Due to the globalized IC market model, the chance of a third party vendor providing a compromised chip multiprocessor by embedding a Hardware Trojan (HT) is high. multicast traffic becomes more common with the develop...
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ISBN:
(纸本)9781728105543
Due to the globalized IC market model, the chance of a third party vendor providing a compromised chip multiprocessor by embedding a Hardware Trojan (HT) is high. multicast traffic becomes more common with the development of parallel applications and diverse programming models. In this paper, we study the effect of denial of service attacks launched by HTs on the performance of multicast routing algorithms. The HT model is proposed which exploits the on-chip temperature sensor information to identify the hotspot nodes and launch an attack on multicast packets to degrade the performance of the network-on-chip. We propose the hierarchical multicast routing algorithm which is an amalgamation of multiple unicast and dual-path routing algorithms. Simulation results confirm that the proposed algorithm is more resilient to this new type of attack than a multipath algorithm.
The existing Internet is a network consisting of several routers and communication links, where each of the routers has the information about its adjacent routers only. As a result, the algorithms in practice are dece...
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ISBN:
(数字)9781728173665
ISBN:
(纸本)9781728173672
The existing Internet is a network consisting of several routers and communication links, where each of the routers has the information about its adjacent routers only. As a result, the algorithms in practice are decentralized and work in a distributed setting. When forwarding a packet from a single source to multiple destinations, multicasting is the concept of sending only one packet for the common path between the source and the destinations and replicating it when the paths are divided. As the routing algorithms operate only on localized information in the present network, multicasting is a difficult task to implement. Software Defined Network (SDN) provides us with a centralized view of the whole topology, opening up the opportunity to deploy more complex and efficient algorithms for multicasting. Much research has already been carried out on this field based on various dimensions of SDN. In this paper, we reviewed some of the existing algorithms associating with our target. We also tested the existing routing algorithms on SDN and conducted a comparative analysis among the algorithms to determine which one would efficiently deploy multicast routing.
In this work, we propose ultra-low-complexity design solutions for multi-group multicast beamforming in large-scale systems. For the quality-of-service (QoS) problem, by utilizing the optimal multicast beamforming str...
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In this work, we propose ultra-low-complexity design solutions for multi-group multicast beamforming in large-scale systems. For the quality-of-service (QoS) problem, by utilizing the optimal multicast beamforming structure obtained recently in [2], we convert the original problem into a non-convex weight optimization problem of a lower dimension and propose two fast first-order algorithms to solve it. Both algorithms are based on successive convex approximation (SCA) and provide fast iterative updates to solve each SCA subproblem. The first algorithm uses a saddle point reformulation in the dual domain and applies the extragradient method with an adaptive step-size procedure to find the saddle point with simple closed-form updates. The second algorithm adopts the alternating direction method of multipliers (ADMM) method by converting each SCA subproblem into a favorable ADMM structure. The structure leads to simple closed-form ADMM updates, where the problem in each update block can be further decomposed into parallel subproblems of small sizes, for which closed-form solutions are obtained. We also propose efficient initialization methods to obtain favorable initial points that facilitate fast convergence. Furthermore, taking advantage of the proposed fast algorithms, for the max-min fair (MMF) problem, we propose a simple closed-form scaling scheme that directly uses the solution obtained from the QoS problem, avoiding the conventional computationally expensive method that iteratively solves the inverse QoS problem. We further develop lower and upper bounds on the performance of this scaling scheme. Simulation results show that the proposed algorithms offer near-optimal performance with substantially lower computational complexity than the state-of-the-art algorithms for large-scale systems.
This paper addresses the challenges of wireless resource allocation for 5G Ultra-reliable Low-latency Communication (URLLC) broadcast/multicast services in Vehicle-to-Everything (V2X) scenarios. It proposes three key ...
