Low-earth-orbit (LEO) satellite mega-constellations enable universal Internet access from space. To ensure mission safety, LEO satellites conduct orbital maneuvers to prevent physical collisions with other space objects. These maneuvers challenge the performance of predictive TCP congestion control, which mitigates unnecessary throughput reductions caused by handover-induced data loss/delay. Our empirical study shows that these maneuvers can downgrade predictive TCP congestion control’s throughput by 17.63% due to the maneuvering satellite’s cumulative position deviations. The root cause is that end users rely on coarse-grained Two-Line Elements (TLEs) to predict satellites’ orbits, thus suffering from inaccurate predictions. Although these negative impacts seem unsolvable, fine-grained ephemeris can help end users enhance the accuracy of predicting handover events and effectively manage the durations of freezing congestion windows to a more affordable level. We then propose MATCP, a Maneuver-Aware TCP congestion control scheme to enhance the performance of TCP transport in maneuverable satellite networks. Compared to existing predictive congestion control, MATCP leverages ephemeris to achieve more accurate predictions of satellite handovers, and freezes the congestion window at predicted handover timing with a shorter freezing duration, thus improving the throughput of predictive congestion control. Our evaluations, driven by Starlink’s space situational awareness (SSA) dataset, validate that MATCP can safely prevent 94.21% of maneuver-induced throughput reductions in predictive TCP congestion control and increase throughput by 3.83× compared to TCP CUBIC and 20.07% compared to existing predictive satellite congestion control (SATCP).

Direct-to-cell satellites enable global network services for our regular phones/IoTs via 4G, 5G, and beyond. To enforce highly available, trustworthy, and roaming policy-compliant network services, they heavily rely on user geolocation and timing information from external global navigation satellite systems (GNSS) to assist with their radio access, authentication, and authorization. Our analysis and field tests reveal that, this cross-technology over-reliance propagates satellite navigation's defects to direct-to-cell satellite networks, leading to diverse issues such as intermittent connectivity, over/under-billing, unauthorized services, and service denials even when direct-to-cell satellites are accessible. Our solution, SN2, adopts the "fate-sharing" principle to reuse direct-to-cell satellites themselves for self-navigating networks. By exploiting the flexible tradeoffs between satellite network availability and navigation accuracy, it enables "good enough" built-in navigation for highly available and functionally correct network services at a negligible cost of hardware or communication resources. Our evaluations with commodity satellite phones and 3GPP NTN protocol stacks demonstrate SN2's 4.4–23.5× network availability boost and 1.9–12.3× access latency reduction over legacy solutions.

The recent renaissance of low Earth orbit (LEO) satellite networks expands the boundaries of global Internet access, but also introduces substantial new challenges for existing end-to-end congestion control algorithms (CCAs). The rapid and continuous movement of LEO satellites leads to infrastructure-level dynamics, resulting in frequent, LEO-dynamics-induced changes in link capacity, delay, and packet loss rate, which can further mislead the rate control in existing CCAs and cause self-limited performance.

Do we really need 10,000s of Low Earth Orbit (LEO) satellites to meet huge global Internet demands? While proven feasible and valuable, such LEO mega-constellation networks have raised concerns about their prohibitive capital expenditures, market monopoly, and unsustainable use of space. Instead, our analysis reveals that most of their satellites can be wasted due to their mismatch with physically uneven demands. We thus propose TinyLEO, a software-defined solution to shrink LEO network size for enormous global demands via dynamic spatiotemporal supply-demand matching. TinyLEO sparsifies satellite supplies on demand by combining diverse yet sparse orbits, hides complexities of this sparse LEO network via orbital model predictive control, and shifts the responsibility for handling these complexities to its geographic segment anycast for higher network usability, lower resource wastes, faster failovers, simpler satellites, and more flexible network orchestration. We have prototyped TinyLEO as a community toolkit for open research. Our evaluation using this toolkit shows that TinyLEO can compress the existing LEO mega-constellation network size by 2.0–7.9×, cut control plane costs by 1–3 orders of magnitude, and maintain the same demands and comparable data plane performance.

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.

In this paper, we perform a systematic study to explore a pivotal problem facing the web community: is current distributed web cache ready for future satellite Internet? First, through a worldwide performance measurement based on the RIPE Atlas platform and Starlink, the largest low-earth orbit (LEO) satellite network (LSN) today, we identify that the uneven deployment of current distributed cache servers, inter-ISP meandering routes and the last-mile congestion on LEO links jointly prevent existing terrestrial web cache from providing low-latency web access for users in emerging LSNs. Second, we propose Spache, a novel web caching system which addresses the limitations of existing ground-only cache by exploiting a bold idea: integrating web cache into LEO satellites to achieve ubiquitous and low-latency web services. Specifically, Spache leverages a key feature of LSNs called communication schedule to efficiently prefetch web contents on satellites, and adopts a schedule-driven partitioning strategy to avoid cache pollution involved by LEO mobility. Finally, we implement a prototype of Spache, and evaluate it based on real-world HTTP traces and data-driven LSN simulation. Extensive evaluations demonstrate that as compared to existing distributed caching solutions, Spache can improve cache hit ratio by 19.8% on average, reduce latency by up to 17.7%, and maintains consistently low web browsing latency for global LSN users.

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.

