Networks are frequently changing due to new technologies. To increase the network performance, companies migrate their existing network to a network with a new technology. Finding an efficient optimization algorithm is an important challenge in the network migration. In this paper, the network migration problem is considered as a set of circuit migration problems in which multiple technicians simultaneously migrate the endpoints of circuits in order to minimize the average latency and average technician travel cost. While average latency indicates how fast the sites can be upgraded, average travel cost estimates the required cost for modernizing the network. First, We derive binary linear program and binary quadratic program formulations for average latency and average technician travel cost, respectively. Then we use the linear scalarization method to obtain a multi-objective optimization problem for simultaneously minimizing both costs. Our approach for solving the derived multi-objective optimization problem is based on converting it to a quadratic unconstrained binary optimization problem (QUBO) using the penalty method. Subsequently, we exploit the third generation of Fujitsu Digital Annealer which is a hybrid system of hardware and software to minimize the derived QUBO. To investigate the performance of our proposed method, we study extensive network migration instances on the 75-node CONUS network topology. Simulation results indicate that both costs can efficiently be optimized using our proposed method. We also directly solve the obtained multi-objective optimization problem with Gurobi solver. The comparison results show that our proposed method outperforms the Gurobi solver.

While self-attention mechanism has shown promising results for many vision tasks, it only considers the current features at a time. We show that such a manner cannot take full advantage of the attention mechanism. In this paper, we present Deep Connected Attention Network (DCANet), a novel design that boosts attention modules in a CNN model without any modification of the internal structure. To achieve this, we interconnect adjacent attention blocks, making information flow among attention blocks possible. With DCANet, all attention blocks in a CNN model are trained jointly, which improves the ability of attention learning. Our DCANet is generic. It is not limited to a specific attention module or base network architecture. Experimental results on ImageNet and MS COCO benchmarks show that DCANet consistently outperforms the state-of-the-art attention modules with a minimal additional computational overhead in all test cases. The code is available at: https://github.com/13952522076/DCANet.

In this paper, we present a conceptually simple, yet powerful method for image recognition. The method, called Cooperative Dynamic Convolution (CoConv), introduces a cooperative learning of dynamic convolution from multiple convolutional experts. CoConv can be used as a substitute for the traditional static convolution, and can be seamlessly integrated in various visual models. Moreover, CoConv is easy to train with only a minimal computational overhead introduced in the inference phase. CoConv is trained by using multiple convolutional experts simultaneously, and the convolutional weights are merged by a weighted summation before convolutional operations for efficiency during inference. Results from extensive experiments show that CoConv leads to consistent improvement for image classification on various datasets, independent of the choice of the base convolutional network. Remarkably, CoConv improves the top-1 classification accuracy of ResNet18 by 3.06% on ImageNet. The code is available at: https://github.com/Nyquixt/CoConv.

In this paper, we analyze the performance of Multiplayer Cloud Gaming (MCG) systems. To that end, we introduce a model and new MCG-Quality of Service (QoS) metric that captures the freshness of the players' updates and fairness in their gaming experience. We introduce an efficient measurement-based Joint Multiplayer Rate Adaptation (JMRA) algorithm that optimizes the MCG-QoS by overcoming large (possibly varying) network transport delays by increasing the associated players' update rates. The resulting MCG-QoS is shown to be Schur-concave in the network delays, leading to natural characterizations and performance comparisons associated with the players' spatial geometry and network congestion. In particular, joint rate adaptation enables service providers to combat variability in network delays and players' geographic spread to achieve high service coverage. This, in turn, allows us to explore the spatial density and capacity of compute resources that need to be provisioned. Finally, we leverage tools from majorization theory, to show how service placement decisions can be made to improve the robustness of the MCG-QoS to stochastic network delays.

Federated learning is a distributed machine learning approach that can be applied to many networking applications. In this paper, we propose a novel blockchain-based secure aggregation protocol for federated learning, which simplifies the existing secure aggregation process by leveraging consensus through blockchain. We demonstrate the prototype by training a general LSTM model for traffic prediction at cell sites based on distributed time series datasets.

