P2P系统的性能优化：研究综述

Peer-to-Peer(P2P)计算模型在利用和管理日益增长的分布式信息和资源方面是一种非常成功的计算模型。但是这种计算模式也有一些缺点,特别是decentralized unstructured P2P系统,它将随机选择邻居和blind floodingsearch机制联合起来使用,使系统的网络开销急速地增加,严重地影响了系统的性能。本文介绍了减少decentralizedunstructured P2P系统网络开销的各类方法,对它们的优缺点进行了详细的比较,并根据比较的结果对今后的发展趋势进行了展望。     

Decentralized Cooperative SLAM for Sparsely-Communicating Robot Networks: A Centralized-Equivalent Approach

Communication between robots is key to performance in cooperative multi-robot systems. In practice, communication connections for information exchange between all robots are not always guaranteed, which adds difficulty in performing state estimation. This paper examines the decentralized cooperative simultaneous localization and mapping (SLAM) problem, in which each robot is required to estimate the map and all robot states under a sparsely-communicating and dynamic network. We show how the exact, centralized-equivalent estimate can be obtained by all robots in the network in a decentralized manner even when the network is never fully connected. Furthermore, a robot only needs to consider its own knowledge of the network topology in order to detect when the centralized-equivalent estimate is obtainable. Our approach is validated through more than 250 min of hardware experiments using a team of real robots. The resulting estimates are compared against accurate groundtruth data for all robot poses and landmark positions. In addition, we examined the effects of communication range limit on our algorithm’s performance.     

Sporadic decentralized resource maintenance for P2P distributed storage networks

In this paper, we propose a novel decentralized resource maintenance strategy for peer-to-peer (P2P) distributed storage networks. Our strategy relies on the Wuala overlay network architecture, (The WUALA Project). While the latter is based, for the resource distribution among peers, on the use of erasure codes, e.g., Reed–Solomon codes, here we investigate the system behavior when a simple randomized network coding strategy is applied. We propose to replace the Wuala regular and centralized strategy for resource maintenance with a decentralized strategy, where users regenerate new fragments sporadically, namely every time a resource is retrieved. Both strategies are analyzed, analytically and through simulations, in the presence of either erasure and network coding. It will be shown that the novel sporadic maintenance strategy, when used with randomized network coding, leads to a fully decentralized solution with management complexity much lower than common centralized solutions.     

Expansion and decentralized search in complex networks

Borrowing from concepts in expander graphs, we study the expansion properties of real-world, complex networks (e.g., social networks, unstructured peer-to-peer, or P2P networks) and the extent to which these properties can be exploited to understand and address the problem of decentralized search. We first produce samples that concisely capture the overall expansion properties of an entire network, which we collectively refer to as the expansion signature. Using these signatures, we find a correspondence between the magnitude of maximum expansion and the extent to which a network can be efficiently searched. We further find evidence that standard graph-theoretic measures, such as average path length, fail to fully explain the level of “searchability” or ease of information diffusion and dissemination in a network. Finally, we demonstrate that this high expansion can be leveraged to facilitate decentralized search in networks and show that an expansion-based search strategy outperforms typical search methods.     

A decentralized Artificial Immune System for solution selection in Cyber–Physical Systems

Centralization has become a de facto standard for implementing networked environments such as the Cyber–Physical Systems (CPS). Though easy to implement and control, centralized systems are difficult and expensive to scale in terms of the number of devices and the flow of information. This set of circumstances calls for a decentralized and distributed architecture for realizing such networked systems. However, due to the absence of global information in decentralized systems, one of the primary challenges is to find the best solution for problems distributed across the devices which are part of the CPS. Since the problems are distributed and no participating device has access to the full information, the devices may need to interact and share the information to select the best solution for a problem occurred. In this paper, we present a decentralized and distributed mechanism, which adapts to a stream of varying problems and continuously evolves and learns the best mappings between the problems and their associated solutions. The proposed approach integrates the concepts propounded in the three major Immune theories and can cater to real-world situations. The evolved mappings are shared across the physical network, thereby accelerating the search for the best set of solutions. In order to validate the performance of the proposed mechanism, we present the results obtained from solving a problem of sorting a stream of varying data in an emulated decentralized and distributed manner. To substantiate its working in real-world scenarios, we also describe the results obtained by embodying the system in real robots that discover the best path-following algorithms.     

