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TOC Alert for Publication# 90 2018June 18<![CDATA[Table of contents]]>263C11034538<![CDATA[IEEE/ACM Transactions on Networking publication information]]>263C2C280<![CDATA[Incentivizing Wi-Fi Network Crowdsourcing: A Contract Theoretic Approach]]>contract-based incentive framework to incentivize such a Wi-Fi network crowdsourcing under incomplete information (where each user has certain private information such as mobility pattern and Wi-Fi access quality). In the proposed framework, the network operator designs and offers a set of contract items to users, each consisting of a Wi-Fi access price (that a user can charge others for accessing his AP) and a subscription fee (that a user needs to pay the operator for joining the community). Different from the existing contracts in the literature, in our contract model, each user’s best choice depends not only on his private information but also on other user’s choices. This greatly complicates the contract design, as the operator needs to analyze the equilibrium choices of all users, rather than the best choice of each single user. We first derive the feasible contract that guarantees the user’s truthful information disclosure based on the equilibrium analysis of the user choice, and then derive the optimal (and feasible) contract that yields a maximal profit for the operator. Our analysis shows that a user who provides a higher Wi-Fi access quality is more likely to choose a higher Wi-Fi access price and subscription fee, regardless of the user mobility pattern. Simulation results further show that when increasing the average Wi-Fi access quality of users, the operator can gain more profit, but (counter-intuitively) offer lower Wi-Fi access prices and subscription fees for users.]]>263103510482045<![CDATA[Joint Bidding and Geographical Load Balancing for Datacenters: Is Uncertainty a Blessing or a Curse?]]>i.e., dynamically routing workloads to locations with cheaper electricity. Under the ideal setting where exact values of market prices and workloads are given, this problem reduces to a simple linear programming and is easy to solve. However, under the realistic setting where only distributions of these variables are available, the problem unfolds into a non-convex infinite-dimensional one and is challenging to solve. One of our main contributions is to develop an algorithm that is proven to solve the challenging problem optimally, by exploring the full design space of strategic bidding. Trace-driven evaluations corroborate our theoretical results, demonstrate fast convergence of our algorithm, and show that it can reduce the cost for the CSP by up to 20% as compared with baseline alternatives. This paper highlights the intriguing role of uncertainty in workloads and market prices, measured by their variances. While uncertainty in workloads deteriorates the cost-saving performance of joint electricity procurement and GLB, counter-intuitively, uncertainty in market prices can be exploited to achieve a cost reduction even larger than the setting without price uncertainty.]]>263104910622694<![CDATA[Adaptive TTL-Based Caching for Content Delivery]]>automatically adapt to the heterogeneity, burstiness, and non-stationary nature of real-world content requests is a major challenge and is the focus of our work. While there is much work on caching algorithms for stationary request traffic, the work on non-stationary request traffic is very limited. Consequently, most prior models are inaccurate for non-stationary production CDN traffic. We propose two TTL-based caching algorithms that provide provable performance guarantees for request traffic that is bursty and non-stationary. The first algorithm called d-TTL dynamically adapts a TTL parameter using stochastic approximation. Given a feasible target hit rate, we show that d-TTL converges to its target value for a general class of bursty traffic that allows Markov dependence over time and non-stationary arrivals. The second algorithm called f-TTL uses two caches, each with its own TTL. The first-level cache adaptively filters out non-stationary traffic, while the second-level cache stores frequently-accessed stationary traffic. Given feasible targets for both the hit rate and the expected cache size, f-TTL asymptotically achieves both targets. We evaluate both d-TTL and f-TTL using an extensive trace containing more than 500 million requests from a production CDN server. We show that both d-TTL and f-TTL converge to their hit rate targets with an error of about 1.3%. But, f-TTL requires a significantly smaller cache size than d-TTL to achieve the same hit rate, since it effectively filters out non-stationary content.]]