This work investigates measurement strategies for link parameter estimation in Quantum Network Tomography (QNT), where network links are modeled as depolarizing quantum channels distributing Werner states. Three distinct measurement schemes are analyzed: local Z-basis measurements (LZM), joint Bell-state measurements (JBM), and pre-shared entanglement-assisted measurements (PEM). For each scheme, we derive the probability distributions of measurement outcomes and examine how noise in the distributed states influences estimation precision. Closed-form expressions for the Quantum Fisher Information Matrix (QFIM) are obtained, and the estimation precision is evaluated through the Quantum Cramer-Rao Bound (QCRB). Numerical analysis reveals that the PEM scheme achieves the lowest QCRB, offering the highest estimation accuracy, while JBM provides a favorable balance between precision and implementation complexity. The LZM method, although experimentally simpler, exhibits higher estimation error relative to the other schemes; however, it outperforms JBM in high-noise regimes for single-link estimation. We further evaluate the estimation performance on a four-node star network by comparing a JBM-only configuration with a hybrid configuration that combines JBM and LZM. When two monitors are used, the JBM-only strategy outperforms the hybrid approach across all noise regimes. However, with three monitors, it achieves a lower QCRB only in low-noise regimes with heterogeneous links. The results establish a practical basis for selecting measurement strategies in experimental quantum networks, enabling more accurate and scalable link parameter estimation under realistic noise conditions.

We consider quantum networks, where entangled-photon pairs are distributed using fibre optic links from a centralized source to entangling nodes. The entanglement is then stored (via an entanglement swap) in entangling nodes' quantum memories until used in, e.g., distributed quantum computing, quantum key distribution, quantum sensing, and other applications. Due to the fibre loss, some photons are lost in transmission. Noise in the transmission link and the quantum memory also reduces fidelity. Thus, entangling nodes must keep updated records of photon-pair arrivals to each destination, and their use by the applications. This coordination requires classical information exchange between each entangled node pair. However, the same fibre link may not admit both classical and quantum transmissions, as the classical channels can generate enough noise (i.e., via spontaneous Raman scattering) to make the quantum link unusable. Here, we consider coordinating entanglement distribution using a standard Internet protocol (IP) network instead, and propose a control protocol to enable such. We analyse the increase in latency from transmission over an IP network, together with the effect of photon loss, quantum memory noise and buffer size, to determine the fidelity and rate of entangled pairs. We characterize the relationship between the latency of the non-ideal IP network and the decoherence time of the quantum memories, providing a comparison of promising quantum memory technologies.

This paper explores the role of data spaces in enabling secure, interoperable data exchange between Network Digital Twins and SDN Controllers. This approach emphasizes data space connectors for faster, scalable, and flexible integration of multiple components across digital ecosystems to work in unison. The proposed approach is to use a common trusted platform (in this paper, the platform used is TRUE Connector) which is responsible for secure information exchange between components(e.g: SDN Controller, Network Digital Twins, Software applications, etc.) in a digital ecosystem, thus encouraging collaboration between multiple technology vendors to provide more efficient network services and encourage interoperability.

We investigate resource allocation for quantum entanglement distribution over an optical network. We characterize and model a network architecture that employs a single broadband quasi-deterministic time-frequency heralded Einstein-Podolsky-Rosen (EPR) pair source, and develop a routing and spectrum allocation scheme for distributing entangled photon pairs over such a network. As our setting allows separately solving the routing and spectrum allocation problems, we first find an optimal polynomial-time routing algorithm. We then employ max-min fairness criterion for spectrum allocation, which presents an NP-hard problem. Thus, we focus on approximately-optimal schemes. We compare their performance by evaluating the max-min and median number of EPR-pair rates assigned by them, and the associated Jain index. We identify two polynomial-time approximation algorithms that perform well, or better than others under these metrics. We also investigate scalability by analyzing how the network size and connectivity affect performance using Watts-Strogatz random graphs. We find that a spectrum allocation approach that achieves higher minimum EPR-pair rate can perform significantly worse when the median EPR-pair rate, Jain index, and computational resources are considered. Additionally, we evaluate the effect of the source node placement on the performance.

