The demand for Low-earth-orbit (LEO) satellite networking and routing is continuously growing. Container-based satellite network emulation becomes an important networking evaluation choice for LEO satellite network. While many container-based satellite network emulators optimize virtual network efficiency, they often overlook route installation latency—the duration of installing computed routes into kernel routing table. This latency represents a significant bottleneck for emulating large-scale LEO constellations, which are characterized by dynamic topologies and frequent, massive routing re-convergences. High routing installation latency not only reduces emulation efficiency but also compromises evaluation reliability and renders subsequent data transmission experiments infeasible. In this paper, we present a Lock-Free and Filtered Route Installation (LFRI) mechanism. LFRI filters out non-effective route installations, thereby shortening the installation procedure without changing the routing results. LFRI employs a lock-free installation mechanism that enables parallelized and efficient route installation. Experimental results demonstrate that, compared to the legacy Netlink installation, LFRI reduces the route installation latency by up to 99.9% in emulations of typical LEO satellite constellations, bringing it down to under ten milliseconds, within a performance level comparable to routing installation on physical machines. Meanwhile, in the case study evaluating path availability ratio and restoration time, LFRI yields more consistent and credible experimental data.

The convergence of computing and networking heralds a promising paradigm for future sixth-generation (6G) systems, enabling low-latency services for end users by deploying modern applications, which typically consist of multiple interdependent functions, at the network edge. However, unstable and short-range Device-to-Device (D2D) links often restrict offloading options, forcing users to either face limited access to nearby devices or rely on distant cloud resources, thereby underutilizing edge resources and diminishing user experience. To address this challenge, we propose utilizing base stations to relay traffic between unconnected edge devices. Additionally, we strategically reuse historical function placements to balance re-deployment costs against dynamic request adaptation. Then, aiming to minimize storage, computation, and transmission resource consumption costs along with function replacement costs, we formulate the Dependent Function Scheduling (DFS) problem as a Mixed Integer Non-Linear Programming (MINLP) problem, which is NP-hard even in single-slot scenarios. We introduce a random rounding-based approach to derive high-quality integer solutions for the single-slot DFS problem. Building on this, we further develop an efficient online algorithm for the general multi-slot DFS problem, which is proved to achieve near-optimal performance with high probability. Extensive real-trace simulations demonstrate that our proposed method significantly outperforms state-of-the-art baselines, achieving up to a 68.22% reduction in total cost.

With the steady development of geostationary orbit (GEO) satellites in China and the accelerated construction of low-orbit (LEO) satellite internet, the integration application of high and low orbit heterogeneous constellations has become an important direction for future development. Current research mainly focuses on resource allocation within a single constellation, such as GEO constellations or LEO constellations, while there is insufficient attention to the collaborative allocation of heterogeneous resources in mixed high and low orbit and cross-constellation scenarios. Therefore, this paper takes the China-Sat, Asia-Pacific and LEO satellite internet systems as research objects, deeply analyzes the service transmission modes and resource characteristics of different satellite systems such as transparent forwarding, high throughput and LEO constellations. At the same time, from the current engineering construction status, a heterogeneous resource allocation strategy for cross-high and low orbit mixed satellite networks is proposed. This strategy takes dynamic communication service demands as input and collaboratively allocates beam bandwidth, frequency, time slot, power and inter-satellite links and other heterogeneous resources. The research results can provide support for the simulation modeling and business planning of high and low orbit mixed satellite networks.

As reliable distributed systems, Blockchains have been widely applied in diverse domains, such as the Internet of Things (IoT). Recent studies have explored Deep Reinforcement Learning (DRL) to enhance blockchain performance. However, existing DRL-based blockchain performance optimization methods rely on implicit and idealized assumptions about node behaviors and transactional workloads, limiting their effectiveness and efficiency on the blockchain with dynamic work-loads and heterogeneous nodes. To alleviate this, we propose CHAINOPT, a novel blockchain performance optimization framework devised for optimal parameter configuration to handle dynamic workloads and heterogeneous nodes. Specifically, we first propose an interaction-aware state representation learning module to model both global system-level and local heterogeneous node feature interactions to generate better state representations. Then, a contrastive learning-enhanced workload identification module is designed to extract discriminative workload-specific state representations to improve workload identification accuracy. Finally, we design a workload-similarity guided policy reuse module to produce effective reuse weights to transfer knowledge from history policies based on workload relevance, thereby improving optimization speed and stability. Extensive experiments show the effectiveness of CHAINOPT in improving blockchain performance, achieving 185.87% higher scalability and 1492.16% stronger security with only a marginal 6.26% latency increase. Moreover, it outperforms baselines in static scenarios while maintaining considerable superiority under varying workloads.

As the frequency of cyber attacks (e.g., DDoS) continues to rise, it becomes increasingly crucial to trace malicious packets and identify which autonomous systems (ASes) they originate from and traverse through. Path-aware networking (PAN) architectures, forwarding packets based on in-packet path identifiers (PIDs), enable end-hosts to gain insights into the AS-level topology and facilitate AS-level path traceback. However, the study on AS-level topology inference and path traceback under PAN has been overlooked, primarily due to the diversity of path identification. Some PAN architectures (e.g., SCION) use deterministic PIDs to identify inter-domain paths, while others (e.g., CoLoR) adopt prefix-deterministic PIDs. In the latter, PID-Prefix is used to identify inter-domain paths, and PID-Suffix is reserved for additional security functions, making the inference problem complicated. This paper focuses on PAN with prefix-deterministic PIDs and investigates how to use in-packet PIDs to infer the AS-level topology. Our goal is to construct a tree-like AS-level topology with the corresponding PID-Prefixes based on in-packet PIDs. We first introduce two macro metrics (i.e., completion rate and precision rate) and two micro metrics (i.e., traceback division and traceback aggregation) to evaluate the inference accuracy. Furthermore, we propose an Alternating Expanding and Checking (AEC) algorithm for AS-level topology inference. AEC algorithm constructs the topology by iteratively expanding and checking the current topology. The expanding phase performs one-hop PID-Prefix inference and generates new child ASes, while the checking phase verifies the consistency of PID sequences with the current topology from the perspective of each new child AS. Experiments based on empirical Internet topology of 201 ASes show that the accuracy of AEC reaches 99.6% when the observer receives only 30 packets from each AS.

Acquiring the status information of candidate servers is important for decision making in edge computing, which is however extremely challenging and time-consuming in high-dense device-assisted task offloading scenarios. In this paper, we propose a Distributed Status-aware response (DiSar) method to balance the status acquisition and task execution latency by utilizing incomplete but necessary status information. Specifically, each candidate server device set a backoff timer based on their computing and transmission status independently, which enables low-latency servers to rapidly respond while avoiding wireless transmission collisions. A salient-driven status mapping algorithm is proposed for the backoff timer setting, and the performances of DiSar are analyzed theoretically. Accordingly the influencing parameters are further optimized to rapidly fit the dynamic offloading environment, whereby the performances of DiSar are guaranteed. Simulation results demonstrate that DiSar can find the optimal server device with 95% probability while reducing the information acquisition latency by 96.33%, compared with centralized complete information acquisition. In addition, the overall task offloading latency can be reduced by 11.63% to 52.67%, using DiSar compared with the state-of-theart methods.

Due to the intermittent nature of wireless transmission, link state awareness is important to enhance the success probability and efficiency of unmanned aerial vehicles (UAV) networking, which is however resource consuming especially in the high mobility scenario. In this paper, we utilize the broadcast feature of wireless channels to infer the real-time link state along with packet forwarding, and propose a reception determined routing (RDR) mechanism. Specifically, for each hop, the transmission node broadcasts the packet, and multiple reception nodes together decide the optimal one to forward the packet based on packet reception, network topology, and historical paths. The key challenge lies in making appropriate decisions based on fused information distributively. A path repair mechanism is also proposed to further enhance routing reliability by dealing with node mobility and link down. Packetlevel simulation results show that RDR achieves a higher packet delivery ratio (up to 62.3 %) and supports larger network scales (up to 79.5 % improvements) compared to state-of-the-art mechanisms.

Low-earth-orbit (LEO) mega-constellations with inter-satellite links (ISLs) are becoming the Internet backbone in space. Satellites within LEO often need the capability to enforce data forwarding paths. For example, they may need to bypass the satellites over the untrusted areas for the data of mission-critical applications or minimize latency for the data of time-sensitive applications. However, typical source/segment routing techniques (e.g., SRv6) suffer from scalability issue, since they record source-route-style forwarding information via the list-based structure. This results in great payload and forwarding overhead. To overcome this drawback, we propose a source/segment routing architecture for LEO mega-constellations, which is named as Link-identified Routing (LiR). LiR leverages in-packet bloom filter (BF) to record source-route-style forwarding information. BF could efficiently record multiple elements via a probabilistic data structure, but overlooks the order of the encoded elements. To address this, LiR identifies each unidirectional ISL, and represents the path by encoding ISL identifiers into BF. We investigate how to optimize BF configuration and ISL encoding policy to address false positives caused by BF. We implement LiR in Linux kernel and develop a container-based emulator for performance evaluation. Results show that LiR significantly outperforms SRv6 in terms of packet forwarding and data delivery efficiency.