Asynchronous broadcast-based decentralized learning in sensor networks

Abstract

In this paper, we study the problem of decentralized learning in sensor networks in which local learners estimate and reach consensus to the quantity of interest inferred globally while communicating only with their immediate neighbours. The main challenge lies in reducing the communication cost in the network, which involves inter-node synchronisation and data exchange. To address this issue, a novel asynchronous broadcast-based decentralized learning algorithm is proposed. Furthermore, we prove that the iterates generated by the developed decentralized method converge to a consensual optimal solution (model). Numerical results demonstrate that it is a promising approach for decentralized learning in sensor networks.     

Distributed multi-task classification: a decentralized online learning approach

Although dispersing one single task to distributed learning nodes has been intensively studied by the previous research, multi-task learning on distributed networks is still an area that has not been fully exploited, especially under decentralized settings. The challenge lies in the fact that different tasks may have different optimal learning weights while communication through the distributed network forces all tasks to converge to an unique classifier. In this paper, we present a novel algorithm to overcome this challenge and enable learning multiple tasks simultaneously on a decentralized distributed network. Specifically, the learning framework can be separated into two phases: (i) multi-task information is shared within each node on the first phase; (ii) communication between nodes then leads the whole network to converge to a common minimizer. Theoretical analysis indicates that our algorithm achieves a \(\mathcal {O}(\sqrt{T})\) regret bound when compared with the best classifier in hindsight, which is further validated by experiments on both synthetic and real-world datasets.     

Synchronization in heterogeneous networks of discrete‐time introspective right‐invertible agents

This paper studies output synchronization problem, formation problem, and regulated synchronization problem for a heterogenous network of discrete‐time introspective right‐invertible agents. We first propose a decentralized control scheme to solve the output synchronization problem for a set of communication topologies. Moreover, if the synchronization trajectories are assumed to be bounded, a universal controller can be constructed for all communication topologies, which contain a directed spanning tree. The design can be applied to solve the formation problem with arbitrary formation vectors. In the regulated synchronization problem, we assume only the root receives information from exosystem. We then design a decentralized controller to solve the problem for a set of communication topologies. Copyright © 2013 John Wiley & Sons, Ltd.     

An analysis on decentralized adaptive MAC protocols for cognitive radio networks

The scarcity of bandwidth in the radio spectrum has become more vital since the demand for more and more wireless applications has increased. Most of the spectrum bands have been allocated although many studies have shown that these bands are significantly underutilized most of the time. The problem of unavailability of spectrum and inefficiency in its utilization has been smartly addressed by the cognitive radio (CR) technology which is an opportunistic network that senses the environment, observes the network changes, and then uses knowledge gained from the prior interaction with the network to make intelligent decisions by dynamically adapting their transmission characteristics. In this paper, some of the decentralized adaptive medium access control (MAC) protocols for CR networks have been critically analyzed, and a novel adaptive MAC protocol for CR networks, decentralized non-global MAC (DNG-MAC), has been proposed. The results show the DNG-MAC outperforms other CR-MAC protocols in terms of time and energy efficiency.     

Algebraic Connectivity Estimation Based on Decentralized Inverse Power Iteration

In this work, we propose a new scheme to estimate the algebraic connectivity of the graph describing the network topology of a multi‐agent system. We consider network topologies modeled by undirected graphs. The main idea is to propose a new decentralized conjugate gradient algorithm and a decentralized compound inverse power iteration scheme. The matrix inversion computation in this scheme is replaced by solving the non‐homogeneous linear equations relying on the proposed decentralized conjugate gradient algorithm. With this scheme, we can achieve a fast convergence rate in estimating the algebraic connectivity by setting the parameter μ properly. Simulation results demonstrate the effectiveness of the proposed scheme.     