We investigate resource allocation for quantum entanglement distribution over an optical network. We characterize and model a network architecture that employs a single quasi-deterministic time-frequency heralded EPR-pair source, and develop a routing scheme for distributing entangled photon pairs over such a network. We focus on max-min fairness in entanglement distribution and compare the performance of various spectrum allocation schemes by examining both the max-min number of EPR pairs assigned by them and the Jain index associated with this assignment.

In past, due to data and model security concerns, modern communication systems mainly focus on the use of edge computing devices for enabling immersive applications and services. Federated learning is one of the preferred solutions but it stresses the computation capability of the edge devices for immersive applications. Much research is now focusing on split learning as an alternative due to its ability of performing joint training with limited computing resources. However, split learning is also vulnerable to data reconstruction, feature space hijacking, and model inversion attacks, which are quite common concerning immersive applications such as Metaverse. In this regard, we propose a ZEro-Trust Attack (ZETA) framework for data reconstruction and model inversion attacks for autonomous vehicles opting for split learning strategies. We propose the joint training of client, server, and shadow models for both the reconstruction and main task to fool existing methods. Our experimental results demonstrate that the proposed method is capable of reconstructing client's data with an error of 0.0032. This study is proposed as a basis to design more sophisticated defense mechanisms for autonomous vehicles to protect user services in 5G/6G networks.

Commercial virtual-desktop computing is well established using computers optimized to serve as thin clients connected to centralized computing systems. Some common residential applications impose more stringent requirements on both communication bandwidth and latency than those of typical commercial applications. This article describes an objective study of residential applications accessed through thin-client virtual desktops for the purpose of investigating the feasibility of applying virtual-desktop computing to residential users. New metrics are introduced to quantify user-received application performance. The results suggest that certain commercial solutions with a commodity datacenter server show a strong potential for being adapted to residential virtual-desktop computing.

This paper focuses on COSMOS - Cloud enhanced Open Software defined MObile wireless testbed for city-Scale deployment. The COSMOS testbed is being deployed in West Harlem (New York City) as part of the NSF Platforms for Advanced Wireless Research (PAWR) program. It will enable researchers to explore the technology "sweet spot" of ultra-high bandwidth and ultra-low latency in the most demanding real-world environment. We describe the testbed's architecture, the design and deployment challenges, and the experience gained during the design and pilot deployment. Specifically, we describe COSMOS' computing and network architectures, the critical building blocks, and its programmability at different layers. The building blocks include software-defined radios, 28 GHz millimeter-wave phased array modules, optical transport network, core and edge cloud, and control and management software. We describe COSMOS' deployment phases in a dense urban environment, the research areas that could be studied in the testbed, and specific example experiments. Finally, we discuss our experience with using COSMOS as an educational tool.

The Cloud-Enhanced Open Software Defined Mobile Wireless Testbed for City-Scale Deployment (COSMOS) platform is a programmable city-scale shared multi-user advanced wireless testbed that is being deployed in West Harlem of New York City [1]. To keep pace with the significantly increased wireless link bandwidth and to effectively integrate the emerging C-RANs, COSMOS is designed to incorporate a fast programmable core network for providing connections across different computing layers. A key feature of COSMOS is its dark fiber based optical x-haul network that enables both highly flexible, user defined network topologies and experimentation directly in the optical physical layer. The optical architecture of COSMOS was presented in [2]. In this abstract, we present the tools and services designed to configure and monitor the performance of optical paths and topologies of the COSMOS testbed. In particular, we present the SDN framework that allows testbed users to implement experiments with application-driven control of optical and data networking functionalities.

