Gossip Communication Research Articles

Rapid 3D modelling: Clustering method based on dynamic load balancing strategy

AbstractThree‐dimensional (3D) reconstruction is a pivotal research area within computer vision and photogrammetry, offering a valuable foundation of data for the development of smart cities. However, existing methods for constructing 3D models from unmanned aerial vehicle (UAV) images often suffer from slow processing speeds and low central processing unit (CPU)/graphics processing unit (GPU) utilization rates. Furthermore, the utilization of cluster‐based distributed computing for 3D modelling frequently results in inefficient resource allocation across nodes. To address these challenges, this paper presents a novel approach to 3D modelling in clusters, incorporating a dynamic load‐balancing strategy. The method divides the 3D reconstruction process into multiple stages to lay the groundwork for distributing tasks across multiple nodes efficiently. Instead of traditional traversal‐based communication, this approach employs gossip communication techniques to reduce the network overhead. To boost the modelling efficiency, a dynamic load‐balancing strategy is introduced that prevents nodes from becoming overloaded, thus optimizing resource usage during the modelling process and alleviating resource waste issues in multidevice clusters. The experimental results indicate that in small‐scale data environments, this approach improves CPU/GPU utilization by 35.8%/23.4% compared with single‐machine utilization. In large‐scale data environments for cluster‐based 3D modelling tests, this method enhances the average efficiency by 61.4% compared with traditional 3D modelling software while maintaining a comparable model accuracy.

TSBFT: A scalable and efficient leaderless byzantine consensus for consortium blockchain

In this paper, we present a high-performance, scalable Byzantine fault tolerance (BFT) protocol TSBFT for the consortium blockchains that does not rely on expensive leader-driven communication. It overcomes the challenges faced by the existing BFT protocol in three aspects: single-point failure, huge total message sizes, and limited by the slowest nodes. The proposed protocol secretly selects block proposers and uses threshold signature as a multi-round voting mechanism to confirm the validity of the proposed block. We adopt transmission pipelining to improve the network utilization while optimizing the gossip communication scheme to reduce the total message sizes. Finally, our protocol guarantees the security and liveness of the system. Experimental results show that, compared with other related BFT protocols (e.g., PBFT), TSBFT can effectively solve these three challenges. In addition, our experiments also show how the different optimization ingredients of TSBFT contribute to its performance and scalability. The results show that compared with the traditional BFT protocol, it can scale from dozens of nodes to hundreds of nodes.

An asynchronous distributed training algorithm based on Gossip communication and Stochastic Gradient Descent

Cyber–Physical Systems (CPS) applications are playing an increasingly important role in our lives, hence the use of centralized distributed machine learning in CPS to secure applications to start widespread use. However, existing centralized distributed machine learning (ML) algorithms have significant shortcomings in CPS scenarios. As a result, its synchronization algorithm has high latency and sensitivity to drop-off, which affects the security of CPS. Therefore, this paper combining the Gossip protocol with Stochastic Gradient Descent (SGD), this paper proposes a communication framework Gossip Ring SGD (GR-SGD) for machine learning. GR-SGD is decentralized and asynchronous, and solves the problem of long communication waiting time. This paper uses the ImageNet data set and the ResNet model to verify the feasibility of the algorithm and compares it with Ring AllReduce and D-PSGD. Moreover, this paper also indicates that some data redundancy can reduce communication overhead and increase system fault tolerance, it can be better applied to CPS and all kinds of machine learning models.

Observer‐based distributed control of large‐scale systems under gossip communication protocol

AbstractThis note is concerned with the observer‐based distributed control problem of large‐scale systems under gossip communication protocol. Random binary variables that satisfy a certain conditional probability distribution are introduced to model gossip communication protocol. In such a protocol, each subcontroller receives information from only one randomly chosen neighbor at each instant. Novel observer‐based distributed controllers are proposed for large‐scale systems. Based on Lyapunov stability theory and stochastic matrix manipulations, sufficient conditions are established such that the closed‐loop system is exponentially mean square stable and the prescribed ‐gain performance is satisfied. Then these conditions are transformed into linear matrix inequalities. The observer gains and controller gains can be obtained by solving a set of linear matrix inequalities. Finally, two numerical examples are used to illustrate the effectiveness of the proposed theoretical results.

