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Regular Expression (RegEx) matching is the industry standard for Deep Packet Inspection (DPI) because RegExes are significantly more expressive than strings. To achieve high matching speed, we need to convert the RegExes to Deterministic Finite State Automata (DFA). However, DFA has the state explosion problem, that is, the number of DFA states and transitions can be exponential with the number of RegExes. Much work has addressed the DFA state explosion problem; however, none has met all the requirements of fast and automated construction, small memory image, and high matching speed. In this paper, we propose a decompositional approach, with fast and automated construction, small memory image, and high matching speed, to DFA state explosion. The first key idea is to decompose a complex RegEx that cause exponential state increases into a set of simpler RegExes that do not cause exponential state increases, where any character string that matches the complex RegEx also matches all the RegExes in the set of simpler RegExes; that is, the set of strings that match the complex RegEx is a subset of strings that match the set of simpler RegExes. The second key idea is to use a stateful post-processing engine to filter the matches that are actually the matches of the complex RegEx. Given an input string for matching, instead of using the large DFA constructed from the original complex RegEx to perform the matching, we first use the small DFA constructed from the set of simpler RegExes to perform the matching, and then, if the small DFA reports a match, we use the post-processing engine to determine whether it is a true match to the original complex RegEx. Because the pre-processing is simple, automaton construction can be automated and fast, and because most on-line processing is done by a DFA, its matching speed is close to that of a DFA alone. Our experimental results show that our decompositional approach achieves orders of magnitude faster DFA construction (in terms of seconds instead of minutes), 30 times smaller memory image, and 43% faster matching speeds, than state-of-the-art software based RegEx matching algorithms.

Communication networks shared by many users are a widespread challenge nowadays. In this paper we address several aspects of this challenge simultaneously: learning unknown stochastic network characteristics, sharing resources with other users while keeping coordination overhead to a minimum. The proposed solution combines Multi-Armed Bandit learning with a lightweight signalling-based coordination scheme, and ensures convergence to a stable allocation of resources. Our work considers single-user level algorithms for two scenarios: an unknown fixed number of users, and a dynamic number of users. Analytic performance guarantees, proving convergence to stable marriage configurations, are presented for both setups. The algorithms are designed based on a system-wide perspective, rather than focusing on single user welfare. Thus, maximal resource utilization is ensured. An extensive experimental analysis covers convergence to a stable configuration as well as reward maximization. Experiments are carried out over a wide range of setups, demonstrating the advantages of our approach over existing state-of-the-art methods.

The recent proliferation of human-carried mobile devices has given rise to mobile crowd sensing (MCS) systems that outsource sensory data collection to the public crowd. In order to identify truthful values from (crowd) workers’ noisy or even conflicting sensory data, truth discovery algorithms , which jointly estimate workers’ data quality and the underlying truths through quality-aware data aggregation, have drawn significant attention. However, the power of these algorithms could not be fully unleashed in MCS systems, unless workers’ strategic reduction of their sensing effort is properly tackled. To address this issue, in this paper, we propose a payment mechanism , named Theseus, that deals with workers’ such strategic behavior, and incentivizes high-effort sensing from workers. We ensure that, at the Bayesian Nash Equilibrium of the non-cooperative game induced by Theseus, all participating workers will spend their maximum possible effort on sensing, which improves their data quality. As a result, the aggregated results calculated subsequently by truth discovery algorithms based on workers’ data will be highly accurate. Additionally, Theseus bears other desirable properties, including individual rationality and budget feasibility . We validate the desirable properties of Theseus through theoretical analysis, as well as extensive simulations.

