INFOCOM 2018 论文列表

Recent years have seen various acoustic applications on mobile devices, e.g. range finding, gesture recognition, and device-to-device data transport, which use near-ultrasound signals at frequencies around 18-24 kHz. Due to the fixed low sound sample rate and hardware limitation, the highest detectable sound frequency on commercial-off-the-shelf (COTS) mobile devices is capped at 24 kHz, presenting a daunting barrier that prevents high-frequency ultrasounds from benefiting acoustic applications. To bridge this gap, we present iChemo, a technology that enables COTS mobile devices to sense high-frequency ultrasound signals. Specifically, we demonstrate how to detect the power spectral density (PSD) of a high-frequency ultrasound signal by customizing the coprime sampling algorithm on COTS devices. Through our prototype and evaluation on extensive mobile devices, we demonstrate that iChemo can sense the PSD of ultrasound at frequency of 60 kHz, which is over twice of the current sensible frequency threshold.

Caching popular content at the wireless edge is recently proposed as a means to reduce congestion at the backbone of cellular networks. The two main actors involved are Mobile Network Operators (MNOs) and Content Providers (CPs). In this work, we consider the following arrangement: an MNO pre-installs memory on its wireless equipment (e.g. Base Stations) and invites a unique CP to use them, with monetary cost. The CP will lease memory space and place its content; the MNO will associate network users to stations. For a given association policy, the MNO may help (or not) the CP to offload traffic, depending on whether the association takes into account content placement. We formulate an optimization problem from the CP perspective, which aims at maximizing traffic offloading with minimum leasing costs. This is a joint optimization problem that can include any association policy, and can also derive the optimal one. We present a general exact solution using Benders decomposition. It iteratively updates decisions of the two actors separately and converges to the global optimum. We illustrate the optimal CP leasing/placement strategy and hit probability gains under different association policies. Performance is maximised when the MNO association follows CP actions.

Bufferbloat is a phenomenon where router buffers are constantly being filled, resulting in high queueing delay and delay variation. Larger buffer size and more delay-sensitive applications on the Internet have made this phenomenon a pressing issue. Active queue management (AQM) algorithms, which play an important role in combating bufferbloat, have not been widely deployed due to complicated manual parameter tuning. Moreover, AQM algorithms are often designed and analyzed based on models with a single bottleneck link, rendering their performance and stability unclear in multi-bottleneck networks. In this paper, we propose a general framework to combat bufferbloat in multi-bottleneck networks. We first conduct an equilibrium analysis for a general multi-bottleneck TCP/ AQM system and develop an algorithm to compute the equilibrium point. We then decompose the system into single-bottleneck subsystems and derive sufficient conditions for the local asymptotic stability of the subsystems. Using the proposed framework, we present a case study to analyze the stability of the recently proposed Controlled Delay (CoDel) in multi-bottleneck networks and devise Self-tuning CoDel to improve the system stability and performance. Extensive simulation results show that Self-tuning CoDel effectively stabilizes queueing delay in multi-bottleneck scenarios, and thus contributes to combating bufferbloat.

We consider the problem of exploring travel demand statistics to optimize ride-sharing routing, in which the driver of a vehicle determines a route to transport multiple customers with similar itineraries and schedules in a cost-effective and timely manner. This problem is important for unleashing economical and societal benefits of ride-sharing. Meanwhile, it is challenging due to the need of (i) meeting travel delay requirements of customers, and (ii) making online decisions without knowing the exact travel demands beforehand. We present a general framework for exploring the new design space enabled by the demand-aware approach. We show that the demand-aware ride-sharing routing is fundamentally a two-stage stochastic optimization problem. We show that the problem is NP-Complete in the weak sense. We exploit the two-stage structure to design an optimal solution with pseudo-polynomial time complexity, which makes it amenable for practical implementation. We carry out extensive simulations based on real-world travel demand traces of Manhattan. The results show that using our demand-aware solution instead of the conventional greedy-routing scheme increases the driver's revenue by 10%. The results further show that as compared to the case without ride-sharing, our ride-sharing solution reduces the customers' payment by 9% and the total vehicle travel time (indicator of greenhouse gas emission) by 17%. The driver can also get 26% extra revenues per slot by participating in ride-sharing.

Mobile sensing enables unobtrusive monitoring of our daily activities, sleep quality, breathing and heart rate, revolutionizing the health-care system. Dietary information is also a critical dimension for health management but has no convenient solution yet. In this paper, we ask whether we can track meal composition unobtrusively. We introduce Smart-U, a new utensil design that can recognize meal composition during the intake process, without user intervention or on-body instruments. Smart-U makes use of the fact that light spectra reflected by foods are dependent on the food ingredients. By analyzing the reflected light spectra, Smart-U can recognize what food is on top of the utensil. We describe the prototype design of Smart-U and the food recognition algorithm. We demonstrate that Smart-U can recognize 20 types of foods with 93 % accuracy. It can work robustly under different conditions. We envision that Smart-U can enable automatic food intake tracking, and provide personalized food suggestions based on nutrient recommendations and prior consumption. In the long run, Smart-U can contribute to the study of chronic diseases.