Many Internet platforms are information-oriented and crowd-based. They collect fresh information of various points of interest (PoIs) relying on users who happen to be nearby the PoIs. The platform will offer rewards to incentivize users and compensate their costs incurred from information acquisition. In practice, a user’s cost is his/her private information, thus both the user cost and its distribution are hidden to the platform, making it challenging to determine the optimal rewarding decision. In this paper, we investigate how the platform dynamically rewards the users, aiming to jointly reduce the age of information (AoI) and the operational expenditure (OpEx). Due to the hidden cost distribution, this is an online non-convex learning problem with bandit feedback. To overcome the challenge, we first design an age-based reward scheme, which decouples the OpEx from the unknown cost distribution and enables the platform to accurately control its OpEx. We then take advantage of the age-based reward scheme and propose an exponentially discretizing and learning (EDAL) policy for platform operation. We prove that the EDAL policy performs asymptotically as well as the optimal decision (derived from the cost distribution). Simulation results show that the age-based reward scheme protects the platform’s OpEx from the influence of the user crowd characteristics, and also verify the asymptotic optimality of the EDAL policy.

Low-Earth-Orbit (LEO) satellite constellation is promising to extend broadband Internet services to where the terrestrial networks cannot reach, forming a satellite-terrestrial integrated network (STIN). The tradeoff between communication latency and topology stability is the core of constellation design and STIN operation. In general, low-altitude orbits reduce the propagation delay of ground-satellite links (GSLs), but result in frequent handover for GSLs. Frequent GSL handover weakens the topology stability, which is often measured by satellite pass duration and communication session duration. In this paper, we investigate the latency-stability tradeoff in STIN, and propose a stochastic model to analyze and evaluate GSL latency and pass/session duration. We analytically derive the distributions and the expectations of these metrics in closed forms, and verify the theoretic results based on empirical distributions (obtained by trajectory simulation). Compared to previous studies, our results exhibit more concise expressions and arguments. We find that the above closed-form expressions take two key arguments, i.e., the orbit period and the maximal central angle. The expected pass/session duration is directly proportional to the product of the two arguments, and the coefficients can be obtained explicitly (e.g., 0.25 for pass duration). We also demonstrate how to utilize the analytic results to draw deep insights on constellation design, aiming to reduce the GSL latency and keep the topology stable. We believe that our results in this paper could empower the operators and researchers to obtain a rapid understanding on the latency-stability tradeoff of STIN without resorting to time-consuming simulations.

In decentralized edge computing environments, user devices need to perceive the status of neighboring devices, including computational availability and communication delays, to optimize task offloading decisions. However, probing the real-time status of all devices introduces significant overhead, and probing only a few devices can lead to suboptimal decision-making, considering the massive connectivity and non-stationarity of edge networks. Aiming to balance the status probing cost and task offloading performance, we study the joint transmission and computation status probing problem, where the status and offloading delay on edge devices are characterized by general, bounded, and non-stationary distributions. The problem is proved to be NP-hard, even with known offloading delay distributions. To handle this case, we design an efficient offline method that guarantees a$(1-1/e)$approximation ratio via leveraging the submodularity of the expected offloading delay function. Furthermore, for scenarios with unknown and non-stationary offloading delay distributions, we reformulate the problem using the piecewise-stationary combinatorial multi-armed bandit framework and develop a change-point detection-based online status probing (CD-OSP) algorithm. CD-OSP can timely detect environmental changes and update probing strategies via using the proposed offline method and estimating offloading delay distributions. We prove that CD-OSP achieves a regret of$\mathcal {O}(NV\sqrt{T\ln T})$, with$N$,$V$, and$T$denoting the numbers of stationary periods, edge devices, and time slots, respectively. Extensive simulations and testbed experiments demonstrate that CD-OSP significantly outperforms state-of-the-art baselines, which can reduce the probing cost by up to 16.18X with a 2.14X increase in the offloading delay.

Low- earth-orbit (LEO) satellite constellations (e.g., Starlink) are becoming a necessary component of future Internet. There have been increasing studies on LEO satellite networking. It is a crucial problem how to evaluate these studies in a systematic and reproducible manner. In this paper, we present OpenSN, i.e., an open source library for emulating large-scale satellite network (SN). Different from Mininet-based SN emulators (e.g., LeoEM), OpenSN adopts container-based virtualization, thus allows for running distributed routing software on each node, and can achieve horizontal scalability via flexible multi-machine extension. Compared to other container-based SN emulators (e.g., StarryNet), OpenSN streamlines the interaction with Docker command line interface and significantly reduces unnecessary operations of creating virtual links. These modifications improve emulation efficiency and vertical scalability on a single machine. Furthermore, OpenSN separates user-defined configuration from container network management via a Key-Value Database that records the necessary information for SN emulation. Such a separation architecture enhances the function extensibility. To sum up, OpenSN exhibits advantages in efficiency, scalability, and extensibility, thus is a valuable open source library that empowers research on LEO satellite networking. Experiment results show that OpenSN constructs mega-constellations 5X-10X faster than StarryNet, and updates link state 2X-4X faster than LeoEM. We also verify the scalability of OpenSN by successfully emulating the five-shell Starlink constellation with a total of 4408 satellites.

Low-earth-orbit (LEO) satellite constellations (e.g., Starlink) are becoming the necessary component of future Internet. There have been increasing studies on LEO satellite networking. It is a crucial problem how to evaluate these studies in a systematic and reproducible manner. In this paper, we present OpenSN, i.e., an open-source library for emulating large-scale satellite network (SN). Different from Mininet-based SN emulators (e.g., LeoEM), OpenSN adopts container-based virtualization, thus allows for running distributed routing software on each node, and can achieve horizontal scalability via flexible multi-machine extension. Compared to other container-based SN emulators (e.g., StarryNet), OpenSN streamlines the interaction with Docker command line interface and significantly reduces unnecessary operation of creating virtual links. These modifications improve emulation efficiency and vertical scalability on a single machine. Furthermore, OpenSN separates user-defined configuration from container network management via a key-value database that records the necessary information of SN emulation. Such a separation architecture enhances the function extensibility. To sum up, OpenSN exhibits advantages in efficiency, scalability, and extensibility, thus is a valuable open-source library that empowers research on satellite networking. Experimental results show that OpenSN can construct mage-constellations 5X-10X faster than StarryNet, and update link state 2X-4X faster than Mininet.

This work proposes a transmission scheme for a 360-degree video streaming system. A critical issue is selecting the proper parts of the images that can cover users’ viewport while simultaneously meeting bandwidth constraints. However, the imperfect prediction of real-time viewpoint and fronthaul bandwidth brings significant challenges. To address this issue, by leveraging the Lagrangian exponentially weighted average (LEWA) method, we propose a LEWA-based transmission online learning scheme that iteratively selects tiles with varying bitrates based on real-time user feedback. This approach not only ensures that Quality of Experience (QoE) approaches the static offline optimal scheme under long-term bandwidth constraints but also effectively handles bandwidth fluctuations and prediction inaccuracies through adaptive bitrate selection. A prototype for 360-degree video streaming is built. The results indicate that the proposed scheme can maintain the proportion of rebuffering time at a stable low value (less than 2.5%) and boost the bandwidth utilization by at least 13.5% and more than 50% in most testcases compared to baseline schemes.

By cutting the 360-degree video into temporal chunks and spacial tiles of different quality levels, layered 360-degree video streaming enables fine-grained bitrate adaptation. A critical challenge is whether to transmit the expected viewport at a higher quality level or pre-fetch the low-rate base layer while avoiding buffer overflow or rebuffering, given the fluctuated and constrained fronthaul bandwidth. In this work, we investigate this interplay to provision better quality of experience (QoE) while guaranteeing the smoothness of 360-degree video streaming. Specifically, an optimization problem is formulated to maximize the expected video quality under a bidirectional constraint on the long-term average buffer. To address this problem, we propose the Lyapunov Stable Point Optimization(LSPO), which is a Lyapunov optimization variant. LSPO achieves a near-optimal time-averaged QoE within an O(1/V )margin, while also upholding bidirectional buffer constraints. A buffer-based algorithm is then proposed and evaluated based on real-trace data. The results demonstrate superior performance in video quality and reduced rebuffering probability compared to existing algorithms.