The inference of the network traffic data from partial measurements data becomes increasingly critical for various network engineering tasks. By exploiting the multi-dimensional data structure, tensor completion is a promising technique for more accurate missing data inference. However, existing tensor completion algorithms generally have the strong assumption that the tensor data have a global low-rank structure, and try to find a single and global model to fit the data of the whole tensor. In a practical network system, a subset of data may have stronger correlation. In this work, we propose a novel localized tensor completion model (LTC) to increase the data recovery accuracy by taking advantage of the stronger local correlation of data to form and recover sub-tensors each with a lower rank. Despite that it is promising to use local tensors, the finding of correlated entries faces two challenges, the data with adjacent indexes are not ones with higher correlation and it is difficult to find the similarity of data with missing tensor entries. To conquer the challenges, we propose several novel techniques: efficiently calculating the candidate anchor points based on locality-sensitive hash (LSH), building sub-tensors around properly selected anchor points, encoding factor matrices to facilitate the finding of similarity with missing entries, and similarity-aware local tensor completion and data fusion. We have done extensive experiments using real traffic traces. Our results demonstrate that LTC is very effective in increasing the tensor recovery accuracy without depending on specific tensor completion algorithms.

When network is undergoing problems such as congestion, scan attack, DDoS attack, etc , measurements are much more important than usual. In this case, traffic characteristics including available bandwidth, packet rate, and flow size distribution vary drastically, significantly degrading the performance of measurements. To address this issue, we propose the Elastic sketch. It is adaptive to currently traffic characteristics. Besides, it is generic to measurement tasks and platforms. We implement the Elastic sketch on six platforms: P4, FPGA, GPU, CPU, multi-core CPU, and OVS, to process six typical measurement tasks. Experimental results and theoretical analysis show that the Elastic sketch can adapt well to traffic characteristics. Compared to the state-of-the-art, the Elastic sketch achieves 44.6 ~ 45.2 times faster speed and 2.0 ~ 273.7 smaller error rate.

Set representation is a crucial functionality in various areas such as networking and databases. In many applications, memory and time constraints allow only an approximate representation where errors can appear for some queried elements. The Variable-Increment Counting Bloom Filter (VI-CBF) is a popular data structure for the representation of dynamically-changing sets, achieving a good tradeoff between memory efficiency and queries accuracy. For some applications, the required accuracy is higher than that enabled by the VI-CBF. In this paper, we present the Tandem Counting Bloom Filter (T-CBF), a new data structure that relies on the interaction among counters to describe sets with higher accuracy. We analyze its performance and show that by a joint consideration of counters, the T-CBF always performs better than the VI-CBF and it can for some configurations reduce its false positive probability by an order of magnitude. The overhead of such an approach is expressed upon an element insertion or query as read or write operations to a pair of counters rather than a single counter in each hash location. The operations themselves also require considering a larger number of scenarios.

Runtime performance variability has been a major issue, hindering predictable and scalable performance in modern distributed systems. Executing requests or jobs redundantly over multiple servers have been shown to be effective for mitigating variability, both in theory and practice. Systems that employ redundancy has drawn significant attention, and numerous papers have analyzed the pain and gain of redundancy under various service models and assumptions on the runtime variability. This paper presents a cost (pain) vs. latency (gain) analysis of executing jobs of many tasks by employing replicated or erasure coded redundancy. The tail heaviness of service time variability is decisive on the pain and gain of redundancy and we quantify its effect by deriving expressions for cost and latency. Specifically, we try to answer four questions: 1) How do replicated and coded redundancy compare in the cost vs. latency tradeoff? 2) Can we introduce redundancy after waiting some time and expect it to reduce the cost? 3) Can relaunching the tasks that appear to be straggling after some time help to reduce cost and/or latency? 4) Is it effective to use redundancy and relaunching together? We validate the answers we found for each of these questions via simulations that use empirical distributions extracted from a Google cluster data.

Big data transfer in next-generation scientific applications is now commonly carried out over dedicated channels in high-performance networks (HPNs), where transport protocols play a critical role in maximizing application-level throughput. Optimizing the performance of these protocols is challenging: i) transport protocols perform differently in various network environments, and the protocol choice is not straightforward; ii) even for a given protocol in a given environment, different parameter settings of the protocol may lead to significantly different performance and oftentimes the default setting does not yield the best performance. However, it is prohibitively time-consuming to conduct exhaustive transport profiling due to the large parameter space. In this paper, we propose a PRofiling Optimization Based DAta Transfer Advisor (ProbData) to help end users determine the most effective transport method with the most appropriate parameter settings to achieve satisfactory performance for big data transfer over dedicated connections in HPNs. ProbData employs a fast profiling scheme based on the Simultaneous Perturbation Stochastic Approximation algorithm, namely, FastProf, to accelerate the exploration of the optimal operational zones of various transport methods to improve profiling efficiency. We first present a theoretical background of the optimized profiling approach in ProbData and then detail its design and implementation. The advising procedure and performance benefits of FastProf and ProbData are illustrated and evaluated by both extensive emulations based on real-life performance measurements and experiments over various physical connections in existing production HPNs.

