INFOCOM 2015 论文列表

In storage outsourcing, highly correlated datasets can occur commonly, where the rich information buried in correlated data can be useful for many cloud data generation/dissemination services. In light of this, we propose to enable a secure and efficient cloud-assisted image sharing architecture for mobile devices, by leveraging outsourced encrypted image datasets with privacy assurance. Different from traditional image sharing, the proposed design aims to save the transmission cost from mobile clients, by directly utilizing outsourced correlated images to reproduce the image of interest inside the cloud for immediate dissemination. While the benefits are obvious, how to leverage the encrypted image datasets makes the problem particular challenging. To tackle the problem, we first propose a secure and efficient index design that allows the mobile client to securely find from the encrypted image datasets the candidate selection pertaining to the image of interest for sharing. We then design two specialized encryption mechanisms that support the secure image reproduction inside the cloud directly from the encrypted candidate selection. We formally analyze the security strength of the design. Our experiments show that up to 90% of the transmission cost at the mobile client can be saved, while achieving all service requirements and security guarantees.

Beaconing is a primitive communication task in which every node locally broadcasts a packet to all of its neighbors within a fixed distance. The problem Minimum Latency Beaconing Schedule (MLBS) seeks a shortest schedule for beaconing subject to the interference constraint. MLBS has been well studied when all the nodes are always awake. However, it is well-known that the networking nodes often switch between the active state and the sleep state to save energy. A node in duty-cycled scenarios may require transmitting multiple times to inform all of its neighbors due to their different active times. Thus, all of the known algorithms for MLBS are not suitable for duty-cycled multihop wireless networks. In this paper, we study MLBS in Duty-Cycled multihop wireless networks (MLBSDC). We first present two constant-approximation algorithms for MLBSDC under the protocol interference model with the approximation bounds independent of the length of a scheduling period. Then, we develop an efficient algorithm for MLBSDC under the physical interference model. To the best of our knowledge, this is the first paper that develops efficient algorithms for MLBSDC under either of these two interference models.

With the advancement of modern technologies, the security battle between a legitimate system (LS) and an adversary is becoming increasingly sophisticated, involving complex interactions in unknown dynamic environments. Stochastic game (SG), together with multi-agent reinforcement learning (MARL), offers a systematic framework for the study of information warfare in current and emerging cyber-physical systems. In practical security games, each player usually has only incomplete information about the opponent, which induces information asymmetry. This work exploits information asymmetry from a new angle, considering how to exploit local information unknown to the opponent to the player's advantage. Two new MARL algorithms, termed minimax-PDS and WoLF-PDS, are proposed, which enable the LS to learn and adapt faster in dynamic environments by exploiting its private local information. The proposed algorithms are provably convergent and rational, respectively. Also, numerical results are presented to show their effectiveness through two concrete anti-jamming examples.

We study the problem of broadcasting packets in wireless networks. At each time slot, a network controller activates non-interfering links and forwards packets to all nodes at a common rate; the maximum rate is referred to as the broadcast capacity of the wireless network. Existing policies achieve the broadcast capacity by balancing traffic over a set of spanning trees, which are difficult to maintain in a large and time-varying wireless network. We propose a new dynamic algorithm that achieves the broadcast capacity when the underlying network topology is a directed acyclic graph (DAG). This algorithm utilizes local queue-length information, does not use any global topological structures such as spanning trees, and uses the idea of in-order packet delivery to all network nodes. Although the in-order packet delivery constraint leads to degraded throughput in cyclic graphs, we show that it is throughput optimal in DAGs and can be exploited to simplify the design and analysis of optimal algorithms. Our simulation results show that the proposed algorithm has superior delay performance as compared to tree-based approaches.

Data centers are key participants in demand response programs, including emergency demand response (EDR), where the grid coordinates large electricity consumers for demand reduction in emergency situations to prevent major economic losses. While existing literature concentrates on owner-operated data centers, this work studies EDR in multi-tenant colocation data centers where servers are owned and managed by individual tenants. EDR in colocation data centers is significantly more challenging, due to lack of incentives to reduce energy consumption by tenants who control their servers and are typically on fixed power contracts with the colocation operator. Consequently, to achieve demand reduction goals set by the EDR program, the operator has to rely on the highly expensive and/or environmentally-unfriendly on-site energy backup/generation. To reduce cost and environmental impact, an efficient incentive mechanism is therefore in need, motivating tenants' voluntary energy reduction in case of EDR. This work proposes a novel incentive mechanism, Truth-DR, which leverages a reverse auction to provide monetary remuneration to tenants according to their agreed energy reduction. Truth-DR is computationally efficient, truthful, and achieves 2-approximation in colocation-wide social cost. Trace-driven simulations verify the efficacy of the proposed auction mechanism.

