Large-scale Networked Systems Research Articles

QuickPoint: Efficiently Identifying Densest Sub-Graphs in Online Social Networks for Event Stream Dissemination

Efficient event stream dissemination is a challenging problem in large-scale Online Social Network (OSN) systems due to the costly inter-server communications caused by the per-user view data storage. To solve the problem, previous schemes mainly explore the structures of social graphs to reduce the inter-server traffic. Based on the observation of high cluster coefficients in OSNs, a state-of-the-art social piggyback scheme can save redundant messages by exploiting an intrinsic hub-structure in an OSN graph for message piggybacking. Essentially, finding the best hub-structure for piggybacking is equivalent to finding a variation of the densest sub-graph. The existing scheme computes the best hub-structure by iteratively removing the node with the minimum weighted degree. Such a scheme incurs a worst computation cost of $O(n^2)$ O ( n 2 ) , making it not scalable to large-scale OSN graphs. Using alternative hub-structure instead of the best hub-structure can speed up the piggyback assignment. However, they greatly sacrifice the communication efficiency of the assignment schedule. Different from the existing designs, in this work, we propose a QuickPoint algorithm, which removes a fraction of nodes in each iteration in finding the best hub-structure. We mathematically prove that QuickPoint converges in $O(log_an) (a>1)$ O ( l o g a n ) ( a > 1 ) iterations in finding the best hub-structure for efficient piggyback. We implement QuickPoint in parallel atop Pregel, a vertex-centric distributed graph processing platform. Comprehensive experiments using large-scale data from Twitter and Flickr show that our scheme is 38.8× more efficient compared to existing schemes.

Event-based multirate control of large-scale distributed nonlinear systems subject to time-driven zero order holds

The event-based output-feedback control is studied for large-scale distributed networked nonlinear systems. All subsystems are described by interval type-2 (IT2) Takagi–Sugeno (T–S) model and communicate information to each other via distributed networks with time-varying transmission delays. The sampled rates of sensors and refresh rates of actuators are allowed to be different among subsystems. We propose an event-based control scheme in the sense that each subsystem determines its information transmission to other subsystems based on its local sampled output measurements. First, combined with the descriptor system and perturbed system approaches, an equivalent model transformation is established. Then, a relaxing Lyapunov functional is introduced by virtue of Wirtinger inequality. The corresponding co-design criteria are obtained in the form of linear matrix inequalities (LMIs), which guarantee stabilization of the closed-loop fuzzy control system with different sampling and updating rates while transmitting information via networks as little as possible. Furthermore, employing some bounding inequality techniques, a considerable reduction in the number of LMIs can be achieved. Finally, a practical application to a DC microgrid with three PV subsystems is exploited.

Foreground Extraction and Motion Recognition Technology for Intelligent Video Surveillance

With the rapid development of computer technology and network technology, it has become possible to build a large-scale networked video surveillance system. The video surveillance system has become a new type of infrastructure necessary for modern cities. In this paper, the problem of foreground extraction and motion recognition in intelligent video surveillance is studied. The three key sub-problems, namely the extraction of motion foreground in video, the deblurring of motion foreground and the recognition of human motion, are studied and corresponding solutions are proposed. A background modeling technique based on video block is proposed. The background is modeled at the block level, which greatly reduces the spatial complexity of the algorithm. It solves the problem that the traditional Gaussian model (GMM) moving target enters the static state and is integrated into the background process. The target starts to move for a long time and there are ghosts and other problems, which reduce the processing efficiency of the lifting algorithm. The test results on the Weizmann dataset show that the proposed algorithm can achieve high human motion recognition accuracy and recognition with low computational complexity. The rate can reach 100%; the local constrained group sparse representation classification (LGSRC) model is used to classify it. The experimental results on Weizmann, KTH, UCF sports and other test datasets confirm the validity of the algorithm in this chapter. KNN, SRC voting classification accuracy.