The low-earth-orbit (LEO) satellite constellation is becoming the large-scale autonomous system (AS) connected to other terrestrial ASes, forming satellite-terrestrial integrated network (STIN). To reduce the latency of inter-domain content delivery, it is necessary to adopt an efficient inter-domain routing architecture in STIN. Previous studies improve the classic Border Gateway Protocol (BGP) and propose BGP-S for STIN, which proactively probes each inter-domain path. However, BGP-S suffers from a tremendous control overhead when the LEO constellation is large. In this paper, we propose a path-identified content retrieval (PCR) architecture for STIN. PCR is an information-centric architecture, and explicitly identifies the inter-domain path between neighbor ASes. Under PCR architecture, users can retrieve their desired contents by specifying the inter-domain path in the request packet. The corresponding data packets will be forwarded according to the reversed inter-domain path. This way, users could unleash the path diversity in STIN by dynamically selecting inter-domain paths according to various learning-based routing algorithms. That is, PCR achieves adaptive inter-domain content retrieval without incurring additional control overhead. We conduct extensive packet-level simulation on OMNeT++. Results show that PCR (with Exp3-SIX algorithm) outperforms state-of-the-art BGP-S in terms of content retrieval latency by 22%.

The nature of datacenter traffic is a high volume of bursty tiny flows and standing long flows, which forms the coexistence of transient and persistent congestion. Traditional congestion control (CC) algorithms have inherent limitations in reconciling fast response and high efficiency towards transients with stability and fairness during persistence. In this paper, we provide an insight that re-architects CC with two control laws, tailored to transient and persistent concerns, respectively. Armed with this key insight, we propose bimodal congestion control (BCC), which is founded on two core ideas: (i) Quaternary network state detection, which further distinguishes transient and persistent states in switches, and (ii) Bimodal control law, which is manifested as the transient controller and persistent controller at sources. The transient controller employs a precise control paradigm that pauses flows to drain backlogged packets and ramps down/up flow rates to bottleneck bandwidth directly, striving for high efficiency. The persistent controller grounds itself in traditional CC algorithms, inheriting stability and fairness. We implement BCC in the Linux kernel and P4-programmable switch. In our evaluation, compared to DCQCN, HPCC, PowerTCP, and Swift, BCC reduces flow completion times by 14% ~ 99%.

The emerging large-scale low earth orbit (LEO) constellation is expected to provide global Internet services. However, large-scale satellite networks meet the challenges of topology dynamics and continuous traffic variation. This paper proposes LoHi, a Load-aware Hierarchical Information-centric (LoHi) Routing Protocol based on constellation Partitioning and logic path identifier (PID), to address the above challenges. Specifically, LoHi divides a constellation into satellite groups and only keeps track of the inter-satellite link (ISL) state within each group instead of the entire constellation, stabilizing global routing. Moreover, LoHi adopts the PID to denote the logic connectivity of adjacent satellite groups, which corresponds to multiple physical ISLs. Accordingly, the hierarchical multipath routing is calculated based on the inner-group and inter-group connectivity information. Furthermore, LoHi adjusts forwarding decisions according to the real-time traffic load on the ISL and group-level inter-group paths to reduce packet drop and queuing delay in a hierarchical pattern. Packet-level experiment results show that LoHi achieves a higher packet delivery ratio than the state-of-the-art mechanisms (up to 132.12%).

Traffic state estimation (TSE) is attracting significant attention due to its importance to the Internet of Vehicles (IoV) for various applications, such as vehicle path planning. In classic IoV, the real-time traffic data collected by road side units requires transferring to the cloud server for processing. Such a centralized manner may raise privacy leakage issues. Split federated learning (SFL) has emerged as one of the prevalent methods to solve these issues. However, recent studies have shown that the existing SFL frameworks are vulnerable to the model inversion (MI) attacks, leading to private raw data leakage. To this end, in this article, we propose ShieldTSE, a privacy-enhanced SFL framework for TSE in IoV. To protect privacy and maintain utility, a variational encoder-decoder-based privacy-preserving feature extraction module with adversarial learning is first proposed to generate better privacy-preserved intermediate activations with a lower-dimensional feature space. Then, a hard attention-based feature selection module is designed to select partial yet crucial features from the intermediate activations by removing redundant sensitive features to further reduce the data privacy leakage. Experimental results demonstrate that ShieldTSE achieves superior privacy-preserving ability when against training-based and optimization-based MI attacks with an average reconstruction mean-square error (MSE) improvement of$18\times $and$35\times $on METR-LA and PEMS-BAY compared to the baseline without the privacy-preserving strategy, respectively. ShieldTSE also successfully maintains better model utility compared to the privacy protection baselines.

Real-time video analytics services aim to provide users with accurate recognition results timely. However, existing studies usually fall into the dilemma between reducing delay and improving accuracy. The edge computing scenario imposes strict transmission and computation resource constraints, making balancing these conflicting metrics under dynamic network conditions difficult. In this regard, we introduce the age of processed information (AoPI) concept, which quantifies the time elapsed since the generation of the latest accurately recognized frame. AoPI depicts the integrated impact of recognition accuracy, transmission, and computation efficiency. We derive closed-form expressions for AoPI under preemptive and non-preemptive computation scheduling policies w.r.t. the transmission/computation rate and recognition accuracy of video frames. We then investigate the joint problem of edge server selection, video configuration adaptation, and bandwidth/computation resource allocation to minimize the long-term average AoPI over all cameras. We propose an online method, i.e., Lyapunov-based block coordinate descent (LBCD), to solve the problem, which decouples the original problem into two subproblems to optimize the video configuration/resource allocation and edge server selection strategy separately. We prove that LBCD achieves asymptotically optimal performance. According to the testbed experiments and simulation results, LBCD reduces the average AoPI by up to 10.94X compared to state-of-the-art baselines.

Efficient and scalable networking is a key enabler of the Unmanned Aerial Vehicle (UAV) swarms, wherein multiple UAVs cooperatively execute complicated tasks. Despite the intermittent connections due to the UAV mobility, stable paths may exist temporally in periods like formation keeping, which is rarely considered or utilized in existing UAV routing designs. In this article, we propose an integrated host- and content-centric routing (IHCR) mechanism to harness the advantages of both routing mechanisms. Specifically, the routing information of stable paths is reused in a host-centric manner to reduce the flooding for path exploring. In addition, the route failure detection and re-routing are content-centric to adjust to the topology dynamics. The challenges lie in the inherent contradiction between host-centric and content-centric routing mechanisms (e.g., naming spaces) and the tradeoff between path reusing and re-routing. To overcome these challenges, we appropriately incorporate node names into content names and then fully exploit reusable paths via time-based route failure detection and delayed forwarding. Packet-level simulation results show that IHCR increases the packet delivery ratio by 60.1%, and enlarges the achievable network scale by 4.2 times compared to state-of-the-art routing mechanisms.

RoCEv2 (RDMA over Converged Ethernet version 2) is the canonical method for deploying RDMA in Ethernet-based datacenters. Traditionally, RoCEv2 runs over the lossless network which is in turn achieved by enabling Priority Flow Control (PFC) within the network. However, as the scale of the datacenter increases, PFC’s side effects, such as head-of-line blocking, congestion spreading, and pause frame storm, are amplified. Datacenter operators can no longer tolerate these problems. In hence, they are seeking PFC alternatives for RDMA networks. Rather than aiming at the lossless RDMA network, we instead handle packet loss effectively to support RDMA over Ethernet. In this paper, we propose Switch-assistant Loss Recovery (SLR), a switch building block to enhance RoCEv2’s loss recovery. Specifically, SLR-enabled switches send loss notifications to request fast retransmissions. To cooperate with go-back-N retransmission, SLR generates loss notifications only when expected packets (i.e., in-order packets expected by receivers) are dropped and then filters out unexpected packets, which can avoid timeouts and prevent exacerbating congestion. Further, we adapt SLR to multi-bottleneck scenarios by inferring expected packets among multiple switch views. We implement SLR prototypes on commodity programmable switches. Evaluations show that SLR reduces the 99.9th-percentile FCT slowdown by up to 21.6$\times$compared to PFC and other state-of-the-arts.

Low-Earth-Orbit (LEO) satellite constellations are becoming the necessary infrastructure in the future. However, the secure operation of LEO constellations is faced with severe risks. Specifically, LEO satellites are constantly orbiting and their channel interfaces are open. The adversary in hostile regions can leverage the global footprint to inject malicious traffic via access satellites. That is, LEO satellites are susceptible to physical and cyber attacks. Therefore, access authentication regarding terrestrial users (TUs) is crucial to ensure the secure operation of LEO constellations. The traditional on-orbit authentication frameworks usually presume that satellites are reliable and mutually trusted, thus one could rely on access satellites to perform authentication. In practice, however, physical and cyber attacks could bring down the satellites (causing fail-stop fault) or even hijack the satellites (causing Byzantine fault). This fact requires that the access authentication framework installed on LEO constellations should be fault-tolerant. In this paper, we aim to achieve Byzantine fault tolerance in access authentication for LEO satellite networks by properly integrating PBFT consensus protocol with traditional on-orbit authentication. Based on the topology characteristics of LEO constellations, we analytically derive the consensus probability, authentication accuracy, and communication overhead under PBFT-based authentication. To reduce the communication overhead, we propose to partition the constellation into multiple consensus groups, and devise a hierarchical PBFT (HPBFT) protocol. Simulation results based on Starlink Shell-I constellation indicate that HPBFT-based authentication could reduce the communication overhead (by an order of magnitude) and maintain almost the same authentication accuracy compared to PBFT-based authentication.