Ubiquitous mobile devices with rich sensors and advanced communication capabilities have given rise to mobile crowdsensing systems. The diverse reliabilities of mobile users and the openness of sensing paradigms raise concerns for data trustworthiness, user privacy, and incentive provision. Instead of considering these issues as isolated modules in most existing researches, we comprehensively capture both conflict and inner-relationship among them. In this paper, we propose a holistic solution for trustworthy and privacy-aware mobile crowdsensing with no need of a trusted third party. Specifically, leveraging cryptographic technologies, we devise a series of protocols to enable benign users to request tasks, contribute their data, and earn rewards anonymously without any data linkability. Meanwhile, an anonymous trust/reputation model is seamlessly integrated into our scheme, which acts as reference for our fair incentive design, and provides evidence to detect malicious users who degrade the data trustworthiness. Particularly, we first propose the idea of limiting the number of issued pseudonyms which serves to efficiently tackle the anonymity abuse issue. Security analysis demonstrates that our proposed scheme achieves stronger security with resilience against possible collusion attacks. Extensive simulations are presented which demonstrate the efficiency and practicality of our scheme.

RDMA is becoming prevalent because of its low latency, high throughput and low CPU overhead. However, in current datacenters, RDMA remains a single path transport which is prone to failures and falls short to utilize the rich parallel network paths. Unlike previous multi-path approaches, which mainly focus on TCP, this paper presents a multi-path transport for RDMA, i.e. MP-RDMA, which efficiently utilizes the rich network paths in datacenters. MP-RDMA employs three novel techniques to address the challenge of limited RDMA NICs on-chip memory size: 1) a multi-path ACK-clocking mechanism to distribute traffic in a congestion-aware manner without incurring per-path states; 2) an out-of-order aware path selection mechanism to control the level of out-of-order delivered packets, thus minimizes the meta data required to them; 3) a synchronise mechanism to ensure in-order memory update whenever needed. With all these techniques, MP-RDMA only adds 66B to each connection state compared to single-path RDMA. Our evaluation with an FPGA-based prototype demonstrates that compared with single-path RDMA, MP-RDMA can significantly improve the robustness under failures ( $2\times \sim 4\times $ higher throughput under 0.5%~10% link loss ratio) and improve the overall network utilization by up to 47%.

Due to the random delay, local maximum and data congestion in vehicular networks, the design of a routing is really a challenging task especially in the urban environment. In this paper, a distributed routing protocol DGGR is proposed, which comprehensively takes into account sparse and dense environments to make routing decisions. As the guidance of routing selection, a road weight evaluation (RWE) algorithm is presented to assess road segments, the novelty of which lies that each road segment is assigned a weight based on two built delay models via exploiting the real-time link property when connected or historic traffic information when disconnected. With the RWE algorithm, the determined routing path can greatly alleviate the risk of local maximum and data congestion. Specially, in view of the large size of a modern city, the road map is divided into a series of Grid Zones (GZs). Based on the position of the destination, the packets can be forwarded among different GZs instead of the whole city map to reduce the computation complexity, where the best path with the lowest delay within each GZ is determined. The backbone link consisting of a series of selected backbone nodes at intersections and within road segments, is built for data forwarding along the determined path, which can further avoid the MAC contentions. Extensive simulations reveal that compared with some classic routing protocols, DGGR performs best in terms of average transmission delay and packet delivery ratio by varying the packet generating speed and density.