Hierarchical Attack Identification for Distributed Robust Nonlinear Control

Developing tools for attack identification in large-scale networked control systems is a research area of increasing significance for the secure and reliable operation of autonomous control systems. Due to scalability limits and privacy issues of individual subsystems, attack identification methods should not rely on global model knowledge. We address systems of interconnected nonlinear subsystems with coupled dynamics or constraints in a distributed control setup. The local controllers share information about the coupling variables of the subsystems and are designed to be robust towards attacks and uncertain influences through neighboring subsystems. We present a scalable hierarchical attack identification method which monitors the evolution of the coupling variables after an attack occurred in some unknown subsystem. Based on the mutual exchange of local sensitivity information among the subsystems, the propagation of the attack through the network is approximated. The propagation equations are used to formulate a quadratic program whose solution determines the attack signal that explains the observed network evolution best. The developed approach is applied to the IEEE 30 bus system to illustrate attack identification in power systems with faulty buses.

Controllability maximization of large-scale systems using projected gradient method

In this work, we formulate two controllability maximization problems for large-scale networked dynamical systems such as brain networks: The first problem is a sparsity constraint optimization problem with a box constraint. The second problem is a modified problem of the first problem, in which the state transition matrix is Metzler. In other words, the second problem is a realization problem for a positive system. We develop a projected gradient method for solving the problems, and prove global convergence to a stationary point with locally linear convergence rate. The projections onto the constraints of the first and second problems are given explicitly. Numerical experiments using the proposed method provide non-trivial results. In particular, the controllability characteristic is observed to change with increase in the parameter specifying sparsity, and the change rate appears to be dependent on the network structure.

Special Issue “Uncertainties in large-scale networked control systems”

Special Issue “Uncertainties in large-scale networked control systems”

Optimisation of classification algorithm of associated data features of large-scale network system

Data feature extraction is vulnerable to external interferences during traditional classification of associated data of large-scale network system, resulting in low classification accuracy and large time consumption, so an optimised classification algorithm of large-scale network system associated data features based on deep learning is proposed. In this algorithm, associated data of large-scale network system is acquired through the multi-sensor quantisation fusion method, and the acquired data is done with spectral decomposition to obtain the convergence conditions for feature components of associated data of large-scale network system; then the feature components are processed based on a spectrum analysis model to extract features of associated data of large-scale network system; the features of the associated data are done with piecewise regression analysis, and data samples are output in classification based on the deep learning algorithm. Simulation results show that the proposed method can accurately classify associated data with relatively short learning steps with accuracy 24.7% higher than that of the ARMA classification method and 22.6% higher than that of the decision tree classification method. It is verified that the method proposed in this paper is obviously better than the traditional methods with higher classification accuracy and less time consumption.

Optimisation of classification algorithm of associated data features of large-scale network system

Data feature extraction is vulnerable to external interferences during traditional classification of associated data of large-scale network system, resulting in low classification accuracy and large time consumption, so an optimised classification algorithm of large-scale network system associated data features based on deep learning is proposed. In this algorithm, associated data of large-scale network system is acquired through the multi-sensor quantisation fusion method, and the acquired data is done with spectral decomposition to obtain the convergence conditions for feature components of associated data of large-scale network system; then the feature components are processed based on a spectrum analysis model to extract features of associated data of large-scale network system; the features of the associated data are done with piecewise regression analysis, and data samples are output in classification based on the deep learning algorithm. Simulation results show that the proposed method can accurately classify associated data with relatively short learning steps with accuracy 24.7% higher than that of the ARMA classification method and 22.6% higher than that of the decision tree classification method. It is verified that the method proposed in this paper is obviously better than the traditional methods with higher classification accuracy and less time consumption.

