INFOCOM 2016 论文列表

Various attacks may disclose sensitive information such as passwords of mobile devices. Residue-based attacks exploit oily or heat residues on the touch screen, computer vision based attacks analyze the hand movement on a keyboard, and sensor based attacks measure a device's motion difference via motion sensors as different keys are tapped. A randomized soft keyboard may defeat these attacks. However, a randomized key layout is counter-intuitive and users may be reluctant to adopt it. In this paper, we introduce a novel and intuitive input system, secure finger mouse, which uses a mobile device's camera sensing the fingertip movement, moves an on-screen cursor and performs clicks by sensing click gestures. We design a randomized mouse acceleration algorithm so that the adversary cannot infer keys clicked on the soft keyboard by observing the finger movement. The secure finger mouse can defeat attacks including residue, computer vision and motion based attacks too. We perform both theoretical analysis and real-world experiments to demonstrate the security and usability of the secure fingertip mouse.

In any caching system, the admission and eviction policies determine which contents are added and removed from a cache when a miss occurs. Usually, these policies are devised so as to mitigate staleness and increase the hit probability. Nonetheless, the utility of having a high hit probability can vary across contents. This occurs, for instance, when service level agreements must be met, or if certain contents are more difficult to obtain than others. In this paper, we propose utility-driven caching, where we associate with each content a utility, which is a function of the corresponding content hit probability. We formulate optimization problems where the objectives are to maximize the sum of utilities over all contents. These problems differ according to the stringency of the cache capacity constraint. Our framework enables us to reverse engineer classical replacement policies such as LRU and FIFO, by computing the utility functions that they maximize. We also develop online algorithms that can be used by service providers to implement various caching policies based on arbitrary utility functions.

Opportunistic resources are often present in various resource sharing networks for the users to exploit, but their qualities often change over time. Fortunately, many user tasks are delay-tolerant, which offers the network users a favorable degree of freedom in waiting for and accessing the opportunistic resource at the time of its best quality. For such delay-tolerant and opportunistic resource sharing networks (DT-ORS-Net), the corresponding optimal accessing strategies developed in existing literature mainly focus on the single-user scenarios, while the potential competition from other peer users in practical multi-user DT-ORS-Net is often ignored. Considering this, a multi-player Markov stopping game (M-MSG) is developed in this work, and the derived Nash equilibrium (NE) strategy of this M-MSG can guide network users to properly handle the potential competition from other peers and thus exploit the time diversity of the opportunistic resource more effectively, which in turn further improves the resource utilization efficiency. Applications in the cloud-computing and the mobile crowdsourcing networks are demonstrated to verify the effectiveness of the proposed method, and simulation results show that using the NE strategy of the proposed M-MSG can provide substantial performance gain as compared to using the conventional single-user optimal one.

We have seen an emerging trend towards wearables nowadays. In this paper, we focus on smart glasses, whose current interfaces are difficult to use, error-prone, and provide no or insecure user authentication. We thus present GlassGesture, a system that improves Google Glass through a gesture-based user interface, which provides efficient gesture recognition and robust authentication. First, our gesture recognition enables the use of simple head gestures as input. It is accurate in various wearer activities regardless of noise. Particularly, we improve the recognition efficiency significantly by employing a novel similarity search scheme. Second, our gesture-based authentication can identify owner through features extracted from head movements. We improve the authentication performance by proposing new features based on peak analyses, and employing an ensemble method. Last, we implement GlassGesture and present extensive evaluations. GlassGesture achieves a gesture recognition accuracy near 96%. For authentication, GlassGesture can accept authorized users in near 92% of trials, and reject attackers in near 99% of trials. We also show that in 100 trials imitators cannot successfully masquerade as the authorized user even once.