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This paper addresses the challenges of wireless resource allocation for 5G Ultra-reliable Low-latency Communication (URLLC) broadcast/multicast services in Vehicle-to-Everything (V2X) scenarios. It proposes three key algorithms: an iterative resource allocation approach that decomposes optimization into power and spectrum subproblems, a federated learning-based multicast resource allocation scheme that protects data privacy while enabling distributed training, and a cooperative multi-agent reinforcement learning solution that treats vehicles as intelligent nodes to jointly optimize system throughput, URLLC delivery rate, and multicast performance. Path loss models, mobility patterns, and interference scenarios are analyzed for both unicast and multicast transmissions. Simulation results demonstrate that the proposed algorithms achieve superior performance in terms of throughput, reliability, and latency compared to traditional and baseline approaches.
Recent advances in distributed machine learning show theoretically and empirically that, for many models, provided that workers will eventually participate in the synchronizations, i) the training still converges, eve...
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Recent advances in distributed machine learning show theoretically and empirically that, for many models, provided that workers will eventually participate in the synchronizations, i) the training still converges, even if only p workers take part in each round of synchronization, and ii) a larger p generally leads to a faster rate of convergence. These findings shed light on eliminating the bottleneck effects of parameter synchronization in large-scale data-parallel distributed training and have motivated several optimization designs. In this paper, we focus on optimizing the parameter synchronization for peer-to-peer distributed learning, where workers broadcast or multicast their updated parameters to others for synchronization, and propose SELMCAST, a suite of expressive and efficient multicast receiver selection algorithms, to achieve the goal. Compared with the state-of-the-art (SOTA) design, which randomly selects exactly p receivers for each worker's multicast in a bandwidth-agnostic way, SELMCAST chooses receivers based on the global view of their available bandwidth and loads, yielding two advantages, i.e., accelerated parameter synchronization for higher utilization of computing resources and enlarged average p values for faster convergence. Comprehensive evaluations show that SELMCAST is efficient for both peer-to-peer Bulk Synchronous Parallel (BSP) and Stale Synchronous Parallel (SSP) distributed training, outperforming the SOTA solution significantly.
We propose fast beamforming algorithms for minimizing the power consumption of wireless communications while assuring unicast and multicast data rates in NOMA wireless networks. Specifically, we put forward the greedy...
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We propose fast beamforming algorithms for minimizing the power consumption of wireless communications while assuring unicast and multicast data rates in NOMA wireless networks. Specifically, we put forward the greedy backward substitution algorithm for unicast beamforming design and the Gram-Schmidt rotation algorithm for multicast beamforming design. The proposed approach adopts zero-forcing techniques, orthogonalization and rotation but does not have to solve complicated convex optimization problems. In addition, we derive novel analytical results for the studied problem. Simulation results indicate that the proposed approach outperforms a number of alternative schemes in the literature in terms of beamforming power consumption and computational complexity.
multicast short video streaming can enhance bandwidth utilization by enabling simultaneous video transmission to multiple users over shared wireless channels. The existing network management schemes mainly rely on the...
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multicast short video streaming can enhance bandwidth utilization by enabling simultaneous video transmission to multiple users over shared wireless channels. The existing network management schemes mainly rely on the sequential buffering principle and general quality of experience (QoE) model, which may deteriorate QoE when users' swipe behaviors exhibit distinct spatiotemporal variation. In this paper, we propose a digital twin (DT)-based network management scheme to enhance QoE. Firstly, user status emulated by the DT is utilized to estimate the transmission capabilities and watching probability distributions of sub-multicast groups (SMGs) for an adaptive segment buffering. The SMGs' buffers are aligned to the unique virtual buffers managed by the DT for a fine-grained buffer update. Then, a multicast QoE model consisting of rebuffering time, video quality, and quality variation is developed, by considering the mutual influence of segment buffering among SMGs. Finally, a joint optimization problem of segment version selection and slot division is formulated to maximize QoE. To efficiently solve the problem, a data-model-driven algorithm is proposed by integrating a convex optimization method and a deep reinforcement learning algorithm. Simulation results based on the real-world dataset demonstrate that the proposed DT-based network management scheme outperforms benchmark schemes in terms of QoE improvement.
With the burgeoning demand for data-intensive services, satellite-terrestrial networks (STNs) face increasing backhaul link congestion, deteriorating user quality of service (QoS), and escalating power consumption. Ca...
