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.

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 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.

Multiplicative Weight Update (MWU) is an online learning meta-algorithm that has been widely used in sequential decision-making problems. The key idea of MWU is to maintain a distribution as the selection probability allocation to the alternative actions. MWU has been independently rediscovered and investigated in many studies. This paper will revisit MWU from the perspective of fair resource allocation. Specifically, we focus on a well-known fairness concept called weighted alpha-fairness, which was introduced by Mo and Walrand in network bandwidth allocation. We first unveil that MWU inherently adopts the weighted alpha-fair probability allocation in each slot. The fairness level equals to the inverse of the step-size parameter in MWU, and the weight assigned to each action decays exponentially in relation to its accumulated cost. This insight suggests that the use of appropriate weighted-alpha fair allocations for action-taking probability can indeed perform as well as the offline optimal allocation in the long-term. Motivated by this insight, we further investigate whether one could solve more general online resource allocation problems (not limited to the one addressed by MWU) by carefully constructing the weighted-alpha fair allocation for each time slot. To this end, we propose a weighted alpha-fair online allocation framework that carefully constructs the merit-based weights and the increasing fairness levels. Under our proposed framework, the allocation outcome in each time slot satisfies the weighted alpha-fairness principal, and the allocation outcomes also perform asymptotically as well as the offline optimal outcome. We demonstrate how this framework functions in two networking applications. In size-based traffic scheduling, our framework enables the network switches to prioritize short flows and avoid flow starvation without the prior flow size information. In file caching, our framework outperforms several state-of-the-art caching policies up to 21% in terms of cache-hit-ratio.

Low Earth Orbit (LEO) observation satellite systems play a critical role in a variety of applications, including environmental monitoring, urban planning, and national security. However, transmitting the data collected by numerous observation satellites to Earth remains a significant challenge. One promising approach to improve data transmission efficiency is to utilize LEO communication satellites as data relays. Existing researches in this area primarily focus on the inter-satellite communication scheduling, often neglecting the importance of satellite observation scheduling, which limits the potential performance gain. In this work, we investigate the joint optimization of observation and transmission scheduling in a satellite network with resource-constrained LEO satellites, taking into account the heterogeneity of observation missions and the dynamics of network topology. Specifically, we first introduce a Time-Expanded Graph (TEG) model to effectively represent dynamic network topology and satellite resource constraints. Based on this model, we formulate a network flow problem that incorporates both mission-specific characteristics and satellite energy costs. To reduce the solution complexity in large-scale networks, we propose a novel low-complexity Augmented Lagrangian-based Distributed Parallel Splitting (ALDPS) algorithm. Simulation results show that our proposed algorithm can improve the network utility by 8.3% to 28.1% compared to baseline methods.

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%).

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.

Multi-arm bandit (MAB) is a classic online learning framework that studies the sequential decision-making in an uncertain environment. The MAB framework, however, overlooks the scenario where the decision-maker cannot take actions (e.g., pulling arms) directly. It is a practically important scenario in many applications such as spectrum sharing, crowdsensing, and edge computing. In these applications, the decision-maker would incentivize other selfish agents to carry out desired actions (i.e., pulling arms on the decision-maker’s behalf). This paper establishes the incentivized online learning (IOL) framework for this scenario. The key challenge to design the IOL framework lies in the tight coupling of the unknown environment learning and asymmetric information revelation. To address this, we construct a special Lagrangian function based on which we propose a socially-optimal mechanism for the IOL framework. Our mechanism satisfies various desirable properties such as agent fairness, incentive compatibility, and voluntary participation. It achieves the same asymptotic performance as the state-of-art benchmark that requires extra information. Our analysis also unveils the power of crowd in the IOL framework: a larger agent crowd enables our mechanism to approach more closely the theoretical upper bound of social performance. Numerical results demonstrate the advantages of our mechanism in large-scale edge computing.

The classic utility maximization framework studies the fairness-efficiency tradeoff in various resource allocation problems (e.g., bandwidth allocation). The weighted alpha-fair utility is a common utilitarian metric. However, this classic framework cannot tackle those allocation problems with the online decision-making requirement (e.g., caching capacity allocation under unknown requests). Existing studies on these online allocation problems largely follow the online learning approaches, thus inevitably overlook the allocation fairness. In this paper, we propose a novel utility maximization framework accommodating the online setting. The major challenge of designing this framework lies in the tight coupling between the desirable fairness guarantee and the unknown allocation efficiency. To tackle this, we integrate the weighted alpha-fair utility with the learning rationale, by properly devising the merit-based weights and the increasing fairness levels. Under our proposed framework, the utility-maximizing allocation in each time slot is weighted alpha-fair. Our framework also performs asymptotically as well as the offline optimal/efficient outcome. We demonstrate how this framework functions in two networking applications. In size-based scheduling, it enables network switches to prioritize short flows and avoid flow starvation without the prior flow size information. In file caching, our framework outperforms several state-of-the-art caching policies up to 21% in terms of cache-hit-ratio.

