INFOCOM 2016 论文列表

Network Function Virtualization (NFV) is emerging as a new paradigm for providing elastic network functions through flexible virtual network function (VNF) instances executed on virtualized computing platforms exemplified by cloud datacenters. In the new NFV market, well defined VNF instances each realize an atomic function that can be chained to meet user demands in practice. This work studies the dynamic market mechanism design for the transaction of VNF service chains in the NFV market, to help relinquish the full power of NFV. Combining the techniques of primal-dual approximation algorithm design with Myerson's characterization of truthful mechanisms, we design a VNF chain auction that runs efficiently in polynomial time, guarantees truthfulness, and achieves near-optimal social welfare in the NFV eco-system. Extensive simulation studies verify the efficacy of our auction mechanism.

The Internet is made of almost 50, 000 ASes exchanging routing information thanks to BGP. Inside each AS, information is redistributed via iBGP sessions. This allows each router to map a destination exterior to the AS with a given egress point. The main redistribution mechanisms used today, (iBGP full mesh, Route Reflectors and BGP confederations), either guarantee selection of the best egress point or enhance scalability, but not both. In this paper, we propose a new way to perform iBGP redistribution in an AS based on its IGP topology, conciliating optimality in route selection and scalability. Our contribution is threefold. First, we demonstrate the tractability of our approach and its benefits. Second, we provide an open-source implementation of our mechanism based on Quagga. Third, we illustrate the feasibility of our approach through simulations performed under ns-3 and compare its performance with full mesh and Route Reflection.

Densely-deployed femtocell networks are used to enhance wireless coverage in public spaces like office buildings, subways, and academic buildings. These networks can increase throughput for users, but edge users can suffer from co-channel interference, leading to service outages. This paper introduces a distributed algorithm for network configuration, called Radius Reduction and Scheduling (RRS), to improve the performance and fairness of the network. RRS determines cell sizes using a Voronoi-Laguerre framework, then schedules users using a scheduling algorithm that includes vacancy requests to increase fairness in dense femtocell networks. We prove that our algorithm always terminate in a finite time, producing a configuration that guarantees user or area coverage. Simulation results show a decrease in outage probability of up to 50%, as well as an increase in Jain's fairness index of almost 200%.

Network functions are increasingly being commoditized as software appliances on off-the-shelf machines, popularly known as Network Functions Virtualization (NFV). While this trend provides economics of scale, a key challenge is to ensure that the performance of virtual appliances match that of hardware boxes. We present the design and implementation of NFV-RT, a system that dynamically provisions resources in an NFV environment to provide timing guarantees. Specifically, given a set of service chains that each consist of some network functions, NFV-RT aims at maximizing the total number of requests that can be assigned to the cloud for each service chain, while ensuring that the assigned requests meet their deadlines. Our approach uses a linear programming model with randomized rounding to efficiently and proactively obtain a near-optimal solution. Our simulation shows that, given a cloud with thousands of machines and service chains, NFV-RT requires only a few seconds to compute the solution, while accepting three times the requests compared to baseline heuristics. In addition, under some special settings, NFV-RT can provide significant performance improvement. Our evaluation on a local testbed shows that 94% of the packets of the submitted requests meet their deadlines, which is three times that of previous reactive-based solutions.

This paper investigates divide-and-conquer based cooperative jamming for physical-layer security enhancement in the presence of multiple eavesdroppers. Different from previous works, we consider a scenario where the eavesdroppers can be located anywhere inside the communication region of the source, no location information of the eavesdroppers is available and no constraint on the number of eavesdroppers is presupposed. The basic idea is to transmit the message in multiple rounds and exploit the helpful interference from the source and the destination to jam the eavesdroppers in close proximity. Stochastic geometry based analytic results as well as Monte Carlo simulations are presented to illustrate the achievable secrecy performances.