We explore a novel, free-space optics based approach for building data center interconnects. It uses a digital micromirror device (DMD) and mirror assembly combination as a transmitter and a photodetector on top of the rack as a receiver (Figure 1). Our approach enables all pairs of racks to establish direct links, and we can reconfigure such links (i.e., connect different rack pairs) within 12 us. To carry traffic from a source to a destination rack, transmitters and receivers in our interconnect can be dynamically linked in millions of ways. We develop topology construction and routing methods to exploit this flexibility, including a flow scheduling algorithm that is a constant factor approximation to the offline optimal solution. Experiments with a small prototype point to the feasibility of our approach. Simulations using realistic data center workloads show that, compared to the conventional folded-Clos interconnect, our approach can improve mean flow completion time by 30-95% and reduce cost by 25-40%.

We consider the problem of efficient content delivery over networks in which individual nodes are equipped with content caching capabilities. We present a flexible methodology for the design of cooperative, decentralized caching strategies that can adapt to real-time changes in regional content popularity. This design methodology makes use of a recently proposed reduced consensus optimization scheme, in which a number of networked agents cooperate in locating the optimum of the sum of their individual, privately known objective functions. The outcome of the design is a set of dynamic update rules that stipulate how much and which portions of each content piece an individual network node ought to cache. In implementing these update rules, the nodes achieve a collectively optimal caching configuration through nearest-neighbor interactions and measurements of local content request rates only. Moreover, individual nodes need not be aware of the overall network topology or how many other nodes are on the network. The desired caching behavior is encoded in the design of individual nodes' costs and can incorporate a variety of network performance criteria. Using the proposed methodology, we develop a set of content-caching update rules designed to minimize the energy consumption of the network as a whole by dynamically trading off transport and caching energy costs in response to changes in content demand.

We consider the problem of designing content-caching strategies for the energy-efficient delivery of content such as video, over an internet-style network. We propose a method for the design of decentralized caching strategies that can adapt to real-time changes in regional content popularity. This design method is based on a recently proposed reduced consensus-optimization scheme wherein a number of agents networked over a general mesh topology cooperate in locating the optimum of the sum of their individual, privately known objective functions. The agents (i.e. network nodes with caching capabilities) achieve the collectively optimal caching configuration via nearest-neighbor interactions and measurements of local content request rates only. The caching behavior of individual nodes, which dynamically trades transport and caching energy costs in response to fluctuations in content demand, is designed to optimize the performance of the network as a whole.

Core and aggregation optical networks are remarkably static, despite the emerging dynamic capabilities of the individual optical devices. This stems from the inability to address optical impairments in real-time. As a result, tasks such as adding and removing wavelengths take a substantial amount of time, and therefore, optical networks are over-provisioned and inefficient in terms of capacity and energy. Optical Performance Monitors (OPMs) that assess the Quality of Transmission (QoT) in real-time can be used to overcome these inefficiencies. However, prior work mostly focused on the single link level. In this paper, we present a network-wide optimization algorithm that leverages OPM measurements to dynamically control the wavelengths' power levels. Hence, it allows adding and dropping wavelengths quickly while mitigating the impacts of impairments caused by these actions, thereby facilitating efficient operation of higher layer protocols. We evaluate the algorithm's performance using a network-scale optical simulator under real-world scenarios and show that the ability to add and drop wavelengths dynamically can lead to significant power savings. Moreover, we experimentally evaluate the algorithm in an optical testbed and discuss the practical implementation issues. To the best of our knowledge, this paper is the first attempt at providing a global power control algorithm that uses live OPM measurements to enable dynamic optical networking.

In this paper, we study the efficacy of error control schemes for energy-efficient reliable delivery of large files (hundreds of GBs) over core optical networks. Specifically, we examine two schemes: automatic repeat request (ARQ), and hybrid ARQ (i.e. ARQ combined with forward error correction (FEC) capability). We focus on Reed-Solomon (RS) FEC codes (in hybrid ARQ) and propose a new model, incorporating different block sizes as well as code error-correction capability, to estimate the energy consumption for performing encoding and decoding operations in optical networks. The model considers the impact of varying pre-FEC bit-error rates (BER) of the optical channel, and the signal processing blocks used to implement RS codes. Our results show that when the pre-FEC channel BER is in excess of 10-5, hybrid ARQ offers better performance than ARQ in terms of energy efficiency. However, both hybrid ARQ and ARQ have similar performance under lower BER.