Modern data-center applications generate a diverse mix of short and long flows with different performance requirements and weaknesses. The short flows are typically delay-sensitive but to suffer the head-of-line blocking and out-of-order problems. Recent solutions prioritize the short flows to meet their latency requirements, while damaging the throughput-sensitive long flows. To solve these problems, we design a Coding-based Adaptive Packet Spraying (CAPS) that effectively mitigates the negative impact of short and long flows on each other. To exploit the availability of multiple paths and avoid the head-of-line blocking, CAPS spreads the packets of short flows to all paths, while the long flows are limited to a few paths with Equal Cost Multi Path (ECMP). Meanwhile, to resolve the out-of-order problem with low overhead, CAPS encodes the short flows using forward error correction (FEC) technology and adjusts the coding redundancy according to the blocking probability. Moreover, since the coding efficiency decreases when the coding unit is too small or large, we demonstrate how to obtain the optimal size of coding unit. The coding layer is deployed between the TCP and IP layers, without any modifications on the existing TCP/IP protocols. The test results of NS2 simulation and small-scale testbed experiments show that CAPS significantly reduces the average flow completion time of short flows by ~30%–70% over the state-of-the-art multipath transmission schemes and achieves the high throughput for long flows with negligible traffic overhead.

In this paper, we study high reliability, low latency and cost effective network planning for industrial wireless mesh networks. Based on the requirements of routing reliability, minimum end-to-end delay and reduced deployment cost in wireless mesh networks, such as WirelessHART network, we propose three network planning approaches following the principles of the shortest hops, the least routers and balance of the shortest hops and the least routers, respectively. We then implement the proposed algorithms respectively to generate the network deployment for a given factory layout. Simulation results show that there exists a performance trade-off between these three algorithms. The proposed algorithms have also been implemented and validated in an NS-2 WirelessHART network simulator.

Thanks to its computational and forwarding capabilities, the mobile network infrastructure can support several third-party (“vertical”) services, each composed of a graph of virtual (network) functions (VNFs). Importantly, one or more VNFs are often common to multiple services, thus the services deployment cost could be reduced by letting the services share the same VNF instance instead of devoting a separate instance to each service. By doing that, however, it is critical that the target KPI (key performance indicators) of all services are met. To this end, we study the VNF sharing problem and make decisions on 1) when sharing VNFs among multiple services is possible, 2) how to adapt the virtual machines running the shared VNFs to the combined load of the assigned services, and 3) how to prioritize the services traffic within shared VNFs. All decisions aim to minimize the cost for the mobile operator, subject to requirements on end-to-end service performance, e.g., total delay. Notably, we show that the aforementioned priorities should be managed dynamically and vary across VNFs. We then propose the FlexShare algorithm to provide near-optimal VNF-sharing and priority assignment decisions in polynomial time. We prove that FlexShare is within a constant factor from the optimum and, using real-world VNF graphs, we show that it consistently outperforms baseline solutions.

Traffic monitoring is essential to manage large networks and validate Service Level Agreements. Passive monitoring is particularly valuable to promptly identify transient fault episodes and react in a timely manner. This article proposes a novel, non-invasive and flexible method to passively monitor large backbone networks. By using only packet counters, commonly available on existing hardware, we can accurately measure packet losses, in different segments of the network, affecting only specific flows. We can monitor not only end-to-end flows, but any generic flow with packets following several different paths in the network (multipoint flows). We also sketch a possible extension of the method to measure average one-way delay for multipoint flows, provided that the measurement points are synchronized. Through various experiments we show that the method is effective and enables easy zooming in on the cause of packet losses. Moreover, the method can scale to very large networks with a very low overhead on the data plane and the management plane.

Due to the popularity of mobile computing and mobile sensing, users’ traces can now be readily collected to enhance applications’ performance. However, users’ location privacy may be disclosed to the untrusted data aggregator that collects users’ traces. Cloaking users’ traces with synthetic traces is a prevalent technique to protect location privacy. But the existing work that synthesizes traces suffers from the social relationship based de-anonymization attacks. To this end, we propose $W^{3}{-}tess$ that synthesizes privacy-preserving traces via enhancing the plausibility of synthetic traces with social networks. The main idea of $W^{3}{-}tess$ is to credibly imitate the temporal, spatial, and social behavior of users’ mobility, sample the traces that exhibit similar three-dimension mobility behavior, and synthesize traces using the sampled locations. By doing so, $W^{3}{-}tess$ can provide “ differential privacy ” on location privacy preservation. In addition, compared to the existing work, $W^{3}{-}tess$ offers several salient features. First, both location privacy preservation and data utility guarantees are theoretically provable. Second, it is applicable to most geo-data analysis tasks performed by the data aggregator. Experiments on two real-world datasets, loc-Gwalla and loc-Brightkite, have demonstrated the effectiveness and efficiency of $W^{3}{-}tess$ .

