Showing papers presented at "International Workshop on Quality of Service in 2016"

An experimental investigation of hyperbolic routing with a smart forwarding plane in NDN

Abstract: Routing in NDN networks must scale in terms of forwarding table size and routing protocol overhead. Hyperbolic routing (HR) presents a potential solution to address the routing scalability problem, because it does not use traditional forwarding tables or exchange routing updates upon changes in network topologies. Although HR has the drawbacks of producing sub-optimal routes or local minima for some destinations, these issues can be mitigated by NDN's intelligent data forwarding plane. However, HR's viability still depends on both the quality of the routes HR provides and the overhead incurred at the forwarding plane due to HR's sub-optimal behavior. We designed a new forwarding strategy called Adaptive Smoothed RTT-based Forwarding (ASF) to mitigate HR's sub-optimal path selection. This paper describes our experimental investigation into the packet delivery delay and overhead under HR as compared with Named-Data Link State Routing (NLSR), which calculates shortest paths. We run emulation experiments using various topologies with different failure scenarios, probing intervals, and maximum number of next hops for a name prefix. Our results show that HR's delay stretch has a median close to 1 and a 95th-percentile around or below 2, which does not grow with the network size. HR's message overhead in dynamic topologies is nearly independent of the network size, while NLSR's overhead grows polynomially at least. These results suggest that HR offers a more scalable routing solution with little impact on the optimality of routing paths.

TrafficAV: An effective and explainable detection of mobile malware behavior using network traffic

Abstract: Android has become the most popular mobile platform due to its openness and flexibility. Meanwhile, it has also become the main target of massive mobile malware. This phenomenon drives a pressing need for malware detection. In this paper, we propose TrafficAV, which is an effective and explainable detection of mobile malware behavior using network traffic. Network traffic generated by mobile app is mirrored from the wireless access point to the server for data analysis. All data analysis and malware detection are performed on the server side, which consumes minimum resources on mobile devices without affecting the user experience. Due to the difficulty in identifying disparate malicious behaviors of malware from the network traffic, TrafficAV performs a multi-level network traffic analysis, gathering as many features of network traffic as necessary. The proposed method combines network traffic analysis with machine learning algorithm (C4.5 decision tree) that is capable of identifying Android malware with high accuracy. In an evaluation with 8,312 benign apps and 5,560 malware samples, TCP flow detection model and HTTP detection model all perform well and achieve detection rates of 98.16% and 99.65%, respectively. In addition, for the benefit of user, TrafficAV not only displays the final detection results, but also analyzes the behind-the-curtain reason of malicious results. This allows users to further investigate each feature's contribution in the final result, and to grasp the insights behind the final decision.

Dynamic flow scheduling for Power-efficient Data Center Networks

Abstract: Power-efficient Data Center Networks (DCNs) have been proposed to save power of DCNs using OpenFlow. In these DCNs, the OpenFlow controller adaptively turns on and off links and OpenFlow switches to form a minimum-power subnet that satisfies traffic demand. As the subnet changes, flows are scheduled dynamically to routes composed of active switches and links. However, existing flow scheduling schemes could cause undesired results: (1) power inefficiency: due to unbalanced traffic allocation on active routes, extra switches and links may be activated to cater to bursty traffic surges on congested routes, and (2) Quality of Service (QoS) fluctuation: because of the limited flow entry processing ability, switches cannot timely install/delete/update flow entries to properly schedule flows. In this paper, we propose AggreFlow, a dynamic flow scheduling scheme that achieves power efficiency in DCNs and improved QoS using two techniques: Flow-set Routing and Lazy Rerouting. Flow-set Routing achieves load balancing and reduces the number of entry installment on switches by routing flows in a coarse-grained flow-set fashion. Lazy Rerouting maintains load balancing and spreads rerouting operations over a relatively long period of time, reducing the burstiness of entry installment/deletion/update on switches. We built a NS3 based fat-tree network simulation platform to evaluate AggreFlow's performance. The simulation results show AggreFlow reduces power consumption by about 18%, achieves load balancing and improved QoS (i.e., low packet loss rate and reducing the number of processing entries for flow scheduling by 98%), compared with baseline schemes.

