The performance of collective communication schedules is crucial for the efficiency of machine learning jobs and GPU cluster utilization. Existing open-source collective communication libraries (such as NCCL and RCCL) rely on fixed schedules and cannot adjust to varying topology and model requirements. State-of-the-art collective schedule synthesizers (such as TECCL and TACCL) utilize Mixed Integer Linear Program for modeling but encounter search space explosion and scalability challenges. In this paper, we propose SyCCL, a scalable collective schedule synthesizer that aims to synthesize near-optimal schedules in tens of minutes for production-scale machine-learning jobs. SyCCL leverages collective and topology symmetries to decompose the original collective communication demand into smaller sub-demands within smaller topology subsets. SyCCL proposes efficient search strategies to quickly explore potential sub-demands, synthesizes corresponding sub-schedules, and integrates these sub-schedules into complete schedules. Our 32-A100 testbed and production-scale simulation experiments show that SyCCL improves collective performance by up to 127% while reducing synthesis time by 2 to 4 orders of magnitude compared to state-of-the-art efforts.

Leveraging direct-to-cell (DTC) satellites in low-earth orbits (LEO) to directly provide communication services for terrestrial cellphones is gaining popularity in recent years. However, the unique characteristics of the wireless medium in space-ground communication, combined with the dynamic behavior of LEO satellites, raise a new privacy leakage risk that an adversary eavesdropping on DTC broadcasts could steal the physical locations of active users. In this paper, we investigate new techniques to analyze the location leakage risks in emerging LEO direct-to-cell satellite networks (DCSN). We present DCATOR1DCATOR indicates the abbreviation of DCSN terminal locator. , a novel location leakage analyzer which continuously monitors DTC signaling messages in broadcast channels, extracts various location clues and combines them with the time-varying satellite trajectories to infer the physical locations of active users. We use DCATOR to analyze the consequences if an adversary is able to continuously monitor and process broadcast DTC signaling to deduce the locations of other users within the same satellite coverage area, in three representative DCSNs: (i) the operational Iridium; (ii) the developing Starlink DTC; and (iii) a DCSN based on the latest 3GPP NTN standards. Our extensive experiments demonstrate the existence of location leakages in real DCSNs, and in the worst case an adversary can precisely track the locations of other users within hundreds of meters. Finally, we propose privacy-enhancing countermeasures for DCSNs.

User-perceived latency is important for the quality of experience (QoE) of wide-area real-time communications (RTC). With the rapid development of low Earth orbit (LEO) mega-constellations, this paper explores a futuristic yet important problem facing the RTC community:can we exploit emerging mega-constellations to facilitate low-latency RTC globally?We carry out our quest in three steps. First, through a measurement study associated with a large number of geo-distributed RTC users, we quantitatively expose that themeandering routesin theclient-to-cloudandinter-cloud-sitesegment of existing cloud-based RTC architecture are critical culprits for the high latency issue suffered by wide-area RTC sessions. Second, we proposeSpaceRTC, a satellite-cloud cooperative framework that dynamically selectsrelay serversupon satellites and cloud sites to build an overlay network which enables diverse close-to-optimal paths.SpaceRTCjudiciously allocates RTC flows of different sessions upon the network to facilitate low-latency interactions and adaptively selects bitrates to offer high user-perceived QoE in energy-limited space circumstance. Finally, we implement a testbed based on public constellation information and real-world RTC traces. Extensive experiments demonstrate thatSpaceRTCcan deliver near-optimal interactive latency, with up to 53.3% average latency reduction and 103.6% average bitrate improvement as compared to other state-of-the-art cloud-based solutions.

End-to-end congestion control algorithms (CCAs) are expected to perform well in any Internet path, including those paths with low-earth orbit (LEO) satellite links. In this paper, we conduct a performance study on various CCAs in an operational LEO satellite network. We find that existing CCAs struggle to deal with the drastic network variations caused by the mobility of LEO satellites, resulting in poor link utilization or high latency. Further, through an in-depth analysis, we identify the fundamental challenge is that existing end-to-end CCAs detect network congestion based on performance changes observed on the sender, but the unique LEO mobility can involve massive non-congestion performance changes which seriously mislead CCA behaviors. Finally, we explore and discuss possible solutions to mitigate the misleading effects of LEO mobility.

Emerging satellite Internet constellations such as SpaceX's Starlink will deploy thousands of broadband satellites and construct Low-Earth Orbit (LEO) satellite networks (LSNs) in space, significantly expanding the boundaries of today's terrestrial Internet. However, due to the unique global LEO dynamics, satellite routers will inevitably pass through uncontrolled areas, suffering from security threats. It should be important for satellite network operators (SNOs) to enable verifiable riskavoidance routing to identify path anomalies. In this paper, we present STARVERI, a novel network path verification framework tailored for emerging LSNs. STARVERI addresses the limitations of existing crypto-based and delay-based verification approaches and accomplishes efficient and accurate path verification by: (i) adopting a dynamic relay selection mechanism deployed in SNO's operation center to judiciously select verifiable relays for each communication pair over LSNs; and (ii) incorporating a lightweight path verification algorithm to dynamically verify each segment path split by distributed relays. We build an LSN simulator based on real constellation information and the results demonstrate that STARVERI can significantly improve the path verification accuracy and achieve lower router overhead compared with existing approaches.