For many important network types, physical coordinate systems and physical distances are either difficult to discern or inapplicable. Accordingly, coordinate systems and characterizations based on hop-distance measurements, such as Topology Preserving Maps (TPMs) and Virtual-Coordinate (VC) systems are attractive alternatives to geographic coordinates for many network algorithms. We present an approach to recover geometric and topological properties of a network with a small set of distance measurements. The approach is a combination of shortest path (often called geodesic) recovery concepts and low-rank matrix completion, generalized to the case of hop-distances in graphs. Results for sensor networks embedded in 2-D and 3-D spaces as well as for social networks indicate that the method can accurately capture the network connectivity with a small set of measurements. TPM generation can now be based on various context appropriate measurements or VC systems instead of distances to a set of global anchors. The proposed method is a significant generalization that allows the topology to be extracted from a random set of graph shortest paths, making it applicable in contexts such as social networks where VC generation may not be possible.

This paper proposes and investigates a framework for clique gossip protocols. As complete subnetworks, the existence of cliques is ubiquitous in various social, computer, and engineering networks. By clique gossiping, nodes interact with each other along a sequence of cliques. Clique-gossip protocols are defined as arbitrary linear node interactions where node states are vectors evolving as linear dynamical systems. Such protocols become clique-gossip averaging algorithms when node states are scalars under averaging rules. We generalize the classical notion of line graph to capture the essential node interaction structure induced by both the underlying network and the specific clique sequence. We prove a fundamental eigenvalue invariance principle for periodic clique-gossip protocols, which implies that any permutation of the clique sequence leads to the same spectrum for the overall state transition when the generalized line graph contains no cycle. We also prove that for a network with $n$ nodes, cliques with smaller sizes determined by factors of $n$ can always be constructed leading to finite-time convergent clique-gossip averaging algorithms, provided $n$ is not a prime number. Particularly, such finite-time convergence can be achieved with cliques of equal size $m$ if and only if $n$ is divisible by $m$ and they have exactly the same prime factors. A proven fastest finite-time convergent clique-gossip algorithm is constructed for clique-gossiping using size- $m$ cliques. Additionally, the acceleration effects of clique-gossiping are illustrated via numerical examples.

In-Network Processing (INP) is an effective way to aggregate and process data from different sources and forward the aggregated data to other nodes for further processing until it reaches the end user. There is a trade-off between energy consumption for processing data and communication energy spent on transferring the data. An essential requirement in the INP process is to ensure that the user expectation of quality of information (QoI) is delivered during the process. Using wireless sensor networks for illustration and with the aim of minimizing the total energy consumption of the system, we study and formulate the trade-off problem as a nonlinear optimization problem where the goal is to determine the optimal data reduction rate, while satisfying the QoI required by the user. The formulated problem is a Signomial Programming (SP) problem, which is a non-convex optimization problem. We propose two solution frameworks. First, we introduce an equivalent problem which is still SP and non-convex as the original one, but we prove that the strong duality property holds, and propose an efficient distributed algorithm to obtain the optimal data reduction rates, while delivering the required QoI. The second framework applies to the system with identical nodes and parameter settings. In such cases, we prove that the complexity of the problem can be reduced logarithmically. We evaluate our proposed frameworks under different parameter settings and illustrate the validity and performance of the proposed techniques through extensive simulation.