Low Earth Orbit (LEO) satellite network is undergoing an explosive expansion to enable high-speed Internet for numerous users anywhere on Earth. However, as a cyber-physical network, the LEO network's sustainable expansion is constrained by its harsh, crowded, and imbalanced physical environment. This position paper dives into two physical constraints for the LEO network scalability: the scale-out limit by satellite safety distances in crowded outer space, and the scale-up limit by the mismatch between the uniform LEO network capacity supply and geographically non-uniform global distribution of user demands. Traditional networking research pays less attention to these physical scaling limits, which may imply a call for a cyber-physical co-design to help the LEO network grow in the challenged space environment.

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.

Recently we have witnessed the active deployment of mega-constellations with hundreds to thousands of low-earth orbit (LEO) satellites, targeting at constructing LEO satellite networks (LSN) to provide ubiquitous Internet services globally. However, while the massive deployment of LEO satellites can improve the network survivability and performance of an LSN, it also involves additional sustainable challenges such as higher deployment cost, risk of satellite conjunction and space debris.In this paper, we investigate an important research problem facing the upcoming satellite Internet: from a network perspective, how many satellites exactly do we need to construct a survivable and performant LSN? To answer this question, we first formulate the survivable and performant LSN design (SPLD) problem, which aims to find the minimum number of needed satellites to construct an LSN that can provide sufficient amount of redundant paths, required link capacity and acceptable latency for traffic carried by the LSN. Second, to efficiently solve the tricky SPLD problem, we propose MegaReduce, a requirement-driven constellation optimization mechanism, which can calculate feasible solutions for SPLD in polynomial time. Finally, we conduct extensive trace-driven simulations to verify MegaReduce’s cost-effectiveness in constructing survivable and performant LSNs on demand, and showcase how MegaReduce can help optimize the incremental deployment and long-term maintenance of future satellite Internet.

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.

In recent years, the rapid proliferation of satellite Internet constellations (SICs) operating in low-earth orbit (LEO) has attracted attention due to their charming merits. Despite the potential of LEO satellite networks, the security aspects of their operation have largely been neglected.Composed by a large number of satellites, these mega-constellation networks are considered to be performant and resilient. In this paper, however, we challenge this intuitive notion by proposing STARMAZE, an impactful ring-based attack which could lead to severe consequences such as network service disruption or causing routing detours. Unlike other existing attack methods, STARMAZE removes the extra assumptions of specific routing algorithms and has the periodicity which contribute to lower-cost yet long-lasting launch of attack. Simultaneously, we also introduce the concept of intentional gaps to emphasize our consideration of real-world attacks, where attackers may not always succeed in precisely targeting all links. Comprehensive evaluations, grounded in practical constellation insights, show that impairing just 2.5% of Inter-Satellite Links (ISLs) can markedly amplify the average propagation delay of chosen long-distance communication pairs by over 63%. Moreover, during more than 47% of the observation time, the value can even reach over 312%.

Low Earth Orbit (LEO) satellite mega-constellations promise ubiquitous network services to "unconnected" users. But their upcoming global routing for Earth will be unstable due to exhaustive topology updates between satellites and Earth, inside an orbital shell, and across heterogeneous orbital shells. In real LEO networks, these multi-dimensional dynamics are interleaved and complicated by chaotic orbital maneuvers and random failures. They are less predictable than most satellite routing proposals expect and threaten these proposals' availability, efficiency, or resiliency at scale.

The emergent direct-to-cell Low-Earth Orbit (LEO) satellite mega-constellations promise ubiquitous LTE/5G access for our commodity phones and IoTs without terrestrial base stations. While their extreme scale and mobility help tolerate diverse attacks, we show that both new features are exploitable to amplify signaling protocol vulnerabilities inherited from LTE/5G and obfuscate attacks to threaten satellite services. We showcase this with SatOver, a control-plane cross-layer attack that lets a greedy terrestrial operator or a man-in-the-middle attacker block all direct-to-cell satellites in urban areas. SatOver can reuse terrestrial LTE/5G base stations or deploy commodity software-defined radios as false satellites, stealthily hijack victim devices, delay their satellite access, stop them from probing other satellites, and block the entire mega-constellation. Our real-world satellite tests, lab tests with commodity 3GPP NR/IoT-NTN stacks, and operational trace-driven emulation validate SatOver’s viability for attacking COTS and upcoming NTN phones/IoTs. We discuss potential defenses against SatOver’s attack amplification/obfuscation.

The integration of Mobile Edge Computing (MEC) and Low Earth Orbit (LEO) satellite networks holds the potential to offer ubiquitous computing services for ground users and has garnered significant attention recently. While there has been extensive research conducted in the field of Satellite Mobile Edge Computing (SMEC), research on edge server deployment in mega-constellation is overlooked. Edge servers require an appropriate quantification and placement before the implementation of computation offloading. The improper deployment strategy can result in high access latency and imbalance workload. In this paper, we propose a two-stage approach, called cluster-based small-scale server deloyment (CSSD), for small-scale placing and dynamic allocating edge servers that enable low access latency and workload balancing. Specifically, the offline stage is employed to determine the optimal placement of edge servers and the initial offloading mapping from access satellites to service satellites. The online stage, building dynamically adjusts the offloading mapping based on the spatiotemporal positions and workload of the satellites to balance system workload. Evaluation results show that CSSD outperforms other approaches with up to 16.37% and 35.38% enhancements in terms of workload standard deviation and user service rate while maintaining low access latency.