Most of today's data center applications are sensitive to latency. In this paper, a Deadline-aware bandwidth Sharing mechanism by Allocating switch Buffer (DSAB) is proposed to satisfy the deadline requirements of flows. As the existing work SAB does, DSAB also leverages the feature that the buffer size of a switch port is usually much larger than the product of bandwidth and round trip delay in data center networks. The basic idea of DSAB is to allocate switch buffer to deadline-sensitive flows in priority and the remaining buffer is fairly allocated to background flows. However, this method will possibly lead to bandwidth wastage in topologies with multiple bottlenecks. Therefore, enlightened by the pre-authorization method in credit card systems, we propose a Pre-Authorization (PA) algorithm to address this problem. The PA algorithm allows switches take back the extra allocated bandwidth by adding a bandwidth confirmation phase after the bandwidth request phase. End hosts and switch functions of DSAB is implemented in Linux kernel and NetFPGA platform, respectively. The results of experiments conducted in a real small testbed indicate that DSAB can indeed satisfy the deadline requirements of deadline-sensitive flows by allocating switch buffer to them in priority.

Channel rendezvous is a prerequisite for secondary users (SUs) to set up communications in cognitive radio networks (CRNs). It is expected that the rendezvous can be achieved within a short finite time for delay-sensitive applications and over all available channels to increase the robustness to unstable channels. Some existing works suffer from a small number of rendezvous channels and can only guarantee rendezvous under the undesired requirements such as synchronous clock, homogeneous available channels, predetermined roles and explicit SUs' identifiers (IDs). In this paper, to address these limitations, we employ the notion of Disjoint Set Cover (DSC) and propose a DSC-based Rendezvous (DSCR) algorithm. We first present an approximation algorithm to construct one DSC. The variant permutations of elements in the ingeniously constructed DSC are then utilized to regulate the order of accessing channels, enabling SUs to rendezvous on all available channels within a short duration. We derive the theoretical maximum and expected rendezvous latency and prove the full rendezvous degree of the DSCR algorithm. Extensive simulations show that the DSCR algorithm can significantly reduce the rendezvous latency compared to existing algorithms.

Near-duplicate detection (NDD) plays an essential role for effective resource utilization and possible traffic alleviation in many emerging network architectures, leveraging in-network storage for various content-centric services. As innetwork storage grows, data security has become one major concern. Though encryption is viable for in-network data protection, current techniques are still lacking for effectively locating encrypted near-duplicate data, making the benefits of NDD practically invalidated. Besides, adopting encrypted innetwork storage further complicates the user authorization when locating near-duplicate data from multiple content providers under different keys. In this paper, we propose a secure and effective NDD system over encrypted in-network storage supporting multiple content providers. Our design bridges locality-sensitive hashing (LSH) with a newly developed cryptographic primitive, multi-key searchable encryption, which allows the user to send only one encrypted query to access near-duplicate data encrypted under different keys. It relieves the users from multiple rounds of interactions or sending multiple different queries respectively. As simply applying LSH does not ensure the detection quality, we then leverage Yao's garbled circuits to build a secure protocol to obtain highly accurate results, without user-side post-processing. We formally analyze the security strength. Experiments demonstrate our system achieves practical performance with comparable accuracy to plaintext.

Screen-camera links for Visible Light Communication (VLC) are diverse, as the link quality varies according to many factors, such as ambient light and camera's performance. This paper presents SoftLight, a channel coding approach that considers the unique channel characteristics of VLC links and automatically adapts the transmission data rate to the link qualities of various scenarios. SoftLight incorporates two new ideas: (1) an expanded color modulation interface that provides soft hint about its confidence in each demodulated bit and establishes a bit-level VLC erasure channel, and (2) a rateless coding scheme that achieves bit-level rateless transmissions with low computation complexity and tolerates the false positive of bits provided by the soft hint enabled erasure channel. SoftLight is orthogonal to the visual coding schemes and can be applied atop any barcode layouts. We implement SoftLight on Android smartphones and evaluate its performance under a variety of environments. The experiment results show that SoftLight can correctly transmit a 22-KByte photo between two smartphones within 0.6 second and improves the average goodput of the state-of-the-art screen-camera VLC solution by 2.2.

While cellular networks connect over 3.7 billion people worldwide, their availability and quality is not uniform across regions. Under-provisioned and overloaded networks lead to poor network performance and an aggravated user experience. To address this problem we propose HybridCell: a system that leverages locally-owned small-scale cellular networks to augment the operation of overloaded commercial networks. HybridCell is the first system to allow a user with their existing SIM card and mobile phone to seamlessly switch between commercial and local networks in order to maintain continuous connectivity. Hybrid-Cell accomplishes this by identifying poorly-performing networks and taking action to provide seamless cellular connectivity to end users. Using traces collected from observing the cellular infrastructure during our visit to the Za'atari refugee camp in Jordan, we demonstrate HybridCell's capability to detect and act upon commercial network overload, offering an alternate communication channel during times of congestion. We show that even in scenarios where provider networks deny calls due to overload, HybridCell is able to accommodate users and facilitate local calling.