Real-time video analytics services are expected to deliver accurate recognition results to users timely. However, existing studies usually fail in the dilemma between reducing delay and improving accuracy. Balancing such conflicting metrics is extremely hard under dynamic network conditions. In this regard, we introduce the age of processed information (AoPI) concept, i.e., the time elapsed since the generation of the latest accurately recognized frame. As a systematic metric, AoPI depicts the integrated impact of recognition accuracy, transmission, and computation efficiency. We derive the closed-form expressions of AoPI under preemptive and non-preemptive computation scheduling policies w.r.t. the transmission/computation rate and recognition accuracy of video frames, which are validated using prototype experiments. Based on the derived results, we study the joint video configuration selection, bandwidth, and computation resource allocation problem to minimize the long-term average AoPI among all cameras. An efficient method is proposed to solve the problem, which utilizes Lyapunov optimization and block coordinate descent to make decisions online without requiring future information about network variations and video content. We prove that our method achieves asymptotically optimal performance. Extensive simulations show that our method reduces the AoPI by up to 4.06X compared with the state-of-the-art baselines.

Many Internet platforms collect fresh information of various points of interest (PoIs) relying on users who happen to be nearby the PoIs. The platform will offer reward to incentivize users and compensate their costs incurred from information acquisition. In practice, the user cost (and its distribution) is hidden to the platform, thus it is challenging to determine the optimal reward. In this paper, we investigate how the platform dynamically rewards the users, aiming to jointly reduce the age of information (AoI) and the operational expenditure (OpEx). Due to the hidden cost distribution, this is an online non-convex learning problem with partial feedback. To overcome the challenge, we first design an age-based rewarding scheme, which decouples the OpEx from the unknown cost distribution and enables the platform to accurately control its OpEx. We then take advantage of the age-based rewarding scheme and propose an exponentially discretizing and learning (EDAL) policy for platform operation. We prove that the EDAL policy performs asymptotically as well as the optimal decision (derived based on the cost distribution). Simulation results show that the age-based rewarding scheme protects the platform’s OpEx from the influence of the user characteristics, and verify the asymptotic optimality of the EDAL policy.

Low earth orbit (LEO) satellite networks have the potential to provide low-latency communication with global coverage. To unleash this potential, it is crucial to achieve efficient data delivery. In this paper, we analyze the topology characteristics of LEO satellite networks, and propose a source-route-style forwarding framework. Specifically, we leverage the deterministic neighbor relationship and identify all the unidi-rectional inter-satellite links (ISLs). Moreover, our framework utilizes the in-packet bloom filter (BF) to store the source-route-style forwarding information. This way, the source satellite could encode multiple ISL identifiers into the BF, which actually specifies the forwarding path. The intermediate satellites only need to check whether the outgoing ISLs are encoded and forward packets accordingly. Due to false positives caused by BF, the more ISLs are encoded at a time, the more redundant forwardings emerge. To reduce forwarding overhead, we take into account segment encoding, allowing the source and intermediate satellites to encode part of ISLs towards the destination. Overall, segment encoding seeks the right balance between forwarding overhead and encoding delay. We characterize a wide range of segment encoding policy in a unified framework, and derive the expected forwarding overhead in a closed-form. The segment encoding design is formulated as a binary non-linear programming, which is NP-hard. To overcome the challenge, we leverage its decomposable structure, and propose an efficient algorithm to solve it optimally. Finally, we validate our analytical results via packet-level experiments. Results also show that our proposed segment encoding policy significantly reduces the queuing delay compared to source encoding.

Accommodating services at the network edge is favorable for time-sensitive applications. However, maintaining service usability is resource consuming in terms of pulling service images to the edge, synchronizing databases of service containers, and hot updates of service modules. Accordingly, it is critical to determine which service to place based on the received user requests and service refreshing (maintaining) cost, which is usually neglected in existing studies. In this work, we study how to cooperatively place timely refreshing services and offload user requests among edge servers to minimize the backhaul transmission costs. We formulate an integer nonlinear programming problem and prove its NP-hardness. This problem is highly nontractable due to the complex spatial-and-temporal coupling effect among service placement, offloading, and refreshing costs. We first decouple the problem in the temporal domain by transforming it into a Markov shortest path problem. We then propose a lightweighted discounted value approximation (DVA) method, which further decouples the problem in the spatial domain by estimating the offloading costs among edge servers. The worst performance of DVA is proved to be bounded. 5G service placement testbed experiments and real-trace simulations show that DVA reduces the total transmission cost by up to 59.1% compared with the state-of-the-art baselines.

The cloud-assisted mobile edge computing system is a critical architecture to process computation-intensive and delay-sensitive mobile applications in close proximity to mobile users with high resource efficiency. Due to the heterogenous dynamics of task arrivals at edge nodes and the distributed nature of the system, the workloads of edge nodes are prone to be unbalanced, which can cause high task response time and resource cost. This paper solves the dynamic task scheduling problem in cloud-assisted mobile edge computing (including both peer task scheduling among edge nodes and cross-layer task scheduling from edge nodes to the cloud), aiming at minimizing average task response time within resource budget limit. To overcome the challenges of task arrival dynamics, edge node heterogeneity, and computation-communication delay tradeoff, we propose aWater-filling BasedDynamic TaskScheduling (WiDaS) algorithm. WiDaS dynamically tunes the usage of cloud resources based on the Lyapunov optimization method and efficiently schedules mobile tasks among edge nodes (and the cloud) by exploiting the idea of water filling. Extensive simulations are conducted to evaluate WiDaS under a trace-driven traffic pattern and two mathematic traffic patterns. The results demonstrate that WiDaS shows two-fold benefits of efficiency and effectiveness. In terms of efficiency, WiDaS can achieve the approximate results with the KKT-based algorithm while reducing the computation complexity from exponential order to polynomial order. In terms of effectiveness, WiDaS can reduce the average task response time by up to 64.4% and 47.2% over the Fair-ratio and the Edge-first algorithm.

In this work, we aim to maintain the timeliness of a multipath-based real-time video analytics system by analyzing the age of processed information (AoPI) at the receiver side. In the system, a camera consistently uploads captured frames to different edge servers for processing via corresponding wireless channels. To measure the timeliness of the system, a novel metric named AoPI is proposed, which is the time elapsed since the generation of the latest successfully recognized frame. Hence, AoPI is impacted by the transmission/computation delay on different paths and whether the frames are successfully recognized. We derive the closed-form average AoPI at the receiver side of the system, which is inversely proportional to the recognition probability. Moreover, we prove that adding an identical path can reduce the average AoPI by 37.5% to 50% compared with using a single path. Nevertheless, numerical results show that utilizing a secondary path to access an available edge node will increase the average AoPI in the case of low spectrum efficiency and constrained bandwidth.

Space–air–ground-integrated networks (SAGINs) are deemed as a promising solution to support multifarious Internet of Vehicles (IoV) services with diversified Quality-of-Service (QoS) requirements in future communication networks. Network function virtualization (NFV) and software-defined networking (SDN) are two complementary and promising technologies to reduce the function provisioning cost and coordinate the heterogeneous physical resources in SAGIN. In this article, we investigate the online dynamic virtual network function (VNF) mapping and scheduling in SAGIN, considering the dynamicity of IoV services. The VNF live migration, VNF reinstantiation, and VNF rescheduling are enabled to increase the service acceptance ratio and service provider’s profits. Considering the heterogeneity of space, air, and ground nodes, we first model the migration cost and additional delay incurred by VNF live migration and reinstantiation. We then formulate the dynamic VNF mapping and scheduling jointly as a mixed-integer linear programming (MILP) problem with specified cost and delay models. We propose two Tabu search (TS)-based algorithms, i.e., TS-based VNF remapping and rescheduling (TS-MAPSCH) algorithm and TS-based pure VNF rescheduling (TS-PSCH) algorithm, to obtain suboptimal solutions to the MILP problem efficiently. Simulation results show that the proposed solution is very close to the optimum and that the proposed dynamic algorithms outperform existing works with respect to multiple performance metrics, including the service provider’s profit, service acceptance ratio, and QoS satisfaction level.

In this paper, the content service provision of information-centric vehicular networks (ICVNs) is investigated from the aspect of mobile edge caching, considering the dynamic driving-related context information. To provide up-to-date information with low latency, two schemes are designed for cache update and content delivery at the roadside units (RSUs). The roadside unit centric (RSUC) scheme decouples cache update and content delivery through bandwidth splitting, where the cached content items are updated regularly in a round-robin manner. The request adaptive (ReA) scheme updates the cached content items upon user requests with certain probabilities. The performance of both proposed schemes are analyzed, whereby the average age of information (AoI) and service latency are derived in closed forms. Surprisingly, the AoI-latency trade-off does not always exist, and frequent cache update can degrade both performances. Thus, the RSUC and ReA schemes are further optimized to balance the AoI and latency. Extensive simulations are conducted on SUMO and OMNeT++ simulators, and the results show that the proposed schemes can reduce service latency by up to 80 percent while guaranteeing content freshness in heavily loaded ICVNs.