Delay-sensitive Web services are crucial applications in emerging low-earth orbit (LEO) satellite networks (LSNs). However, our real-world measurement study based on SpaceX's Starlink, the most widely used commercial LSN today, reveals that the endless and bursty packet losses over unstable LEO satellite links impose significant challenges on guaranteeing the quality of experience (QoE) of Web applications. We propose SatGuard, a distributed in-orbit loss recovery mechanism that can reduce user-perceived delay by completely concealing packet losses in the unstable and lossy LSN environment from endpoints. Specifically, SatGuard adopts a series of techniques to: (i) correctly migrate on-board packet buffer to support link-local retransmission under LEO dynamics; (ii) efficiently detect packet losses on satellite links; and (iii) ensure packet ordering for endpoints. We implement a SatGuard prototype, and conduct extensive trace-driven evaluations guided by public constellation information and real-world measurements. Our experiments demonstrate that, in comparison with other state-of-the-art approaches, SatGuard can significantly improve Web-based QoE, by reducing: (i) up to 48.3% of page load time for Web browsing; and (ii) up to 57.4% end-to-end communication delay for WebRTC.

Deep neural networks (DNNs) are widely deployed on real-world devices. Concerns regarding their security have gained great attention from researchers. Recently, a new weight modification attack called bit flip attack (BFA) was proposed, which exploits memory fault inject techniques such as row hammer to attack quantized models in the deployment stage. With only a few bit flips, the target model can be rendered useless as a random guesser or even be implanted with malicious functionalities. In this work, we seek to further reduce the number of bit flips. We propose a training-assisted bit flip attack, in which the adversary is involved in the training stage to build a high-risk model to release. This high-risk model, obtained coupled with a corresponding malicious model, behaves normally and can escape various detection methods. The results on benchmark datasets show that an adversary can easily convert this high-risk but normal model to a malicious one on victim’s side by flipping only one critical bit on average in the deployment stage. Moreover, our attack still poses a significant threat even when defenses are employed. The codes for reproducing main experiments are available at https://github.com/jianshuod/TBA.

Exploiting a constellation of small satellites to realize continuous earth observations (EO) is gaining popularity. Large-volume EO data acquired from space needs to be transferred to the ground. However, existing EO delivery approaches are either: (a) efficiency-limited, suffering from long delivery completion time due to the intermittent ground-space communication, or (b) scalability-limited since they fail to support concurrent delivery for multiple satellites in an EO constellation.To make big data delivery for emerging EO constellations fast and scalable, we propose Falcon, a multi-path EO delivery framework that wisely exploits diverse paths in broadband constellations to collaboratively deliver EO data effectively. In particular, we formulate the constellation-wide EO data multi-path download (CEOMD) problem, which aims at minimizing the delivery completion time of requested data for all EO sources. We prove the hardness of solving CEOMD, and further present a heuristic multipath routing and bandwidth allocation mechanism to tackle the technical challenges caused by time-varying satellite dynamics and flow contention, and solve the CEOMD problem efficiently. Evaluation results based on public orbital data of real EO constellations show that as compared to other state-of-the-art approaches, Falcon can reduce at least 51% delivery completion time for various data requests in large EO constellations.

The emerging satellite internet constellation aims to deploy hundreds of low-orbit satellites to provide high-speed broadband internet services to global ground terminals. However, this poses a significant challenge for large-scale and highly dynamic satellite networking due to the traditional satellite constellations’ uniform structure. This structure is limited by four laser links per low-orbit satellite, using default connections of two intra-orbit links and two inter-orbit links, and is difficult to match with the uneven population distribution and user traffic on the ground, resulting in unnecessary overheads in propagation delay and transmission hops. In recent years, researchers have developed methods for matching structure and traffic distribution that overcome the limitations of traditional connection methods, reducing transmission delay and hops. Despite this progress, these methods still maintain the characteristics of uniform configuration. To address this issue, a new link connection design for large-scale low-orbit satellite network driven by user distribution has been proposed. This mechanism enables the dynamic matching of satellite structure and users, facilitating elastic networking under dynamic topology conditions, improving the overall capacity and utilization of satellite networks. Through simulation, this user-driven link connection design has been verified to reduce the average hop count by at least 41% in many scenarios, demonstrating its effectiveness for a variety of new large-scale low-orbit satellite networks.

Entering the “NewSpace” era, satellite Internet constellation (SIC) is expanding rapidly. However, while operating a large number of broadband satellites in free space enables great opportunities for ubiquitous and low-latency Internet services, it also involves new threats that were previously ignored to the energy-limited satellite systems. In this paper, we investigate the feasibility and impact of a new class of real risk in emerging SICs: energy drain attack. To this end, we play the role of an attacker and propose Starmelt,an energy drain attack mechanism that persistently injects malicious traffic that goes through the victim satellite from various geo-distributed locations, preventing the victim from hibernating and overusing the satellite battery to cut its lifetime. Further, we design a series of techniques to: (i) handle the path ambiguity challenge caused by various access selection and routing schemes to guarantee that the deliberately crafted traffic can precisely pass through the victim; (ii) leverage the tail energy consumption characteristics to reduce the detectability as well as the traffic cost of an attack. Extensive simulations based on real constellation knowledge demonstrate that Starmeltcan substantially increase on-board energy consumption, reducing up to 76% lifetime of the victim satellite under various power models and traffic patterns.

