Error-sensitive proof-labeling schemes

Verifying that a network configuration satisfies a given boolean predicate is a fundamental problem in distributed computing. Many variations of this problem have been studied, for example, in the context of proof labeling schemes (PLS), locally checkable proofs (LCP), and non-deterministic local decision (NLD). In all of these contexts, verification time is assumed to be constant. Korman, Kutten and Masuzawa presented a proof-labeling scheme for MST, with poly-logarithmic verification time, and logarithmic memory at each vertex. In this paper we introduce the notion of a t-PLS, which allows the verification procedure to run for super-constant time. Our work analyzes the tradeoffs of t-PLS between time, label size, message length, and computation space. We construct a universal t-PLS and prove that it uses the same amount of total communication as a known one-round universal PLS, and t factor smaller labels. In addition, we provide a general technique to prove lower bounds for space- time tradeoffs of t-PLS. We use this technique to show an optimal tradeoff for testing that a network is acyclic (cycle free). Our optimal t-PLS for acyclicity uses label size and computation space O((log n)/t). We further describe a recursive O(log* n) space verifier for acyclicity which does not assume previous knowledge of the run-time t.

Classical mathematical models of information sharing and updating in multi-agent networks use linear operators. In the paradigmatic DeGroot model, for example, agents update their states with linear combinations of their neighbors’ current states. In prior work, an accelerated averaging model employing the use of memory has been suggested to accelerate convergence to a consensus state for undirected networks. There, the DeGroot update on the current states is followed by a linear combination with the previous states. We propose a modification where the DeGroot update is applied to the current and previous states and is then followed by a linear combination step. We show that this simple modification applied to undirected networks permits convergence even for periodic networks. Further, it allows for faster convergence than DeGroot and accelerated averaging models for suitable networks and model parameters.

This article examines the current state of the road network of Uzbekistan. The state of the road network of Uzbekistan in relation to various countries of the world in terms of density, the specific weight of paved roads and the specific weight of gross domestic product (GDP) per capita is analyzed. The dynamics of funds spent on the public road network in Uzbekistan is considered. Conclusions and proposals for the development of the road network are presented.

Packet loss in a networked control system (NCS) is a stochastic event that depends on the current network state, which, as an abstract and hidden variable, can not be well defined but can be perceived by the observation of the packet-loss process. We model the network in an NCS as a Markov chain and the packet losses as stochastic events whose probability depends on the Markov chain state (i.e. network state). Thus, the network state estimation is a hidden Markov model (HMM) estimation problem. To derive the HMM of the NCS under study, we propose the expectation maximization (EM) algorithm to estimate the parameters of the HMM based on the packet dropping sequence. Finally, a simulation example illustrates the effectiveness of the proposed approach.

In this paper, a proactive dynamic spectrum sharing scheme between 4G and 5G systems is proposed. In particular, a controller decides on the resource split between NR and LTE every subframe while accounting for future network states such as high interference subframes and multimedia broadcast single frequency network (MBSFN) subframes. To solve this problem, a deep reinforcement learning (RL) algorithm based on Monte Carlo Tree Search (MCTS) is proposed. The introduced deep RL architecture is trained offline whereby the controller predicts a sequence of future states of the wireless access network by simulating hypothetical bandwidth splits over time starting from the current network state. The action sequence resulting in the best reward is then assigned. This is realized by predicting the quantities most directly relevant to planning, i.e., the reward, the action probabilities, and the value for each network state. Simulation results show that the proposed scheme is able to take actions while accounting for future states instead of being greedy in each subframe. The results also show that the proposed framework improves system-level performance.