In this paper, we investigate the problem of optimal base station (BS) ON-OFF switching and user association in a heterogeneous network (HetNet) with massive MIMO, with the objective to maximize the system energy efficiency (EE). The joint BS ON-OFF switching and user association problem is formulated as an integer programming problem. We first develop a centralized scheme, in which we relax the integer constraints and employ a series of Lagrangian dual methods that transform the original problem into a standard linear programming (LP) problem. Due to the special structure of the LP, we prove that the optimal solution to the relaxed LP is also feasible and optimal to the original problem. We then propose a distributed scheme by formulating a repeated bidding game for users and BS's, and prove that the game converges to a Nash Equilibrium (NE). Simulation studies demonstrate that the proposed schemes can achieve considerable gains in EE over several benchmark schemes in all the scenarios considered.

Antenna selection, a cost-effective way to enhance network performance, has been employed in a limited manner in wireless local area networks (WLANs) due to the lack of channel information at the transmitter. In this paper, a practical antenna selection system without using channel information is proposed. We first describe the practical issues of antenna selection system for infrastructure-based WLANs, and then, analyze its characteristics through an extensive measurement study. Based on that, we propose antenna selection algorithms of access point (AP) for both (1) unicast transmission and (2) multicast transmission and reception. The proposed algorithms are comparatively evaluated using prototype implementation in a commercial AP. It is demonstrated that the proposed algorithms achieve up to 34% throughput gain and 54% frame error rate reduction for unicast and multicast transmissions, respectively.

Air quality monitoring has attracted a lot of attention from governments, academia and industry, especially for PM2.5due to its significant impact on our respiratory systems. In this paper, we present the design, implementation, and evaluation of Mosaic, a low cost urban PM2.5 monitoring system based on mobile sensing. In Mosaic, a small number of air quality monitoring nodes are deployed on city buses to measure air quality. Current low-cost particle sensors based on light-scattering, however, are vulnerable to airflow disturbance on moving vehicles. In order to address this problem, we build our air quality monitoring nodes, Mosaic-Nodes, with a novel constructive airflow-disturbance design based on a carefully tuned airflow structure and a GPS-assisted filtering method. Further, the buses used for system deployment are selected by a novel algorithm which achieves both high coverage and low computation overhead. The collected sensor data is also used to calculate the PM2.5of locations without direct measurements by an existing inference model. We apply the Mosaic system in a testing urban area which includes more than 70 point-of-interests. Results show that the Mosaic system can accurately obtain the urban air quality with high coverage and low cost.

Aggregation of streamed data is key to the expansion of the Internet of Things. This paper addresses the problem of designing a topology for reliably aggregating data flows from many devices arriving at a datacenter. Reliability here means ensuring operation without data loss. We seek a frugal solution that prevents wasteful resource consumption (over-provisioning). This problem is salient when building an aggregation service out of components (here aggregation nodes) that exhibit hard constraints on the amount of information they can handle per unit of time. We first formalize the problem and provide an analysis of the relation between monitored devices (plus information they send), and the operations performed at aggregation nodes, in terms of data rates. Building on this rate analysis, we devise a novel algorithm, which we call CSA, that basically outputs an aggregation topology capable of handling those incoming data rates, preventing thereby empirical trial-and-error design. We analyze the algorithm, before validating it on the Amazon Kinesis platform, using a device dataset from a European telco operator.

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.

