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TOC Alert for Publication# 90 2017February 16<![CDATA[Table of Contents]]>251C12540<![CDATA[IEEE/ACM Transactions on Networking publication information]]>251C2C277<![CDATA[Incentivizing Sharing in Realtime D2D Streaming Networks: A Mean Field Game Perspective]]>2513172013<![CDATA[A New Constant Factor Approximation to Construct Highly Fault-Tolerant Connected Dominating Set in Unit Disk Graph]]>$m$ -dominating set problem in unit disk graph (UDG) with any positive integer $m geq 1$ for the first time in the literature. We observe that it is difficult to modify the existing constant factor approximation algorithm for the minimum three-connected $m$ -dominating set problem to solve the minimum four-connected $m$ -dominating set problem in UDG due to the structural limitation of Tutte decomposition, which is the main graph theory tool used by Wang et al. to design their algorithm. To resolve this issue, we first reinvent a new constant factor approximation algorithm for the minimum three-connected $m$ -dominating set problem in UDG and later use this algorithm to design a new constant factor approximation algorithm for the minimum four-connected $m$ -dominating set problem in UDG.]]>25118282476<![CDATA[The Value-of-Information in Matching With Queues]]>$mathtt {LRAM}$ ) and Dual-$mathtt {LRAM}$ ($mathtt {DRAM}$ ) to effectively resolve both challenges. Both algorithms are equipped with a learning module for estimating the matching-reward information, while $mathtt {DRAM}$ incorporates an additional module for learning the system dynamics. We show that both algorithms achieve an $O(epsilon +delta _{r})$ close-to-optimal utility performance for any $epsilon >0$ , while $mathtt {DRAM}$ achieves a faster convergence speed and a better delay compared with $mathtt {LRAM}$ , i.e., $O(delta _{pi }/epsilon + log (1/epsilon )^{2})$ delay and $O(delta _{pi }/epsilon )$ convergence under $mathtt {DRAM}$ compared with $O(1/epsilon )$ delay and convergence under $mathtt {LRAM}$ ($delta _{r}$ and $delta _{pi }$ are maximum estimation errors for reward and system dynamics). Our results show that the information of different system components can play very different roles in algorithm performance and provide a novel way for designing the joint learning-control algorithms.]]>25129421032<![CDATA[Enabling Network Anti-Inference via Proactive Strategies: A Fundamental Perspective]]>25143551123<![CDATA[Radio Frequency Traffic Classification Over WLAN]]>25156683273<![CDATA[From Rateless to Hopless]]>$1.7times $ and $1.3times $ goodput gain over EXOR and MIXIT, respectively. We further implement HOPE on a sensor network testbed, achieving the goodput gains over CTP.]]>25169822053<![CDATA[Information-Centric Multilayer Networking: Improving Performance Through an ICN/WDM Architecture]]>25183971892<![CDATA[Optimally Approximating the Coverage Lifetime of Wireless Sensor Networks]]>$ln n$ by any polynomial time algorithm, where $n$ is the number of targets. This provides closure to the long-standing open problem of showing optimality of previously known $ln n$ approximation algorithms. We also derive a new $ln n$ approximation to the problem by showing the $ln n$ approximation to the related maximum disjoint set cover problem. We show that this approach has many advantages over algorithms in the literature, including a simple and optimal extension that solves the problem with multiple coverage constraints. For the 1-D network topology, where sensors can monitor contiguous line segments of possibly different lengths, we show that the optimal coverage lifetime can be found in polynomial time. Finally, for the 2-D topology in which coverage regions are unit squares, we combine the existing results to derive a $1+epsilon $ approximation algorithm for the problem. Extensive simulation experiments validate our theoretical results, showing that our algorithms not only have optimal worst case guarantees but also match the performance of the existing algorithms on special network topologies. In addition, our algorithms sometimes run orders of magnitude faster than the existing state of the art.]]