Low-earth-orbit (LEO) satellite mega-constellations, such as SpaceX Starlink, are under rocket-fast deployments and promise broadband Internet to remote areas that terrestrial networks cannot reach. For mission safety and sustainable uses of space, Starlink has adopted a proprietary onboard autonomous driving system for its extremely mobile LEO satellites. This paper demystifies and diagnoses its impacts on the LEO mega-constellation and satellite networks. We design a domain-specific method to characterize key components in Starlink's autonomous driving from various public space situational awareness datasets, including continuous orbit maintenance, collision avoidance, and maneuvers between orbital shells. Our analysis shows that, these operations have mixed impacts on the stability and performance of the entire mega-constellation, inter-satellite links, topology, and upper-layer network functions. To this end, we investigate and empirically assess the potential of networking-autonomous driving co-designs for the upcoming satellite networks.

Internet content providers (ICPs) typically exploit content distribution networks (CDNs) to provide wide-area data access with high availability and low latency. However, our analysis on a large-scale trace collected from seven major CDN operators has revealed that: from a global perspective, there are still a large portion of users suffering from high user-perceived latency due to the insufficient deployment of terrestrial cloud infrastructures, especially in remote or rural areas where even the closest available cache server is too far away. This paper presents STAR FRONT, a cost-effective content distribution framework to optimize global CDNs and enable low content access latency anywhere. STAR FRONT collaboratively builds CDNs upon emerging low earth orbit (LEO) constellations and existing cloud platforms to satisfy the low latency requirements while minimizing the operational cost. Specifically, STAR FRONT exploits a key insight that emerging mega-constellations will consist of thousands of LEO satellites which can be equipped with high-speed data links and storage, and thus can potentially work as “cache in space” to enable pervasive and low-latency data access. STAR FRONT judiciously places replicas on either LEO satellite caches or terrestrial cloud caches, and dynamically assigns user requests to proper cache servers based on different constellation parameters, cloud/user distributions and pricing policies. We have implemented a STAR FRONT prototype in our testbed, and extensive trace-driven evaluations covering multiple geo-distributed vantage points have demonstrated that STAR FRONT can effectively reduce the global content access latency with acceptable operational cost under representative CDN traffic.

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.

The network topology design is critical for achieving low latency and high capacity in future integrated satellite and terrestrial networks (ISTN). However, existing studies mainly focus on the design of inter-satellite topology of ISTN, and very little is known about the design of satellite-ground topology, as well as its impact on the attainable network performance.In this paper, we conduct a quantitative study on the impact of various satellite-ground designs on the network performance of ISTN. We identify that the high-density and high-dynamicity characteristics of emerging mega-constellations have jointly imposed big challenges, such as significant routing instability, low network reachability, high latency and jitter on the ISTN paths. To alleviate the above challenges, we formulate the Low-latency Satellite-Ground Interconnecting (LSGI) problem, targeting at the integration of space and ground segment in the ISTN, while minimizing the maximum transmission latency and keeping routing stable. We further design algorithms to solve the LSGI problem through wisely coordinating the establishment of ground-to-satellite links among distributed ground stations. Comprehensive experiment results demonstrate that our solution can outperform existing related schemes by about 19% reduction of the latency and 70% reduction of the jitter on average, while sustaining the highest network reachability.

The low-earth-orbit (LEO) satellite networks promise low-latency broadband network services to remote areas. LEO networks require deploying many satellites to serve numerous users in crowded terrestrial areas, thus forming a satellite mega-constellation. It is well known that this practice would lower network utilization, which may be exacerbated with recent LEO satellite mega-constellations. For LEO satellite mega-constellation networks with complex and dynamic structures, multiple functions, and diversified user demands, this paper presents a methodology for systematically analyzing utilization. Our study shows that operational mega-constellations today suffer from < 10% low network utilization. The root cause is twofold. First, today's evenly distributed satellites do not match the unevenly distributed terrestrial users, and the law of satellite movement keeps a satellite above sparsely populated areas most of the time. Second, a uniform LEO mega-constellation cannot simultaneously meet the heterogeneous demands from local satellite access (requiring more satellites to serve the enormous population in hotspots) and global satellite routing (requiring fewer satellites for shorter paths). Based on these findings, we analyze how to improve utilization from angles of constellation structure and network architecture design. We showcase it can increase the utilization with fewer and divergent satellites while retaining comparable performance to state-of-the-art.

Low-earth-orbit (LEO) satellite mega-constellations promise broadband, low-latency network infrastructure from space for terrestrial users in remote areas. However, they face new QoS bottlenecks from infrastructure mobility due to the fast-moving LEO satellites and earth’s rotations. Both cause frequent space-ground link churns and challenge the network latency, bandwidth, and availability at the global scale. Today’s LEO networks mask infrastructure mobility with fixed anchors (ground stations) but cause single-point bandwidth/latency bottlenecks. Instead, we design LBP to remove the LEO network’s QoS bottlenecks from infrastructure mobility. LBP removes remote terrestrial fixed anchors via geographic addressing for shorter latencies and more bandwidth. It adopts local, orbit direction-aware geographic routing to avoid global routing updates for high network availability. LBP further shortens the routing paths by refining handover policies by satellites’ orbital directions. Our experiments in controlled testbeds and trace-driven emulations validate LBP’s 1.64× network latency reduction, 9.66× more bandwidth, and improve network availability to 100%.