There has been a proliferation of public Wi-Fi hotspots that serve a significant amount of global mobile traffic today. In this paper, we propose a general Wi-Fi monetization model for public Wi-Fi hotspots deployed by venue owners (VOs), where VOs generate revenue from providing both the premium Wi-Fi access and the advertising sponsored Wi-Fi access to mobile users (MUs). With the premium access, MUs directly pay VOs for their Wi-Fi usage; while with the advertising sponsored access, MUs watch advertisements for the free usage of Wi-Fi. VOs sell their ad spaces to advertisers (ADs) via an ad platform, and share a proportion of the revenue with the ad platform. We formulate the economic interactions among the ad platform, VOs, MUs, and ADs as a three-stage Stackelberg game. By analyzing the equilibrium, we show that the ad platform's advertising revenue sharing policy affects a VO's Wi-Fi price but not the VO's advertising price. Moreover, we prove that a single term called equilibrium indicator determines whether a VO will fully rely on the premium access, or fully rely on the advertising sponsored access, or obtain revenue from both types of access. Numerical results show that the VO obtains a large revenue under a large advertising concentration level and a medium MU visiting frequency.

Opportunistic mobile networks consisting of intermittently connected mobile devices have been exploited for various applications, such as computational offloading and mitigating cellular traffic load. Different from existing work, in this paper, we focus on cooperatively offloading data among mobile devices to maximally improve the probability of data delivery from a mobile device to an intermittently connected remote server or data center within a given time constraint, which is referred to as the cooperative offloading problem. Unfortunately, cooperative offloading is NP-hard. To this end, a heuristic algorithm is designed based on the proposed probabilistic framework, which provides the estimation of the probability of successful data delivery over the opportunistic path, considering both data size and contact duration. Due to the lack of global information, a distributed algorithm is further proposed. The performance of the proposed approaches is evaluated based on both synthetic networks and real traces, and simulation results show that cooperative offloading can significantly improve the data delivery probability and the performance of both heuristic algorithm and distributed algorithm outperforms other approaches.

Privacy has become a major concern in wireless networks, especially in networks with weak or no password protection. While it has been proposed to send jamming signals to thwart the reception of the eavesdropper, the jamming signal typically cannot introduce enough errors to cover the entire packet when the jamming signal is not very strong, mainly due to the use of error correction codes in the system. Fortunately, error amplifiers can be easily constructed using block ciphers to amplify and spread the errors without requiring any secret keys. With error amplification, the jamming strategy needs to be revisited, because introducing a few errors is sufficient protect the privacy of the entire packet, instead of having to jam the entire packet. We propose a novel jamming strategy, called jMax, with proven performance bounds to the optimal jamming signal power. The idea of jMax is to rotate jamming vectors such that the jamming power can be maximized when the jamming vector close to the optimal jamming vector is used. We collect real-world CSI data with the Intel 5300 wireless card and use the Microsoft Sora Software-Defined Radio to process jammed data packets. Our results show that jMax achieves significant gains over other approaches.

Fast and efficient recovery from node failure, with minimal disruption of routes and the consequent traffic loss is of the utmost importance for any routing protocol. Link-state protocols, albeit preferred to distance vector ones because of faster convergence, still suffer from a trade-off between control message overhead and performance. This work formalizes the routes' disruption following a node failure as an optimization problem depending on the nodes' centrality in the topology, constrained to a constant signaling overhead. Next, it shows that the solution can be found using Lagrange Multipliers. The solution complexity is low enough to be computed on-line on the network routers, thus obtaining the optimal setting of control message timers that minimize the traffic loss following a node failure. The gain obtained is quantified in power-law synthetic topologies, and it is also tested on real network topologies extending the OLSR protocol to use the modified timers, showing that the inevitable approximations introduced in the analysis do not hamper the very good results achievable through this novel approach. The technique can be applied to any link state protocol, including OSPF, and improves route convergence not only upon failures but on every topology modification.

We propose a graph-based framework for privacy preserving data publication, which is a systematic abstraction of existing anonymity approaches and privacy criteria. Graph is explored for dataset representation, background knowledge specification, anonymity operation design, as well as attack inferring analysis. The framework is designed to accommodate various datasets including social networks, relational tables, temporal and spatial sequences, and even possible unknown data models. The privacy and utility measurements of the anonymity datasets are also quantified in terms of graph features. Our experiments show that the framework is capable of facilitating privacy protection by different anonymity approaches for various datasets with desirable performance.