Distributed denial of service (DDoS) flooding attacks are very harmful and difficult to prevent due to the default-on nature of both inter-domain and intra-domain routing adopted by the current Internet. Accordingly, many future Internet initiatives try to eliminate DDoS attacks by design. Among these efforts, using path identifiers (PIDs) as inter-domain routing objects has attracted much research interest. However, existing approaches either advertise PIDs throughout the Internet or use secret but forgeable (whether static or dynamic) PIDs, which makes it easy to launch DDoS flooding attacks. To address this issue, in this paper we propose K-PID that uses cryptographic (thus unforgeable) PIDs as inter-domain routing objects. In particular, an${N}$-bit PID is comprised of an${n}$-bit prefix and (${N}\,\,-\,\,{n}$) bits that are cryptographic hash over per-flow/request information and a secret number. The prefix is used for inter-domain packet forwarding and the cryptographic hash is used as a token for allowing a data packet to enter into (or, pass through) a domain. We analyze K-PID’s performance in preventing DDoS flooding attacks and implement K-PID in a prototype to verify K-PID’s feasibility. The results show that K-PID can effectively prevent DDoS flooding attacks at a low cost of reducing the forwarding capability of routers.

To efficiently allocate heterogeneous resources for customized services, in this article, we propose a network virtualization (NV)-based network architecture in cybertwin-enabled 6G core networks. In particular, we investigate how to optimize the virtual network (VN) topology (which consists of several virtual nodes and a set of intermediate virtual links) and determine the resultant VN embedding in a joint way over a cybertwin-enabled substrate network. To this end, we formulate an optimization problem whose objective is to minimize the embedding cost, while ensuring that the end-to-end (E2E) packet delay requirements are satisfied. The queueing network theory is utilized to evaluate each service’s E2E packet delay, which is a function of the resources assigned to the virtual nodes and virtual links for the embedded VN. We reveal that the problem under consideration is formally a mixed-integer nonlinear program (MINLP) and propose an improved brute-force search algorithm to find its optimal solutions. To enhance the algorithm’s scalability and reduce the computational complexity, we further propose an adaptively weighted heuristic algorithm to obtain near-optimal solutions to the problem for large-scale networks. Simulations are conducted to show that the proposed algorithms can effectively improve network performance compared to other benchmark algorithms.

Mobile edge computing is emerging as a new computing paradigm that provides enhanced experience to mobile users via low latency connections and augmented computation capacity. As the amount of user requests is time-varying, while the computation capacity of edge hosts is limited, Cloud Assisted Mobile Edge (CAME) computing framework is introduced to improve the scalability of the edge platform. By outsourcing mobile requests to clouds with various types of instances, the CAME framework can accommodate dynamic mobile requests with diverse quality of service requirements. In order to provide guaranteed services at minimal system cost, the edge resource provisioning and cloud outsourcing of the CAME framework should be carefully designed in a cost-efficient manner. Specifically, two fundamental issues should be answered: (1) what is the optimal edge computation capacity configuration? and (2) what types of cloud instances should be tenanted and what is the amount of each type? To solve these issues, we formulate the resource provisioning in CAME framework as an optimization problem. By exploiting the piecewise convex property of this problem, the Optimal Resource Provisioning (ORP) algorithms with different instances are proposed, so as to optimize the computation capacity of edge hosts and meanwhile dynamically adjust the cloud tenancy strategy. The proposed algorithms are proved to be with polynomial computational complexity. To evaluate the performance of the ORP algorithms, extensive simulations and experiments are conducted based on both the widely-used traffic models and the Google cluster usage tracelogs, respectively. It is shown that the proposed ORP algorithms outperform the local-first and cloud-first benchmark algorithms in system flexibility and cost-efficiency.

In this paper, we design dynamic probabilistic caching for the scenario when the instantaneous content popularity may vary with time while it is possible to predict the average content popularity over a time window. Based on the average content popularity, optimal content caching probabilities can be found, e.g., from solving optimization problems, and existing results in the literature can implement the optimal caching probabilities via static content placement. The objective of this work is to design dynamic probabilistic caching that: i) converge (in distribution) to the optimal content caching probabilities under time-invariant content popularity, and ii) adapt to the time-varying instantaneous content popularity under time-varying content popularity. Achieving the above objective requires a novel design of dynamic content replacement because static caching cannot adapt to varying content popularity while classic dynamic replacement policies, such as LRU, cannot converge to target caching probabilities (as they do not exploit any content popularity information). We model the design of dynamic probabilistic replacement policy as the problem of finding the state transition probability matrix of a Markov chain and propose a method to generate and refine the transition probability matrix. Extensive numerical results are provided to validate the effectiveness of the proposed design.

Mobile edge caching can effectively reduce service delay but may introduce information staleness, calling for timely content refreshing. However, content refreshing consumes additional transmission resources and may degrade the delay performance of mobile systems. In this work, we propose a freshness-aware refreshing scheme to balance the service delay and content freshness measured by Age of Information (AoI). Specifically, the cached content items will be refreshed to the up-to-date version upon user requests if the AoI exceeds a certain threshold (named as refreshing window). The average AoI and service delay are derived in closed forms approximately, which reveals an AoI-delay tradeoff relationship with respect to the refreshing window. In addition, the refreshing window is optimized to minimize the average delay while meeting the AoI requirements, and the results indicate to set a smaller refreshing window for the popular content items. Extensive simulations are conducted on the OMNeT++ platform to validate the analytical results. The results indicate that the proposed scheme can restrain frequent refreshing as the request arrival rate increases, whereby the average delay can be reduced by around 80% while maintaining the AoI below one second in heavily-loaded scenarios.

Mobile edge computing is beneficial for reducing service response time and core network traffic by pushing cloud functionalities to network edge. Equipped with storage and computation capacities, edge nodes can cache services of resource-intensive and delay-sensitive mobile applications and process the corresponding computation tasks without outsourcing to central clouds. However, the heterogeneity of edge resource capacities and mismatch of edge storage and computation capacities make it difficult to fully utilize both the storage and computation capacities in the absence of edge cooperation. To address this issue, we consider cooperation among edge nodes and investigate cooperative service caching and workload scheduling in mobile edge computing. This problem can be formulated as a mixed integer nonlinear programming problem, which has non-polynomial computation complexity. Addressing this problem faces challenges of sub-problem coupling, computation-communication tradeoff, and edge node heterogeneity. We develop an iterative algorithm named ICE to solve this problem. It is designed based on Gibbs sampling, which has provably near-optimal performance, and the idea of water filling, which has polynomial computation complexity. Simulation results demonstrate that our algorithm can jointly reduce the service response time and the outsourcing traffic, compared with the benchmark algorithms.

Space-air-ground integrated networks (SAGIN) extend the capability of wireless networks and will be the essential building block for many advanced applications, like autonomous driving, earth monitoring, and etc. However, coordinating heterogeneous physical resources is very challenging in such a large-scale dynamic network. In this paper, we propose a reconfigurable service provisioning framework based on service function chaining (SFC) for SAGIN. In SFC, the network functions are virtualized and the service data needs to flow through specific network functions in a predefined sequence. The inherent issue is how to plan the service function chains over large-scale heterogeneous networks, subject to the resource limitations of both communication and computation. Specifically, we must jointly consider the virtual network functions (VNFs) embedding and service data routing. We formulate the SFC planning problem as an integer non-linear programming problem, which is NP-hard. Then, a heuristic greedy algorithm is proposed, which concentrates on leveraging different features of aerial and ground nodes and balancing the resource consumptions. Furthermore, a new metric, aggregation ratio (AR) is proposed to elaborate the communication-computation tradeoff. Extensive simulations shows that our proposed algorithm achieves near-optimal performance. We also find that the SAGIN significantly reduces the service blockage probability and improves the efficiency of resource utilization. Finally, a case study on multiple intersection traffic scheduling is provided to demonstrate the effectiveness of our proposed SFC-based service provisioning framework.

Utilizing the intelligence at the network edge, edge computing paradigm emerges to provide time-sensitive computing services for Internet of Things. In this paper, we investigate sustainable computation offloading in an edge-computing system that consists of energy harvesting-enabled mobile devices (MDs) and a dispatcher. The dispatcher collects computation tasks generated by IoT devices with limited computation power, and offloads them to resourceful MDs in exchange for rewards. We propose an online Rewards-optimal Auction (RoA) to optimize the long-term sum-of-rewards for processing offloaded tasks, meanwhile adapting to the highly dynamic energy harvesting (EH) process and computation task arrivals. RoA is designed based on Lyapunov optimization and Vickrey-Clarke-Groves auction, the operation of which does not require a prior knowledge of the energy harvesting, task arrivals, or wireless channel statistics. Our analytical results confirm the optimality of tasks assignment. Furthermore, simulation results validate the analytical analysis, and verify the efficacy of the proposed RoA.