To reduce the amount of transmitted data, feature map-based fusion is recently proposed as a practical solution to cooperative 3-D object detection by autonomous vehicles (AVs). The precision of object detection, however, may require significant improvement, especially for objects that are far away or occluded. To address this critical issue for the safety of AVs and human beings, we propose a cooperative spatial feature fusion (CoFF) method for AVs to effectively fuse feature maps for achieving a higher 3-D object detection performance. Especially, CoFF differentiates weights among feature maps for a more guided fusion, based on how much new semantic information is provided by the received feature maps. It also enhances the inconspicuous features corresponding to far/occluded objects to improve their detection precision. The experimental results show that CoFF achieves a significant improvement in terms of both detection precision and effective detection range for AVs, compared to previous feature fusion solutions.

Motivated by the potentially high downlink traffic demands of commuters in future autonomous vehicles, we study a network architecture where vehicles use Vehicle-to-Vehicle (V2V) links to form relay network clusters, which in turn use Vehicle-to-Infrastructure (V2I) links to connect to one or more Road Side Units (RSUs). Such cluster-based multihoming offers improved performance, e.g., in coverage and per user shared rate, but depends on the penetration of V2V+V2I capable vehicles and possible blockage, by legacy vehicles, of line of sight based V2V links, such as those based on millimeter-wave and visible light technologies. This paper provides a performance analysis of a typical vehicle's connectivity and throughput on a highway in the free-flow regime, exploring its dependence on vehicle density, sensitivity to blockages, number of lanes and heterogeneity across lanes. The results, backed up by simulations of realistic vehicular traffic, show that even with moderate vehicle densities and penetration of V2V+V2I capable vehicles, such architectures can achieve substantial improvements in connectivity and reduction in per-user rate variability as compared to V2I based networks. The typical vehicle's performance is also shown to improve considerably in the multilane highway setting as compared to a single lane road. This paper also sheds light on how the network performance is affected when vehicles can control their relative positions, by characterizing the connectivity-throughput tradeoff faced by the clusters of vehicles.

In this demo, we demonstrate a novel blockchain based protocol for federated learning. We present the system architecture and describe the blockchain based protocol that seamlessly provides secure communication in federated learning with physically distributed data sets.

The automotive industry is undergoing disruptive changes, e.g., ride sharing and self-driving cars which, in addition to leveraging wireless connectivity, may lead to dramatic changes in the volume of infotainment and work related data consumption of vehicle bound passengers. This paper studies the potential gains of leveraging clusters of V2V interconnected vehicles to enable: (1) improved opportunistic access to the cellular infrastructure; and (2), balancing traffic loads across cells through cluster multihoming. A stochastic geometric model and associated analysis are used to obtain a preliminary understanding of possible gains of cluster-based opportunistic relaying and its sensitivity to the system parameters, e.g., base station density, vehicular cluster size and density etc. An optimal network utility maximization formulation is then developed to serve as a baseline to evaluate a simple distributed cluster management algorithm which for the scenarios considered proves to be near-optimal. Overall the results suggest that 3-10x throughput gains are possible along with significant improvements in user rate fairness depending on the system parameters.

In 5G, network slicing is considered as an important enabler to support various verticals simultaneously, by providing dedicated and isolated slice services over a common infrastructure. Many verticals have specific Service Level Agreement (SLA), which should be guaranteed by mechanisms like service assurance (SA). In 5G, SA is required to collaborate with orchestration and management to automate the slice provisioning process. In this paper, we first propose a hierarchical, distributive, and modular SA architecture to assure the entire slice services from end to end (E2E), based on distributed modules assuring underlying services, functions, and resources. Secondly, a closed loop is formed between assurance and orchestration to enable automation by cooperation between monitoring, analytics, and orchestration. In addition, correlation across layers (infrastructure, network function, network service and E2E slice) is considered to form a closed loop through all layers to further improve efficiency. The joint approach also allows to optimize the tradeoff between the cost and the performance of SA. Simulation results illustratively demonstrate the benefit of the proposed approach.

In this paper, we consider the problem of dynamic space-time resource allocation for optimizing the movements of connected autonomous vehicles (CAVs) through intersections without traffic signals. We design a three-dimensional (3D) space-time resource model for maintaining the intersection resource information in both the two-dimensional (2D) space domain and the time domain. In the 3D resource model, the trajectory of a CAV through an intersection is assigned a specific parallelepiped resource that spans both 2D space and time domains. Moreover, the dynamic space-time resource allocation problem is simplified to a classic 3D container-packing problem. We propose a dynamic heuristic algorithm, Best Parallelepiped Fit (BPF), to maintain smooth traffic flow and maximize spacetime resource usage by adjusting the speed and entry time of each approaching CAV through intersections. We evaluate the performance of the proposed algorithm under different traffic loads, and simulation results indicate that our algorithm can greatly reduce the average travel delay of CAVs.