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.

The efficiency of a large-scale edge computing system primarily depends on three aspects: i) edge server provision, ii) task migration, and iii) computing resource configuration. In this paper, we study the dynamic resource configuration for hybrid edge server provision under two decentralized task migration schemes. We formulate the dynamic resource configuration as an online cost minimization problem, aiming to jointly minimize performance degradation and operation expenditure. Due to the stochastic nature, it is an online learning problem with partial feedback. To address it, we derive a deterministic mean field model to approximate the stochastic edge computing system. We show that the mean field model provides the increasingly accurate full feedback as the system scales. We then propose a learning policy based on the mean field model, and show that our proposed policy performs asymptotically as well as the offline optimal configuration. We provide two ways of setting the policy parameters, which achieve a constant competitive ratio (under certain mild conditions) and a sub-linear regret, respectively. Numerical results show that the mean field model significantly improves the convergence speed. Moreover, our proposed policy under the decentralized task migration schemes considerably reduces the operating cost (by 23%) and incurs little communication overhead.

Mobile network operators (MNOs) provide wireless data services based on a tariff data plan with a month data cap. Traditionally, the data cap is only valid for domestic data consumption and users have to pay extra roaming fees for overseas data consumption. A recently emerged location-flexible service allows users to access the domestic data cap in overseas locations (by configuring location-flexibility with a daily fee). This paper studies the economic impact of the location-flexibility on the overseas market. The overseas market tracks the travelers on a monthly basis, hence it is month-variant. Each user in overseas market decides his joint flexibility configuration and data consumption (J-FCDC) every day, which corresponds to an on-line payoff maximization problem. We first analyze the off-line version of J-FCDC problem (which is NP-hard), and then we design an on-line strategy with a provable performance guarantee. Moreover, we propose a pricing policy for the location-flexible service without the need of knowing the market statistic information. We find that the location-flexibility induces users to consume more data in low-valuation days, and the MNO benefits from stimulating users’ data consumption through an appropriate pricing. Numerical results show that the location-flexibility improves the MNO’s revenue and the users’ payoffs.

In mobile Internet ecosystem, mobile users (MUs) purchase wireless data services from Internet service provider (ISP) to access to Internet and acquire the interested content services (e.g., online game) from Content Provider (CP). The popularity of intelligent functions (e.g., AI and 3D modeling) increases the computation-intensity of the content services, leading to a growing computation pressure for the MUs’ resource-limited devices. To this end,edge computing serviceis emerging as a promising approach to alleviate the MUs’ computation pressure while keeping their quality-of-service, via offloading some computation tasks of MUs to edge (computing) servers deployed at the local network edge. Thus, edge service provider (ESP), who deploys the edge servers and offers the edge computing service, becomes an upcoming new stakeholder in the ecosystem. In this work, we study the economic interactions of MUs, ISP, CP, and ESP in the new ecosystem with edge computing service, where MUs can acquire the computation-intensive content services (offered by CP) and offload some computation tasks, together with the necessary raw input data, to edge servers (deployed by ESP) through ISP. We first study the MU's Joint Content Acquisition and Task Offloading (J-CATO) problem, which aims to maximize his long-term payoff. We derive theoff-linesolution with crucial insights, based on which we design anonlinestrategy with provable performance. Then, we study the ESP's edge service monetization problem. We propose a pricing policy that can achieve aconstant fractionof the ex post optimal revenue with an extraconstant lossfor the ESP. Numerical results show that the edge computing service can stimulate the MUs’ content acquisition and improve the payoffs of MUs, ISP, and CP.