Network Function Virtualization (NFV) is a novel paradigm that enables flexible and scalable implementation of network services on cloud infrastructure. A key factor in the success of NFV is the ability to dynamically allocate physical resources according to the demand. This is particularly important when dealing with the data plane since additional resources are required in order to support the virtual switching of the packets between the Virtual Network Functions (VNFs). The exact amount of these resources depends on the way service chains are deployed and the amount of network traffic being handled. Thus, orchestrating service chains that require high traffic throughput is a very complex task and most existing solutions either concentrate on handcrafted tuning of the servers to achieve the needed performance level, or present theoretical placement functions that assume that the switching cost is part of the input. In this work, we bridge this gap by presenting a deployment algorithm for service chains that optimizes performance by minimizing the actual cost of virtual switching. The results are based on extensive measurements of the actual switching cost and the performance of service chains in a realistic NFV environment. Our evaluation indicates that this new algorithm significantly reduces virtual switching resource utilization when compared to the de-facto standard placement in OpenStack/Nova - allowing a much higher acceptance ratio of network services.

This paper proposes and analyzes a stochastic multiagent opinion dynamics model. We are interested in a multi-leveled opinion of each agent which is randomly influenced by the binary actions of its neighbors. It is shown that, as far as the number of agents in the network is finite, the model asymptotically produces consensus. The consensus value corresponds to one of the absorbing states of the associated Markov system. However, when the number of agents is large, we emphasize that partial agreements are reached and these transient states are metastable, i.e., the expected persistence duration is arbitrarily large. These states are characterized using an N-intertwined mean field approximation (NIMFA) for the Markov system. Numerical simulations validate the proposed analysis.

Online Social Networks (OSNs) attract billions of users to share information and communicate where viral marketing has emerged as a new way to promote the sales of products. An OSN provider is often hired by an advertiser to conduct viral marketing campaigns. The OSN provider generates revenue from the commission paid by the advertiser which is determined by the spread of its product information. Meanwhile, to propagate influence, the activities performed by users such as viewing video ads normally induce diffusion cost to the OSN provider. In this paper, we aim to find a seed set to optimize a new profit metric that combines the benefit of influence spread with the cost of influence propagation for the OSN provider. Under many diffusion models, our profit metric is the difference between two submodular functions which is challenging to optimize as it is neither submodular nor monotone. We design a general two-phase framework to select seeds for profit maximization and develop several bounds to measure the quality of the seed set constructed. Experimental results with real OSN datasets show that our approach can achieve high approximation guarantees and significantly outperform the baseline algorithms, including state-of-the-art influence maximization algorithms.

Thanks to network slicing, 5G networks will support a variety of services in a flexible and swift manner. In this context, we seek to make high-quality, joint optimal decisions concerning the placement of VNFs across the physical hosts for realizing the services, and the allocation of CPU resources in VNFs sharing a host. To this end, we present a queuing-based system model, accounting for all the entities involved in 5G networks. Then, we propose a fast and efficient solution strategy yielding near-optimal decisions. We evaluate our approach in multiple scenarios that well represent real-world services, and find it to consistently outperform state-of-the-art alternatives and closely match the optimum.

We first study, at a microscopic level, the effects of various types of packet losses on TCP performance in a small data center. Then based on the findings we propose a simple recovery mechanism to combat the drawbacks of the long retransmission timeout. We emphasize through our empirical study that packet losses that occur at the tail of short-lived flows$a$nd/or bursty losses that span a large fraction of the congestion window are frequent in data center networks; and, in most cases, especially for short-lived flows, they result in a loss recovery that incurs waiting for a long retransmission timeout (RTO). The negative effect of frequent RTOs on the FCT is dramatic, yet recovery via RTO is merely a symptom of the pathological design of TCP's minimum RTO mechanism (set by default to the Internet scale). We propose the so-called Timely Retransmitted ACKs (T-RACKs), a very simple recovery mechanism for data centers, implemented as a shim layer between the virtual machines layer and the end-host NIC, to bridge the gap between TCP's huge RTO and the actual round trip times experienced in the data center. Compared to alternative solutions such as DCTCP, our T-RACKS has the virtue of not requiring any modification to TCP, which makes it readily deployable in virtualized multi-tenant public data centers. Experimental results show considerable improvements in the FCT distribution.