In cloud computing, workloads that lease instances on demand get to execute exclusively for a set time. In contrast, workloads that lease spot instances execute until a competing workload outbids the current lease. Spot instances cost less than on-demand instances, but few workloads can use spot instances because of the variable leasing period. We present BOSS, a framework that uses spot instances to reduce costs for in-memory storage workloads. BOSS uses on-demand instances to create and update objects. It uses spot instances to handle read-only queries. BOSS leases instances from multiple sites and exploits varying prices between the sites. When spot instances stop abruptly at one site, BOSS places newly created objects at other sites, reducing the impact on response time. BOSS proposes a novel, online replication approach (1) avoids placing data at too many sites and (2) provides O(1.5)-competitive ratio under skewed cost distributions. Within a site, BOSS manages the tradeoff between savings and risks from replicating to spot instances. We implemented BOSS on top of Cassandra and deployed it on up to 78 instances across 8 sites in Amazon and Google clouds. With BOSS hosting TPC-W data, we spent $8 per hour on Amazon. For the same service, we spent $55 per hour to use ElastiCache and $49 per hour to use on-demand instances only. BOSS saved 85% and 84% respectively. Further, BOSS achieved 95th percentile response time within 13% of ElastiCache.

Wireless multicast has been exploited to bridge the gap between the limited wireless bandwidth and the rapidly increasing mobile video traffic demand. Multicast of videos to a set of heterogeneous users over multiple wireless access points, however, is challenging because of the trade-offs between high transmission rate, load balancing, and multicast opportunities. In this paper, we present JurCast, a joint user and rate allocation scheme for video multicast over multiple APs. Our approach balances the trade-off between these factors by determining user to Access Points (APs) association, the video resolution version (quality) to be delivered for each session, and the transmission link rate for each video version. The aim of our solution is to maximize the overall received video quality over all users. We have implemented and evaluated our solution on a WiFi testbed as well as the simulation of a large scale deployment. The results indicate that our method considerably outperforms the baseline schemes and achieves up to 3dB and 55% improvements in terms of peak signal-to-noise ratio (PSNR) and goodput, respectively.

Software-Defined Networking (SDN) has emerged as the paradigm of the next-generation networking through separating the data control plane from the data plane. The forwarding routing table at each of its switch nodes is usually implemented by expensive and power-hungry Ternary Content Addressable Memory (TCAM) that only has limited number of entries, and the bandwidth at each of its links is bounded too. Under this new network architecture, providing a quality service to users by admitting user requests to meet their resource demands is challenging, and very little attention has ever been paid in this regard. In this paper, we will study online unicast and multicast request admissions in SDNs with the aim to maximize the network throughput under both critical network resources and user bandwidth demand constraints, for which we first propose a novel model to characterize the usage costs of node and link resources. We then devise efficient online algorithms for unicast and multicast requests. We also analyze the competitive ratios of the proposed online algorithms, which are O(log n) and O(Kϵlog n) for unicasting and multicasting, respectively, where n is the network size, K is the maximum number of members in a multicast request, and ϵ is a constant with 0 <; e ≤ 1. We finally evaluate the proposed algorithms empirically through simulations. The simulation results demonstrate that the proposed algorithms are very promising.

Erasure coding has been applied in many cloud storage systems to enhance reliability at a lower storage cost than replication. While a large amount of prior work aims to enhance recovery performance and reliability, the overall access delay in coded storage still needs to be optimized. As most production systems adopt a systematic code and place the original copy of each block on only one server to be read normally, it is harder to balance server loads and more likely to incur latency tails in coded storage than in three-way replication, where a block can be read from any of the 3 servers storing the block. In this paper, we propose to reduce the access latency in coded storage systems by moving blocks with anti-correlated demands onto same servers for statistical load balancing. We formulate the optimal block placement as a problem similar to Min-k-Partition, propose a local block migration scheme, and derive an approximation ratio as a function of demand variation across blocks. Based on request traces from Windows Azure Storage, we demonstrate that our scheme can significantly reduce the access latency with only a few block moves, especially when the request demand is skewed.

Time-varying graphs are a useful model for networks with dynamic connectivity such as vehicular networks, yet, despite their great modeling power, many important features of time-varying graphs are still poorly understood. In this paper, we study the survivability properties of time-varying networks against unpredictable interruptions. We first show that the traditional definition of survivability is not effective in time-varying networks, and propose a new survivability framework. To evaluate the survivability of time-varying networks under the new framework, we propose two metrics that are analogous to MaxFlow and MinCut in static networks. We show that some fundamental survivability-related results such as Menger's Theorem only conditionally hold in time-varying networks. Then we analyze the complexity of computing the proposed metrics and develop approximation algorithms. Finally, we conduct trace-driven simulations to demonstrate the application of our survivability framework in the robust design of a real-world bus communication network.