Distributed consensus-based estimation and control of large-scale systems under gossip communication protocol

This paper studies the distributed consensus-based estimation and control of large-scale systems under gossip communication protocol. Novel observers which combine local Luenberger-like observers with consensus-based strategies are proposed. Based on Lyapunov stability theory, sufficient conditions in the form of matrix inequalities are established such that the considered system is exponentially mean-square stable with a prescribed L2-gain. The observer and controller gains can be obtained efficiently by the proposed numerical algorithm. Finally, the effectiveness of the theoretical results is illustrated by a numerical example.

Nash Equilibrium Seeking with Non-doubly Stochastic Communication Weight Matrix

A distributed Nash equilibrium seeking algorithm is presented for networked games. We assume an incomplete information available to each player about the other players' actions. The players communicate over a strongly connected digraph to send/receive the estimates of the other players' actions to/from the other local players according to a gossip communication protocol. Due to asymmetric information exchange between the players, a non-doubly (row) stochastic weight matrix is defined. We show that, due to the non-doubly stochastic property, the total average of all players' estimates is not preserved for the next iteration which results in having no exact convergence. We present an almost sure convergence proof of the algorithm to a Nash equilibrium of the game. Then, we extend the algorithm for graphical games in which all players' cost functions are only dependent on the local neighboring players over an interference digraph. We design an assumption on the communication digraph such that the players are able to update all the estimates of the players who interfere with their cost functions. It is shown that the communication digraph needs to be a superset of a transitive reduction of the interference digraph. Finally, we verify the efficacy of the algorithm via a simulation on a social media behavioral case.

Gossip and Attend: Context-Sensitive Graph Representation Learning

Graph representation learning (GRL) is a powerful technique for learning low-dimensional vector representation of high-dimensional and often sparse graphs. Most studies explore the structure and metadata associated with the graph using random walks and employ an unsupervised or semi-supervised learning schemes. Learning in these methods is context-free, resulting in only a single representation per node. Recently studies have argued on the adequacy of a single representation and proposed context-sensitive approaches, which are capable of extracting multiple node representations for different contexts. This proved to be highly effective in applications such as link prediction and ranking.However, most of these methods rely on additional textual features that require complex and expensive RNNs or CNNs to capture high-level features or rely on a community detection algorithm to identify multiple contexts of a node.In this study we show that in-order to extract high-quality context-sensitive node representations it is not needed to rely on supplementary node features, nor to employ computationally heavy and complex models. We propose Goat, a context-sensitive algorithm inspired by gossip communication and a mutual attention mechanism simply over the structure of the graph. We show the efficacy of Goat using 6 real-world datasets on link prediction and node clustering tasks and compare it against 12 popular and state-of-the-art (SOTA) baselines. Goat consistently outperforms them and achieves up to 12% and 19% gain over the best performing methods on link prediction and clustering tasks, respectively.

Distributed L 2-gain control of large-scale systems under gossip communication protocol

This paper studies the distributed controller design problem for linear discrete-time large-scale systems under gossip communication protocol. In this communication protocol, each sub-controller transmits the subsystem's state information to only one randomly chosen neighbour at transmission instants. Distributed controllers are designed such that the closed-loop system is exponentially mean-square stable and that the prescribed -gain performance is satisfied in the mean-square sense. The design condition is obtained based on Lyapunov stability theory and given in terms of linear matrix inequalities. Finally, two numerical examples are presented to illustrate the advantages of the proposed scheme.

Broadcast Gossip Algorithms for Distributed Peer-to-Peer Control in AC Microgrids

This paper focuses on a fully distributed peer-to-peer control scheme for voltage regulation and reactive power sharing of multiple inverter-based distributed energy resources (DERs) in ac microgrids. The proposed peer-to-peer control strategy is fully distributed enabled through broadcast gossip communication, where each DER unit only requires local voltage and current measurement from its own and some (but not all) nearby neighbors for the voltage and reactive power sharing control. Employing the broadcast gossip communication protocol with attractive scalability and reliability properties, the control inputs can be updated to restore the voltage magnitudes at the point of common coupling to a desired value ensuring an accurate reactive power sharing for each DER. Since the proposed distributed controllers are implemented on local DERs, the central controller and hierarchy are no longer required. Accordingly, the microgrid system stability is preserved in the peer-to-peer requirements of line switches, which in turn, enables a plug-and-play operation of DERs and their robustness against microgrid topology change scenarios. Simulation studies in a modified IEEE 34-bus test network demonstrate the effectiveness and applicability of the proposed control strategy.