The scheduling policy installed on switches of datacenters plays a significant role on congestion control. Shortest-Remaining-Processing-Time (SRPT) achieves the near-optimal average message completion time (MCT) in various scenarios, but is difficult to deploy as viewed by the industry. The reasons are two-fold: 1) many commodity switches only provide FIFO queues, and 2) the information of remaining message size is not available. Recently, the idea of emulating SRPT using only a few FIFO queues and the original message size has been coined as the approximate and deployable SRPT (ADS) design. In this paper, we provide the first theoretical study on the optimal ADS design. Specifically, we first characterize a wide range of feasible ADS scheduling policies via a unified framework, and then derive the steady-state MCT, slowdown, and impoliteness in the M/G/1 setting. Hence we formulate the optimal ADS design as a non-linear combinatorial optimization problem, which aims to minimize the average MCT given the available FIFO queues. We also take into account the proportional fairness and temporal fairness constraints based on the maximal slowdown and impoliteness, respectively. The optimal ADS design problem is NP-hard in general, and does not exhibit monotonicity or sub-modularity. We leverage its decomposable structure and devise an efficient algorithm to solve the optimal ADS policy. We carry out extensive flow-level simulations and packet-level experiments to evaluate the proposed optimal ADS design. Results show that the optimal ADS policy installed on eight FIFO queues is capable of emulating the true SRPT.

Many online platforms are providing valuable real-time contents (e.g., traffic) by continuously acquiring the status of different Points of Interest (PoIs). In status acquisition, it is challenging to determine how frequently a PoI should upload its status to a platform, since they are self-interested with private and possibly time-varying preferences. This paper considers a general multi-period status acquisition system, aiming to maximize the aggregate social welfare and ensure the platform freshness. The freshness is measured by a metric termed age of information. For this goal, we devise a long-term decomposition (LtD) mechanism to resolve the time-varying information asymmetry. The key idea is to construct a virtual social welfare that only depends on the current private information, and then decompose the per-period operation into multiple distributed bidding problems for the PoIs and platforms. The LtD mechanism enables the platforms to achieve a tunable trade-off between payoff maximization and freshness conditions. Moreover, the LtD mechanism retains the same social performance compared to the benchmark with symmetric information and asymptotically ensures the platform freshness conditions. Numerical results based on real-world data show that when the platforms pay more attention to payoff maximization, each PoI still obtains a non-negative payoff in the long-term.

The scheduling policy installed on switches of datacenters plays a significant role on congestion control. Shortest-Remaining-Processing-Time (SRPT) achieves the near-optimal average message completion time (MCT) in various scenarios, but is difficult to deploy as viewed by the industry. The reasons are two-fold: 1) many commodity switches only provide FIFO queues, and 2) the information of remaining message size is not available. Recently, the idea of emulating SRPT using only a few FIFO queues and the original message size has been coined as the approximate and deployable SRPT (ADS) design. In this paper, we provide the first theoretical study on ADS design. Specifically, we first characterize a wide range of feasible ADS scheduling policies via a unified framework, and then derive the steady-state MCT and slowdown in the M/G/1 setting. We formulate the optimal ADS design as a non-linear combinatorial optimization problem, which aims to minimize the average MCT given the available FIFO queues. To prevent the starvation of long messages, we also take into account the fairness condition based on the steady-state slowdown. The optimal ADS design problem is NP-hard in general, and does not exhibit monotonicity or sub-modularity. We leverage its decomposable structure and devise an efficient algorithm to solve the optimal ADS policy. Numerical results based on the realistic heavy-tail message size distribution show that the optimal ADS policy installed on eight FIFO queues is capable of emulating the true SRPT in terms of MCT and slowdown.

The performance of an edge computing system primarily depends on the edge server provision mode, the task migration scheme, and the computing resource configuration. This paper studies how to perform dynamic resource configuration for hybrid edge server provision under two decentralized task migration schemes. We formulate the dynamic resource configuration as a multi-period online cost minimization problem, aiming to jointly minimize the performance degradation (i.e., execution latency) and the operation expenditure. Due to the stochastic nature, one can only observe the system performance for the currently installed configuration, which is also known as the partial feedback. To overcome this challenge, we derive a deterministic mean field model to approximate the large-scale stochastic edge computing system. We then propose an online mean field aided resource configuration policy, and show that the proposed policy performs asymptotically as good as the offline optimal configuration. Numerical results show that the mean field model can significantly improve the convergence speed in the online resource configuration problem. Moreover, our proposed policy under the two decentralized task migration schemes considerably reduces the operating cost (by 23%) and incurs little communication overhead.