Is it worth and feasible to push mobile core network functions to low-earth-orbit (LEO) satellite mega-constellations? While this paradigm is being tested in space and promises new values, it also raises scalability, performance, and security concerns based on our study with datasets from operational satellites and 5G. A major challenge is today's stateful mobile core, which suffers from signaling storms in satellites' extreme mobility, intermittent failures in outer space, and attacks when unavoidably exposed to untrusted foreign locations. To this end, we make a case for a stateless mobile core in space. Our solution, SpaceCore, decouples states from orbital core functions, simplifies location states via geospatial addressing, eliminates unnecessary state migrations in satellite mobility by shifting to geospatial service areas, and localizes state retrievals with device-as-the-repository. Our evaluation with datasets from operational satellites and 5G shows SpaceCore's 17.5× over existing solutions signaling reductions and resiliency to failures/attacks.

We study a new design objective for "Internet in space" for terrestrial users: To align logical network topology, address, and route in the virtual cyberspace with movements of satellite mega-constellations and earth's rotations in the real physical world. We explain why this is particularly desirable by the recent low-earth-orbit (LEO) mega-constellations, and how it welcomes mobility to streamline the network design for stability, efficiency, and scalability in an unstable space-ground environment. We showcase its feasibility with the common fixed satellite sub-point trajectory in mega-constellations. We describe how it permits stable recursive topology, unifies cyber-physical locations in the address, and naturally embeds the geographical routing into the topological routing.

Internet content providers typically exploit cloud-based content delivery/distribution networks (CDNs) to provide wide-area data access with high availability and low latency. However, from a global perspective, a large portion of users still suffer from high content access latency due to the insufficient deployment of terrestrial cloud infrastructures.This paper presents StarFront, a cost-effective content distribution framework to optimize global CDNs and enable low content access latency anywhere. StarFront builds CDNs upon emerging low Earth orbit (LEO) constellations and existing cloud platforms to satisfy the low-latency requirements while minimizing the operational cost. Specifically, StarFront exploits a key insight that emerging mega-constellations will consist of thousands of LEO satellites equipped with high-speed data links and storage, and thus can potentially work as "cache in space" to enable pervasive and low-latency data access. StarFront judiciously places replicas on either LEO satellites or clouds, and dynamically assigns user requests to proper cache servers based on constellation parameters, cloud/user distributions and pricing policies. Extensive trace-driven evaluations covering geo-distributed vantage points have demonstrated that: StarFront can effectively reduce the global content access latency with acceptable operational cost under representative CDN traffic.

Satellite-based Earth Observation (EO) systems are gaining popularity and widely used in many time-sensitive scenarios, including disaster monitoring, emergency response, forecasting and defense. Existing efforts for gathering EO data mainly rely on either ground station networks or geostationary (GEO) satellites. However, our quantitative analysis reveals that existing approaches are either limited as their achievable latency is far away from the desired value due to the insufficient coverage of ground stations, or hard to scale as the number of sensing satellites increases because of the high cost of GEO satellite relays.This paper explores the feasibility and performance of a novel approach that leverages emerging low Earth orbit (LEO) constellations to enable low-latency and scalable EO data delivery from space. We present OrbitCast, a hybrid EO data delivery architecture upon LEO constellations and geo-distributed ground stations to forward EO data from the source remote sensing satellite to a collection of end users. To handle the network dynamicity caused by LEO satellite movements and achieve stable communication over the satellite network, we propose a geo-location driven scheme to forward and deliver data packets. To demonstrate the effectiveness of OrbitCast, we build a testbed driven by public constellation information and implement the OrbitCast prototype on top of the testbed. Extensive realistic-data-driven simulations demonstrate that OrbitCast can significantly reduce the latency as compared to other state-of-the-art approaches, and complete the data delivery within five minutes for representative EO data traffic.

The booming of satellite mega-constellations to provide Internet access around the Earth is attracting more attention recently. To provide broadband Internet globally, satellites will be responsible to undertake IP address management of ground terminals. However, the movement of the LEO (Low Earth Orbit) satellite will cause difficulties to IP address management, because every LEO satellite can just stay in sight of a ground terminal for several minutes. On one hand, stateful IP address management faces a short lifetime, signal storm, and unstable address space problems. On the other hand, stateless IP address management cannot avoid high overhead in DAD (Duplicate Address Detection). This paper proposes GAMS, Geographical Addressing Management for Satellite, an efficient IP address management mechanism for mega-constellations networks. By embedding both satellites' feature information and geographical location into IPv6 addresses, GAMS enables efficient DAD with assigned address tables ONLY on respective satellite routers. Theoretical proof and simulation results validate that GAMS could effectively provide unique and long-lifetime IPv6 addresses to ground terminals with negligible cost.

The rapid development of high-speed rails (HSRs) has brought great convenience to people's travel. But related scholars found that the network status on the train is very inefficient. In order to solve that, some scholars proposed to use multipath TCP (MPTCP). However, MPTCP suffers when different paths have heterogeneous quality. In HSR scenarios, frequent handover (less than 10 seconds) occurs when devices connect to cellular network base stations. If MPTCP is used, each sub-flow has to go through handover and packets will be heavily garbled. In turn, it occupies a large amount of the receiver's buffer, even up to 100%. The latency of data submission to application will also be higher (10ms or more). It will affect the overall transfer performance. The reason for the above problem is that MPTCP does not react quickly enough to link changes and cannot accurately schedule packets between sub-flows. After consulting the measurement data of relevant scholars, we found that the trains in HSR have some recurring phenomena. For example, base station handover always occurs in the same place for devices in HSR. Based on relevant recurring information, this paper proposes Hysteresis Optimized Multipath TCP Data Scheduling Algorithm (HoMPTCP) in Predictable Networks. Combined with the prediction information formed by using historical trajectories in HSR, HoMPTCP can more accurately schedule packets between sub-flows and control the congestion window of sub-flows. After the experiments of HSR scenario, HoMPTCP can improve throughput performance by 5%-15% while increasing sequential arrival rate by 10%-15%.