Emerging integrated space and terrestrial networks (ISTN) built upon low earth orbit (LEO) satellite constellations aim at providing planet-wide Internet services, not only for residential users, but also for mobile users (e.g., in airplane and cruise scenarios). Efficiently managing global mobility and keeping connections active for mobile users is critical for ISTN operators. However, our quantitative analysis identifies that existing mobility management (MM) schemes suffer from frequent connection interruptions and long latency in ISTN scenarios. The fundamental challenge stems from a unique characteristic of ISTNs: not only users are mobile, but also core network infrastructures (i.e., LEO satellites) are frequently changing their locations in the network.To facilitate seamless and low-latency satellite Internet services, this paper presents SkyCastle, a novel network-based global mobility management mechanism. SkyCastle incorporates two key techniques to address frequent connection interruptions in ISTNs. First, to reduce the interruption time, SkyCastle adopts distributed satellite anchors to track the location changes of mobile nodes, manage handovers and avoid routing convergence. Second, SkyCastle leverages an anchor manager to schedule MM functionalities at satellites to reduce deployment costs while guaranteeing low latency. Extensive evaluations combining real constellation information and mobile user trajectories show that: SkyCastle can improve up to 55.8% uninterrupted time and reduce 47.8% latency as compared to other existing MM solutions.

With the rapid evolution of space-borne capabilities, space edge computing (SEC) is becoming a new computation paradigm for future integrated space and terrestrial networks. Satellite edges adopt advanced on-board hardware, which not only enables new opportunities to perform complex intelligent tasks in orbit, but also involves new challenges due to the additional energy consumption in power-constrained space environment.In this paper, we present Phoenix, an energy-efficient task scheduling framework for emerging SEC networks. Phoenix exploits a key insight that in the SEC network, there always exist a number of sunlit edges which are illuminated during the entire orbital period and have sufficient energy supplement from the sun. Phoenix accomplishes energy-efficient in-orbit computing by judiciously offloading space tasks to "sunlight-sufficient" edges or to the ground. Specifically, Phoenix first formulates the SEC battery energy optimizing (SBEO) problem which aims at minimizing the average battery energy consumption while satisfying various task completion constraints. Then Phoenix incorporates a sunlight-aware scheduling mechanism to solve the SBEO problem and schedule SEC tasks efficiently. Finally, we implement a Phoenix prototype and build an SEC testbed. Extensive data-driven evaluations demonstrate that as compared to other state-of-the-art solutions, Phoenix can effectively reduce up to 54.8% SEC battery energy consumption and prolong battery lifetime to 2.9× while still completing tasks on time.

Emerging low earth orbit (LEO) satellites and geo-distributed ground station networks can assist pervasive and efficient Internet data delivery on a global scale. However, while promising, the improper integration of ingress satellite selection (ISS) and inter-satellite routing (ISR) can result in significantly high propagation latency and low network utilization. In this paper, we propose AeroPath, a ground-station-driven data delivery architecture that enables high-throughput data transmission while maintaining low latency. Specifically, to accomplish transmission efficiency, geo-distributed ground stations independently schedule flows over ground-satellite links in collaboration with ISR and cooperatively select inter-satellite paths to avoid bandwidth competition between different ground stations. Finally, we evaluate the effectiveness of AeroPath via extensive simulations driven by realistic constellation information. Evaluation results show that AeroPath can outperform other approaches with up to 24.1% and 18.5% improvement in terms of average system throughput and ground station utilization respectively under state-of-the-art constellation patterns.

User-perceived latency is important for the quality of experience (QoE) of wide-area real-time communications (RTC). This paper explores a futuristic yet important problem facing the RTC community: can we exploit emerging mega-constellations to facilitate low-latency RTC globally? We carry out our quest in three steps. First, through a measurement study associated with a large number of geo-distributed RTC users, we quantitatively expose that the meandering routes in the client-cloud and inter-cloud-site segment of existing cloud-based RTC architecture are critical culprits for the high latency issue suffered by wide-area RTC sessions. Second, we propose SPACERTC, a satellite-cloud cooperative framework that adaptively selects relay servers upon satellites and cloud sites to build an overlay network which enables diverse close-to-optimal paths, and then judiciously allocates RTC flows upon the network to facilitate low-latency interactions. Finally, we implement our SPACERTC prototype on an experimental environment based on public constellation information and RTC trace, and extensive experiments demonstrate that SPACERTC can deliver near-optimal interactive latency, with up to 64.9% latency reduction as compared to other state-of-the-art cloud-based solutions under representative videoconferencing traffic.