Mobile Network Operators (MNOs) provide wireless data services based on a tariff data plan with a month data cap. Traditionally, the data cap is only valid for domestic data consumption and users have to pay extra roaming fees for overseas data consumption. A recent location-flexible service allows the user to access the domestic data cap in overseas locations (by configuring location-flexibility with a daily fee). This paper studies the economic effect of the location-flexibility on the overseas market. The overseas market comprises users who travel overseas within the month, thus is monthly variant. Each user decides his joint flexibility configuration and data consumption (J-FCDC) every day. The user's J-FCDC problem is an on-line payoff maximization. We analyze its off-line problem (which is NP-hard) and design an on-line strategy with provable performance. Moreover, we propose a pricing policy for the location-flexible service without relying on the market statistic information. We find that the location-flexibility induces users to consume more data in low-valuation days, and the MNO benefits from stimulating users' data consumption through an appropriate pricing. Numerical results based on empirical data show that the location-flexibility improves the MNO's revenue by 18% and the users' payoffs by 12% on average.

The growing competition drives the mobile network operators (MNOs) to explore adding time flexibility to the traditional data plan, which consists of a monthly subscription fee, a data cap, and a per-unit fee for exceeding the data cap. The rollover data plan, which allows the unused data of the previous month to be used in the current month, provides the subscribers with the time flexibility. In this paper, we formulate two MNOs' market competition as a three-stage game, where the MNOs decide their data mechanisms (traditional or rollover) in Stage I and the pricing strategies in Stage II, and then users make their subscription decisions in Stage III. Different from the monopoly market where an MNO always prefers the rollover mechanism over the traditional plan in terms of profit, MNOs may adopt different data mechanisms at an equilibrium. Specifically, the high-QoS MNO would gradually abandon the rollover mechanism as its QoS advantage diminishes. Meanwhile, the low-QoS MNO would progressively upgrade to the rollover mechanism. The numerical results show that the market competition significantly limits MNOs' profits, but both MNOs obtain higher profits with the possible choice of the rollover data plan.

An effective way for a Mobile network operator (MNO) to improve its revenue is price discrimination, i.e., providing different combinations of data caps and subscription fees. Rollover data plan (allowing the unused data in the current month to be used in the next month) is an innovative data mechanism with time flexibility. In this paper, we study the MNO's optimal multi-cap data plans with time flexibility in a realistic asymmetric information scenario. Specifically, users are associated with multi-dimensional private information, and the MNO designs a contract (with different data caps and subscription fees) to induce users to truthfully reveal their private information. This problem is quite challenging due to the multi-dimensional private information. We address the challenge in two aspects. First, we find that a feasible contract (satisfying incentive compatibility and individual rationality) should allocate the data caps according to users' willingness-to-pay (captured by the slopes of users' indifference curves). Second, for the non-convex data cap allocation problem, we propose a Dynamic Quota Allocation Algorithm, which has a low complexity and guarantees the global optimality. Numerical results show that the time-flexible data mechanisms increase both the MNO's profit (25 percent on average) and users' payoffs (8.2 percent on average) under price discrimination.

Mobile Network Operators (MNOs) are providing more flexible wireless data services to attract subscribers and increase revenues. For example, the data trading market enables user-flexibility by allowing users to sell leftover data to or buy extra data from each other. The rollover mechanism enables time-flexibility by allowing a user to utilize his own leftover data from the previous month in the current month. In this paper, we investigate the economic viability of offering the data trading market together with the rollover mechanism, to gain a deeper understanding of the interrelationship between the user-flexibility and the time-flexibility. We formulate the interactions between the MNO and mobile users as a multi-slot dynamic game. Specifically, in each time slot (e.g., every day), the MNO first determines the selling and buying prices with the goal of revenue maximization, then each user decides his trading action (by solving a dynamic programming problem) to maximize his long-term payoff. Due to the availability of monthly data rollover, a user's daily trading decision corresponds to a dynamic programming problem with two time scales (i.e., day-to-day and month-to-month). Our analysis reveals an optimal trading policy with a target interval structure, specified by a buy-up-to threshold and a sell-down-to threshold in each time slot. Moreover, we show that the rollover mechanism makes users sell less and buy more data given the same trading prices, hence it increases the total demand while decreasing the total supply in the data trading market. Finally, numerical results based on real-world data unveil that the time-flexible rollover mechanism plays a positive role in the user-flexible data trading market, increasing the MNO's revenue by 25% and all users' payoff by 17% on average.