This work presents the first experimental evaluation of the Floating Content (FC) communication paradigm in a campus/large office setting. By logging information transfer events we have characterized mobility patterns, and we have assessed the performance of services implemented using the FC paradigm. Our results unveil the key relevance of group dynamics in user movements for the FC performance. Surprisingly, in such an environment, our results show that a relatively low user density is enough to guarantee content persistence over time, contrarily to predictions from available models. Based on these experimental findings, we develop a novel simple analytical model that accounts for the peculiarities of the mobility patterns in such a setting, and that can accurately predict the effectiveness of FC for the implementation of services in a campus/large office setting.

Data deduplication, a space-efficient and bandwidth-saving technology, plays an important role in bandwidth-efficient data transmission in various data-intensive network and cloud applications. Rabin-based and MAXP-based Content-Defined Chunking (CDC) algorithms, while robust in finding suitable cut-points for chunk-level redundancy elimination, face the key challenges of (1) low chunking throughput that renders the chunking stage the deduplication performance bottleneck and (2) large chunk-size variance that decreases deduplication efficiency. To address these challenges, this paper proposes a new CDC algorithm called the Asymmetric Extremum (AE) algorithm. The main idea behind AE is based on the observation that the extreme value in an asymmetric local range is not likely to be replaced by a new extreme value in dealing with the boundaries-shift problem, which motivates AE's use of asymmetric (rather than symmetric as in MAXP) local range to identify cut-points and simultaneously achieve high chunking throughput and low chunk-size variance. As a result, AE simultaneously addresses the problems of low chunking throughput in MAXP and Rabin and high chunk-size variance in Rabin. The experimental results based on four real-world datasets show that AE improves the throughput performance of the state-of-the-art CDC algorithms by 3x while attaining comparable or higher deduplication efficiency.

The classification of Internet traffic using supervised or semi-supervised statistical learning techniques, both for anomaly detection and identification of Internet applications, has been impaired by difficulties in obtaining a reliable ground-truth, required both to train the classifier and to evaluate its performance. A perfect ground-truth is increasingly difficult, or sometimes impossible, to obtain due to the growing percentage of cyphered traffic, the sophistication of network attacks, and the constant updates of Internet applications. In this paper, we study the impact of the ground-truth on training the classifier and estimating its performance measures. We show both theoretically and through simulation that ground-truth imperfections can severely bias the performance estimates. We then propose a latent class model that overcomes this problem by combining estimates of several classifiers over the same dataset. The model is evaluated using a high-quality dataset that includes the most representative Internet applications and network attacks. The results show that our latent class model produces very good performance estimates under mild levels of ground-truth imperfection, and can thus be used to correctly benchmark Internet traffic classifiers when only an imperfect ground-truth is available.

Cognitive radio networks (CRNs) have been proposed to solve the spectrum scarcity problem. One of their fundamental procedures is to construct a communication link on a common channel for the users, which is referred as rendezvous. In reality, the capability to sense the spectrum may vary from user to user, and such users form what is known as a heterogeneous cognitive radio network (HCRN). The licensed spectrum is divided in to n channels, U = {1, 2,..., n}. We denote the capability of user i as Ci⊆ U and the set of available channels (i.e. the channels not occupied by the paying users) as Vi⊆ Ci. We study the rendezvous problem in HCRN under two circumstances: fully available spectrum (Vi= Ci) and partially available spectrum (Vi≠ Ci). For any two users a, b, we propose the Traversing Pointer (TP) algorithm that guarantees rendezvous in O(max{|Ca|,|Cb|}log log n) time slots for the fully available spectrum scenario. This result is only O (log log n) larger than our constructive lower bound. Moreover, it removes an O(min{|Ca|, |Cb|}) factor as compared to the state-of-the-art result (O(|Ca||Cb|) in [26]). For the partially available spectrum scenario, we propose the Moving Traversing Pointers (MTP) algorithm to guarantee rendezvous in O((max{|Va|, |Vb|})2log log n) time slots, which works more efficiently than the previous best result (O(|Ca||Cb|) in [25]) in various circumstances. We also conduct extensive simulations and the results corroborate our analysis.