Certificate-aware encrypted traffic classification using Second-Order Markov Chain

Abstract: With the prosperity of network applications, traffic classification serves as a crucial role in network management and malicious attack detection. The widely used encryption transmission protocols, such as the Secure Socket Layer/Transport Layer Security (SSL/TLS) protocols, leads to the failure of traditional payload-based classification methods. Existing methods for encrypted traffic classification suffer from low accuracy. In this paper, we propose a certificate-aware encrypted traffic classification method based on the Second-Order Markov Chain. We start by exploring reasons why existing methods not perform well, and make a novel observation that certificate packet length in SSL/TLS sessions contributes to application discrimination. To increase the diversity of application fingerprints, we develop a new model by incorporating the certificate packet length clustering into the Second-Order homogeneous Markov chains. Extensive evaluation results show that the proposed method lead to a 30% improvement on average compared with the state-of-the-art method, in terms of classification accuracy.

Demystifying the energy efficiency of Network Function Virtualization

Abstract: Middleboxes are prevalent in today's enterprise and data center networks. Network function virtualization (NFV) is a promising technology to replace dedicated hardware middleboxes with virtualized network functions (VNFs) running on commodity servers. However, no prior study has examined the energy efficiency of different NFV implementations. In this paper, we conduct a measurement study on the power efficiency of software data planes, the virtual I/O and the software middleboxes, which are important parts of the NFV implementations. We run two popular software middleboxes (Snort and Bro) on three common software data planes (i.e., DPDK-OVS, Click Modular Router and Netmap). Our results show significant differences on power among those different NFV implementations. We analyze the underlying design choices and give implications on how to build more power efficient NFV implementations.

Towards QoE assessment of encrypted YouTube adaptive video streaming in mobile networks

Abstract: Video streaming has become one of the most prevalent mobile applications, and takes a huge portion of the traffic on mobile networks today. YouTube is one of the most popular and volume-dominant video content providers. Understanding the user perception on the quality (i.e., Quality of Experience or QoE) of YouTube video streaming services is thus paramount for the content provider as well as its content delivery network (CDN) providers. Although various video QoE assessment approaches proposed to use different Key Performance Indicators (KPIs), they are all essentially related to a common parameter: Bitrate. However, after YouTube adopted HTTPS as its adaptive video streaming method to better protect user privacy and network security, bitrate cannot be obtained anymore from encrypted video traffic via typical deep packet inspection (DPI) method. In this paper, we tackle this challenge by proposing a machine learning based bitrate estimation (MBE) approach to parse bitrate information from IP packet level measurement. For evaluating the effectiveness of MBE, we have chosen video Mean Opinion Score (vMOS) proposed by a leading telecom vendor, as the QoE assessment framework, and have conducted comprehensive experiments to study the impact of bitrate estimation accuracy on its KPIs for HTTPS YouTube video streaming service. Experimental results show that MBE is a feasible and highly effective approach to obtain in real time the bitrate information from encrypted video streaming traffic.

Enhancing participant selection through caching in mobile crowd sensing

Abstract: With the rapid increasing of smart phones and their embedded sensing technologies, mobile crowd sensing (MCS) becomes an emerging sensing paradigm for performing large-scale sensing tasks. One of the key challenges of large-scale mobile crowd sensing systems is how to effectively select the minimum set of participants from the huge user pool to perform the tasks and achieve certain level of coverage. In this paper, we introduce a new MCS architecture which leverages the cached sensing data to fulfill partial sensing tasks in order to reduce the size of selected participant set. We present a newly designed participant selection algorithm with caching and evaluate it via extensive simulations with a real-world mobile dataset.

Selecting most informative contributors with unknown costs for budgeted crowdsensing

Abstract: Mobile crowdsensing has become a novel and promising paradigm in collecting environmental data. A critical problem in improving the QoS of crowdsensing is to decide which users to select to perform sensing tasks, in order to obtain the most informative data, while maintaining the total sensing costs below a given budget. The key challenges lie in (i) finding an effective measure of the informativeness of users' data, (ii) learning users' sensing costs which are unknown a priori, and (iii) designing efficient user selection algorithms that achieve low-regret guarantees. In this paper, we build Gaussian Processes (GPs) to model spatial locations, and provide a mutual information-based criteria to characterize users' informativeness. To tackle the second and third challenges, we model the problem as a budgeted multi-armed bandit (MAB) problem based on stochastic assumptions, and propose an algorithm with theoretically proven low-regret guarantee. Our theoretical analysis and evaluation results both demonstrate that our algorithm can efficiently select most informative users under stringent constraints.