Vehicular networks are expected to support diverse content applications with multi-dimensional quality of service (QoS) requirements, which cannot be realized by the conventional one-fit-all network management method. In this paper, a service-oriented hierarchical soft slicing framework is proposed for the cache-enabled vehicular networks, where each slice supports one service and the resources are logically isolated but opportunistically reused to exploit the multiplexing gain. The performance of the proposed framework is studied in an analytical way considering two typical on-road content services, i.e., the time-critical driving related context information service (CIS) and the bandwidth-consuming infotainment service (IS). Two network slices are constructed to support the CIS and IS, respectively, where the resource is opportunistic reused at both intra- and inter-slice levels. Specifically, the throughput of the IS slice, the content freshness (i.e., age of information) and delay performances of the CIS slice are analyzed theoretically, whereby the multiplexing gain of soft slicing is obtained. Extensive simulations are conducted on the OMNeT++ and MATLAB platforms to validate the analytical results. Numerical results show that the proposed soft slicing method can enhance the IS throughput by 30% while guaranteeing the same level of CIS content freshness and service delay.

Space-air-ground integrated network (SAGIN) brings great potentials to extend the terrestrial networks and satisfy the diverse service demands from many emerging applications. The major challenge is the coordination of large-scale networks with heterogeneous communication and computation resources. In this paper, flexible and reconfigurable service provisioning based on service function chaining (SFC) is exploited to address the challenge, where the traffic flow of the network services need to pass through specified virtual network functions (VNFs) in a given order. Our main target is to optimize the planning of the service function chains under limited heterogeneous resources and to map them on physical networks, considering the balance of resource utilization of both communication and computation. The SFC planning problem is formulated as an integer non-linear programming problem, which is NP-hard. Then, we propose a heuristic SFC planning algorithm (HSP) to reduce the computational complexity. Moreover, we propose a new metric, aggregation ratio (AR), to observe the tradeoff between communication and computation resource consumptions. The simulations results demonstrate that the HSP achieves near-optimal performance and the communication and computation resources can be well tradeoffed via tuning AR. The service blockage probability is significantly decreased and the efficiency of resource utilization is improved by integrating SAGIN based on SFC.

Mobile edge caching is an effective way to reduce the service delay of content delivery, where the popular contents can be pro-actively stored in proximity to users. In practice, the cached contents should be updated timely to avoid information staleness, in case that the information of a content changes with time and environment. However, cache update consumes additional transmission resources, which can degrade the delay performance. This work studies the fundamental tradeoff relationship between the content freshness (depicted by the age of information (AoI)) and service delay in mobile edge caching networks, and proposes a freshness-aware cache update scheme to achieve the AoI-delay balance. In specific, the base station will fetch the latest version of a content before delivery, if the AoI is larger than a certain threshold (i.e., update window size). The average AoI and service delay are derived in closed forms through approximated analysis of queueing systems, revealing a tradeoff relationship with respect to the update window size. Extensive simulations are conducted on the OMNeT++ platform, which validates the analytical results. Both the analytical and simulation results show that the proposed scheme can flexibly balance the average AoI and delay on demand, by tuning the update window size. Furthermore, the proposed scheme can also avoid frequent update in case of heavy content requests, whereby the AoI and delay are regulated by setting the appropriate update window size.

Mobile edge computing extends computing and storage resources to the proximity of mobile users, facilitating a number of innovative mobile applications. Particularly, video streaming is the most prevailing one that consumes substantial edge resources. In this paper, we investigate the multi-dimensional resource allocation for video service provisioning, with the objective of ensuring satisfied streaming experience at high resource utilization. Considering the diversified and constantly changing user preferences on the quality of video contents, the edge resource allocation process is modeled as a long-term utility maximization problem. To address this problem, we propose an online learning algorithm that actively estimates user preferences according to regression analysis on user feedback. This algorithm requires no training phase, and hence is adaptive to dynamic user interests and available edge resources. Both theoretical analysis and numerical results demonstrate that the performance of the proposed algorithm asymptotically approaches the hindsight optimal resource allocation strategy.

By exploiting the storage resources of end devices, local caching becomes a promising approach to reduce the latency and improve the throughput of content delivery. Considering battery constraints at end devices, this paper investigates energy-aware caching policy to minimize the power consumed in content delivery, taking into account the heterogeneity of interests and sharing willingness of different MSs. Specifically, MSs are divided into disjoint groups based on the preferences and sharing willingness, and each group customizes the caching policy accordingly. Both the non-coordinated and coordinated caching scenarios are considered. For the non-coordinated case, the problem is formulated as an optimization problem, which is proved to be concave and solved by Lagrange methods. For the coordinated caching case, a water-filling based iterative algorithm is proposed to get the optimal caching policies. Numerical results demonstrate that the designed caching policies can reduce the system energy consumption by around 10% - 20% comparing to the conventional ones without considering the heterogeneity of users.

Mobile edge caching aims to enable content delivery within the radio access network, which effectively alleviates the backhaul burden and reduces response time. To fully exploit edge storage resources, the most popular contents should be identified and cached. Observing that user demands on certain contents vary greatly at different locations, this paper devises location-customized caching schemes to maximize the total content hit rate. Specifically, a linear model is used to estimate the future content hit rate. For the case with zero-mean noise, a ridge regression-based online algorithm with positive perturbation is proposed. Regret analysis indicates that the hit rate achieved by the proposed algorithm asymptotically approaches that of the optimal caching strategy in the long run. When the noise structure is unknown, an$H_{\infty }$filter-based online algorithm is devised by taking a prescribed threshold as input, which guarantees prediction accuracy even under the worst-case noise process. Both online algorithms require no training phases and, hence, are robust to the time-varying user demands. The estimation errors of both algorithms are numerically analyzed. Moreover, extensive experiments using real-world datasets are conducted to validate the applicability of the proposed algorithms. It is demonstrated that those algorithms can be applied to scenarios with different noise features, and are able to make adaptive caching decisions, achieving a content hit rate that is comparable to that via the hindsight optimal strategy.

The flourishing vehicular applications require vehicles to download huge amount of Internet data, which consumes significant backhaul bandwidth and considerable time for data content delivery. Caching the popular data at network edge station can alleviate the congestion at backhaul network and reduce the data delivery delay. In this paper, we propose a dynamic edge caching policy for Heterogeneous Vehicular Network via Reinforcement Learning on adaptive traffic intensity and hot content popularity. We aim to enhance the download rate of vehicles adapting to dynamic vehicle velocity and hot file pool by caching the popular content on heterogeneous network edge stations. The proposed policy makes use of real time information and jointly considers download rate for each file and utility for edge stations to improve overall download performance. Simulation results show that our proposed policy can achieve better download rate than random and fixed caching policies.

By employing the exponentially increasing communication and computing capabilities of vehicles brought by the development of connected and autonomous vehicles, vehicular cloud computing (VCC) can improve the overall computational efficiency by offloading the computing tasks from the edge or remote cloud. In this paper, we study the computation migration problem in VCC, where a vehicle transfers unfinished computing missions to other vehicles before leaving a network edge to avoid mission failures. Specifically, we consider a computing mission offloaded from edge cloud to the vehicular cloud. The mission has a linear logical topology, i.e., consisting of tasks which should be executed sequentially. The migration problem is formulated as a sequential decision making problem aiming to minimize the overall response time. Considering the vehicular mobility, communication time, and heterogeneous vehicular computing capabilities, the problem is difficult to model and solve. We thus propose a novel on-policy reinforcement learning based computation migration scheme, which learns on-the-fly the optimal policy of the dynamic environment. Numerical results demonstrate that the proposed scheme can adapt to the uncertain and changing environment, and guarantee low computing latency.

Space information network (SIN) is a promising networking architecture to significantly broaden the observation area and realize continuous information acquisition for earth observation. Over the dynamic and complex SIN environment, it is a key issue to coordinate multi-dimensional heterogeneous network resources (e.g., observation resource and transmission resource) in the presence of multi-resource variations and severe conflicts, such that diverse earth observation service requirements can be satisfied. To this end, this paper studies the multi-resource coordinate scheduling problem in SINs. Specifically, we first characterize the relationship among multi-resource using an event-driven time-expanded graph (EDTEG). Based on the EDTEG, observation resource and transmission resource are jointly considered, and an integer linear programming optimization problem is formulated to maximize the sum priorities of the successfully scheduled tasks. An iterative optimization technique is employed to decompose the problem into separate observation scheduling and transmission scheduling sub-problems, which can be efficiently solved by extended transmission time sharing graph and directed acyclic graph methods, respectively. Simulation results demonstrate the effectiveness of the proposed algorithm and performance impacts of different network parameters.