Recently, the mobile network operators (MNOs) are exploring more time flexibility with the rollover data plan, which allows the unused data from the previous month to be used in the current month. Motivated by this industry trend, we propose a general framework for designing and optimizing the mobile data plan with time flexibility. Such a framework includes the traditional data plan, two existing rollover data plans, and a new credit data plan as special cases. Under this framework, we formulate a monopoly MNO's optimal data plan design as a three-stage Stackelberg game: In Stage I, the MNO decides the data mechanism. In Stage II, the MNO further decides the corresponding data cap, subscription fee, and the per-unit fee. Finally, in Stage III, users make subscription decisions based on their own characteristics. Through backward induction, we analytically characterize the MNO's profit-maximizing data plan and the corresponding users' subscriptions. Furthermore, we conduct a market survey to estimate the distribution of users' two-dimensional characteristics, and evaluate the performance of different data mechanisms using the real data. We find that a more time-flexible data mechanism increases MNO's profit and users' payoffs, hence improves the social welfare.

Mobile network operators (MNOs) have been offering mobile data plans with different data caps and subscription fees as an effective way of achieving price discrimination and improving revenue. Recently, some MNOs are investigating innovative data plans with time flexibility based on the multi-cap scheme. The rollover data plan and the credit data plan are such innovative data plans with time flexibility. In this paper, we study how the MNO optimizes its multi-cap data plan with time flexibility in the realistic asymmetric information scenario, where each user is associated with multidimensional private information, i.e., the data valuation and the network substitutability. Specifically, we consider a multi-dimensional contract-theoretic approach, and analyze the optimal data caps and the subscription fees design systematically. We find that each user's willingness-to-pay for a particular data cap can be captured by the slope of his indifference curve on the contract plane, and the feasible contract (satisfying the incentive compatibility and individual rationality conditions) will allocate larger data caps for users with higher willingness-to-pay. Furthermore, we conduct a market survey to estimate the statistical distribution of users' private information, and examine the performance of our proposed multi-dimensional contract design using the empirical data. Numerical results further reveal that the optimal contract may provide price discounts (i.e., negative subscription fees) to attract low valuation users to select a small-cap (possibly zero-cap) contract item. A data mechanism with better time flexibility brings users higher payoffs and the MNO more profit, hence increases the social welfare.

Today, many mobile network operators (MNOs) provide data services through a three-part tariff data plan, which involves a fixed subscription fee, a data cap, and a per-unit fee for the data over-usage exceeding the data cap. To increase their market competitiveness, MNOs have been trying to provide more time flexibility in the data plans. One of such innovations is the rollover data plan, which allows a subscriber to use the unused data of the previous month in the current month. Depending on the consumption priority of the rollover data, different rollover data plans can have different levels of time flexibility. The interactions among multiple MNOs offering rollover data plans, however, are quite complicated and sometimes counter-intuitive. To examine this issue, in this paper we build a simple market model of two MNOs competing to serve the same pool of heterogeneous users. We formulate the market competition as a two-stage game: in Stage I, the MNOs simultaneously decide their pricing strategies of their chosen data mechanisms; In Stage II, users make their subscription decisions among the two MNOs. We characterize the sub-game perfect equilibrium (SPE) of the two-stage game through backward induction. Comparing with a monopoly market where a better time flexibility always improves the MNO's profit, our analysis reveals a rather complicated story in the duopoly market: (i) with a mild competition, the stronger MNO will increase both MNOs' profits by adopting a data plan with a better time flexibility, while the weaker MNO will decrease both MNOs' profits by adopting a data plan with a better time flexibility; (ii) with a fierce competition, any MNO will increase its profit and decrease the competitor's profit by adopting a data plan with a better time flexibility.

Rollover data plans are attractive to mobile users by allowing them to keep their unused data for future use, and hence has been widely implemented by Mobile Network Operators (MNOs) around the world. In this work, we formulate a three-stage Stackelberg game to analyze the interactions between an MNO and its subscribed users under both traditional and rollover data plans. Specifically, in Stage I, the MNO decides which data plan(s) to implement; In Stage II, the MNO decides the price(s) of the data plan(s) to maximize its expected revenue; In Stage III, users make their individual subscription decisions to maximize their expected payoffs. Our analysis shows that in general, high evaluation users are more likely to choose the rollover data plan than medium evaluation users. More precisely, as the network substitutability increases, high evaluation users tend to choose the rollover data plan, while medium evaluation users tend to choose the traditional data plan. We further prove that the MNO can achieve the maximum revenue by only providing the rollover data plan (without bundling with the traditional data plan). Numerical results show that the rollover data plan can increase not only the MNO's revenue but also the users' payoffs (and hence the social welfare) comparing with the traditional data plan. We also compare two rollover data plans that differ in whether the rollover data is consumed prior to monthly data cap, and show that allowing the rollover data to be consumed before the monthly data cap is more beneficial to both users and the MNO.