How to combine multiple data-streams for transmission in aggregate is a very interesting problem, especially for the emerging software-defined networks nowadays. The conventional packet-based protocols cannot provide the flexibility for combining data-streams in the ad hoc nature. If the number of data-streams changes over time, the existing packet formats cannot handle the transmission of multiple data-streams effectively. The effectiveness is measured by two performance metrics, namely coding efficiency and data-transmission intermittency. We propose a new software-defined multiplexing code (SDMC) approach, which can combine (multiplex) multiple data-streams easily and is much more effective than the conventional packet-based method. Three SDMC schemes (distributed, hierarchical, and hybrid) are compared theoretically and by simulation. A trade-off between these two performance metrics can be found when one selects one of the three SDMC schemes for combining multiple data-streams. The hierarchical SDMC scheme brings about the highest coding efficiency while the hybrid SDMC scheme suffers from the smallest overall intermittency.

Data center has become an important facility for hosting various applications. For data center networks, deadline missing rate and average flow completion time are two main metrics for the performance of applications. In this paper, we find deadline-aware methods can only reduce the percentage of flows missing deadline, while flowsize-aware and information-cumulative methods can only optimize the average flow completion time. However, traffic in data center is the mixture of various flows and focusing on the single goal is not enough. We advocate to incorporate deadline and flow duration time into flow rate control. Then we design DA&FD (Deadline-Aware and Flow Duration) based rate control mechanism and analyze its performance in theory. At last, we evaluate DA&FD under different topologies, real world traffic and load scenarios, both by simulation and in real testbed. Our results show that DA&FD performs close to D 2 TCP and about 15%, 25%, 30%, 35% better than Ameon, L 2 DCT, Karuna, DCTCP on deadline missing rate. For average FCT, the performance of DA&FD is similar to L 2 DCT and compared with Ameon, D 2 TCP, Karuna, DCTCP, DA&FD can reduce average FCT by 10%, 15%, 20%, 25%.

In edge-cloud computing, a set of servers (called edge servers) are deployed near the mobile devices to allow these devices to offload their jobs to and subsequently obtain their results from the edge servers with low latency. One fundamental problem in edge-cloud systems is how to dispatch and schedule the jobs so that the job response time (defined as the interval between the release of the job and the arrival of the computation result at the device) is minimized. In this paper, we propose a general model for this problem, where the jobs are generated in arbitrary order and at arbitrary times at the mobile devices and then offloaded to servers with both upload and download delays. Our goal is to minimize the total weighted response time of all the jobs. The weight is set based on how latency-sensitive the job is. We derive the first online job dispatching and scheduling algorithm in edge-clouds, called OnDisc , which is scalable in the speed augmentation model; that is, OnDisc is $(1 + \varepsilon )$ -speed $O(1/\varepsilon )$ -competitive for any small constant $\varepsilon >0$ . Moreover, OnDisc can be easily implemented in distributed systems. We also extend OnDisc with a fairness knob to incorporate the trade-off between the average job response time and the degree of fairness among jobs. Extensive simulations based on a real-world data-trace from Google show that OnDisc can reduce the total weighted response time dramatically compared with heuristic algorithms.

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We propose a new latent Boolean feature model for complex networks that captures different types of node interactions and network communities. The model is based on a new concept in graph theory, termed the Boolean intersection representation of a graph, which generalizes the notion of an intersection representation. We mostly focus on one form of Boolean intersection, termed cointersection, and describe how to use this representation to deduce node feature sets and their communities. We derive several general bounds on the minimum number of features used in cointersection representations and discuss graph families for which exact cointersection characterizations are possible. Our results also include algorithms for finding optimal and approximate cointersection representations of a graph.

We consider scheduling in wireless networks and formulate it as Maximum Weighted Independent Set (MWIS) problem on a "conflict" graph that captures interference among simultaneous transmissions. We propose a novel, low-complexity, and fully distributed algorithm that yields high-quality feasible solutions. Our proposed algorithm consists of two phases, each of which requires only local information and is based on message-passing. The first phase solves a relaxation of the MWIS problem using a gradient projection method. The relaxation we consider is tighter than the simple linear programming relaxation and incorporates constraints on all cliques in the graph. The second phase of the algorithm starts from the solution of the relaxation and constructs a feasible solution to the MWIS problem. We show that our algorithm always outputs an optimal solution to the MWIS problem for perfect graphs. Simulation results compare our policies against Carrier Sense Multiple Access (CSMA) and other alternatives and show excellent performance.