Distributed auction optimization algorithm for the nonconvex economic dispatch problem based on the gossip communication mechanism

This paper presents an efficient distributed auction optimization algorithm (DAOA) based on the gossip communication mechanism for the nonconvex economic dispatch problem. The problem contains several constraints such as generation output limits, valve-point loading effects, multiple fuels, and the supply-demand balance. The gossip communication mechanism runs as two layers. The first layer triggers the leaders to conduct the local auction optimization, and the second layer is the auction protocol that selects the optimal neighbors to cooperate with the leaders to implement the auction decision. Auction optimization is a local optimization method, in which the units evaluate the bids of a certain amount of output power and the paired bid winners update their output power to reduce the generation cost. Better solutions can be obtained through gossip communication and local optimizations. Numerous simulations are conducted to demonstrate the effectiveness of the proposed strategy.

Breathe before speaking: efficient information dissemination despite noisy, limited and anonymous communication

Distributed computing models typically assume reliable communication between processors. While such assumptions often hold for engineered networks, e.g., due to underlying error correction protocols, their relevance to biological systems, wherein messages are often distorted before reaching their destination, is quite limited. In this study we take a first step towards reducing this gap by rigorously analyzing a model of communication in large anonymous populations composed of simple agents which interact through short and highly unreliable messages. We focus on the broadcast problem and the majority-consensus problem. Both are fundamental information dissemination problems in distributed computing, in which the goal of agents is to converge to some prescribed desired opinion. We initiate the study of these problems in the presence of communication noise. Our model for communication is extremely weak and follows the push gossip communication paradigm: In each round each agent that wishes to send information delivers a message to a random anonymous agent. This communication is further restricted to contain only one bit (essentially representing an opinion). Lastly, the system is assumed to be so noisy that the bit in each message sent is flipped independently with probability $$1/2-\epsilon $$ , for some small $$\epsilon >0$$ . Even in this severely restricted, stochastic and noisy setting we give natural protocols that solve the noisy broadcast and majority-consensus problems efficiently. Our protocols run in $$O(\log n/\epsilon ^2)$$ rounds and use $$O(n \log n / \epsilon ^2)$$ messages/bits in total, where n is the number of agents. These bounds are asymptotically optimal and, in fact, are as fast and message efficient as if each agent would have been simultaneously informed directly by an agent that knows the prescribed desired opinion. Our efficient, robust, and simple algorithms suggest balancing between silence and transmission, synchronization, and majority-based decisions as important ingredients towards understanding collective communication schemes in anonymous and noisy populations.

Distributed randomized algorithms for opinion formation, centrality computation and power systems estimation: A tutorial overview

In this tutorial paper, we study three specific applications: opinion formation in social networks, centrality measures in complex networks and estimation problems in large-scale power systems. These applications fall under a general framework which aims at the construction of algorithms for distributed computation over a network. The two key ingredients of randomization and time-averaging are used, together with a local gossip communication protocol, to obtain convergence of these distributed algorithms to the global synchronous dynamics.

Broadcast Protocol Guaranteeing Causal Delivery Order Consistency Condition for Social Networks

The social networks are based on sensor networks by mobile phones where social applications, such as real-time collaborative video watching applications, work out well, sensing their events collaboratively and socially. Recently, these networks use a contentbased distributed P/S (publish/subscribe) infrastructure that acts as a scalable group communication backbone. P/S based gossip protocols is elastically to scale in and out and provides suitable consistency guarantees for data safety and high availability but, does not deal with end-to-end message delay and message order-based consistency, which are addressed in real-time collaborative applications. Especially in broadcasting in real-time collaboration applications, it is possible for the messages to take different time and arrive at end users in different order. So, these applications should be based on P/S infrastructure including dealing with message timeliness and message ordering consistencies. Gossip communication is becoming one of the promising solutions for addressing P/S scalability problems in providing information propagation functionality by exploiting a mixture of diverse consistency options. In this paper, we present deadline-constraints causal order protocol respecting Δ(lifetime) based on P/S architecture for broadcasting in real-time collaborative applications in social networks to guarantee causally ordered messages delivery from brokers to subscribers. In the proposed protocol, every broker manages a 2dimensional vector, representing its knowledge of the last message sent by each broker at time t. But, every broker disseminates a broadcast message only with one scalar variable, the time-stamped information that represents the maximum gossip round and is the deadline (lifetime) of the immediate message of it, to subscribers because all messages disseminated by brokers have the same lifetime as the maximum number of gossip rounds. Therefore, the proposed protocol implemented for P/S based on gossip protocols results in very low communication overhead from brokers to subscribers in the context of broadcast respecting Δ.