>263106310772211<![CDATA[MobiT: Distributed and Congestion-Resilient Trajectory-Based Routing for Vehicular Delay Tolerant Networks]]>263107810912397<![CDATA[FINE: A Framework for Distributed Learning on Incomplete Observations for Heterogeneous Crowdsensing Networks]]>distributed record completion algorithm that allows each node to obtain the global consensus by an efficient communication with neighbors, and a distributed dual average algorithm that achieves the efficiency of minimizing non-smooth error functions. Our analysis shows that all these algorithms converge, of which the convergence rates are also derived to confirm their efficiency. We evaluate the performance of our framework with experiments on synthetic and real-world networks.]]>263109211091489<![CDATA[Minimum-Weight Link-Disjoint Node-“Somewhat Disjoint” Paths]]>node-disjoint and link-disjoint solutions. Specifically, we formalize several optimization problems that aim at finding minimum-weight link-disjoint paths while restricting the number of its common nodes. We establish that some of these variants are computationally intractable, while for other variants we establish polynomial-time algorithmic solutions. Finally, through extensive simulations, we show that, by allowing link-disjoint paths share a few common nodes, a major improvement is obtained in terms of the quality (i.e., total weight) of the solution.]]>263111011223045<![CDATA[Ghost Riders: Sybil Attacks on Crowdsourced Mobile Mapping Services]]>Sybil devices that expose crowdsourced map systems to a variety of security and privacy attacks. Our experiments show that a single Sybil device with limited resources can cause havoc on Waze, reporting false congestion and accidents and automatically rerouting user traffic. More importantly, we describe techniques to generate Sybil devices at scale, creating armies of virtual vehicles capable of remotely tracking precise movements for large user populations while avoiding detection. To defend against Sybil devices, we propose a new approach based on co-location edges, authenticated records that attest to the one-time physical co-location of a pair of devices. Over time, co-location edges combine to form large proximity graphs that attest to physical interactions between devices, allowing scalable detection of virtual vehicles. We demonstrate the efficacy of this approach using large-scale simulations, and how they can be used to dramatically reduce the impact of the attacks. We have informed Waze/Google team of our research findings. Currently, we are in active collaboration with Waze team to improve the security and privacy of their system.]]>263112311362206<![CDATA[Accurate Recovery of Internet Traffic Data Under Variable Rate Measurements]]>263113711503540<![CDATA[Memory-Efficient and Ultra-Fast Network Lookup and Forwarding Using Othello Hashing]]>263115111643041<![CDATA[ICE Buckets: Improved Counter Estimation for Network Measurement]]>independent counter estimation buckets, a novel algorithm that improves estimation accuracy for all counters. This is achieved by separating the flows to buckets and configuring the optimal estimation function according to each bucket’s counter scale. We prove a tighter upper bound on the relative error and demonstrate an accuracy improvement of up to 57 times on real Internet packet traces.]]>263116511782715<![CDATA[Sponsoring Mobile Data: Analyzing the Impact on Internet Stakeholders]]>263117911922627<![CDATA[<italic>TrafficShaper:</italic> Shaping Inter-Datacenter Traffic to Reduce the Transmission Cost]]>$q$ th percentile charging model. In such a charging model, the time slots with top ($100-q$ ) percent of data transmission do not affect the total transmission cost and can be viewed as “free.” This brings the opportunity to optimize the scheduling of inter-DC transfers to minimize the entire transmission cost. However, a very little work has been done to exploit those “free” time slots for scheduling inter-DC transfers. The crux is that existing work either lacks a mechanism to accumulate traffic to “free” time slots, or inevitably relies on prior knowledge of future traffic arrival patterns. In this paper, we present TrafficShaper, a new scheduler that shapes the inter-DC traffic to exploit the “free” time slots involved in the $q$ th percentile charging model, so as to reduce or even minimize the transmission cost. When shaping traffic, TrafficShaper advocates a simple principle: more traffic peaks should be scheduled in “free” time slots, while less traffic differentiation should be maintained among the remaining time slots. To this end, TrafficShaper designs a pricing-aware control framework, which makes online decisions for inter-DC tr-