With low latency and wide coverage, Low Earth Orbit (LEO) satellite networks can provide network services for places that cannot be reached by the terrestrial network and become a critical supplement to the traditional network, playing an increasingly important role. While the wide coverage and broadcasting enable LEO satellite networks (LSNs) accessible to more devices, this also raises the risk of being attacked by potential adversaries. Previous research on secure routing is either mainly based on the design of encryption-based algorithms or is only suitable for the relative static topology of the traditional terrestrial networks. However, given limited computational resources and the highly dynamic changes of the LEO satellite, encryption-based algorithms can hardly meet the demands of LSNs. Unlike these works, this paper proposes a lightweight risk-avoidance routing algorithm (LRAR). It allows users' data packets to be forwarded by avoiding specified high-risk areas to reduce the risk of user data being attacked. As can be seen in extensive simulation experiments, the LRAR implements results close to the optimal path with little overhead. Also, it enhances security and flexible extensibility.

Due to the simplification of path selection in traditional IP routing schemes and the uneven distribution of traffic, problems such as congestion and low system performance are still unavoidable in satellite networks. Compared with terrestrial networks, satellite networks have much more equal-cost paths between any two points. So, this paper uses path diversity to solve the above problems. Researchers have proposed many traffic engineering algorithms to solve similar problems, but high-dynamics and high-latency of the satellite network make these algorithms ineffective. This paper proposed a Multi-routing-plane based Flow Scheduling Strategy (MFSS), using different routing schemes in different routing planes. It can make full use of path diversity to solve simplification of path selection and solve the high-dynamics problem through cooperation between satellites and the terrestrial networks. The simulation result on the topology of Starlink shows that, in our scenarios, MFSS can increase the system throughput by 60.9% while the delay growth does not exceed 3%.

With the rapid evolution of space communication technologies, satellite networks have entered a booming period. In recent years, Integrated Satellite and Terrestrial Network (ISTN) has been recognized as the trend of future networks. In order to adapt the existing routing technology to the network with high dynamic characteristics, a large number of research have tried to divide the timeslot in the satellite network and regard the network as static in a timeslot. However, The existing methods do not consider the existence of network convergence process in ISTN. With the massive increase in satellite handoff and the consensus of running distributed routing protocol, the network availability drops sharply in the timeslot divided by the existing methods. This paper proposes Handoff Synchronization to reduce the long convergence time. Moreover, STARSLOT is proposed to divide timeslots and improve the ISTN's availability. Extensive simulation show that for the typical ISTN architecture, STARSLOT can improve the network availability by 52.63% as compared to other existing methods. Moreover, STARSLOT is much less affected by the increase in network scale.

"Newspace" mega-constellations, such as Starlink and OneWeb are gaining tremendous popularity, with the promising potential to provide high-capacity and low-latency communication globally. However, very little is known about the architecture and performance of such emerging systems, the workload they have to face, as well as the impact of topological options on the attainable network performance. This paper presents StarPerf, a mega-constellation performance simulation platform that enables constellation manufacturers and content providers to estimate and understand the achievable performance under a variety of constellation options. The proposed platform integrates two key techniques: (1) performance simulation for mega-constellation, which captures the impact of the inherent high mobility in satellite networks and profiles the area-to-area attainable network performance; (2) constellation scaling, which synthesizes various topological options by scaling the space resource and enables exploration on multiple operating conditions that can not be easily reproduced. To demonstrate the effectiveness of StarPerf on understanding and optimizing satellite networks, we leverage StarPerf to evaluate and compare the performance of several state-of-the-art low earth orbit (LEO) constellations and obtain insights on optimizing the architectural design to improve area-to-area network performance. Finally, to further show how applications can benefit from the proposed simulator, we propose an adaptive relay selection algorithm that can intelligently choose the optimal relay on cloud platforms and LEO satellites to achieve reduced latency. Evaluation results show that by properly selecting a relay in the satellite-cloud integrated infrastructure, end-to-end communication latency can be reduced by up to 62% for typical interactive traffic.

A trusted network path is a desired property of the Internet. Previous works introduced new protocol headers based on source routing for source authentication and path verification. It is obvious that any extra protocol headers will increase the network burden, and network path privacy deserves attention, especially when we use source routing. The emergence of IPv6 Segment Routing (SRv6) may bring the opportunity to assemble trusted network paths with a lightweight header. In this paper, we propose SR-TPP, a novel mechanism based on SRv6 to support network path verification meanwhile hides both-end and path information. Different from existing works, SR-TPP extends SRv6 function instead of introducing a new protocol header to meet the requirement of path compliance. Path information is sequentially encoded into the segment list in SR-TPP so that path information is partially visible to each intermediate router. The distributed verification of SR-TPP also makes it easier to locate faults. Finally, the security analysis and evaluation show that SR-TPP can assemble private and trusted network paths with acceptable performance.