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With the burgeoning demand for data-intensive services, satellite-terrestrial networks (STNs) face increasing backhaul link congestion, deteriorating user quality of service (QoS), and escalating power consumption. Cache-aided STNs are acknowledged as a promising paradigm for accelerating content delivery to users and alleviating the load of backhaul links. However, the dynamic nature of low earth orbit (LEO) satellites and the complex interference among satellite beams and terrestrial base stations pose challenges in effectively managing limited edge resources. To address these issues, this paper proposes a method for dynamically scheduling caching and communication resources, aiming to reduce network costs in terms of transmission power consumption and backhaul traffic, while meeting user QoS demands and resource constraints. We formulate a mixed timescale problem to jointly optimize cache placement, LEO satellite beam direction, and cooperative multicast beamforming among satellite beams and base stations. To tackle this intricate problem, we propose a two-stage solution framework, where the primary problem is decoupled into a short-term content delivery subproblem and a long-term cache placement subproblem. The former subproblem is solved by designing an alternating optimization approach with whale optimization and successive convex approximation methods according to the cache placement state, while cache content in STNs is updated using an iterative algorithm that utilizes historical information. Simulation results demonstrate the effectiveness of our proposed algorithms, showcasing their convergence and significantly reducing transmission power consumption and backhaul traffic by up to 52%.
Consistent routing updates through Software-Defined Networking (SDN) can be difficult due to the asynchronous and distributed nature of the data plane. Recent studies have achieved consistent unicast routing updates. ...
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Consistent routing updates through Software-Defined Networking (SDN) can be difficult due to the asynchronous and distributed nature of the data plane. Recent studies have achieved consistent unicast routing updates. However, achieving consistent updates with drop-freeness and duplicate-freeness remains a challenge for multicast with fewer known results. This paper proposes a Novel Ordered Update Scheme called Nous, a novel approach that offers a comprehensive solution for consistently updating multicast routing based on SDN. To avoid duplicate entries, Nous configures the inport match field in the forwarding rules. Nous implements a dependency graph to schedule update operations dynamically. It also solves the Replace Operation Tree Migration Problem (ROTMP) using a greedy solution. To compare the greedy solution with the optimal solution, we employ the state-of-the-art mathematical programming solver Gurobi Optimizer 7.5 (for solving the optimization problem), Mininet 2.0, and Floodlight 1.2 (for simulation and comparison) to obtain a near-optimal solution. Simulation results show that using the greedy solution, Nous can usually achieve near-optimal solutions to the ROTMP with an average of fewer than 1.2 rounds and within 10 ms in different scenarios. This makes Nous the first ordered update scheme to guarantee two consistent states simultaneously.
Virtual reality (VR) imaging is 360 degrees, which requires a large bandwidth for video transmission. To address this challenge, tile-based streaming has been proposed to deliver only the focused part of the video ins...
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Virtual reality (VR) imaging is 360 degrees, which requires a large bandwidth for video transmission. To address this challenge, tile-based streaming has been proposed to deliver only the focused part of the video instead of the entire one. However, the impact of cybersickness, akin to motion sickness, on tile selection in VR has not been explored. In this paper, we investigate Multi-user Tile Streaming with Cybersickness Control (MTSCC) in an adaptive 360(degrees) video streaming system with multicast and cybersickness alleviation. We propose a novel m(2)-competitive online algorithm that utilizes Individual Sickness Indicator (ISI) and Bitrate Restriction Indicator (BRI) to evaluate user cybersickness tendency and network bandwidth efficiency. Moreover, we introduce the Video Loss Indicator (VLI) and Quality Variance Indicator (QVI) to assess video quality loss and quality difference between tiles. We also propose a multi-armed bandit (MAB) algorithm with confidence bound-based reward (video quality) and cost (cybersickness) estimation. The algorithm learns the weighting factor of each user's cost to slow down cybersickness accumulation for users with high cybersickness tendencies. We prove that the algorithm converges to an optimal solution over time. According to simulation with real network settings, our proposed algorithms outperform baselines in terms of video quality and cybersickness accumulation.
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