nsfers without requiring a prior knowledge of traffic arrivals. To verify the performance of TrafficShaper, we conduct rigorous theoretical analysis based on Lyapunov optimization techniques, large-scale trace-driven simulations, and small-scale testbed implementation. Results from rigorous mathematical analyses demonstrate that TrafficShaper can make the transmission cost arbitrarily close to the optimum value. Extensive trace-driven simulation results show that TrafficShaper can reduce the transmission cost by up to 40.23%, compared with the state-of-the-art solutions. The testbed experiments further verify that TrafficShaper can realistically reduce the transmission cost by up to 19.38%.]]>263119312062962<![CDATA[Resource Demand Misalignment: An Important Factor to Consider for Reducing Resource Over-Provisioning in Cloud Datacenters]]>263120712212673<![CDATA[On-Line Anomaly Detection With High Accuracy]]>263122212353018<![CDATA[Privacy-Preserving Crowdsourced Spectrum Sensing]]>− and PriCSS^{+}, two different schemes under distinct design objectives and assumptions. PriCSS^{−} is an approximately truthful scheme that achieves differential location privacy and an approximate minimum payment, while PriCSS^{+} is a truthful scheme that achieves differential location privacy and an approximate minimum social cost. The detailed theoretical analysis and simulation studies are performed to demonstrate the efficacy of both schemes.]]>263123612491361<![CDATA[Node Virtualization for IP Level Resilience]]>263125012632573<![CDATA[Distributed Packet Forwarding and Caching Based on Stochastic Network Utility Maximization]]>263126412771609<![CDATA[Stable Local Broadcast in Multihop Wireless Networks Under SINR]]>$R$ . We investigate the maximum packet injection rate and the minimum packet latency that can be achieved in a stable protocol. This paper assumes the signal-to-interference-plus-noise-ratio (SINR) interference model, which reflects more accurately the physical characteristics of the wireless interference, such as fading and signal accumulation, than conventional local interference models, e.g., graph-based models. More specifically, we present a stable protocol that can handle both stochastic and adversarial injection patterns. The protocol is asymptotically optimal in terms of both injection rate and packet latency. To the best of our knowledge, this paper is the first one studying the properties of stable protocols for the basic primitive of local broadcast in a multi-hop setting under SINR. Our proposed protocol utilizes a static local broadcast algorithm as a subroutine. This static algorithm is of independent interest, and it closes the $O(log n)$ gap between the upper and lower bounds for static local broadcast. Simulation results indicate that our proposed algorithms can perform well in realistic environments.]]>263127812913930<![CDATA[Oblivious Routing in IP Networks]]>263129213051463<![CDATA[Attack Vulnerability of Power Systems Under an Equal Load Redistribution Model]]>$N$ transmission lines with initial loads $L_{1}, ldots , L_{N}$ and capacities $C_{1}, ldots , C_{N}$ , respectively; the capacity $C_{i}$ defines the maximum flow allowed on line $i$ . Under an equal load redistribution model, where load of failed lines is redistributed equally among all remaining lines, we study the optimization problem of finding the best $k$ lines to attack so as to minimize the number of alive lines at the steady-state (i.e., when cascades stop). This is done to reveal the worst-case attack vulnerability of the system as well as to reveal its most vulnerable lines. We derive optimal attack strategies in several special cases of load-capacity distributions that are practically relevant. We then consider a modified optimization problem where the adversary is also constrained by the total load (in addition to the number) of the initial attack set, and prove that this problem is NP-hard. Finally, we develop heuristic algorithms for selecting the attack set for both the original and modified problems. Through extensive simulations, we show that these heuristics outperform benchmark algorithms under a wide range of settings.]]