LTE-Advanced (LTE-A) macro-cell deployments are being enhanced with small cells, i.e., low-power base stations, to increase the network coverage and capacity. However, simultaneous co-channel transmissions from macro and small cells cause increased inter-cell interference and under-utilize the spectrum resources at the small cells. The following LTE-A design techniques are used to improve system performance in such deployments: (i) Carrier Aggregation (CA) to increase capacity by using additional carrier bandwidth; (ii) enhanced Inter-Cell Interference Coordination (elCIC), that includes (a) Cell Selection Biasing (CSB) to increase small cell spectrum utilization via cell range expansion; and (b) blanking data transmission on the macro cells for a certain duration of time to increase cell-edge user throughput Our objective is to maximize the CSB of the small cell, subject to user QoS constraints and blanking support from the macro cell. Towards this end, we develop an analytical model that captures the inter-dependency between elCIC techniques. We observe that, not accounting for the complex inter-dependencies between these techniques leads to a degraded network performance. We propose a framework that jointly optimizes elCIC and the assignment of multiple component carriers in an LTE-A deployment for increasing spectrum utilization at the small cells with appropriate blanking support from the macro cells. Our simulation results show that our approach increases the small cell spectrum utilization and aggregate cell-edge throughput by as much as 200%.

To steadily gaining benefit from the exponential growth in mobile traffic, operators are eager to find solutions to maximize profits. Two very attractive strategies have been proposed to complement the existing macro cellular architecture: deploying low power bases stations and offloading data traffic to other networks. Each strategy has different costs and yields different benefits for operators. The offloading option could be cheaper in the short run; nevertheless, it might be more expensive in the long run than cell densification due to the varying cost. On the other hand, small cells, since having to be deployed in advance, may be underutilized or not fully meet future demands. In the latter case, offloading techniques can be used to increase capacity with additional costs. Further, uncertainty of future data demands and electricity prices also impact operators profitability, making the best network strategy difficult to achieve. To address such problem, an optimal cell configuration algorithm is proposed by formulating a stochastic programming model that considers both network design and data offloading. This algorithm can maximize the profit, under future demand and price uncertainty. Numerical studies are extensively performed in which the results show that operators' profits can be improved with our proposed algorithm.

Given the increasing popularity of wearable devices, this paper explores the potential to use wearables for steering and driver tracking. Such capability would enable novel classes of mobile safety applications without relying on information or sensors in the vehicle. In particular, we study how wrist-mounted inertial sensors, such as those in smart watches and fitness trackers, can track steering wheel usage and angle. In particular, tracking steering wheel usage and turning angle provide fundamental techniques to improve driving detection, enhance vehicle motion tracking by mobile devices and help identify unsafe driving. The approach relies on motion features that allow distinguishing steering from other confounding hand movements. Once steering wheel usage is detected, it further uses wrist rotation measurements to infer steering wheel turning angles. Our on-road experiments show that the technique is 99% accurate in detecting steering wheel usage and can estimate turning angles with an average error within 3.4 degrees.

The connected vehicles have been considered as a remedy for modern traffic issues, potentially saving hundreds of thousands of lives every year worldwide. The Dedicated Short-Range Communications (DSRC) technology is an essential building block of this promising vision. DSRC faces volatile vehicular environments, where not only wireless propagation channels but also network topologies vary rapidly. Moreover, traffic congestions during rush hours may lead to an unprecedentedly high density of broadcasting radios, resulting in compromised reliability, efficiency and fairness of DSRC. In order to optimize the performance of DSRC, we develop a novel Online Control Approach of power and Rates (OnCAR). Supported by systematic control theories, OnCAR performs stably even in the dynamic and unpredictable vehicular environments. To the best of our knowledge, OnCAR is the first solution to address the strong coupling between communication variables. It adopts a multi-variable control model to synchronously adjust transmission power and data rates, which are two major variables determining the performance of DSRC. In addition, OnCAR leverages receiver-side measurements of performance metrics to strike a balance between overall performance and fairness. Compared with the state of the art, OnCAR enhances the overall reliability and efficiency of DSRC by 23.7% and 30.1%, respectively. Meanwhile, these numbers are achieved with a 40.1% improvement in fairness.