MCTCP: Congestion-aware and robust multicast TCP in Software-Defined networks

Abstract: Continuously enriched distributed systems in data centers generate much network traffic in push-style one-to-many group mode, raising new requirements for multicast transport in terms of efficiency and robustness. Existing reliable multicast solutions, which suffer from low robustness and inefficiency in either host-side protocols or multicast routing, are not suitable for data centers. In order to address the problems of inefficiency and low robustness, we present a sender-initiated, efficient, congestion-aware and robust reliable multicast solution mainly for small groups in SDN-based data centers, called MCTCP. The main idea behind MCTCP is to manage the multicast groups in a centralized manner, and reactively schedule multicast flows to active and low-utilized links, by extending TCP as the host-side protocol and managing multicast groups in the SDN-controller. The multicast spanning trees are calculated and adjusted according to the network status to perform a better allocation of resources. Our experiments show that, MCTCP can dynamically bypass the congested and failing links, achieving high efficiency and robustness. As a result, MCTCP outperforms the state-of-the-art reliable multicast schemes. Moreover, MCTCP improves the performance of data replication in HDFS compared with the original and TCP-SMO based ones, e.g., achieves 101% and 50% improvements in terms of bandwidth, respectively.

Dynamic resource scheduling in cloud radio access network with mobile cloud computing

Abstract: Nowadays, by integrating the cloud radio access network (C-RAN) with the mobile cloud computing (MCC) technology, mobile service provider (MSP) can efficiently handle the increasing mobile traffic and enhance the capabilities of mobile users' devices to provide better quality of service (QoS). But the power consumption has become skyrocketing for MSP as it gravely affects the profit of MSP. Previous work often studied the power consumption in C-RAN and MCC separately while less work had considered the integration of C-RAN with MCC. In this paper, we present a unifying framework for optimizing the power-performance tradeoff of MSP by jointly scheduling network resources in C-RAN and computation resources in MCC to minimize the power consumption of MSP while still guaranteeing the QoS for mobile users. Our objective is to maximize the profit of MSP. To achieve this objective, we first formulate the resource scheduling issue as a stochastic problem and then propose a Resource onlIne sCHeduling (RICH) algorithm using Lyapunov optimization technique to approach a time average profit that is close to the optimum with a diminishing gap (1/V) for MSP while still maintaining strong system stability and low congestion to guarantee the QoS for mobile users. With extensive simulations, we demonstrate that the profit of RICH algorithm is 3.3× (18.4×) higher than that of active (random) algorithm.

Toward online virtual network function placement in Software Defined Networks

Abstract: Network function virtualization (NFV) and Software Defined Networks (SDN) separate and abstract network functions from underlying hardware, creating a flexible virtual networking environment that reduces cost and allows policy-based decisions. One of the biggest challenges in NFV-SDN is to map the required virtual network functions (VNFs) to the underlying hardware in substrate networks in a timely manner. In this paper, we formulate the VNF placement problem via Graph Pattern Matching, with an objective function that can be easily adapted to fit various applications. Previous work only considers off-line VNF placement as it is time consuming to find an appropriate mapping path while considering all software and hardware constraints. To reduce this time, we investigate the feasibility and effectiveness of path-precomputing, where paths are calculated prior to placement. Our approach enables online VNF placement in SDNs, allowing VNF requests to be processed as they arrive. An online placement approach (OPA) is proposed to place VNF requests on substrate networks. To the best of our knowledge, this is the first work in the literature that considers the online chaining VNF placement in SDNs. In addition, we present an application of OPA over cost minimization. Simulation results demonstrate that our online approach provides competitive performance compared with off-line algorithms.

