows through VNFs comprising a Service Chain (SC). The joint VNF placement and traffic routing is usually referred as SC mapping. In a Wide Area Network (WAN), a situation may arise where several traffic flows, generated by many distributed node pairs, require the same SC, one single instance (or occurrence) of that SC might not be enough. SC mapping with multiple SC instances for the same SC turns out to be a very complex problem, since the sequential traversal of VNFs has to be maintained while accounting for traffic flows in various directions. Our study is the first to deal with SC mapping with multiple SC instances to minimize network resource consumption. Exact mathematical modeling of this problem results in a quadratic formulation. We propose a two-phase column-generation-based model and solution in order to get results over large network topologies within reasonable computational times. Using such an approach, we observe that an appropriate choice of only a small set of SC instances can lead to solution very close to the minimum bandwidth consumption.

Network Function Virtualization (NFV) aims to abstract the functionality of traditional proprietary hardware into software as Virtual Network Functions (VNFs), which can run on commercial off the shelf (COTS) servers. Besides reducing dependency on proprietary support, NFV helps network operators to deploy multiple services in a agile fashion. Service deployment involves placement and in sequence routing through VNFs comprising a Service Chain (SC). Our study is the first to focus on the computationally complex problem of multiple VNF SC placement and routing while considering VNF service chaining explicitly. We propose a novel column generation model for placing multiple VNF SCs and routing, which reduces the computational complexity of the problem significantly. Our aim here is to determine the ideal NFV Infrastructure (NFVI) for minimizing network resource consumption. Our results indicate that a Network enabled Cloud (NeC) results in lower networkresource consumption than a centralized NFVI (e.g., Data Center) while avoiding infeasibility with a distributed NFVI.

Network functions (e.g., firewalls, load balancers, etc.) have been traditionally provided through proprietary hardware appliances. Often, hardware appliances need to be hardwired back to back to form a service chain providing chained network functions. Hardware appliances cannot be provisioned on demand since they are statically embedded in the network topology, making creation, insertion, modification, upgrade, and removal of service chains complex, and also slowing down service innovation. Hence, network operators are starting to deploy Virtual Network Functions (VNFs), which are virtualized over commodity hardware. VNFs can be deployed in Data Centers (DCs) or in Network Function Virtualization (NFV) capable network elements (nodes) such as routers and switches. NFV capable nodes and DCs together form a Network enabled Cloud (NeC) that helps to facilitate the dynamic service chaining required to support evolving network traffic and its service demands. In this study, we focus on the VNF service chain placement and traffic routing problem, and build a model for placing a VNF service chain while minimizing network resource consumption. Our results indicate that a NeC having a DC and NFV capable nodes can significantly reduce network-resource consumption.

Communication networks, such as core optical networks, heavily depend on their physical infrastructure, and hence they are vulnerable to man-made disasters, such as Electromagnetic Pulse (EMP) or Weapons of Mass Destruction (WMD) attacks, as well as to natural disasters. Large-scale disasters may cause huge data loss and connectivity disruption in these networks. As our dependence on network services increases, the need for novel survivability methods to mitigate the effects of disasters on communication networks becomes a major concern. Software-Defined Networking (SDN), by centralizing control logic and separating it from physical equipment, facilitates network programmability and opens up new ways to design disaster-resilient networks. On the other hand, to fully exploit the potential of SDN, along with data-plane survivability, we also need to design the control plane to be resilient enough to survive network failures caused by disasters. Several distributed SDN controller architectures have been proposed to mitigate the risks of overload and failure, but they are optimized for limited faults without addressing the extent of large-scale disaster failures. For disaster resiliency of the control plane, we propose to design it as a virtual network, which can be solved using Virtual Network Mapping techniques. We select appropriate mapping of the controllers over the physical network such that the connectivity among the controllers (controller-to-controller) and between the switches to the controllers (switch-to-controllers) is not compromised by physical infrastructure failures caused by disasters. We formally model this disaster-aware control-plane design and mapping problem, and demonstrate a significant reduction in the disruption of controller-to-controller and switch-to-controller communication channels using our approach., Comment: 6 pages