We consider a cognitive radio network, where primary users (PUs) share their spectrum with energy harvesting (EH) enabled secondary users (SUs), conditioned on a limited SUs' interference at PU receivers. Due to the lack of information exchange between SUs and PUs, the SU-PU interference channels are subject to uncertainty in channel estimation. Besides channel uncertainty, SUs' EH profile is also subject to spatial and temporal variations, which enforce an energy causality constraint on SUs' transmit power control and affect SUs' interference at PU receivers. Considering both the channel and EH uncertainties, we propose a robust design for SUs' power control to maximize SUs' throughput performance. Our robust design targets at the worst-case interference constraint to provide a robust protection for PUs, while guarantees a transmission probability to reflect SUs' minimum QoS requirements. To make the non-convex throughput maximization problem tractable, we develop a convex approximation for each robust constraint and successfully design a successive approximation approach that converges to the global optimum of the throughput objective. Simulations show that SUs will change transmission strategies according to PUs' sensitivity to interference, and we also exploit the impact of SUs' EH profile (e.g., mean, variance, and correlation) on SUs' power control.

Crowdsourcing systems allocate tasks to a group of workers over the Internet, which have become an effective paradigm for human-powered problem solving such as image classification, optical character recognition and proofreading. In this paper, we focus on incentivizing crowd workers to label a set of binary tasks under strict budget constraint. We properly profile the tasks' difficulty levels and workers' quality in crowdsourcing systems, where the collected labels are aggregated with sequential Bayesian approach. To stimulate workers to undertake crowd labeling tasks, the interaction between workers and the platform is modeled as a reverse auction. We reveal that the platform utility maximization could be intractable, for which an incentive mechanism that determines the winning bid and payments with polynomial-time computation complexity is developed. Moreover, we theoretically prove that our mechanism is truthful, individually rational and budget feasible. Through extensive simulations, we demonstrate that our mechanism utilizes budget efficiently to achieve high platform utility with polynomial computation complexity.

Reducing the energy consumption to the minimum is a crucial design requirement for all body area sensor networks. Sensors deployed on the human body, especially at the limbs often move along different positions. Usually, the transmit power is set to a sufficiently high value to achieve reliable transmission for the constellation with highest attenuation. For periodic movements, data transmission can be carried out at the position of the lowest path loss between the sender and the receiver, provided this position can be reliably identified. We propose a novel framework that predicts this position using acceleration data and the received signal strength. By learning a correlation between these signals, accurate predictions can be performed and up to 24.7% of the power spent by a Bluetooth Low Energy module for the transmission of a packet can be saved while still achieving the same packet error rate as with sending using the higher transmit power.

Recently, both academia and industry are moving their research attention to the fifth-generation (5G) wireless networks - the next new era of wireless networks. The wireless full-duplex transmission, as one of promising candidate techniques for 5G, can significantly boost the spectrum efficiency of the wireless networks, thus providing a powerful thrust to optimize the quality-of-service (QoS) performances for the wireless networks. However, due to the heterogeneity caused by different types of simultaneous traffics over the wireless full-duplex link, supporting QoS guarantees for wireless full-duplex networks imposes the new challenges that we need to provide heterogeneous QoS guarantees for different types of traffics over the same link simultaneously. To overcome the aforementioned problems, in this paper we propose the heterogeneous statistical QoS provisioning framework for bidirectional transmission based wireless full-duplex networks. In particular, we formulate the optimization problems to maximize the system throughput subject to heterogeneous statistical delay-bound QoS requirements. Then, we convert the resulted non-convex optimization problem into an equivalent convex optimization problem, solving which we can derive the optimal QoS-driven power allocation scheme to maximize the system throughput while guaranteeing the heterogeneous statistical delay-bound QoS requirements. The extensive simulation results obtained show that our proposed QoS-driven power allocation scheme for heterogeneous statistical delay-bound QoS requirements can achieve larger aggregate system throughput than the scheme for the homogeneous statistical delay-bound QoS requirement over 5G mobile wireless full-duplex networks.

Network applications commonly maintain local copies of remote data sources in order to provide caching, indexing, and data-mining services to their clients. Modeling performance of these systems and predicting future updates usually requires knowledge of the inter-update distribution at the source, which can only be estimated through blind sampling - periodic downloads and comparison against previous copies. In this paper, we first introduce a stochastic modeling framework for this problem, where the update and sampling processes are both renewal. We then show that all previous approaches are biased unless the observation rate tends to infinity or the update process is Poisson. To overcome these issues, we propose four new algorithms that achieve various levels of consistency, which depend on the amount of temporal information revealed by the source and capabilities of the download process.