Online influence maximization in non-stationary Social Networks

Abstract: Social networks have been popular platforms for information propagation. An important use case is viral marketing: given a promotion budget, an advertiser can choose some influential users as the seed set and provide them free or discounted sample products; in this way, the advertiser hopes to increase the popularity of the product in the users' friend circles by the world-of-mouth effect, and thus maximizes the number of users that information of the production can reach. There has been a body of literature studying the influence maximization problem. Nevertheless, the existing studies mostly investigate the problem on a one-off basis, assuming fixed known influence probabilities among users, or the knowledge of the exact social network topology. In practice, the social network topology and the influence probabilities are typically unknown to the advertiser, which can be varying over time, i.e., in cases of newly established, strengthened or weakened social ties. In this paper, we focus on a dynamic non-stationary social network and design a randomized algorithm, RSB, based on multi-armed bandit optimization, to maximize influence propagation over time. The algorithm produces a sequence of online decisions and calibrates its explore-exploit strategy utilizing outcomes of previous decisions. It is rigorously proven to achieve an upper-bounded regret in reward and applicable to large-scale social networks. Practical effectiveness of the algorithm is evaluated using real-world datasets, which demonstrates that our algorithm outperforms previous stationary methods under non-stationary conditions.

Collusion-resisting secure nearest neighbor query over encrypted data in cloud, revisited

Abstract: It is a challenging problem to securely resist the collusion of cloud server and query users while implementing nearest neighbor query over encrypted data in cloud. Recently, CloudBI-II is put forward to support nearest neighbor query on encrypted cloud data, and declared to be secure while cloud server colludes with some untrusted query users. In this paper, we propose an efficient attack method which indicates CloudBI-II will reveal the difference vectors under the collusion attack. Further, we show that the difference vector disclosure will result in serious privacy breach, and thus attain an efficient attack method to break CloudBI-II. Namely, CloudBI-II cannot achieve their declared security. Through theoretical analysis and experiment evaluation, we confirm our proposed attack approach can fast recover the original data from the encrypted data set in CloudBI-II. Finally, we provide an enhanced scheme which can efficiently resist the collusion attack.

Truthful incentive mechanism for vehicle-based nondeterministic crowdsensing

Abstract: Nowadays, vehicles have shown great potential in crowdsensing. To guarantee a good Quality of Service (QoS), stimulating enough vehicles to participate in crowdsensing is very necessary. In this paper, we focus on the incentive mechanism design in the vehicle-based nondeterministic crowdsensing. Different from existing works, we take into consideration that each vehicle performs sensing tasks along some trajectories with different probabilities, and each task must be successfully performed with a joint probability no less than a threshold. Designing an incentive mechanism for such a nondeterministic crowdsensing system is challenging, which contains a non-trivial set cover problem with non-linear constraints. To solve the problem, we propose a truthful incentive mechanism based on reverse auction, including an approximation algorithm to select winning bids with a nearly minimum social cost, and a payment algorithm to determine the payments for all participants. Through theoretical analysis, we prove that our incentive mechanism is truthful and individual rational, and we give an approximation ratio of the winning bid selection algorithm. In addition, we conduct extensive simulations, based on a real vehicle trace, to validate the performances of the proposed incentive mechanism.

sdnMAC: A software defined networking based MAC protocol in VANETs

Abstract: In this paper, we propose a hierarchical architecture based on software defined networking (SDN) to manage the physical resources in vehicular ad-hoc networks (VANETs), namely sdnMAC. First of all, a novel roadside unit (denoted by ROFS) is designed, which is an OpenFlow switch equipped with a wireless interface. Then, a hierarchical architecture is proposed for sdnMAC, consisting of two tiers, one is the management of the ROFSs by the Controller, the other is management of vehicles by ROFSs. Due to the cooperative share of slots information, sdnMAC can provide pre-warning of collisions and agility to topology change and varying densities of vehicles.