Static decentralized control of a single-integrator network with Markovian sensing topology

Static stabilization of a decentralized discrete-time single-integrator network that is subject to Markovian variation in the communication/sensing topology is considered. In particular, we develop sufficient conditions on the Markovian topology for mean-square stabilization using a decentralized static controller that has no knowledge of the underlying Markov state. Our analysis exposes a deep connection between decentralized control of single-integrator networks with Markovian topology and those with fixed topology: static stabilization of the network with Markovian topology is possible whenever the steady-state time-average of the Markovian topology is amenable to static (fixed-topology-based) decentralized control.     

基于熵值理论的区块链网络去中心化程度量化评估方法

去中心化是区块链网络的重要优势。然而,尽管人们普遍认识到这一性质的重要性,但大多数关于这一主题的研究缺乏量化,而且没有对它们在实践中实现的去中心化程度进行计算。熵是随机变量不确定性的度量,也就是数据集的随机性度量,正好可以用来衡量区块链网络的去中心化程度。本文以比特币和以太坊为例,提出一种基于信息论中熵值理论来量化计算这些区块链网络去中心化程度的方法。利用信息熵计算出比特币和以太坊的出块数量和地址余额的随机程度,以此作为量化比特币和以太坊去中心化程度的指标。计算结果表明,基于出块数量的全样本统计,比特币比以太坊要高出大约12%的去中心化程度,并且基于10000个地址余额的样本统计,比特币也比以太坊要高出大约9.2%的去中心化程度。本文提出的方法具有普适性,可以用来量化评估任何区块链网络的去中心化程度。     

关于补偿谱和闭环谱的相交性问题

本文使用分散控制方法,讨论并解决了控制系统的补偿谱和闭环谱的相交性问题。文中引进了消失性的概念,得到了关于集中系统和分散系统存在使补偿器和闭环谱不相交的动态补偿器的充分必要条件。此外,该条件不成立时,给出了使用任何动态补偿器所得到的补偿谱与闭环谱的交。     

非结构化离散型对等网络的枢纽副本复制机制

P2P网络使得网络中的数据传输更加方便和高效.当前大多数P2P相关研究集中在路由算法及结构化网络拓扑方面,忽略了非结构化离散型副本复制的研究.提出了一种基于非结构化离散型对等网络的枢纽节点副本复制机制(JRM).通过该机制,可以降低非结构化离散型对等网络中的数据流量并实现更好的负载平衡.给出了相关算法伪代码,并通过分析证明了该算法的优势.     

A decentralized mechanism for improving the functional robustness of distribution networks

Most real-world distribution systems can be modeled as distribution networks, where a commodity can flow from source nodes to sink nodes through junction nodes. One of the fundamental characteristics of distribution networks is the functional robustness, which reflects the ability of maintaining its function in the face of internal or external disruptions. In view of the fact that most distribution networks do not have any centralized control mechanisms, we consider the problem of how to improve the functional robustness in a decentralized way. To achieve this goal, we study two important problems: 1) how to formally measure the functional robustness, and 2) how to improve the functional robustness of a network based on the local interaction of its nodes. First, we derive a utility function in terms of network entropy to characterize the functional robustness of a distribution network. Second, we propose a decentralized network pricing mechanism, where each node need only communicate with its distribution neighbors by sending a "price" signal to its upstream neighbors and receiving "price" signals from its downstream neighbors. By doing so, each node can determine its outflows by maximizing its own payoff function. Our mathematical analysis shows that the decentralized pricing mechanism can produce results equivalent to those of an ideal centralized maximization with complete information. Finally, to demonstrate the properties of our mechanism, we carry out a case study on the U.S. natural gas distribution network. The results validate the convergence and effectiveness of our mechanism when comparing it with an existing algorithm.     

Output feedback stabilization for time-delay nonlinear interconnected systems using neural networks.

