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To cope with the complex and constantly changing communication environment, Multi-Link Operation (MLO) has attracted extensive attention in research and development of next-generation Wi-Fi technology, IEEE 802.11be standard (Wi-Fi 7). MLO can transmit information simultaneously on different channels in the same device, which can significantly increase the capacity of Wi-Fi for future communication systems. Previous relevant studies have shown that network throughput is not simply and positively correlated with frame aggregation length. Furthermore, due to the variability of communication environments, the optimal frame aggregation length in the scheduling process is not unique within a given time, and the traditional methods are limited to solving non-convex optimization problems. To fill this gap, we present a novel approach using deep reinforcement learning (DRL) to tackle the optimization of frame aggregation lengths in 802.11be for multiple links. Our research offers a comprehensive depiction of the communication and interaction structure among multiple links and DRL, which helps drive the advancement of artificial intelligence (AI) solutions in future network designs and demonstrates the feasibility of exploiting DRL in next-generation wireless networks. Extensive simulation experiments show that the proposed method can achieve superior performance compared to the existing methods.

Multi-UAV systems rely on the communication network to exchange mission-critical data for their coordination and deployment, while communication delays could cause significant challenges to both tasks. The impact of the delays becomes even more severe if the delay, network structure and formation are all time-varying, a common challenge faced by real-world multi-UAV systems. To address this challenge, we consider time-varying delays that exist in multiple channels caused by transmitting information and internal delays that exist in UAVs themselves caused by obtaining and processing their own data. We design an effective distributed formation control protocol for a multi-UAV system to achieve time-varying formation; this protocol is particularly useful for dealing with time-varying multi-UAV network topologies as well. We provide rigorous convergence analysis for different scenarios with or without hybrid delays and obtain sufficient conditions for achieving the time-varying formation. Furthermore, we propose an algorithm for quantifying the maximum delay allowed by the system. Based on the designed formation algorithm, a deployment strategy is proposed to coordinate multi-UAV systems in a practical environment. Numerical analysis and UAV hardware experiments are conducted to evaluate the performance of the theoretical results and investigate the feasibility of generated flight trajectories.

The combination of 5G/6G and edge computing has been envisioned as a promising paradigm to empower pervasive and intensive computing for the Internet-of-Things (IoT). High deployment cost is one of the major obstacles for realizing 5G/6G edge computing. Most existing works tried to deploy the minimum number of edge servers to cover a target area by avoiding coverage overlaps. However, following this framework, the resource requirement per server will be drastically increased by the peak requirement during workload variations. Even worse, most resources will be left under-utilized for most of the time. To address this problem, we propose CoopEdge, a cost-effective server deployment scheme for cooperative multi-access edge computing. The key idea of CoopEdge is to allow deploying overlapped servers to handle variable requested workloads in a cooperative manner. In this way, the peak demands can be dispersed into multiple servers, and the resource requirement for each server can be greatly reduced. We propose a Two-step Incremental Deployment (TID) algorithm to jointly decide the server deployment and cooperation policies. For the scenarios involving multiple network operators that are unwilling to cooperate with each other, we further extend the TID algorithm to a distributed TID algorithm based on the game theory. Extensive evaluation experiments are conducted based on the measurement results of seven real-world edge applications. The results show that compared with the state-of-the-art work, CoopEdge significantly reduces the deployment cost by 38.7% and improves resource utilization by 36.2%, and the proposed distributed algorithm can achieve a comparable deployment cost with CoopEdge, especially for small-coverage servers.

Virtual machine (VM) consolidation strategies are widely used in cloud data centers (CDC) to optimize resource utilization and reduce total energy consumption. Although existing strategies consider current and future resource utilization, the impact of sudden bursts in historical resource utilization on the hosts has been underestimated in uncertain future periods. Insufficient analysis of historical resource utilization may increase the risk of host overloading and Service Level Agreement Violation (SLAV). By defining historical and future trends based on resource utilization, we propose a novel combined trend VM consolidation (CTVMC) strategy which can effectively reduce energy consumption and SLAV. The VMs with the largest combined trend are selected for migration to prevent host overloading. Based on the temporal locality and prediction technique, CTVMC then employs the past, present, and future resource utilization to filter candidate hosts, and identifies the most complementary host to place VM using combined trends. We conduct extensive simulation experiments with PlanetLab Trace and Google Cluster Trace in the CloudSim simulator. Compared with the well-known strategies, CTVMC strategy using the PlanetLab Trace can reduce the number of migrations by over 72.39%, SLAV by over 75.85%, and ESV (a combined metric that judges the trade-off between energy consumption and SLAV) by over 81.54%. According to the Google Cluster Trace, our strategy can reduce the number of migrations by over 61.51%, SLAV by over 37.37%, and ESV by over 35.30%.