Coverage and distinguishability requirements for Traffic Flow Monitoring Systems

Abstract: Traffic flow monitoring systems aim to measure and monitor vehicle trajectories in smart cities. Their critical applications include vehicle theft prevention, vehicle localization, and traffic congestion solution. This paper studies an RoadSide Unit (RSU) placement problem in traffic flow monitoring systems. Given some traffic flows on streets, the objective is to place a minimum number of RSUs to cover and distinguish all traffic flows. A traffic flow is covered and distinguishable, if the set of its passing RSUs is non-empty and unique among all traffic flows. The RSU placement problem is NP-hard, monotonic, and non-submodular. It is a non-trivial extension of the traditional set cover problem that is submodular. We show that, to cover and distinguish an arbitrary pair of traffic flows (ƒ and ƒ′), two RSUs should be placed on streets from two different subsets of ƒ∖ƒ′, ƒ′∖ƒ, and ƒ ⋂ ƒ′. Three bounded RSU placement algorithms are proposed. Their approximation ratios are n ln n(n−1)/2, n+1/2 ln 3n(n−1)/2, and ln n(n+1)/2, respectively. Here, n is the number of given traffic flows. Extensive real data-driven experiments demonstrate the efficiency and effectiveness of the proposed algorithms.

HybridFlow: A lightweight control plane for hybrid SDN in enterprise networks

Abstract: Software-Defined Networking (SDN) has great potentials in changing the fragile and complex enterprise networks. One operational challenge to SDN deployment is the settlement of legacy switches. A hybrid SDN consisting of both SDN and legacy switches may be a tradeoff. Nevertheless most of the current SDN control planes can not handle legacy switches. To overcome this problem, we present HybridFlow, a lightweight control plane for hybrid SDN. HybridFlow can abstract a hybrid network into a logical SDN network and existing SDN control applications can run on it transparently.

Multicast routing with uncertain sources in software-defined network

Abstract: Multicast is designed to jointly deliver content from a single source to a set of destinations. It can efficiently save the bandwidth consumption and reduce the load on the source. The appearance of SDN provides opportunities to deploy flexible protocols, including multicast and its variants. However, in many important applications, it is not necessary that the source of a multicast transfer has to be in specific location as long as certain constraints are satisfied. Such facts bring a novel multicast with uncertain sources, abbreviated as uncertain multicast. It brings new opportunities and challenges to reduce the bandwidth consumption. In this paper, we focus on the uncertain multicast and construct a forest with the minimum cost (MCF), to enable that each destination reaches to one and only one source. Prior approaches, relying on traditional multicast, remain inapplicable to the MCF problem. Therefore, we propose two (2+e)-approximation methods, named P-MCF and E-MCF, which can be deployed in SDN controllers. We conduct experiments on our SDN testbed together with large-scale simulations under the random SDN network. All manifest that our MCF approach always occupies less network links and incurs less network cost for an uncertain multicast than the traditional Steiner minimum tree (SMT) of any related multicast, irrespective of the used network topology and the setting of multicast transmissions.

Your trajectory privacy can be breached even if you walk in groups

Abstract: The enterprise Wi-Fi networks enable the collection of large-scale users' mobility information at an indoor level. The collected trajectory data is very valuable for both research and commercial purposes, but the use of the trajectory data also raises serious privacy concerns. A large body of work tries to achieve k-anonymity (hiding each user in an anonymity set no smaller than k) as the first step to solve the privacy problem. Yet it has been qualitatively recognized that k-anonymity is still risky when the diversity of the sensitive information in the k-anonymity set is low. There, however, still lacks a study that provides a quantitative understanding of that risk in the trajectory dataset. In this work, we present a large-scale measurement based analysis of the low-diversity risk over four weeks of trajectory data collected from Tsinghua, a campus that covers an area of 4 km2, on which 2,670 access points are deployed in 111 buildings. Using this dataset, we highlight the high risk of the low diversity. For example, we find that even when 5-anonymity is satisfied, the sensitive attributes of 25% of individuals can be easily guessed. We also find that although a larger k increases the size of anonymity sets, the corresponding improvement on the diversity of anonymity sets is very limited (decayed exponentially). These results suggest that diversity-oriented solutions are necessary.

Using recurrent neural networks toward black-box system anomaly prediction

Abstract: Component based enterprise systems are becoming extremely complex in which the availability and usability are influenced intensively by the system's anomalies. Anomaly prediction is highly important for ensuring a system's stability, which aims at preventing anomaly from occurring through pre-failure warning. However, due to the system's complex nature and the noise from monitoring, capturing pre-failure symptoms is a challenging problem. In this paper, we present a sequential and an averaged recurrent neural networks (RNN) models for distributed systems and component based systems. Specifically, we use cycle representation to capture cyclical system behaviors, which can be used to improve prediction accuracy. The anomaly data used in the experiments is collected from RUBis, IBM System S, and the component based system of enterprise T. The experimental results show that our proposed methods can achieve high prediction accuracy with satisfying lead time. Our recurrent neural networks model also demonstrates time efficiency for monitoring large-scale systems.