Event-based social networks (EBSNs) are the newly emerging social platforms for users to publish events online and attract others to attend events offline. The content information of events plays an important role in event recommendation. However, the content-based approaches in existing event recommender systems cannot fully represent the preference of each user on events since most of them focus on exploiting the content information from events' perspective, and the bag-of-words model, commonly used by them, can only capture word frequency but ignore word orders and sentence structure. In this paper, we shift the focus from events' perspective to users' perspective, and propose a Deep User Modeling framework for Event Recommendation (DUMER) to characterize the preference of users by exploiting the contextual information of events that users have attended. Specifically, we utilize convolutional neural network (CNN) with word embedding to deeply capture the contextual information of a user's interested events and build up a user latent model for each user. We then incorporate the user latent model into probabilistic matrix factorization (PMF) model to enhance the recommendation accuracy. We conduct experiments on the real-world dataset crawled from a typical EBSN, Meetup.com, and the experimental results show that DUMER outperforms the compared benchmarks.

Ultra densification along with the use of wider bands at higher frequencies are likely to be key elements towards meeting the throughput/coverage objectives of 5G wireless networks. In addition to increased parallelism, densification leads to improved, but eventually bounded, benefits from proximity of users to base stations, while resulting in increased aggregate interference. Such networks are expected to be interference limited, and in higher frequency regimes, the interference is expected to become spatially variable due to the increased sensitivity of propagation to obstructions and the proximity of active interferers. This paper studies the characteristics of the spatial random fields associated with interference and Shannon capacity in ultra-dense limiting regimes. They rely on the theory of Gaussian random fields which arise as natural limits under densification. Our models show how densification and operation at higher frequencies, could lead to increasingly rough temporal variations in the interference process. This is characterized by the Holder exponent of the interference field. We show that these fluctuations make it more difficult for mobile users to adapt modulation and coding. We further study how the spatial correlations in users' rates impact backhaul dimensioning. Therefore, this paper identifies and quantifies challenges associated with densification in terms of the resulting unpredictability and the correlation of interference on the achievable rates.

Vital signs such as heart rate and heartbeat interval are currently measured by electrocardiograms (ECG) or wearable physiological monitors. These techniques either require contact with the patient's skin or are usually uncomfortable to wear, rendering them too expensive and user-unfriendly for daily monitoring. In this paper, we propose a new noninvasive technology to generate an Acousticcardiogram (ACG) that precisely monitors heartbeats using inaudible acoustic signals. ACG uses only commodity microphones and speakers commonly equipped on ubiquitous off-the-shelf devices, such as smartphones and laptops. By transmitting an acoustic signal and analyzing its reflections off human body, ACG is capable of recognizing the heart rate as well as heartbeat rhythm. We employ frequency-modulated sound signals to separate reflection of heart from that of background motions and breath, and continuously track the phase changes of the acoustic data. To translate these acoustic data into heart and breath rates, we leverage the dual microphone design on COTS mobile devices to suppress direct echo from speaker to microphones, identify heart rate in frequency domain, and adopt an advanced algorithm to extract individual heartbeats. We implement ACG on commercial devices and validate its performance in real environments. Experimental results demonstrate ACG monitors user's heartbeat accurately, with median heart rate estimation error of 0.6 beat per minute (bpm), and median heartbeat interval estimation error of 19 ms.

In recent years, real-world attacks against PKI take place frequently. For example, malicious domains' certificates issued by compromised CAs are widespread, and revoked certificates are still trusted by clients. In spite of a lot of research to improve the security of SSL/TLS connections, there are still some problems unsolved. On one hand, although log-based schemes provided certificate audit service to quickly detect CAs' misbehavior, the security and data consistency of log servers are ignored. On the other hand, revoked certificates checking is neglected due to the incomplete, insecure and inefficient certificate revocation mechanisms. Further, existing revoked certificates checking schemes are centralized which would bring safety bottlenecks. In this paper, we propose a blockchain-based public and efficient audit scheme for TLS connections, which is called Certchain. Specially, we propose a dependability-rank based consensus protocol in our blockchain system and a new data structure to support certificate forward traceability. Furthermore, we present a method that utilizes dual counting bloom filter (DCBF) with eliminating false positives to achieve economic space and efficient query for certificate revocation checking. The security analysis and experimental results demonstrate that CertChain is suitable in practice with moderate overhead.

Since the mobile camera is often small in size and easy to conceal, existing anti-piracy solutions are inefficient in defeating mobile-camera-based piracy, thus it remains a serious threat to copyright of Intellectual property. This paper presents Rainbow, a low-cost lighting system which prevents mobile-camera-based piracy attacks on intellectual property in the physical world, e.g., art paintings. Through embedding invisible illuminance flickers and chromatic changes into the light, our system can significantly degrade the imaging quality of a camera while maintaining a good visual experience for human eyes. Extensive objective evaluations under different scenarios demonstrate that Rainbow is robust to different confounding factors and can effectively defeat piracy attacks on various mobile devices. Subjective tests on volunteers further evidence that our system can not only significantly pollute pirated photos but also be able to provide good lighting conditions.