Orchestration across network domains is essential for providing end-to-end network services in software-defined infrastructures. In this paper, we propose a vertex-centric distributed computing algorithm for finding all feasible mappings of a mesh virtual network request across domains. Our proposed algorithm is based on a distributed orchestration framework, where the topology information is locally maintained within each domain without disclosing to any centralized broker. The proposed algorithm first partitions a mesh virtual network request into a set of linear sub-requests and applies a vertex-centric distributed computing algorithm to find all feasible mappings of each individual linear sub-request. The feasible mappings of sub- requests are then merged to obtain all feasible mappings of the original virtual network request. Our simulation results show that partitioning a virtual network request to longer, balanced-length, non-overlap-link linear sub-requests is more scalable by significantly lowering the total computation time.

Current frameworks for network programming assume that the network contains a collection of homogenous devices that can be rapidly reconfigured in response to changing policies and network conditions. Unfortunately, these assumptions are incompatible with the realities of modern networks, which contain legacy devices that offer diverse functionality and can only be reconfigured slowly. Additionally, network service providers need to walk a fine line between providing flexibility to users, and maintaining the integrity and reliability of their core networks. These issues are particularly evident in optical networks which are used by ISPs and WANs and provide high bandwidth at the cost of limited flexibility and long reconfiguration times. This paper presents a different approach to implementing high-level policies, by pushing functionality to the edge and using the core merely for transit. Building on the NetKAT framework and leveraging linear programming problem solvers, we develop techniques for analyzing and transforming policies into configurations that can be installed at the edge of the network. Furthermore, our approach is extensible to include constraints crucial to optical networks such as path constraints and fault tolerance. We develop a working implementation using off-the-shelf solvers and evaluate our approach on a set of large-scale optical topologies.

In this paper, we study the reliable virtual network mapping (RVNM) problem for allocating virtual machines (VMs) from multiple data centers (DCs) with the objective of maximizing the total reliability of a virtual network under two capacity constraints: computing capacity constraint at each DC and bandwidth capacity constraint on each link. We first describe graph models of RVNM, formulate the RVNM problem, and prove RVNM is NP-complete. We then formulate the problem as an integer linear programming (ILP) and give results for small-scale cases. A game theory based approach, named Link Mapping First (LMF), is proposed by modeling RVNM to the capacity-constrained potential game and is proved to be convergent to a pure Nash Equilibrium. Numerical results show that LMF achieves high reliability, which is close to the optimal solution, in small-scale cases and outperforms an existing Node Mapping First (NMF) algorithm, especially for large-scale cases.

In this paper, we study a new optical network paradigm, where statistical sharing is supported in optical networks. This new paradigm is motivated by the recent revolution of Software Defined Optics (SDO). Software defined variable-bandwidth transponders can support variable data rates for a single connection, i.e. base rates and peak rates. Guaranteeing the peak rates for all the connections simultaneously requires the spectrum for all circuits to be provisioned for peak rates, leading to a large amount of bandwidth usage with low utilization. However, if resources are provisioned for the base rate of each connection, with some shared spectrum resources set aside to allow a fraction of these connections to dynamically switch to their peak rates, then spectrum resources can be allocated more efficiently, allowing a greater number of connections to be accommodated. We reserve a fraction of the spectrum resources for provisioning of base rate of the dynamic traffic while the rest of the spectrum resources are reserved for peak rate, allowing statistical sharing. Our goal is to minimize the blocking of the arriving connection requests, while at the same time maximizing the chance that existing connection requests are able to switch from base rate to peak rate. These two goals conflict with each other; therefore, we need to find a trade-off based on the amount of spectrum resources set aside for the peak rate, and based on the routing, modulation format selection, and spectrum allocation (RMSA) scheme. Our evaluation can help network operators to determine the amount of spectrum that requires to be set aside for peak rates in order to maximize revenue.

In this paper, we study a new paradigm in optical networking in which optical spectrum is allowed to be statistically shared between optical circuits, allowing the oversubscription of optical links. We propose a probabilistic model which estimates the capacity requirements for the optical networks under a certain density of statistical sharing. Simulation results indicate that our proposed model can help with network design decisions, such as admission control and capacity and bandwidth allocation in elastic optical networks.