Distributed Nonlinear Control Design Using Separable Control Contraction Metrics

This paper gives convex conditions for the synthesis of a distributed control system for large-scale networked nonlinear dynamic systems. It is shown that the technique of control contraction metrics can be extended to this problem by utilizing separable metric structures, resulting in controllers that only depend on information from local sensors and communications from immediate neighbors. The conditions given are pointwise linear matrix inequalities, and are necessary and sufficient for linear positive systems and certain monotone nonlinear systems. Distributed synthesis methods for systems on chordal graphs are also proposed based on semidefinite program decompositions. The results are illustrated on a problem of vehicle platooning with heterogeneous vehicles, and a network of nonlinear dynamic systems with over 1000 states that is not feedback linearizable and has an uncontrollable linearization.

On Fundamental Limitations of Dynamic Feedback Control in Regular Large-Scale Networks

In this paper, we study fundamental performance limitations of distributed feedback control in large-scale networked dynamical systems. Specifically, we address the question of whether dynamic feedback controllers perform better than static (memoryless) ones when subject to locality constraints. We consider distributed linear consensus and vehicular formation control problems modeled over toric lattice networks. For the resulting spatially invariant systems, we study the large-scale asymptotics (in network size) of global performance metrics that quantify the level of network coherence. With static feedback from relative state measurements, such metrics are known to scale unfavorably in lattices of low spatial dimensions, preventing, for example, a one-dimensional string of vehicles to move like a rigid object. We show that the same limitations in general apply also to dynamic feedback control that is locally of first order. This means that the addition of one local state to the controller gives a similar asymptotic performance to the memoryless case. This holds unless the controller can access noiseless measurements of its local state with respect to an absolute reference frame, in which case the addition of controller memory may fundamentally improve performance. In simulations of platoons with 20–200 vehicles, we show that the performance limitations we derive manifest as unwanted accordionlike motions. Similar behaviors are to be expected in any network that is embeddable in a low-dimensional toric lattice, and the same fundamental limitations would apply. To derive our results, we present a general technical framework for the analysis of stability and performance of spatially invariant systems in the limit of large networks.

On Mean Field Games for Agents With Langevin Dynamics

Mean field games (MFG) have emerged as a viable tool in the analysis of large-scale self-organizing networked systems. In particular, MFGs provide a game-theoretic optimal control interpretation of the emergent behavior of noncooperative agents. The purpose of this paper is to study MFG models in which individual agents obey multidimensional nonlinear Langevin dynamics, and analyze the closed-loop stability of fixed points of the corresponding coupled forward-backward partial differential equation (PDE) systems. In such MFG models, the detailed balance property of the reversible diffusions underlies the perturbation dynamics of the forward–backward system. We use our approach to analyze closed-loop stability of two specific models. Explicit control design constraints, which guarantee stability, are obtained for a population distribution model and a mean consensus model. We also show that static state feedback using the steady-state controller can be employed to locally stabilize an MFG equilibrium.

Distributed Formal Analysis for Power Networks With Deep Integration of Distributed Energy Resources

A scalable distributed formal analysis (DFA) via reachable set computation is presented to efficiently evaluate the stability of large-scale interconnected power networks under heterogeneous disturbances induced by high penetration of distributed energy resources (DERs). Based on rigorous mathematical derivation, DFA is able to directly compute the boundaries of all possible dynamics and provide stability information, which is unattainable by traditional time-domain simulations or direct methods. An $N+M$ decomposition approach is established to decouple a large-scale networked system and enable distributed reachable set calculations while also preserving the privacy of each subsystem. Numerical examples on a networked microgrid system show that DFA facilitates the efficient calculation and analysis of the impact DER disturbances can have on power network dynamics, which provides a potent means of optimizing the system's operation. Therefore, DFA provides an invaluable tool for designing and operating the interconnected power networks of the future, which will feature the deep integration of DERs.