Modern datacenter schedulers apply a static policy to partition resources among different tasks. The amount of allocated resource won't get changed during a task's lifetime. However, we found that resource usage during a task's runtime demonstrates high dynamics and it only reaches full usage at few moments. Therefore, the static allocation policy doesn't exploit the dynamic nature of resource usage, leading to low system resource utilization. To address this hard problem, a recently proposed task-consolidation approach packs as many tasks as possible on the same node based on real-time resource demands. However, this approach may cause resource over-allocation and harm application performance. In this paper, we propose and develop ECS, an elastic container based scheduler that leverages resource usage variation within the task lifetime to exploit the potential utilization and parallelism. The key idea is to proactively select and shift tasks backward so that the inherent paralleled tasks can be identified without over-allocation. We formulate the scheduling scheme as an online optimization problem and solves it using a resource leveling algorithm. We have implemented ECS in Apache Yarn and performed evaluations with various MapReduce benchmarks in a cluster. Experimental results show that ECS can efficiently utilize resource and achieves up to 29% reduction on average job completion time while increasing CPU utilization by 25%, compared to stock Yarn.

In this paper, we investigate the problem of beam alignment in millimeter wave (mmWave) systems, and design an optimal algorithm to reduce the overhead. Specifically, due to directional communications, the transmitter and receiver beams need to be aligned, which incurs high delay overhead since without a priori knowledge of the transmitter/receiver location, the search space spans the entire angular domain. This is further exacerbated under dynamic conditions (e.g., moving vehicles) where the access to the base station (access point) is highly dynamic with intermittent on-off periods, requiring more frequent beam alignment and signal training. To mitigate this issue, we consider an online stochastic optimization formulation where the goal is to maximize the directivity gain (i.e., received energy) of the beam alignment policy within a time period. We exploit the inherent correlation and unimodality properties of the model, and demonstrate that contextual information improves the performance. To this end, we propose an equivalent structured Multi-Armed Bandit model to optimally exploit the exploration-exploitation tradeoff. In contrast to the classical MAB models, the contextual information makes the lower bound on regret (i.e., performance loss compared with an oracle policy) independent of the number of beams. This is a crucial property since the number of all combinations of beam patterns can be large in transceiver antenna arrays, especially in massive MIMO systems. We further provide an asymptotically optimal beam alignment algorithm, and investigate its performance via simulations.

In this paper, we study the correlation of graph da-ta's anonymity, utility, and de-anonymity. Our main contributions include four perspectives. First, to the best of our knowledge, we conduct the first Anonymity-Utility-De-anonymity (AUD) correlation quantification for graph data and obtain close-forms for such correlation under both a preliminary mathematical model and a general data model. Second, we integrate our AUD quantification to SecGraph [31], a recently published Secure Graph data sharing/publishing system, and extend it to Sec-Graph+. Compared to SecGraph, SecGraph+ is an improved and enhanced uniform and open-source system for comprehensively studying graph anonymization, de-anonymization, and utility evaluation. Third, based on our AUD quantification, we evaluate the anonymity, utility, and de-anonymity of 12 real world graph datasets which are generated from various computer systems and services. The results show that the achievable anonymity/de-anonymity depends on multiple factors, e.g., the preserved data utility, the quality of the employed auxiliary data. Finally, we apply our AUD quantification to evaluate the performance of state-of-the-art anonymization and de-anonymization techniques. Interestingly, we find that there is still significant space to improve state-of-the-art de-anonymization attacks. We also explicitly and quantitatively demonstrate such possible improvement space.

Neighbor discovery is a critical first step in establishing communication in a wireless ad-hoc network. Existing quorum-based neighbor discovery algorithms only consider a pair of nodes and ensure that this pair can communicate at least once in a bounded interval. However, when the node density of a wireless network increases, collisions are more likely to happen, which makes these quorum-based algorithms inefficient in practice. We propose a novel self-adapting quorum-based neighbor discovery algorithm that can dynamically adjust its cycle pattern to decrease the impact of such collisions. We first assess the collision problem in wireless networks when using quorum-based neighbor discovery algorithms and then establish a theoretical framework to analyze the discovery delay when considering collision effects. Guided by these theoretical results, we design a self-adapting mechanism for cycle patterns in quorum-based algorithms. Simulation results show that our algorithm can achieve complete neighbor discovery in less time than existing quorum-based neighbor discovery algorithms.