In this paper, we study the minimum failure resource allocation (MFRA) problem of allocating virtual machines (VMs) across multiple optically interconnected data centers (DCs) with the objective of minimizing the total failure probability based on the information obtained from the optical network virtulization. We first describe the framework of resource allocation, formulate the MFRA problem, and prove that MFRA is NP-complete. We then provide ILP formulation to obtain the optimal solution for small scale problems and two heuristic algorithms, named Minimum SRG Cover (MSC) and Reliable DC Selection (RDS), to solve large scale problems. Numerical results show that both heuristics achieve results close to optimal solutions for small scale problems. Numerical results also show that although RDS has higher time complexity, it outperforms MSC especially when the requested VMs are small.

In this paper, we study the reliable resource allocation (RRA) problem of allocating virtual machines (VMs) from multiple optically interconnected data centers (DCs) with the objective of minimizing the total failure probability based on the information obtained from the optical network virtulization. We first describe the framework of resource allocation, formulate the RRA problem, and prove that RRA is NP-complete. We provide an algorithm, named Minimum Failure Cover (MFC), to obtain optimal solutions for small scale problems. We then provide a greedy algorithm, named VM-over-Reliability (VOR), to solve large scale problems. Numerical results show that VOR achieves results close to optimal solutions gained by MFC for small scale problems. Numerical results also show that VOR outperforms the resource allocation through random DC selection (RDS).

In this paper, we aim to minimize the total network cost in flexible-grid optical networks with multiple line rates. Besides transponder cost, regenerator cost, and shared infrastructure cost, the cost of regenerator sites is also considered. We first provide the problem definition and formulate the problem as an integer linear program (ILP). We also propose a heuristic algorithm considering both selection and placement of equipment to minimize the total network cost. Simulation results show the heuristic algorithm results in up to 28% cost saving, with no significant increase in spectrum usage.

In this paper, we study the problem of regenerator pool site selection for mixed line rate optical networks (MLR-RPSS), with the objective of minimizing the number of regenerator pool sites for a given set of requests. We first provide the problem definition of MLR-RPSS and show that the MLR-RPSS problem is NP-complete. We then present four algorithms, named Independent algorithm, Sequential algorithm, MLR-combined algorithm, and Weighted MLR-combined algorithm. The performance of the algorithms is compared via simulation and results show that the Weighted MLR-combined algorithm has better performance in most cases. Also, when network load is high, the minimum number of regenerator pool sites will approach a certain limit, and some specific nodes will be more likely to be selected as regenerator pool sites.

In this paper, we address the problem of survivable traffic grooming and regenerator placement in optical WDM networks with impairment constraints. The working connections are protected end to end by provisioning bandwidth along a sequence of lightpaths through a dedicated connection-level protection scheme. An auxiliary-graph-based approach is proposed to address the placement of regenerators and grooming equipment for both working and dedicated backup connections in the network with the goal of minimizing the total equipment cost. Simulation results show that the proposed algorithm outperforms a lightpath-level protection algorithm, in which each lightpath is protected separately. We also show the effect of different cost models on equipment placement and evaluate the performance for networks with different line rates.

In this paper, we address the problem of survivable traffic grooming in optical WDM networks in which lightpaths are hop constrained. Survivability is provisioned at the wavelength granularity through either dedicated or shared path protection schemes. We propose an auxiliary-graph-based algorithm that addresses grooming, protection, and impairment constraints in a combined manner and that determines the placement of regenerators and grooming equipment in the network with the goal of minimizing equipment cost. Numerical results illustrate that the proposed algorithm outperforms an algorithm in which grooming, protection, and impairments are handled separately. We also evaluate effects of different equipment placement policies on the network cost and evaluate the cost-performance trade-offs for different network line rates.

A light-trail is a generalization of a lightpath such that multiple nodes can take part in communication along the path. A light-trail is a good candidate for optical layer traffic grooming. In this paper we investigate the grooming aspect of light-trails from a sub-system perspective. We introduce a new sub-system called burstponder card that enables efficient grooming of traffic in a light-trail. We describe the implementation of the burstponder card using FPGA and burst-mode optics. We show experiment results on efficiency and latency for a 4-node light-trail network demonstrating it as an effective solution for optical grooming.