>251981111981<![CDATA[Adaptive Influence Maximization in Dynamic Social Networks]]>2511121252320<![CDATA[Delay Network Tomography Using a Partially Observable Bivariate Markov Chain]]>2511261381434<![CDATA[Tunable QoS-Aware Network Survivability]]>tunable survivability offers major performance improvements over traditional approaches. Indeed, while the traditional approach aims at providing full (100%) protection against network failures through disjoint paths, it was realized that this requirement is too restrictive in practice. Tunable survivability provides a quantitative measure for specifying the desired level (0%–100%) of survivability and offers flexibility in the choice of the routing paths. Previous work focused on the simpler class of “bottleneck” criteria, such as bandwidth. In this paper, we focus on the important and much more complex class of additive criteria, such as delay and cost. First, we establish some (in part, counter-intuitive) properties of the optimal solution. Then, we establish efficient algorithmic schemes for optimizing the level of survivability under additive end-to-end quality of service (QoS) bounds. Subsequently, through extensive simulations, we show that, at the price of negligible reduction in the level of survivability, a major improvement (up to a factor of 2) is obtained in terms of end-to-end QoS performance. Finally, we exploit the above findings in the context of a network design problem, in which, for a given investment budget, we aim to improve the survivability of the network links.]]>2511391491548<![CDATA[Correctness of Routing Vector Protocols as a Property of Network Cycles]]>251150163962<![CDATA[Scheduling in Densified Networks: Algorithms and Performance]]>2511641781016<![CDATA[How Much Cache is Needed to Achieve Linear Capacity Scaling in Backhaul-Limited Dense Wireless Networks?]]>2511791881722<![CDATA[On the Problem of Optimal Path Encoding for Software-Defined Networks]]>$8/7$ is NP-hard. Thus, at best, we can hope for a constant-factor approximation algorithm. We then present such an algorithm, approximating the optimal path-encoding problem to within a factor 2. Finally, we provide the empirical results illustrating the effectiveness of the proposed algorithm.]]>251189198765<![CDATA[Optimizing Throughput in Optical Networks: The Joint Routing and Power Control Problem]]>2511992091876<![CDATA[Optimal Monitor Assignment for Preferential Link Tomography in Communication Networks]]>2512102232490<![CDATA[RFID Estimation With Blocker Tags]]>2512242372947<![CDATA[Online Allocation of Virtual Machines in a Distributed Cloud]]>2512382492643<![CDATA[How CSMA/CA With Deferral Affects Performance and Dynamics in Power-Line Communications]]>2512502631973<![CDATA[Multi-Category RFID Estimation]]>2512642775312<![CDATA[Fast Tracking the Population of Key Tags in Large-Scale Anonymous RFID Systems]]>2512782914300<![CDATA[Resource Allocation and Rate Gains in Practical Full-Duplex Systems]]>2512923053994<![CDATA[Packet-Scale Congestion Control Paradigm]]>2513063191164<![CDATA[PASE: Synthesizing Existing Transport Strategies for Near-Optimal Data Center Transport]]>2513203342685<![CDATA[DX: Latency-Based Congestion Control for Datacenters]]>2513353482007<![CDATA[Improved Utility-Based Congestion Control for Delay-Constrained Communication]]>2513493623144<![CDATA[Temporal Update Dynamics Under Blind Sampling]]>2513633763145<![CDATA[Throughput-Optimal Multihop Broadcast on Directed Acyclic Wireless Networks]]>2513773912563<![CDATA[Collision-Aware Churn Estimation in Large-Scale Dynamic RFID Systems]]>2513924051577<![CDATA[HetNets Selection by Clients: Convergence, Efficiency, and Practicality]]>2514064192672<![CDATA[Optimal Spectrum Auction Design With 2-D Truthful Revelations Under Uncertain Spectrum Availability]]>2514204332073<![CDATA[Network Capability in Localizing Node Failures via End-to-End Path Measurements]]>2514344502222<![CDATA[Self-Stabilized Distributed Network Distance Prediction]]>$L_{1}$ -norm regularization to preserve the sparseness of the square matrix. Simulation results and a PlanetLab-based experiment confirm that RMF converges to stable positions within 10 to 15 rounds, and decreases the prediction errors by 10% to 20%.]]>2514514641912<![CDATA[Robust and Optimal Opportunistic Scheduling for Downlink Two-Flow Network Coding With Varying Channel Quality and Rate Adaptation]]>2514654791530<![CDATA[Accurate Per-Packet Delay Tomography in Wireless Ad Hoc Networks]]>2514804912092<![CDATA[A High Efficiency MAC Protocol for WLANs: Providing Fairness in Dense Scenarios]]>2514925051583<![CDATA[<italic>eBA</italic>: Efficient Bandwidth Guarantee Under Traffic Variability in Datacenters]]>eBA, an efficient solution to bandwidth allocation that provides end-to-end bandwidth guarantee for VMs under large numbers of short flows and massive bursty traffic in datacenters. eBA leverages a novel distributed VM-to-VM rate control algorithm based on the logistic model under the control-theoretic framework. eBA’s implementation requires no changes to hardware or applications and can be deployed in standard protocol stack. The theoretical analysis and the experimental results show that eBA not only guarantees the bandwidth for VMs, but also provides fast convergence to efficiency and fairness, as well as smooth response to bursty traffic.]]