Despite the growing market penetration, today's high-end virtual reality (VR) systems remain tethered, which not only limits users' VR experience but also creates a safety hazard. In this paper, we perform a systematic design study of the “elephant in the room” facing the VR industry - is it feasible to enable high-quality VR apps on untethered mobile devices such as smartphones? Our quantitative, performance-driven design study makes two contributions. First, we show that the QoE achievable for high-quality VR applications on today's mobile hardware and wireless networks via local rendering or offloading is about 10X away from the acceptable QoE, yet waiting for future mobile hardware or next-generation wireless networks (e.g., 5G) is unlikely to help, because of power limitation and the higher CPU utilization needed for processing packets under higher data rate. Second, we present Furion, a VR framework that enables high-quality, immersive mobile VR on today's mobile devices and wireless networks. Furion exploits a key insight about the VR workload that foreground interactions and background environment have contrasting predictability and rendering workload, and employs a split renderer architecture running on both the phone and the server. Supplemented with video compression, use of panoramic frames, parallel decoding on multiple cores on the phone, and view-based bitrate adaptation we demonstrate Furion can support high-quality VR apps on today's smartphones over WiFi, with under 14 ms latency and 60 FPS (the phone display refresh rate).

Cloud computing platforms enable applications to offer low-latency services to users by deploying data storage in multiple geo-distributed data centers. In this paper, through benchmark measurements on Amazon AWS and Microsoft Azure together with an analysis of a large-scale dataset collected from a major cloud CDN provider, we identify the high tail latency problem in cloud CDNs, which can substantially undermine the efficacy of cloud CDNs. One crucial idea to reduce the tail latency is to send requests in parallel to multiple clouds in cloud CDNs. However, since application providers often have a budget for using cloud services, deciding how many chunks to download from each cloud and when to download chunks in a cost-efficient manner still remain as open problems in our concerned scenario. To address the problem, we present TailCutter, a workload scheduling framework that aims at optimizing the tail latency while meeting cost constraints given by application providers. Specifically, we formulate the tail latency minimization (TLM) problem in cloud CDNs and design the receding horizon control based maximum tail minimization algorithm (RHC-based MTMA) to efficiently solve the TLM problem in practice. We implement TailCutter across multiple data centers of Amazon AWS and Microsoft Azure. Extensive evaluations using a large-scale real-world data trace (collected from a major ISP) illustrate that TailCutter can reduce up to 58.9% of the 100th-percentile user-perceived latency, as compared with alternative solutions under the cost constraint.

While the bandwidth and latency improvement of both WiFi and cellular data networks in the past decades are plenty evident, the extent of signal strength fluctuation and network disruptions (unexpected switching or disconnections) experienced by mobile users in today's network deployment remains less clear. This paper makes three contributions. First, we conduct the first extensive measurement of network disruptions and significant signal strength fluctuations (together denoted as network instabilities) experienced by 2000 smartphones in the wild. Our results show that network disruptions and signal strength fluctuations remains prevalent as we moved into the 4G era. Second, we study how well popular mobile apps today handle such network instabilities. Our results show that even some of the most popular mobile apps do not implement any disruption-tolerant mechanisms. Third, we present Janus, an intelligent interface management framework that exploits the multiple interfaces on a handset to transparently handle network disruptions and satisfy apps' performance requirement. We have implemented a prototype of Janus and our evaluation using a set of popular apps shows that Janus can: 1) transparently and efficiently handle network disruptions; 2) reduce video stalls by 2.9 times and increase 31% of the time of good voice quality; 3) reduce traffic size by 26.4% and energy consumption by 16.3% compared to naive solutions.

While the bandwidth and latency improvement of both WiFi and cellular data networks in the past decade are plenty evident, the extent of signal strength fluctuation and network disruptions (unexpected switching or disconnections) experienced by mobile users in today's network deployment remains less clear. This paper makes three contributions. First, we conduct the first extensive measurement of network disruptions and signal strength fluctuations (together denoted as instabilities) experienced by 2000 smartphones in the wild. Our results show that network disruptions and signal strength fluctuations remain prevalent as we moved into the 4G era. Second, we study how well popular mobile apps today handle such network instabilities. Our results show that even some of the most popular mobile apps do not implement any disruption-tolerant mechanisms. Third, we present JANUS, an intelligent interface management framework that exploits the multiple interfaces on a handset to transparently handle network disruptions and improve apps' QoE. We have implemented JANUS on Android and our evaluation using a set of popular apps shows that Janus can (1) transparently and efficiently handle network disruptions, (2) reduce video stalls by 2.9 times and increase 31% of the time of good voice quality compared to naive solutions.

In this paper, we perform a systematic design study of the "elephant in the room" facing the VR industry -- is it feasible to enable high-quality VR apps on untethered mobile devices such as smartphones? Our quantitative, performance-driven design study makes two contributions. First, we show that the QoE achievable for high-quality VR applications on today's mobile hardware and wireless networks via local rendering or offloading is about 10X away from the acceptable QoE, yet waiting for future mobile hardware or next-generation wireless networks (e.g. 5G) is unlikely to help, because of power limitation and the higher CPU utilization needed for processing packets under higher data rate. Second, we present Furion, a VR framework that enables high-quality, immersive mobile VR on today's mobile devices and wireless networks. Furion exploits a key insight about the VR workload that foreground interactions and background environment have contrasting predictability and rendering workload, and employs a split renderer architecture running on both the phone and the server. Supplemented with video compression, use of panoramic frames, and parallel decoding on multiple cores on the phone, we demonstrate Furion can support high-quality VR apps on today's smartphones over WiFi, with under 14ms latency and 60 FPS (the phone display refresh rate).