Quantitative performance analysis plays a pivotal role in theoretically investigating the performance of Vehicular Edge Computing (VEC) systems. Although considerable research efforts have been devoted to VEC performance analysis, all of the existing analytical models were designed to derive the average system performance, paying insufficient attention to the worst-case performance analysis, which hinders the practical deployment of VEC systems to support mission-critical vehicular applications, such as collision avoidance. To bridge this gap, we develop an original performance analytical model by virtue of Stochastic Network Calculus (SNC) to investigate the worst-case end-to-end performance of VEC systems. Specifically, to capture the bursty feature of task generation, an innovative bivariate Markov Chain is first established and rigorously analysed to derive the stochastic task envelope. Then, an effective service curve is created to investigate the severe resource competition among vehicular applications. Driven by the stochastic task envelope and effective service curve, a closed-form end-to-end analytical model is derived to obtain the latency bound for VEC systems. Extensive simulation experiments are conducted to validate the accuracy of the proposed analytical model under different system configurations. Furthermore, we exploit the proposed analytical model as a cost-effective tool to investigate the resource allocation strategies in VEC systems.

Electric Vehicle-assisted Multi-access Edge Computing (EV-MEC) is a promising paradigm where EVs share their computation resources at the network edge to perform intensive computing tasks while charging. In EV-MEC, a fundamental problem is to jointly decide the charging power of EVs and computation task allocation to EVs, for meeting both the diverse charging demands of EVs and stringent performance requirements of heterogeneous tasks. To address this challenge, we propose a new joint charging scheduling and computation offloading scheme (OCEAN) for EV-MEC. Specifically, we formulate a cooperative two-timescale optimization problem to minimize the charging load and its variance subject to the performance requirements of computation tasks. We then decompose this sophisticated optimization problem into two sub-problems: charging scheduling and computation offloading. For the former, we develop a novel safe deep reinforcement learning (DRL) algorithm, and theoretically prove the feasibility of learned charging scheduling policy. For the latter, we reformulate it as an integer non-linear programming problem to derive the optimal offloading decisions. Extensive experimental results demonstrate that OCEAN can achieve similar performances as the optimal strategy and realize up to 24% improvement in charging load variance over three state-of-the-art algorithms while satisfying the charging demands of all EVs.

Network Function Virtualization (NFV) introduces a new network architecture that offers different network services flexibly and dynamically in the form of Service Function Chains (SFCs), which refer to a set of Virtualization Network Functions (VNFs) chained in a specific order. However, the service latency often increases linearly with the length of SFCs due to the sequential execution of VNFs, resulting in sub-optimal performance for most delay-sensitive applications. In this paper, a novel Parallel VNF Placement (PVFP) approach is proposed for real-world networks via Federated Deep Reinforcement Learning (FDRL). PVFP has three remarkable characteristics distinguishing from previous work: 1) PVFP designs a specific parallel principle, with three parallelism identification rules, to reasonably decide partial VNF parallelism; 2) PVFP considers SFC partition in multi-domains built on their remaining resources and potential parallel VNFs to ensure that VNFs can be reasonably distributed for resource balancing among domains; 3) FDRL-based framework of parallel VNF placement is designed to train a global intelligent model, with time-variant local autonomy explorations, for cross-domain SFC deployment, avoiding data sharing among domains. Simulation results in different scenarios demonstrate that PVFP can significantly reduce the end-to-end latency of SFCs at the medium resource expenditures to place VNFs in multiple administrative domains, compared with the state-of-the-art mechanisms.

A large number of distributed applications necessitate accurate network distance, for example, in the form of delay or latency, to ensure the Quality of Service (QoS). Due to high network measurement overhead and severe traffic congestion, network distance prediction has been introduced, instead of direct network measurements, to infer the unknown network distance with the partial measurements. However, most existing efforts neglect to fully capitalize on the potential latent factors, such as spatial correlations, long-existing temporal results and multi-rule exploration fusion, to achieve better accuracies with quicker convergence. To fill this gap, in this paper, we propose an Accurate Prediction of Network Distance (APND) solution via Federated Deep Reinforcement Learning (FDRL), which has four novel features distinguishing from the previous work. Firstly, a local feature-based matrix with low rank is established in each network cluster, referring to a set of neighbor nodes, to represent the potential spatial correlations among reachable node-pairs. Secondly, the parallel FDRL-based matrix factorization with multi-rule exploration fusion is introduced into APND and executed in all local clusters to minimize prediction errors and accelerate learning convergence. Thirdly, the long-existing learning experience is designed for local model training via Deep Reinforcement Learning (DRL) with rapid convergence. Fourthly, following the real-world routing paths, the cross-domain network nodes are simultaneously classified into adjacent clusters, built on the spatial correlations among them, and their coordinates will be further refined with error-based and average-based policies. Extensive experiments built on available real-world datasets illustrate that APND can accurately predict network distance compared with state-of-the-art approaches at the moderate computing cost.