In this paper, dynamic output feedback control problem is investigated for a class of nonlinear interconnected systems with time delays. Decentralized observer independent of the time delays is first designed. Then, we employ the bounds information of uncertain interconnections to construct the decentralized output feedback controller via backstepping design method. Based on Lyapunov stability theory, we show that the designed controller can render the closed-loop system asymptotically stable with the help of the changing supplying function idea. Furthermore, the corresponding decentralized control problem is considered under the case that the bounds of uncertain interconnections are not precisely known. By employing the neural network approximation theory, we construct the neural network output feedback controller with corresponding adaptive law. The resulting closed-loop system is stable in the sense of semiglobal boundedness. The observers and controllers constructed in this paper are independent of the time delays. Finally, simulations are done to verify the effectiveness of the theoretic results obtained.     

Intra-domain routing convergence with centralized control

The decentralized control scheme for routing in current IP networks has been questioned, and a centralized routing scheme has been proposed as an alternative. In this paper, we compare the convergence of centralized control scheme with decentralized link-state routing protocols. We first review the architectural advantages and challenges of centralized control. Thereafter, we identify and discuss the components of the convergence time in both schemes. We present how to achieve fast routing convergence in networks with centralized control. In particular, we analyze how to distribute forwarding information efficiently. Finally, we perform simulation studies on the convergence time for both real and synthetic network topologies and study the impact of control element location, link weights, and number of failures on the convergence time. The results show that the centralized control scheme can provide faster routing convergence than link-state routing protocols.     

Decentralized optimality conditions of stochastic differential decision problems via Girsanov’s measure transformation

In this paper, we apply two methods to derive necessary and sufficient decentralized optimality conditions for stochastic differential decision problems with multiple Decision Makers (DMs), which aim at optimizing a common pay-off, based on the notions of decentralized global optimality and decentralized person-by-person (PbP) optimality. Method 1: We utilize the stochastic maximum principle to derive necessary and sufficient conditions which consist of forward and backward Stochastic Differential Equations (SDEs), and conditional variational Hamiltonians, conditioned on the information structures of the DMs. The sufficient conditions for decentralized PbP optimality are local conditions, closely related to the necessary conditions for decentralized PbP optimality. However, under certain convexity condition on the Hamiltonian, and a global version of the sufficient conditions for decentralized PbP optimality, we show decentralized global optimality. Method 2: We utilize the value processes of decentralized PbP optimal policies, we relate them to solutions of backward SDEs, we identify sufficient conditions for decentralized PbP optimality, and we show these are precisely those derived via the maximum principle. For both methods, as usual, we utilize Girsanov’s theorem to transform the initial decentralized stochastic optimal decision problems, to equivalent decentralized stochastic optimal decision problems on a reference probability space, in which the controlled process and the information processes which generate part of the information structures of the DMs, are independent of any of the decisions.     

Autonomous decentralized community communication for information dissemination

To meet the increasing demand for real-time content delivery, the proposed autonomous decentralized community communication system offers an efficient information dissemination infrastructure with a decentralized architecture. ADCC's aim is to help end-user communities communicate and exchange information efficiently; to meet this goal, the system uses an application-level multicast technique that arbitrarily scales to large groups. The ADCC system also features a scalable community construction and maintenance scheme that eases the burden of organizing an online community network.     

Dynamic Web Service discovery architecture based on a novel peer based overlay network

Service Oriented Computing and its most famous implementation technology Web Services (WS) are becoming an important enabler of networked business models. Discovery mechanisms are a critical factor to the overall utility of Web Services. So far, discovery mechanisms based on the UDDI standard rely on many centralized and area-specific directories, which poses information stress problems such as performance bottlenecks and fault tolerance. In this context, decentralized approaches based on Peer to Peer overlay networks have been proposed by many researchers as a solution. In this paper, we propose a new structured P2P overlay network infrastructure designed for Web Services Discovery. We present theoretical analysis backed up by experimental results, showing that the proposed solution outperforms popular decentralized infrastructures for web discovery, Chord (and some of its successors), BATON (and it’s successor) and Skip-Graphs.