Reliability evaluation and management of PetroChina's large-scale system of natural gas pipeline networks

To adapt to the current situation of large-scale and network development of China's natural gas pipeline industry, we must pay attention to the gas transmission support capability of the entire pipeline network system during its operation period and evaluate its reliability with respect to the fulfillment of transmission tasks. This study focuses on the safe and efficient operation and management of large-scale natural gas pipeline network systems, with the objectives of analyzing the reliability indicators, system reliability, unit reliability, reliability data, and reliability management framework of the pipeline network system. The above incentives have lain a foundation for future sustainable reliability management of large-scale systems of natural gas pipeline networks in China. Herein, the purpose, significance, and content of reliability studies on natural gas pipeline networks are discussed in detail at the system level. We establish a distributed step calculation model of physical pipeline network system reliability based on pipeline process simulation technology, which can simplify and rapidly calculate the reliability of the pipeline network/pipelines/station system. The reliability calculation model of the system was tested in a compressor station. According to the sequence of station yard-typical structure-basic circuit-equipment unit, the network topology structure was divided into reliability block diagrams of different levels from top to bottom, which we used to calculate the reliability of individual equipment from bottom to top. Finally, the reliability of the compressor station under different start-up and shutdown schemes was obtained. Overall, the discussed methods are promising and will provide technical and decision-making support for the safe operation of natural gas pipeline networks.

ADMM-Based Distributed State Estimation of Smart Grid Under Data Deception and Denial of Service Attacks

Smart grid (SG) represents a large-scale network system with the tight integration of a physical power network and an information network, which makes it more vulnerable to hybrid cyber attacks against different regional subsystems. First, an alternating direction method of multipliers-based distributed state estimation method is developed to overcome the limitation of conventional state estimation and performance analysis of SG against a single type of cyber attacks. Regional subsystems are partitioned via the ${K}$ -means method. Second, a novel distributed state estimation method integrated with the characteristics of data deception attacks and denial of service (DoS) attacks is proposed to account for the simultaneous presence of different cyber attacks on individual regional subsystems. Third, the convergence of a distributed state estimation algorithm under hybrid cyber attacks is proved theoretically. Furthermore, the relationships between the convergence and algorithm parameters as well as the occurring probability of DoS attacks are established. Finally, the simulations on a modified IEEE 118-bus system are given to demonstrate the feasibility and effectiveness of the proposed method.

Noise-induced limitations to the scalability of distributed integral control

We study performance limitations of distributed feedback control in large-scale networked dynamical systems. Specifically, we address the question of how the performance of distributed integral control is affected by measurement noise. We consider second-order consensus-like problems modeled over a toric lattice network, and study asymptotic scalings (in network size) of H2 performance metrics that quantify the variance of nodal state fluctuations. While previous studies have shown that distributed integral control fundamentally improves these performance scalings compared to distributed proportional feedback control, our results show that an explicit inclusion of measurement noise leads to the opposite conclusion. The noise’s impact on performance is shown to decrease with an increased inter-nodal alignment of the local integral states. However, even though the controller can be tuned for acceptable performance for any given network size, performance will degrade as the network grows, limiting the scalability of any such controller tuning. In particular, the requirement for inter-nodal alignment increases with network size. We show that this may in practice imply that very large and sparse networks will require any integral control to be centralized, rather than distributed. In this case, the best-achievable performance scaling, which is shown to be that of proportional feedback control, is retrieved.

Block-based minimum input design for the structural controllability of complex networks

Traditional control system design to complex networks is generally implemented by integrated structural analysis aiming at a global network. However, such a global method may be inefficient, in particular, when a massive network with a huge number of nodes and associations is considered. In this paper, motivated by the idea of “dividing and dealing”, we propose a block-based approach to the issue of minimum input design for structural controllability (MIDSC) of complex networks that potentially incurs in higher efficiency. Specifically, we consider a large-scale networked system that consists of several local blocks. The main challenge for control configuration design of this class of systems is how to find the minimum inputs of global network according to the local block information while maintaining system’s structural controllability. To this end, two block-based graphical algorithms are developed to meet the conditions required for achieving structural controllability, and meanwhile determine an optimal solution for addressing the MIDSC problem. The complexity of the proposed method is analyzed, which is also compared with existing algorithms designed mainly based on monolithic model. In particular, we show that, under some mild conditions on blocking structure, the complexity of the proposed algorithm is strictly lower than that of existing algorithms to the MIDSC problem.