This paper aims at designing an ecoconscious future-proof sensor enhanced fiber-wireless (SFiWi) network based on EPON, WLAN, wireless sensor (WS), and fiber optic sensor (FOS) technologies as a shared communications infrastructure for broadband access and smart grids. A total cost of ownership (TCO) model is developed to help utilities decide whether to deploy WSs or FOSs in different scenarios and estimate sensor-related costs. To prolong battery life of wireless devices and maximize the overall energy efficiency, a novel energy conservation scheme for SFiWi networks (ECO-SFiWi) is proposed. ECO-SFiWi designs the whole network in three TDMA layers to enhance network performance, while scheduling network components to sleep outside their transmission slots. A comprehensive energy saving model accounting for both optical backhaul and wireless front-end components and a delay analysis based on M/G/1 queuing are presented. Results reveal that with their extremely long lifetime and ability to sustain in harsh environments, FOSs are superior to WSs when advanced interrogation techniques are deployed to reduce their total cost. ECO-SFiWi achieves more than 89% of energy savings, while maintaining low delay for both broadband and smart grid traffic in typical scenarios. FPGA hardware emulation and analytical results match well verifying the effectiveness of ECO-SFiWi.

Network virtualization with bandwidth guarantee is essential for the performance predictability of cloud applications because of the shared multi-tenant nature of the cloud. Several virtual network abstractions have been proposed for the tenants to specify and reserve their virtual clusters with bandwidth guarantee. However, they require pre-determined fixed cluster size and bandwidth, and do not support the scaling of the cluster in size and bandwidth requirements. On the other hand, the existing works on virtual cluster scaling focus on dynamically adjusting the cluster size without considering any bandwidth guarantee targeted by current network abstractions. To fill the gap, this paper considers the problem of scaling up a virtual network abstraction with bandwidth guarantee. Efficient algorithms are proposed to find the valid allocation for the scaled cluster abstraction with optimization on the VM locality of the cluster. We also point out the case that a virtual cluster cannot be scaled without changing its original VM placement, and propose an optimal allocation algorithm that exploits the VM migration to address this issue while minimizing the total migration cost for the virtual cluster scaling. Extensive simulations demonstrate the effectiveness and efficiency of our algorithms.

We present a study on minimizing non-renewable energy for the Internet. The classification of renewable and non-renewable energy brings in several challenges. First, it is necessary to understand how the routing system can distinguish the two types of energy in the power supply. Second, the routing problem changes due to renewable energy; and so do the algorithm designs and analysis. We first clarify the model of how routers can distinguish renewable and non-renewable energy supporting their power supply. This cannot be determined by the routing system alone, and involves modeling the energy generation and supply of the grid. We then present the router power consumption model, which has a fixed startup power and a dynamic traffic-dependent power. We formulate a minimum non-renewable energy routing problem, and two special cases representing either the startup power dominates or the traffic-dependent power dominates. We analyze the complexity of these problems, develop optimal and sub-optimal algorithms, and jointly consider QoS requirements such as path stretch. We evaluate our algorithms using real data from both National and European centers. As compared to the algorithms minimizing the total energy, our algorithms can reduce the non-renewable energy consumption for more than 20% under realistic assumptions.

Most distributed wireless scheduling schemes that are provably efficient have been developed under the protocol model, which describes interference constraints in a binary form. However, the oversimplified interference model imposes fundamental limitations on the performance in practice. The signal-to-interference-plus-noise-ratio (SINR) based interference model is more accurate and realistic accounting for the cumulative nature of the interference signals, but its complex structure makes the design of scheduling schemes much more challenging. In this paper, we focus on the scheduling performance under the SINR model and develop random access scheduling schemes that are amenable to implement in a distributed fashion with only local information. We analytically show that they are provably efficient under the SINR model, and through simulations demonstrate that they empirically perform better than the theoretical performance bound.

As mobile technology grows rapidly, the smartphone has become indispensable for transmitting private user data, storing the sensitive corporate files, and conducting secure payment transactions. However, with mobile security research lagging, smartphones are extremely vulnerable to unauthenticated access. In this paper, we present, EyeVeri, a novel eye-movement-based authentication system for smartphone security protection. Specifically, EyeVeri tracks human eye movement through the built-in front camera and applies the signal processing and pattern matching techniques to explore volitional and non-volitional gaze patterns for access authentication. Through a comprehensive user study, EyeVeri performs well and is a promising approach for smartphone user authentication. We also discuss the evaluation results in-depth and analyze opportunities for future work.