Consider a content delivery network in which storage and transport resources, characterized by their capacity and cost (e.g., energy) efficiency, are used to meet users' content object requests. The goal is to find the evolution of the objects being stored and transported by the network resources that meets user requests, satisfies network resource capacities and minimizes overall network cost. We first present a constructive offline solution that provides the maximum network efficiency (or minimum cost per object delivered) that can be achieved by dynamically exploiting network-coded caching and multicasting under arbitrary time-varying demands. We refer to the solution scheme as a dynamic network-coded caching-aided multicast (NCCAM) scheme, and illustrate it in a 6-node butterfly network. We then consider a single time period in which each user requests an arbitrary subset of content objects. We formulate the problem as a network coding problem on a caching-augmented graph and show that under uniform demand, random linear coded caching and multicasting is sufficient for achieving minimum cost caching-aided multicast. For the arbitrary demand scenario, we provide the transport-storage-popularity tradeoff of a polynomial-time solution that uses uncoded caching according to object popularity and random linear coded transmission. We show that while for skewed Zipf object popularity such a simple scheme achieves close to optimal performance, as the Zipf parameter approaches zero (uniform popularity), significant cost reductions can be obtained by optimizing the transport configuration at the expense of increased computational complexity.

In recent years, technical challenges are emerging on how to efficiently distribute the rapid growing user-generated contents (UGCs) with long-tailed nature. To address this issue, we have previously proposed cloud centric media network (CCMN) for cost-efficient UGCs delivery. In this paper, we further study the content placement problem in CCMN. Our objective is to minimize the monetary cost incurred by using cloud resources to orchestrate an elastic and global content delivery network (CDN) service. In particular, this objective is achieved via a two-step method. First, for a single content, we map it into a k-center problem, and find a logarithmic relationship between the mean hop distance from users to contents, and the reciprocal of replica number. Second, for multiple contents, we formulate a convex optimization with storage and bandwidth capacity constraints, which can be solved by our proposed algorithm. Finally, we verify the algorithm based on real-world traces collected from a popular video website in China. Our numerical results suggest that, the optimal number of replica for each content follows a power law in respect to its popularity, under feasible storage and bandwidth constraints, in a set of deployed backbone networks.

Core and aggregation optical networks are remarkably static, despite the emerging dynamic capabilities of the individual optical devices. This stems from the inability to address optical impairments in real-time. As a result, tasks such as adding and removing wavelengths take a substantial amount of time, and therefore, optical networks are over-provisioned and inefficient in terms of capacity and energy. Optical Performance Monitors (OPMs) that assess the Quality of Transmission (QoT) in real-time can be used to overcome these inefficiencies. However, prior work mostly focused on the single link level. In this paper, we present a network-wide optimization algorithm that leverages OPM measurements to dynamically control the wavelengths' power levels. Hence, it allows adding and dropping wavelengths quickly while mitigating the impacts of impairments caused by these actions, thereby facilitating efficient operation of higher layer protocols. We evaluate the algorithm's performance using a network-scale optical simulator under real-world scenarios and show that the ability to add and drop wavelengths dynamically can lead to significant power savings. Moreover, we experimentally evaluate the algorithm in an optical testbed and discuss the practical implementation issues. To the best of our knowledge, this paper is the first attempt at providing a global power control algorithm that uses live OPM measurements to enable dynamic optical networking.

Consider a network of prosumers of media content in which users dynamically create and request content objects. The request process is governed by the objects' popularity and varies across network regions and over time. In order to meet user requests, content objects can be stored and transported over the network, characterized by the capacity and energy efficiency of the storage and transport resources. The energy efficient dynamic in-network caching problem aims at finding the evolution of the network configuration, in terms of the content objects being cached and transported over each network element at any given time, that meets user requests, satisfies network resource capacities and minimizes overall energy use. We provide 1) an information-centric optimization framework for the energy efficient dynamic in-network caching problem, 2) an offline solution, EE-OFD, based on an integer linear program (ILP) that obtains the maximum efficiency gains that can be achieved with global knowledge of user requests and network resources, and 3) an efficient fully distributed online solution, EEOND, that allows network nodes to make local caching decisions based on their current estimate of the global energy benefit. Our solutions take into account the network heterogeneity, in terms of capacity, energy efficiency and content popularity, and adapt to changing network conditions minimizing overall energy use.