In this paper, an Air-Ground Integrated VEhicular Network (AGIVEN) architecture is proposed, where the aerial high-altitude platforms (HAPs) proactively push contents to vehicles through large-area broadcast, while the ground roadside units (RSUs) provide high-rate unicast services on demand. To efficiently manage the multi-dimensional heterogeneous resources, a service-oriented network slicing approach is introduced, where the AGIVEN is virtually divided into multiple slices and each slice supports a specific application with guaranteed quality of service (QoS). Specifically, the fundamental problem of multi-resource provisioning in AGIVEN slicing is investigated by taking into account the typical vehicular applications of location-based map and popularity-based content services. For the location-based map service, the capability of HAP-vehicle proactive pushing is derived with respect to the HAP broadcast rate and vehicle cache size, wherein a saddle point exists, indicating the optimal communication-cache resource trading. For the popular contents of common interests, the average on-board content hit ratio is obtained with HAPs pushing newly generated contents to keep on-board cache fresh. Then, the minimal RSU transmission rate is derived to meet the average delay requirements of each slice. The obtained analytical results reveal the service-dependent resource provisioning and trading relationships among RSU transmission rate, HAP broadcast rate, and vehicle cache size, which provides guidelines for multi-resource network slicing in practice. Simulation results demonstrate that the proposed AGIVEN network slicing approach matches the multi-resources across slices, whereby the RSU transmission rate can be saved by 40% while maintaining the same QoS.

In this paper, we study the performance of base station (BS) coordination in heterogeneous networks (HetNets) with cache-enabled and renewable energy-powered small cell BSs (SBSs). Macrocell base stations (MBSs) provide basic coverage, while the SBSs, powered by harvested energy, conduct content-aware coordinated transmission to provide high data rate and further improve the network coverage. Specifically, a joint transmission strategy is performed based on the knowledge of the energy states and the cached contents of SBSs, along with the awareness of the availability of channel resources and the average received signal strength (RSS) of the corresponding link. Stochastic geometry is applied to characterize the statistics of the cell load at MBSs and SBSs, as well as the aggregated information and interference signal strength. Then, the average user capacity for the joint transmission is obtained. Additionally, the coverage probability is derived with gamma approximation for the aggregated information and interference signal strength. Analytical results reveal that the average user capacity and coverage probability can be maximized with optimal cache size, energy harvesting rate and cooperative RSS threshold. Finally, extensive numerical and simulation results are provided.

With files proactively stored at base stations (BSs), mobile edge caching enables direct content delivery without remote file fetching, which can reduce the end-to-end delay while relieving backhaul pressure. To effectively utilize the limited cache size in practice, cooperative caching can be leveraged to exploit caching diversity, by allowing users served by multiple base stations under the emerging user-centric network architecture. This paper explores delay-optimal cooperative edge caching in large-scale user-centric mobile networks, where the content placement and cluster size are optimized based on the stochastic information of network topology, traffic distribution, channel quality, and file popularity. Specifically, a greedy content placement algorithm is proposed based on the optimal bandwidth allocation, which can achieve (1 - 1/e)-optimality with linear computational complexity. In addition, the optimal user-centric cluster size is studied, and a condition constraining the maximal cluster size is presented in explicit form, which reflects the tradeoff between caching diversity and spectrum efficiency. Extensive simulations are conducted for analysis validation and performance evaluation. Numerical results demonstrate that the proposed greedy content placement algorithm can reduce the average file transmission delay up to 45 percent compared with the non-cooperative and hit-ratio-maximal schemes. Furthermore, the optimal clustering is also discussed considering the influences of different system parameters.

In this paper, we investigate the power allocation in a multi-user wireless system to maximize the energy efficiency, while meeting the power constrains of each individual user and the whole system. Specifically, a geometric ceiled-water-filling algorithm is proposed to solve this non-linear fractional optimization problem, which can compute exact solutions with a low degree of polynomial computational complexity. Optimality of the proposed algorithm is strictly proved with mathematical analysis. In addition, the proposed algorithm is further extended to the general case with the minimum system-level throughput constraint, considering the quality of service requirement. To the best of our knowledge, no prior algorithm in the open literature offered such optimal solutions to the target problems, with the merit of exactness and the efficiency. Simulation results demonstrate that the proposed power allocation algorithms can improve the energy efficiency by nearly 50%, compared with the conventional Dinkelbach's method with the same amount of computations.

Mobile edge computing (MEC) is emerging as an effective computing paradigm which alleviates the conflict between computation-intensive mobile applications and resource-constrained mobile devices. In this paper, a Cloud Assisted Mobile Edge computing (CAME) framework is adopted to enhance the adaptability of MEC to time-varying mobile requests. The resource provisioning problem is investigated to provide guaranteed quality of service (QoS) with minimum system cost. By exploiting the piecewise convexity of the problem, the Optimal Resource Provisioning (ORP) algorithm is developed, which determines the computation capacity at mobile edge and dynamically tunes the usage of cloud resources. Extensive simulations demonstrate that the ORP algorithm yields the minimum system cost, compared with the local-first and the cloud-first algorithms. In addition, the ORP algorithm can flexibly adapt to the time-varying mobile requests.

In Earth observation satellite networks (EOSNs), imbalance between the observation and transmission opportunities can cause poor network performance, e.g., a reduced number of successfully scheduled targets. To tackle this issue, in this paper, we investigate the multi-dimensional resource scheduling problem in EOSNs to ensure that each EOS can observe an appropriate subset of targets with matched downloading capacity to the destination. Specifically, an optimization problem is formulated and proved to be NP-hard. Then, an iterative optimization technique is employed to decompose the problem into separate observation scheduling and transmission scheduling subproblems, which are further efficiently solved by acyclic directed graph and particle optimization methods, respectively. Extensive simulations have been conducted to demonstrate the efficiency of the proposed scheduling algorithm.

Mobile edge caching enables content delivery directly within the radio access network, which effectively alleviates the backhaul burden and reduces round-trip latency. To fully exploit the edge resources, the most popular contents should be identified and cached. Observing that content popularity varies greatly at different locations, to maximize local hit rate, this paper proposes an online learning algorithm that dynamically predicts content hit rate, and makes location-differentiated caching decisions. Specifically, a linear model is used to estimate the future hit rate. Considering the variations in user demand, a perturbation is added to the estimation to account for uncertainty. The proposed learning algorithm requires no training phase, and hence is adaptive to the time-varying content popularity profile. Theoretical analysis indicates that the proposed algorithm asymptotically approaches the optimal policy in the long term. Extensive simulations based on real world traces show that, the proposed algorithm achieves higher hit rate and better adaptiveness to content popularity fluctuation, compared with other schemes.

Mobile edge caching has the potential to reduce file transmission delay as well as core network load, by utilizing the cache of base stations to store content with high hit rates. However, in practice, the performance of mobile edge caching can be constrained by BS cache size. Traffic steering can enable end users to obtain requested file directly from the cache of a non-homing BS without remote file fetching, and thus enlarge the set of cached contents by exploiting the content diversity in space. On the other hand, traffic steering can also degrade spectrum efficiency, due to the higher path loss of steered users. In this paper, we investigate the performance of traffic steering on mobile edge caching, taking into account the tradeoff between content diversity and spectrum efficiency. The average file transmission delay is derived by applying stochastic geometry, under constraints of cache size and radio resources. Specifically, a greedy content placement algorithm is proposed, which can achieve near- optimal delay performance with low polynomial computational complexity. Simulation results demonstrate that the average file transmission delay can be reduced up to 55% when 10% contents can be stored in cache, by introducing traffic steering in mobile edge caching.

In this paper, we study a small-cell caching system consisting of one privately-owned small base station (SBS) and multiple content providers (CPs), where CPs leverage the caching capabilities of SBSs to efficiently provide content delivery service to mobile subscribers. Specifically, an incentive cache mechanism is proposed, to stimulate the privately- owned SBS and CPs to participate in the caching system. A two-stage Stackelberg game is formulated for the interaction between the SBS and CPs. In the first stage, the private SBS first decides the price policy to maximize the profit. In the second stage, according to the charge price, each CP determines the amount of caching storage to maximize its utility. The impact of transmission congestion on CP utility is also taken into consideration, which also influences CPs' decisions. We prove the existence and uniqueness of the equilibrium, and design an optimal pricing algorithm to maximize the SBS's revenue. Simulation results are provided to evaluate the performance of the proposed mechanism, which demonstrates the efficiency and feasibility on the SBS resource sharing.