On the performance of cloud storage applications with global measurement

Abstract: In recent years, Dropbox, Google, and Microsoft have been competing in the market of consumer cloud storage (CCS) services. While once the key comparative metric, storage capacity per user has outgrown the needs of most users. Today, third-party applications based on CCS's RESTful Web APIs are becoming a primary way for users to utilize their expanded storage resources. Unfortunately, there is very little visibility into the performance of these Web APIs, even though they are primary determinants of the end user experience on these storage applications. In this paper, we report results from a comprehensive measurement study of the Web APIs of five popular CCS providers. Our results reveal significant differences and limitations in API performance, which result in performance bottlenecks visible to the user through the storage application. We analyze the underlying system designs of the five providers' Web APIs, and present the performance implications of their different design choices. Our research provides practical guidance for service providers to optimize their API performance, for developers to improve the experience of third-party applications, and for users to pick appropriate services that best match their requirements.

Smartphone-assisted smooth live video broadcast on wearable cameras

Abstract: Wearable cameras require connecting to cellular-capable devices (e.g., smartphones) so as to provide live broadcast services for worldwide users when Wi-Fi is unavailable. However, the constantly changing cellular network conditions may substantially slow down the upload of recorded videos. In this paper, we consider the scenario where wearable cameras upload live videos to remote distribution servers under cellular networks, aiming at maximizing the quality of uploaded videos while meeting the delay requirements. To attain the goal, we propose a dynamic video coding approach that utilizes dynamic video recording resolution adjustment on wearable cameras and Lyapunov based video preprocessing on smartphones. Our proposed resolution adjustment algorithm adapts to network condition changes, and reduces the overheads of video preprocessing. Due to the property of Lyapunov optimization framework, our proposed video preprocessing algorithm delivers near-optimal video quality while meeting the upload delay requirements. Our evaluation results show that our approach achieves up to 50% reduction in power consumption on smartphones and up to 60% reduction in average delay, at the cost of slightly compromised video quality.

A decision-tree-based on-line flow table compressing method in Software Defined Networks

Abstract: It is a common view in Software Defined Network (SDN) that the flow table plays the most significant role in SDN architecture, but suffers from the limited TCAM chips. The shortage of flow table storage strongly impacts the quality of service (QoS) provided by SDN, but requires rational solutions. In this paper, we present a practical on-line approach based on the decision tree structure to solve this problem. Our performance is evaluated by the comparison with other existing technologies.

Real-time detection of performance anomalies for cloud services

Abstract: Service performance degradation and downtimes are a common on the Internet today. Many on-line services (e.g. Amazon.com, Spotify, and Netflix, etc.) report huge loss in revenue and traffic per episode. This is perhaps due to the correlation between performance and end-users's satisfaction.

FDALB: Flow distribution aware load balancing for datacenter networks

Abstract: We present FDALB, a flow distribution aware load balancing mechanism aimed at reducing flow collisions and achieving high scalability. FDALB, like the most of centralized methods, uses a centralized controller to get the view of networks and congestion information. However, FDALB classifies flows into short flows and long flows. The paths of short flows and long flows are controlled by distributed switches and the centralized controller respectively. Thus, the controller handles only a small part of flows to achieve high scalability. To further reduce the controller's overhead, FDALB leverages end-hosts to tag long flows, thus switches can easily determine long flows by inspecting the tag. Besides, FDALB can adaptively adjust the threshold at each end-host to keep up with the flow distribution dynamics.