>2515065192379<![CDATA[eICIC Configuration Algorithm with Service Scalability in Heterogeneous Cellular Networks]]>2515205354426<![CDATA[Amazon in the White Space: Social Recommendation Aided Distributed Spectrum Access]]>2515365491977<![CDATA[Performance Modeling, Analysis, and Optimization of Delayed Mobile Data Offloading for Mobile Users]]>2515505642194<![CDATA[Path-Based Epidemic Spreading in Networks]]>contact-based infection. This strongly associates the epidemic spreading process with node degrees. The role of the infection transmission medium is often neglected. In real-world networks, however, the infectious agent as the physical contagion medium usually flows from one node to another via specific directed routes (path-based infection). Here, we use continuous-time Markov chain analysis to model the influence of the infectious agent and routing paths on the spreading behavior by taking into account the state transitions of each node individually, rather than the mean aggregated behavior of all nodes. By applying a mean field approximation, the analysis complexity of the path-based infection mechanics is reduced from exponential to polynomial. We show that the structure of the topology plays a secondary role in determining the size of the epidemic. Instead, it is the routing algorithm and traffic intensity that determine the survivability and the steady-state of the epidemic. We define an infection characterization matrix that encodes both the routing and the traffic information. Based on this, we derive the critical path-based epidemic threshold below which the epidemic will die off, as well as conditional bounds of this threshold which network operators may use to promote/suppress path-based spreading in their networks. Finally, besides artificially generated random and scale-free graphs, we also use real-world networks and traffic, as case studies, in order to compare the behaviors of contact- and path-based epidemics. Our results further corroborate the recent empirical observations that epidemics in communication networks are highly persistent.]]>2515655782545<![CDATA[Guaranteeing Deadlines for Inter-Data Center Transfers]]>Amoeba that implements DNA. Our simulations and test bed experiments show that Amoeba, by harnessing DNA’s malleability, accommodates 15% more user requests with deadlines, while achieving 60% higher WAN utilization than prior solutions.]]>2515795953126<![CDATA[STPP: Spatial-Temporal Phase Profiling-Based Method for Relative RFID Tag Localization]]>2515966093680<![CDATA[Design and Implementation of a Stateful Network Packet Processing Framework for GPUs]]>$16.2times $ speedup compared with different monolithic GPU-based implementations of the same applications.]]>2516106232248<![CDATA[Inter-Session Network Coding Schemes for 1-to-2 Downlink Access-Point Networks With Sequential Hard Deadline Constraints]]>deadline-constrained unicast sessions. In particular, each unicast session aims to transmit a file, whose packets have hard sequential deadline constraints. We first characterize the corresponding deadline-constrained capacity region under heterogeneous channel conditions and heterogeneous deadline constraints. We show that this deadline-constrained capacity region can be achieved asymptotically by modifying the existing generation-based (G-B) schemes. However, despite its asymptotic optimality, the G-B scheme has very poor performance for small and medium file sizes. To address these problems, we develop a new immediately-decodable network coding (IDNC) scheme that empirically demonstrates much better performance for short file sizes, and we prove analytically its asymptotic optimality when used to send large files. Our analysis uses a novel version of drift analysis, which could also be of independent interest to other IDNC schemes.]]>2516246381017<![CDATA[List of Reviewers]]>25163964370<![CDATA[IEEE/ACM Transactions on Networking society information]]>251C3C379<![CDATA[IEEE/ACM Transactions on Networking information for authors]]>251C4C475