Vehicular communication networks expect to accommodate the ever-increasing on-road wireless traffic by deploying roadside units (RSUs) and meanwhile exploiting existing wireless infrastructures. To achieve flexible deployment, energy-saving operation and low-latency services, a new type of RSUs, namely cache-enabled green RSUs are introduced, which can store popular contents locally and harvest renewable energy as power source. In this paper, we investigate cost-effective planning of heterogeneous vehicular networks consisting of conventional macro base stations and cache-enabled green RSUs. Specifically, the RSU density, cache size, and energy harvesting rate are jointly optimized to minimize network deployment cost, under the constraints of quality of service (QoS) requirements and limited backhaul capacities. For QoS guarantee, the lower bound of average data rate is derived in closed form by applying the theory of stochastic geometry, based on which the cost-effective network deployment scheme is proposed. Analytical results reveal the tradeoff between cache size and backhaul capacity, indicate renewable energy harvesting rate should be sufficient to support rush-hour demands, and also provide the optimal RSU density for the given vehicular traffic demands. Extensive simulations are conducted for validation. In addition, numerical results of optimal network planning are provided in details to offer insights into practical system design.

Mobile edge computing is envisioned as a promising computing paradigm with the advantage of low latency. However, compared with conventional mobile cloud computing, mobile edge computing is constrained in computing capacity, especially under the scenario of dense population. In this paper, we propose a Cloud Assisted Mobile Edge computing (CAME) framework, in which cloud resources are leased to enhance the system computing capacity. To balance the tradeoff between system delay and cost, mobile workload scheduling and cloud outsourcing are further devised. Specifically, the system delay is analyzed by modeling the CAME system as a queuing network. In addition, an optimization problem is formulated to minimize the system delay and cost. The problem is proved to be convex, which can be solved by using the Karush-Kuhn-Tucker (KKT) conditions. Instead of directly solving the KKT conditions, which incurs exponential complexity, an algorithm with linear complexity is proposed by exploiting the linear property of constraints. Extensive simulations are conducted to evaluate the proposed algorithm. Compared with the fair ratio algorithm and the greedy algorithm, the proposed algorithm can reduce the system delay by up to 33% and 46%, respectively, at the same outsourcing cost. Furthermore, the simulation results demonstrate that the proposed algorithm can effectively deal with the challenge of heterogeneous mobile users and balance the tradeoff between computation delay and transmission overhead.

In this paper, we study worker selection in spatial crowdsourcing, which is the recruitment of human workers in a specific location to collect geographical data. To achieve better performance, spatial crowdsourcing task relies on both worker's effort and skill. Therefore, to maximize the long-term platform utility, we exploit fog platform as a service to identify valuable workers through learning their performance information. Worker's historical performance data are recorded at local fog server, based on which valuable workers are identified and selected to perform the tasks. During worker selection, we aim at balancing the exploration and exploitation, and propose an online algorithm that promotes workers who are not fully explored. With budget constraint, the proposed algorithm is able to maximize the long-term platform utility. Theoretical analysis indicates that the proposed learning algorithm achieves asymptotically diminishing regret. Finally, extensive simulations on real-world dataset are conducted, which demonstrate the advantage of our algorithm over other methods.

In this paper, we propose a novel user cooperation scheme based on weighted fractional Fourier transform (WFRFT), to enhance the physical (PHY) layer security of wireless transmissions against eavesdropping. Specifically, instead of dissipating additional transmission power for friendly jamming, by leveraging the features of WFRFT, the information bearing signal of cooperators can create an identical artificial noise effect at the eavesdropper while causing no performance degradation on the legitimate receiver. Furthermore, to form the cooperation set in an autonomous and distributed manner, we model WFRFT-based PHY-layer security cooperation problem as a coalitional game with non-transferable utility. A distributed merge-and-split algorithm is devised to facilitate the autonomous coalition formation to maximize the security capacity while accounting for the cooperation cost in terms of power consumption. We analyze the stability of the proposed algorithm and also investigate how the network topology efficiently adapts to the mobility of intermediate nodes. Simulation results demonstrate that the WFRFT-based user cooperation scheme leads to a significant performance advantage, in terms of secrecy ergodic capacity, compared with the conventional security-oriented user cooperation schemes, such as relay-jamming and cluster-beamforming.

With small cell base stations (SBSs) densely deployed in addition to conventional macro base stations (MBSs), the heterogeneous cellular network (HCN) architecture can effectively boost network capacity. To support the huge power demand of HCNs, renewable energy harvesting technologies can be leveraged. In this paper, we aim to make efficient use of the harvested energy for on-grid power saving while satisfying the quality of service (QoS) requirement. To this end, energy-aware traffic offloading schemes are proposed, whereby user associations, ON-OFF states of SBSs, and power control are jointly optimized according to the statistical information of energy arrival and traffic load. Specifically, for the single SBS case, the power saving gain achieved by activating the SBS is derived in closed form, based on which the SBS activation condition and optimal traffic offloading amount are obtained. Furthermore, a two-stage energy-aware traffic offloading (TEATO) scheme is proposed for the multiple-SBS case, considering various operating characteristics of SBSs with different power sources. Simulation results demonstrate that the proposed scheme can achieve more than 50% power saving gain for typical daily traffic and solar energy profiles, compared with the conventional traffic offloading schemes.

Energy harvesting (EH), which explores renewable energy as a supplementary power source, is a promising 5G technology to support the huge energy demand of heterogeneous cellular networks (HCN). However, the random arrival of renewable energy brings great challenges to network management. By adjusting the distribution of traffic load in spatial domain, traffic shaping helps to balance the cell-level power demand and supply, and thus improves the utilization of renewable energy. In this paper, we investigate the power saving performance of traffic shaping in an analytical way, based on the statistic information of energy arrival and traffic load. Specifically, an energy-optimal traffic shaping scheme (EOTS) is devised for HCNs with EH, whereby the on-off state of the off-grid small cell and the amount of offloading traffic are adjusted dynamically with the energy variation, to minimize the on-grid power consumption. Numerical results are given to demonstrate that for the daily traffic and solar energy profiles, EOTS scheme can significantly reduce the energy consumption, compared with the greedy method where users are always offloaded to the off-grid small cell with priority.

To further improve the energy efficiency of heterogeneous networks, a separation architecture called hyper-cellular network (HCN) has been proposed, which decouples the control signaling and data transmission functions. Specifically, the control coverage is guaranteed by macro base stations (MBSs), whereas small cells (SCs) are only utilized for data transmission. Under HCN, SCs can be dynamically turned off when traffic load decreases for energy saving. A fundamental problem then arises: how many SCs can be turned off as traffic varies? In this paper, we address this problem in a theoretical way, where two sleeping schemes (i.e., random and repulsive schemes) with vertical inter-layer offloading are considered. Analytical results indicate the following facts: 1) under the random scheme where SCs are turned off with certain probability, the expected ratio of sleeping SCs is inversely proportional to the traffic load of SC-layer and decreases linearly with the traffic load of MBS-layer; 2) the repulsive scheme, which only turns off the SCs close to MBSs, is less sensitive to the traffic variations; and 3) deploying denser MBSs enables turning off more SCs, which may help to improve network energy-efficiency. Numerical results show that about 50% SCs can be turned off on average under the predefined daily traffic profiles, and 10% more SCs can be further turned off with inter-layer channel borrowing.

To further improve energy efficiency from the view of the whole network, a separation architecture has been proposed, where the control plane and data plane are separated and implemented by different base stations. Under this architecture, the data base stations (DBS) can be turned off adaptively according to the traffic load while signaling base stations (SBS) provide the guarantee of coverage. A key issue of this architecture is the design of effective BS sleeping mechanisms, which should guarantee the quality of service (QoS) and minimize network power consumption. In this paper, a probabilistic DBS sleeping mechanism is proposed and optimized under the separation architecture. Users within the sleeping DBSs are offloaded to SBSs for QoS guarantee. An optimization problem is formulated, where the sleeping probability and spectrum resource allocation are jointly optimized to minimize network power consumption. The optimal BS sleeping scheme is found to be threshold-based. When the ratio of sleeping DBSs is below a certain threshold which depends on the traffic load, the lightly-loaded DBSs should be turned off first; otherwise, only the heavily loaded DBSs go into sleep. Numerical results show nearly 30% energy can be saved under a typical daily traffic profile, and there exists a tradeoff between energy saving and network capacity.

The existing architecture of heterogeneous networks is not energy and spectrum efficient as many lightly loaded base stations (BS) can not be turned off for coverage guarantee. To further improve energy and spectrum efficiency, a new architecture called “hyper-cellular” network has been proposed in our previous work. Under this architecture, the function of different types of BSs may not be the same, and the mechanism of user association should consider many factors, such as user mobility, traffic load distribution, and differentiated service demands, which are usually ignored in the existing studies. In addition, the spectrum allocation strategy also has great influence on the network performance. As a starting point, we explore the user association mechanism based on the differentiated service demands of the network users, and jointly optimize it with spectrum allocation, in order to maximize the network capacity with quality of service constraints. Although closed-form expression of the optimal solution can not be derived, numerical results are obtained. Our approach is shown to improve the network capacity more than four times over the baseline strategy, where the conventional user association method is adopted and all BSs use all available spectrum.