LCC-Graph: A high-performance graph-processing framework with low communication costs

Abstract: With the rapid growth of data, communication overhead has become an important concern in applications of data centers and cloud computing. However, existing distributed graph-processing frameworks routinely suffer from high communication costs, leading to very long waiting times experienced by users for the graph-computing results. In order to address this problem, we propose a new computation model with low communication costs, called LCC-BSP. We use this model to design and implement a high-performance distributed graph-processing framework called LCC-Graph. This framework eliminates the high communication costs in existing distributed graph-processing frameworks. Moreover, LCC-Graph also minimizes the computation workload of each vertex, significantly reducing the computation time for each superstep. Evaluation of LCC-Graph on a 32-node cluster, driven by real-world graph datasets, shows that it significantly outperforms existing distributed graph-processing frameworks in terms of runtime, particularly when the system is supported by a high-bandwidth network. For example, LCC-Graph achieves an order of magnitude performance improvement over GPS and GraphLab.

Traffic big data based path planning strategy in public vehicle systems

Abstract: Public vehicle (PV) systems will be efficient traffic-management platforms in future smart cities, where PVs provide ridesharing trips with balanced QoS (quality of service). PV systems differ from traditional ridesharing due to that the paths and scheduling tasks are calculated by a server according to passengers' requests, and all PVs corporate with each other to achieve higher transportation efficiency. Path planning is the primary problem. The current path planning strategies become inefficient especially for traffic big data in cities of large population and urban area. To ensure real-time scheduling, we propose one efficient path planning strategy with balanced QoS (e.g., waiting time, detour) by restricting search area for each PV, so that a large number of computation is saved. Simulation results based on the Shanghai (China) urban road network show that, the computation can be reduced by 34% compared with the exhaustive search method since many requests violating QoS are excluded.

Efficient and proactive V2V information diffusion using Named Data Networking

Abstract: Due to high mobility and intermittent connections in vehicular networks, reliable and efficient Vehicle-to-Vehicle (V2V) communication is a challenging task. The Named Data Networking (NDN) paradigm is recently being applied to achieve efficient V2V communication, however, proactive V2V information diffusion conflicts with the receiver-initiated nature of NDN. This paper bridges this gap by exploiting hierarchical data names to achieve efficient and proactive V2V information diffusion. We first identify a popular subgroup of vehicles, then select them as the diffusion seeds with 3G/4G capability, while others are only equipped with short-range V2V communication. We also design a namespace-based method to optimize data transmission when vehicles are close, in order to maximize the information distribution across geographical space. We evaluate our solution via a real-world taxicab dataset. Experimental results demonstrate that our approach significantly outperforms state-of-the-art solutions in terms of diffusion speed and success rate of data retrieval.

Reliability in future radio access networks: From linguistic to quantitative definitions

Abstract: For the first time since the advent of mobile networks, the idea of advancing their pervasiveness by co-opting them into most aspects of daily life has taken hold and this idea is, henceforth, intended to be a mainstay of future networks (5G and beyond). As a result, a term one frequently encounters in the latest literature pertinent to radio access networks is reliability. It is, however, fairly evident that it is mostly used in a colloquial linguistic sense or that, in some cases, it is used synonymously with availability. This work is, to the best of our knowledge, the first to provide a quantitative definition of reliability which stems from its characterization in the dictionary and is based on quantifiable definitions of resilience, availability, and other parameters important to radio access networks. The utility of this quantitative definition is demonstrated by developing a reliability-aware scheduler which takes predictions of the channel quality into account. The scheduler developed here is also compared with the classical proportional fair scheduler in use today. This comparison not only succeeds in highlighting the practicality of the definition provided, but it also shows that the anticipatory reliability-aware scheduler is able to provide an improvement of about 35 – 50% in reliability when compared to a proportional fair scheduler which is common in contemporary use.

A secure hierarchical deduplication system in cloud storage

Abstract: Data deduplication is commonly adopted in cloud storage services to improve storage utilization and reduce transmission bandwidth. It, however, conflicts with the requirement for data confidentiality offered by data encryption. Hierarchical authorized deduplication alleviates the tension between data deduplication and confidentiality and allows a cloud user to perform privilege-based duplicate checks before uploading the data. Existing hierarchical authorized deduplication systems permit the cloud server to profile cloud users according to their privileges. In this paper, we propose a secure hierarchical deduplication system to support privilege-based duplicate checks and also prevent privilege-based user profiling by the cloud server. Our system also supports dynamic privilege changes. Detailed theoretical analysis and experimental studies confirm the security and high efficiency of our system.