>263130613192356<![CDATA[Optimal Network Service Chain Provisioning]]>exact mathematical model using decomposition methods whose solution is scalable in order to conduct such an investigation. We conduct extensive numerical experiments, and show we can solve exactly the routing of service chain requests in a few minutes for networks with up to 50 nodes, and traffic requests between all pairs of nodes. Detailed analysis is then made on the best compromise between minimizing the bandwidth requirement and minimizing the number of VNFs and optimizing their locations using different data sets.]]>263132013332357<![CDATA[Context-Aware Hierarchical Online Learning for Performance Maximization in Mobile Crowdsourcing]]>263133413473268<![CDATA[CompVM: A Complementary VM Allocation Mechanism for Cloud Systems]]>263134813613628<![CDATA[Assuring String Pattern Matching in Outsourced Middleboxes]]>263136213753635<![CDATA[FUSO: Fast Multi-Path Loss Recovery for Data Center Networks]]>and has spare congestion window slots. FUSO is fast in that it does not need to wait for timeout on the lossy sub-flow, and it is cautious in that it does not violate the congestion control algorithm. Testbed experiments and simulations show that FUSO decreases the latency-sensitive flows’ $99^{th}$ percentile FCT by up to ~82.3% in a 1-Gb/s testbed, and up to ~87.9% in a 10 Gb/s large-scale simulated network.]]>263137613892843<![CDATA[Statistical Admission Control in Multi-Hop Cognitive Radio Networks]]>average throughput is challenging and remains an open problem. We solve this problem analytically and use the solution as vehicle for BRAND–a centralized heuristic for computing the average bandwidth available with randomized scheduling between a source destination pair in cognitive radio networks. Driven by practical considerations, we introduce a distributed version of BRAND and prove its correctness. An extensive numerical analysis demonstrates the accuracy of BRAND and its enabling value in performing admission control.]]>263139014032407<![CDATA[Network-Aware Feasible Repairs for Erasure-Coded Storage]]>263140414173972<![CDATA[A Non-Monetary Mechanism for Optimal Rate Control Through Efficient Cost Allocation]]>263141814311621<![CDATA[Have You Recorded My Voice: Toward Robust Neighbor Discovery in Mobile Wireless Networks]]>263143214451868<![CDATA[A New Privacy Breach: User Trajectory Recovery From Aggregated Mobility Data]]>263144614593896<![CDATA[Taming Both Predictable and Unpredictable Link Failures for Network Tomography]]>network tomography, is an effective and efficient way to facilitate various network operations, such as network monitoring, load balancing, and fault diagnosis. Recently, there is a growing interest in the monitor placement problem that ensures link identifiability in a network with link failures. Unfortunately, existing work either assumes an ideal failure prediction model where all failures can be predicted perfectly or makes pessimistic assumptions that all failures are unpredictable. In this paper, we study the problem of placing a minimum number of monitors to identify additive link metrics [or additive by using the log($cdot $ ) function, e.g., loss rates] from end-to-end measurements among monitors with considering both predictable and unpredictable link failures. We propose a set of robust monitor placement algorithms with different performance-complexity tradeoffs to solve this tomography problem. In particular, we show that the optimal (i.e., minimum) monitor placement is the solution to a hitting set problem, for which, we provide a polynomial-time algorithm to construct the input. We formally prove that the proposed algorithms can guarantee network identifiability against failures based on the graph theory. Trace-driven evaluation results show the effectiveness and the robustness of our algorithms.]]>263146014731929<![CDATA[Node-Based Distributed Channel Access With Enhanced Delay Characteristics]]>263147414871392<![CDATA[OpenFunction: An Extensible Data Plane Abstraction Protocol for Platform-Independent Software-Defined Middleboxes]]>263148815012496<![CDATA[Data and Spectrum Trading Policies in a Trusted Cognitive Dynamic Network Architecture]]>263150215162298<![CDATA[Enhancing Localization Scalability and Accuracy via Opportunistic Sensing]]>263151715304189<![CDATA[Corrections to “Smoothed Online Resource Allocation in Multi-Tier Distributed Cloud Networks”]]>[1], there are two symbol typos in equations. In (1b), the “$forall t$ , $forall t$ ” should be “$forall i$ , $forall t$ ”. In (1d), the “$forall t$ , $forall t$ ” should be “$forall j$ , $forall ~t$ ”. IEEE regrets the error.]]>2631531153199<![CDATA[IEEE/ACM Transactions on Networking society information]]>263C3C31451<![CDATA[IEEE/ACM Transactions on Networking information for authors]]>263C4C480