In data center networks, micro-burst is a common traffic pattern and the packet dropping caused by it usually leads to serious performance degradation. Meanwhile, most of the current commodity switches employ on-chip shared memory, and the buffer management policies of them ensure fair sharing of memory among all ports. Among various polices, Dynamic Threshold (DT) is widely used by switch vendors. However, because DT needs to reserve a fraction of switch buffer, there is free buffer space while packets from micro-burst traffic are dropped. In this paper, we theoretically deduce the sufficient conditions for packet dropping caused by micro-burst traffic, and estimate the corresponding free buffer size. The results show that the free buffer size is very large when the number of overloaded ports is small. What's worse, to ensure fair sharing of memory among output ports, packets from micro-burst traffic may be dropped even when the traffic size is much smaller than the buffer size. In light of these results, we propose Enhanced Dynamic Threshold (EDT) policy, which can alleviate packet dropping caused by micro-burst traffic through fully utilizing the switch buffer and temporarily relaxing the fairness constraint. The simulation results show that EDT can absorb more micro-burst traffic than DT.

We consider the optimal scheduling problem for a large-scale parallel server system with one large pool of statistically identical servers and multiple classes of jobs under the expected long-run average (ergodic) cost criterion. Jobs of each class arrive as a Poisson process, are served in the FCFS discipline within each class and may elect to abandon while waiting in their queue. The service and abandonment rates are both class-dependent. Assume that the system is operating in the Halfin-Whitt regime, where the arrival rates and the number of servers grow appropriately so that the system gets critically loaded while the service and abandonment rates are fixed. The optimal solution is obtained via the ergodic diffusion control problem in the limit, which forms a new class of problems in the literature of ergodic controls. A new theoretical framework is provided to solve this class of ergodic control problems. The proof of the convergence of the values of the multiclass parallel server system to that of the diffusion control problem relies on a new approximation method, spatial truncation, where the Markov policies follow a fixed priority policy outside a fixed compact set.

Stimulating both service users and service providers is of paramount importance to mobile crowdsourcing. A few incentive mechanisms have been proposed, but all of them have focused only on one-sided interactions either among service users or among service providers. For the first time, to the best of our knowledge, we investigate the important two-sided online interactions among service users and service providers in mobile crowdsourcing. We model such interactions as online double auctions, explicitly taking the dynamic nature of both users and providers into account We propose a general framework for the design of truthful online double auctions for dynamic mobile crowdsourcing. The framework is expressive and can work with different price schedules. We propose price-ranked online double auctions with four price schedules to implement the framework, which are suitable for different scenarios. With theoretical analysis and extensive simulations we demonstrate that the proposed auctions are strategy-proof, individual rational, and ensure budget balance.

With the prosperity of smart devices, crowdsourcing has emerged as a new computing/networking paradigm. Through the crowdsourcing platform, service requesters can buy service from service providers. An important component of crowdsourcing is its incentive mechanism. We study three models of crowdsourcing, which involve cooperation and competition among the service providers. Our simplest model generalizes the well-known user-centric model studied in a recent Mobicom paper. We design an incentive mechanism for each of the three models, and prove that these incentive mechanisms are individually rational, budget-balanced, computationally efficient, and truthful.

Network coding (NC) based opportunistic routing has been well studied, but the impact of routing diversity on the performance of NC-based routing remains largely unexplored. Towards understanding the importance of routing diversity in NC-based routing, we study the problems of estimating and minimizing the data delivery cost in NC-based routing. In particular, we propose an analytical framework for estimating the total number of packet transmissions for NC-based routing in arbitrary topologies. We design a greedy algorithm that minimizes the total transmission cost of NC-based routing and determines the corresponding forwarder set for each node. We prove the optimality of this algorithm and show that 1) nodes on the shortest path may not always be favored when selecting forwarders for NC-based routing and 2)the minimal cost of NC-based routing is upper-bounded by the cost of shortest path routing. Based on the greedy, optimal algorithm, we design and implement ONCR, a distributed minimal cost NC-based routing protocol. Using the NetEye sensor testbed, we comparatively study the performance of ONCR and existing approaches such as the single path routing protocol CTP and the NC-based opportunistic routing protocols MORE and CodeOR. Results show that ONCR achieves close to 100% delivery reliability while having the lowest delivery cost among all the protocols and 25-28% less than the second best protocol CTP. This low delivery cost also enables ONCR to achieve the highest network goodput, i.e., about two-fold improvement over MORE and CodeOR. Our findings demonstrate the significance of optimizing data forwarding diversity in NC-based routing for data delivery reliability, efficiency, and goodput.