This paper provides a theoretical framework of energy-optimal mobile cloud computing under stochastic wireless channel. Our objective is to conserve energy for the mobile device, by optimally executing mobile applications in the mobile device (i.e., mobile execution) or offloading to the cloud (i.e., cloud execution). One can, in the former case sequentially reconfigure the CPU frequency; or in the latter case dynamically vary the data transmission rate to the cloud, in response to the stochastic channel condition. We formulate both scheduling problems as constrained optimization problems, and obtain closed-form solutions for optimal scheduling policies. Furthermore, for the energy-optimal execution strategy of applications with small output data (e.g., CloudAV), we derive a threshold policy, which states that the data consumption rate, defined as the ratio between the data size (L) and the delay constraint (T), is compared to a threshold which depends on both the energy consumption model and the wireless channel model. Finally, numerical results suggest that a significant amount of energy can be saved for the mobile device by optimally offloading mobile applications to the cloud in some cases. Our theoretical framework and numerical investigations will shed lights on system implementation of mobile cloud computing under stochastic wireless channel.

In content-centric networking (CCN), the in-network caching feature provides several attractive advantages such as low dissemination latency and network transport load reduction. Thus, CCN requires less transport energy but additional energy to provide a caching capability at every content router. In this paper, we investigate the minimum energy consumption that CCN can achieve with optimal cache locations by considering different caching hardware technologies, number of downloads per hour, and content popularity. We first set up an energy consumption model for CCN and then formulate linear and nonlinear programming problems to minimize total energy consumption of CCN. Also, a genetic algorithm (GA) approach is proposed to find energy-efficient cache locations. Using reported energy efficiency of computational hardware and network equipment, we show CCN yield greater energy savings for very popular content and small-sized catalog, compared to conventional CDN. Our results also indicate that two aspects of the memory technology, energy-proportional caching and sufficient memory capacity, are critical to the overall energy efficiency gain of CCN.

In-network processing of media streams will be necessary in order to meet the personalization, interactive, and real time requirements of future video centric media services. Using multi-view video (MVV) streaming as an example, we investigate the energy tradeoff between video processing and transport for the delivery of personalized media services. We focus on evaluating the energy benefit of distributed vs. centralized processing architectures. We provide solutions for the relative energy efficiency regions as a function of the user viewing preferences and the processing-transport efficiency ratio. Our results show that a small number of requests and a homogeneous interest among viewing regions favors the centralized processing of personalized video streams and multicast transport to end users, while a larger number of requests and a heterogeneous interest favors the processing of personalized views at a distributed subset of nodes in the network.

We investigate the potential trade-off between energy efficiency and latency of file transmission over IP and Optical domains. We consider various queueing disciplines that are applicable to these domains, and show that existing schemes achieve only one at the expense of the other. We show that we can simultaneously achieve low-latency file transfers in an energy-efficient manner using an intuitive approach that is easy to implement in next-generation networks.

We examine the basic energy tradeoffs between video transport and video processing for services such as multi-view video (MVV) streaming, where multiple media streams are combined and processed to create an immersive and personalized user experience. We analyze and compare the energy efficiency of different architectural options for the location of video processing functions and illustrate how the architecture of choice is influenced by the network topology, the users' view preferences, and the relative transport-processing energy efficiency. We provide an integer linear programming formulation for the energy efficient functional resource allocation problem, which we show it can be solved as a linear program, and an easily implementable algorithm that generates optimal solutions in polynomial time.

In this paper, we examine how energy constraints might shape future optical communication networks and the impact that current technology trends may have on future energy use. Historical factors and prevailing complications associated with fiber capacity point to an increased focus on energy to enable tighter photonic and electronic component integration and larger networks. Energy requirements and associated challenges are described at the component, system, and network level.