The efficiency of operation and interaction of “sea ferry/cruise company-sea passenger port-land transport infrastructure” systems directly depends on the dynamic influence of the external environment, which today determines the transition from deterministic models of decision-making to probabilistic ones. These changes on the basis of current trends in the industry in the form of the introduction of large cruise and ferry ships create the need to form a decision- making system for the reasonable modernization of infrastructure for the heads of ports and terminals. It is necessary to systematically modernize both the berthing infrastructure and change the internal processes of passenger handling in the terminal. The problems of forecasting the development of seaports in connection with the gradual recovery of passenger traffic, caused by a long period of restrictions due to COVID-19, are considered in the paper, as the first priority is the resumption of the route networks of the sea regions, which provide local passenger mobility. Based on the investigated models of ports and terminals evolution (Anyport model, Three Generations model, Workport model) it is revealed that today it is necessary to evaluate possible predictive scenarios of port development. The necessity of having the tools allowing to operatively analyze the situation in the sphere of sea ferry transportation and to model the scenarios of port congestion has been revealed and proved. The approach to estimate the variants of berths loading at simultaneous call of several big cruise vessels on the basis of models of mass service systems for subsequent estimation of infrastructure is considered in the paper. The object of the research is the Baltic Sea region and existing routes of cruise and ferry lines. A new theoretical partitioning of the region and sea passenger ports into four sections is proposed. The systematic analysis of all existing cruise and ferry line routes in each area has been carried out; data on cruise and ferry ship size, passenger capacity, average ticket price, and distance between terminals on the route have been presented. On the basis of the formed approach a new digital dynamic route map that allows both to investigate the current state of the route network and to estimate the possible changes is built. On the basis of the received data it is proved that the best development of passenger sea ports and terminals infrastructure is observed in the segment between the countries of Russia-Estonia-Finland-Sweden. Presented sea terminals have successfully realized the possibility of simultaneous reception of two big cruise vessels and modernized infrastructure during the period of restrictions on the reason of COVID-19, which corresponds to the formation of new points of growth in the Baltic Sea region, taking into account recovery of route networks. The generated big data on route networks accurately define the current state in the sphere of sea ferry/cruise transportation and they should be used in new digital simulation transport models when modeling different variants of sea passenger port operation, both at the stage of short-term planning and when solving the group of questions of development forecasting, which is especially relevant in the situation of competition between terminals for passenger traffic.

Current State and Trend of Facility Network

Mobility in ad hoc networks causes frequent link failures, which in turn causes packet losses. TCP attributes these packet losses to congestion. This incorrect inference results in frequent TCP re-transmission time-outs and therefore a degradation in TCP performance even at light loads. In this work, a new TCP enhanced scheme for mobile ad hoc networks is presented. The key design novelty is to identify the network states based on end-to-end measurements in TCP receiver, which successively dispatches the feedback on current network states to TCP sender via the ACK packets. Consequently, the sender takes corresponding measures. Moreover, this paper investigates the reasons of TCP instability in static ad hoc wireless networks and proposes schemes combining MAC layer and route layer enhancements to solve this problem.

Our previous work suggests that the current congestion controls for cable networks are inadequate when network participants act strategically. A feedback control could result in greater utilization and fairness of cable networks. In order to design a controller for a network, an observer is needed to evaluate the current network state and traffic flows. This paper proposes an observer model and design for cable networks. The observer design is recast into an online traffic classification problem. Using dynamic network traffic phases observed through simulation, the network classifier is designed with a hidden Markov model to determine the network states.

Military communications at the tactical edge consists of unreliable, disrupted, and limited bandwidth networks, which can lead to the delay and loss of critical information. These networks are increasingly being used for the transmission of digital command and control (C2) information, requiring timely and accurate transmission, and play a vital role in the outcome of military operations. Machine Learning (ML) techniques have the potential to improve operational outcomes by autonomously prioritizing the delivery of the most important information through these networks, using observations of the current mission and network state. This paper covers the experimental process and the operational metric used for comparison between the ML and a non-ML approach that sorts messages in a fixed order. We present two regression-based supervised-learning methods that were shown to be more effective in both medium and high congested networks than the non-ML approach.