Piggyback Game: Efficient Event Stream Dissemination in Online Social Network Systems

Event stream dissemination dominates the workloads in large-scale Online Social Network (OSN) systems. Based on the de facto per-user view data storage, event stream dissemination raises a large amount of inter-server traffic due to the complex interconnection among OSN users. The state-of-the-art schemes mainly explore the structure features of social graphs to reduce the inter-server communications for event stream dissemination. Different sub-graph structures are exploited for achieving approximated optimal solutions. However, such schemes incur prohibitively high cost of either computation or communication. In this work, we follow a different design philosophy by using a game theoretic approach, which decomposes the highly complex graph computation problem into rational decision making of every individual social link. Specifically, we propose a novel social piggyback game to achieve a more efficient solution. We mathematically prove the existence of the Nash Equilibrium of the social piggyback game. Moreover, we propose an efficient best response dynamic algorithm to achieve the Nash Equilibrium, which quickly converges in a small number of iterations for large-scale OSNs. We further show that the communication cost of this design achieves a 1.5-approximation of the theoretical social optimum. We conduct comprehensive experiments using large-scale real-world traces from popular OSN systems as well as implement a prototype system to evaluate the performance of this design. Results show that the social piggyback game achieves a significant 302× improvement in system efficiency compared to existing schemes.

Distributed model predictive control for networked plant-wide systems with neighborhood cooperation

For large-scale networked plant-wide systems composed by physically &#x0028 or geographically &#x0029 divided subsystems, only limited information is available for local controllers on account of region and communication restrictions. Concerning the optimal control problem of such subsystems, a neighbor-based distributed model predictive control &#x0028 NDMPC &#x0029 strategy is presented to improve the global system performance. In this scheme, the performance index of local subsystems and that of its neighbors are minimized together in the determination of the optimal control input, which makes the local control decision also beneficial to its neighboring subsystems and further contributes to improving the convergence and control performance of overall system. The stability of the closed-loop system is proved. Moreover, the parameter designing method for distributed synthesis is provided. Finally, the simulation results illustrate the main characteristics and effectiveness of the proposed control scheme.

Deep reinforcement learning for scheduling in large-scale networked control systems

This work considers the problem of control and resource allocation in networked systems. To this end, we present DIRA a Deep reinforcement learning based Iterative Resource Allocation algorithm, which is scalable and control-aware. Our algorithm is tailored towards large-scale problems where control and scheduling need to act jointly to optimize performance. DIRA can be used to schedule general time-domain optimization based controllers. In the present work, we focus on control designs based on suitably adapted linear quadratic regulators. We apply our algorithm to networked systems with correlated fading communication channels. Our simulations show that DIRA scales well to large scheduling problems.

Nonconformity of cooperators promotes the emergence of pure altruism in tag-based multi-agent networked systems

Designing cooperation-enhancing protocols for large-scale multiagent networked systems has been a grand transdisciplinary challenge. In recent years, tag-based interactions and conformity bias have been studied extensively but separately as two viable mechanisms for cooperation enhancement in such systems. Inspired by recent studies on interaction effects in social dilemmas, we herein develop a hybrid, multiagent-based, co-evolutionary model of tag-mediated cooperation and conformity with conditional and unconditional strategies. Through a series of extensive Monte Carlo simulation experiments, we study four variants of this computational model, finding that under the majority rule, the nonconforming unconditional cooperators and conformity biased transmission of other strategies can lead to global altruistic dominance. Employing a random pinning control mechanism, we further observe that only a small fraction of nonconforming altruists is actually required to drive the system towards a robust persistence of pure altruism. Our analytic results in combination with further computational experiments reveal that spatial structure and nonconformity of cooperators are the two indispensable ingredients for the stable dominance of altruistic behavior in tag-based multiagent systems. Our findings can be beneficial for developing novel cooperation-controlling techniques in distributed self-organizing systems such as peer-to-peer networks or in various social networking and viral marketing technologies.