Mobile cloud storage services have gained phenomenal success in recent few years. In this paper, we identify, analyze, and address the synchronization (sync) inefficiency problem of modern mobile cloud storage services. Our measurement results demonstrate that existing commercial sync services fail to make full use of available bandwidth, and generate a large amount of unnecessary sync traffic in certain circumstances even though the incremental sync is implemented. For example, a minor document editing process in Dropbox may result in sync traffic 10 times that of the modification. These issues are caused by the inherent limitations of the sync protocol and the distributed architecture. Based on our findings, we propose QuickSync, a system with three novel techniques to improve the sync efficiency for mobile cloud storage services, and build the system on two commercial sync services. Our experimental results using representative workloads show that QuickSync is able to reduce up to 73.1 percent sync time in our experiment settings.

In cellular networks, it is important to conserve energy while at the same time satisfying different user performance requirements. In this paper, we first propose a comprehensive metric to capture the user performance cost due to task delay, deadline violation, different application profiles, and user preferences. We prove that finding the energy-optimal scheduling solution while meeting the requirements on the performance cost is NP-hard. Then, we design an adaptive online scheduling algorithm PerES to minimize the total energy cost on data transmissions subject to user performance constraints. We prove that PerES can make the energy consumption arbitrarily close to that of the optimal scheduling solution. Further, we develop offline algorithms to serve as the evaluation benchmark for PerES. The evaluation results demonstrate that PerES achieves average 2.5 times faster convergence speed compared to state-of-art static methods, and also higher performance than peers under various test conditions. Using 821 million traffic flows collected from a commercial cellular carrier, we verify our scheme could achieve on average 32-56 percent energy savings over the total transmission energy with different levels of user experience.

Cloud computing platforms enable applications to offer low latency access to user data by offering storage services in several geographically distributed data centers. In this paper, we identify the high tail latency problem in cloud CDN via analyzing a large-scale dataset collected from 783,944 users in a major cloud CDN. We find that the data downloading latency in cloud CDN is highly variable, which may significantly degrade the user experience of applications. To address the problem, we present TailCutter, a workload scheduling mechanism that aims at optimizing the tail latency while meeting the cost constraint given by application providers. We further design the Maximum Tail Minimization Algorithm (MTMA) working in TailCutter mechanism to optimally solve the Tail Latency Minimization (TLM) problem in polynomial time. We implement TailCutter across data centers of Amazon S3 and Microsoft Azure. Our extensive evaluation using large-scale real world data traces shows that TailCutter can reduce up to 68% 99th percentile user-perceived latency in comparison with alternative solutions under cost constraints.

Streaming services are gaining popularity and have contributed a tremendous fraction of today's cellular network traffic. Both playback fluency and battery endurance are significant performance metrics for mobile streaming services. However, because of the unpredictable network condition and the loose coupling between upper layer streaming protocols and underlying network configurations, jointly optimizing rebuffering time and energy consumption for mobile streaming services remains a significant challenge. In this paper, we propose a novel framework that effectively addresses the above limitations and optimizes video transmission in cellular networks. We design two complementary algorithms, Rebuffering Time Minimization Algorithm (RTMA) and Energy Minimization Algorithm (EMA) in this framework, to achieve smoothed playback and energy-efficiency on demand over multi-user scenarios. Our algorithms integrate cross-layer parameters to schedule video delivery. Specifically, RTMA aims at achieving the minimum rebuffering time with limited energy and EMA tries to obtain the minimum energy consumption while meeting the rebuffering time constraint. Extensive simulation demonstrates that RTMA is able to reduce at least 68% rebuffering time and EMA can achieve more than 27% energy reduction compared with other state-of-the-art solutions.

Mobile cloud storage services have gained phenomenal success in recent few years. In this paper, we identify, analyze and address the synchronization (sync) inefficiency problem of modern mobile cloud storage services. Our measurement results demonstrate that existing commercial sync services fail to make full use of available bandwidth, and generate a large amount of unnecessary sync traffic in certain circumstance even though the incremental sync is implemented. These issues are caused by the inherent limitations of the sync protocol and the distributed architecture. Based on our findings, we propose QuickSync, a system with three novel techniques to improve the sync efficiency for mobile cloud storage services, and build the system on two commercial sync services. Our experimental results using representative workloads show that QuickSync is able to reduce up to 52.9% sync time in our experiment settings.

In cellular networks, it is important to conserve energy while at the same time ensuring users to have good transmission experiences. The energy cost can result from tail energy due to the radio resource control strategies designed in cellular networks and data transmission. Existing efforts generally consider one of the energy issues, and also ignore the adverse impact on user transmission performance due to energy conservation. In addition, many existing algorithms are based on prediction and knowledge on future traffic, which are hard to apply in a practical wireless system with dynamic user traffic and channel condition. The goal of this work is to design an efficient online scheduling algorithm to minimize energy consumption both due to tail energy and transmissions while meeting user performance expectation. We prove the problem to be NP-hard, and design a practical online scheduling algorithm PerES to minimize the total energy cost of multiple mobile applications subject to user performance constraints. We propose a comprehensive performance cost metric to capture the impacts due to task delay, deadline violation, different application profiles and user preferences. We prove that our proposed scheduling algorithm can make the energy consumption arbitrarily close to that of the optimal scheduling solution. The evaluation results demonstrate the effectiveness of our scheme and its higher performance than peers. Moreover, by supporting dynamic performance requirement by mobile users, PerES can achieve 2 times faster convergence to both the performance degradation bound and optimal energy conversation bound than those of traditional static methods. Using 821 million traffic flows collected from a commercial cellular carrier, we verify our scheme could achieve on average 32%-56% energy savings with different levels of user experience.