Virtual network embedding (VNE), as the key challenge of network resource management technology, lies in the contradiction between online embedding decision and pursuing long-term average revenue goals. Most of the previous work ignored the dynamics in Virtual Network (VN) modeling, or could not automatically detect the complex and time-varying network state to provide a reasonable network embedding scheme. In view of this, we model a network embedding framework where the topology and resource allocation change dynamically with the number of network users and workload, and then introduce a deep reinforcement learning method to solve the VNE problem. Further, a dynamic virtual network embedding algorithm based on Deep Reinforcement Learning (DRL), named DVNE-DRL, is proposed. In DVNE-DRL, VNE is modeled as a Markov Decision Process (MDP), and then deep learning is introduced to perceive the current network state through historical data and embedded knowledge, while utilizing reinforcement learning decision-making capabilities to implement the network embedding process. In addition, we improve the method of feature extraction and matrix optimization, and consider the characteristics of virtual network and physical network together to alleviate the problem of redundancy and slow convergence. The simulation results show that compared with the existing advanced algorithms, the acceptance rate and average revenue of DVNE-DRL are increased by about 25% and 35%, respectively.

We present Statesman, a network-state management service that allows multiple network management applications to operate independently, while maintaining network-wide safety and performance invariants. Network state captures various aspects of the network such as which links are alive and how switches are forwarding traffic. Statesman uses three views of the network state. In observed state, it maintains an up-to-date view of the actual network state. Applications read this state and propose state changes based on their individual goals. Using a model of dependencies among state variables, Statesman merges these proposed states into a target state that is guaranteed to maintain the safety and performance invariants. It then updates the network to the target state. Statesman has been deployed in ten Microsoft Azure datacenters for several months, and three distinct applications have been built on it. We use the experience from this deployment to demonstrate how Statesman enables each application to meet its goals, while maintaining network-wide invariants.

When diagnosing network problems, it is desirable to have a view of the traffic inside the network. This can be achieved by profiling the traffic. A fully profiled traffic can contain significant information of the network's current state, and can be further used to detect anomalous traffic. Many has addressed problems of profiling network traffic, but unfortunately there are no specific profiles could lasts forever for one particular network, since network traffic characteristic always changes over and over based on the sum of nodes, software that being used, type of access, etc. This paper introduces an online adaptive system using evolving connectionist systems based connectionist model to profile network traffic in continuous manner while at the same time try to detect anomalous activity inside the network in real-time and adapt with changes if necessary. Different from an offline approach, which usually profile network traffic using previously captured data for a certain period of time, an online and adaptive approach can use a shorter period of data capturing and evolve its profile if the characteristics of the network traffic has changed.

Understanding user equilibrium states of road networks: Evidence from two Chinese mega-cities using taxi trajectory mining

Existing compact routing schemes, e.g., Thorup and Zwick [SPAA 2001] and Chechik [PODC 2013], often have no means to tolerate failures, once the system has been setup and started. This paper presents, to our knowledge, the first self-healing compact routing scheme. Besides, our schemes are developed for low memory nodes, i.e., nodes need only O(log2n) memory, and are thus, compact schemes.We introduce two algorithms of independent interest: The first is CompactFT, a novel compact version (using only O(log n) local memory) of the self-healing algorithm Forgiving Tree of Hayes et al. [PODC 2008]. The second algorithm (CompactFTZ) combines CompactFT with Thorup-Zwick's tree-based compact routing scheme [SPAA 2001] to produce a fully compact self-healing routing scheme. In the self-healing model, the adversary deletes nodes one at a time with the affected nodes self-healing locally by adding few edges. CompactFT recovers from each attack in only O(1) time and Δ messages, with only +3 degree increase and O(logΔ) graph diameter increase, over any sequence of deletions (Δ is the initial maximum degree).Additionally, CompactFTZ guarantees delivery of a packet sent from sender s as long as the receiver t has not been deleted, with only an additional O(y logΔ) latency, where y is the number of nodes that have been deleted on the path between s and t. If t has been deleted, s gets informed and the packet removed from the network.