Gossip-based Causal Order Delivery Protocol Respecting Deadline Constraints in Publish/Subscribe Systems

Publish/subscribe systems based on gossip protocols are elastically to scale in and out and provides suitable consistency guarantees for data safety and high availability but, does not deal with end-to-end message delay and message order-based consistency. Especially in real-time collaborative applications, it is possible for the messages to take each a different time to arrive at end users. So, these applications should be based on P/S infrastructure including dealing with message deadlines and message ordering consistencies. Gossip communication is becoming one of the promising solutions for addressing P/S scalability problems in providing information propagation functionality by exploiting a mixture of diverse consistency options. In this paper, we present a new causal order protocol based on scalable P/S architecture for real-time collaborative applications in social web platforms to guarantee causally ordered message delivery, respecting deadline-constraints from brokers to subscribers. In the proposed protocol, every broker manages a 2-dimensional vector, representing its knowledge of the last message sent by each broker at a certain time. But, every broker disseminates a multicast message with a 1-dimensional vector, the time-stamped information that represents the maximum number of gossip rounds to subscribers because all messages disseminated by brokers have the same deadline as the maximum number of gossip rounds. Therefore, the proposed protocol for P/S based on gossiping results in very low communication overhead from brokers to subscribers in the context of respecting deadline-constraints.

Gossip algorithms for heterogeneous multi-vehicle routing problems

In this paper we address a class of heterogeneous multi-vehicle task assignment and routing problems. We propose two distributed algorithms based on gossip communication: the first algorithm is based on a local exact optimization and the second is based on a local approximate greedy heuristic. We consider the case where a set of heterogeneous tasks arbitrarily distributed in a plane has to be serviced by a set of mobile robots, each with a given movement speed and task execution speed. Our goal is to minimize the maximum execution time of robots.

An efficient selection algorithm for building a super-peer overlay

Super-peer overlay provides an efficient way to run applications by exploring the heterogeneity of nodes in a Peer-to-Peer overlay network. Identifying nodes with high capacity as super-peers plays an important role in improving the performance of P2P applications, such as live streaming. In this paper, we present a super-peer selection algorithm (SPS) to select super-peers for quickly building a super-peer overlay. In the SPS, each peer periodically builds its set of super-peer candidates through gossip communication with its neighbors, in order to select super-peers and client peers. Simulation results demonstrate that the SPS is efficient in selecting super-peers, and in quickly building a super-peer overlay. The proposed SPS also possesses good scalability and robustness to failure of super-peers.

A Gossip Based Heuristic Algorithm for Heterogeneous Multi-Vehicle Routing Problems

In this paper we propose a novel algorithm based on gossip communication for the Heterogeneous Multi-Vehicle Routing (HMVR) problem. A collection of tasks is arbitrarily placed in a plane and a set of robots cooperates to minimize the maximum time required to visit and execute all tasks. Each task (resp. each robot) has different cost (resp. speed). The proposed algorithm relies only upon pairwise asynchronous communications between robots and attempts to balance the routing and execution cost of the task assignment through a heuristic. The proposed heuristic exploits polynomial-time approximation algorithms to solve the Euclidean Traveling Salesman Problem (ETSP). Some characterization of the convergence properties of the algorithm are provided together with extensive simulations to corroborate the results.

Gossip Coverage Control for Robotic Networks: Dynamical Systems on the Space of Partitions

Future applications in environmental monitoring, delivery of services, and transportation of goods motivate the study of deployment and partitioning tasks for groups of autonomous mobile agents. These tasks may be achieved by recent coverage algorithms, based upon the classic methods by Lloyd. These algorithms, however, rely upon critical requirements on the communication network: information is exchanged synchronously among all agents and long-range communication is sometimes required. This work proposes novel coverage algorithms that require only gossip communication, i.e., asynchronous, pairwise, and possibly unreliable communication. Which robot pair communicates at any given time may be selected deterministically or randomly. A key innovative idea is describing coverage algorithms for robot deployment and environment partitioning as dynamical systems on a space of partitions. In other words, we study the evolution of the regions assigned to each agent rather than the evolution of the agents' positions. The proposed gossip algorithms are shown to converge to centroidal Voronoi partitions under mild technical conditions. Our treatment features a broad variety of results in topology, analysis, and geometry. First, we establish the compactness of a suitable space of partitions with respect to the symmetric difference metric. Second, with respect to this metric, we establish the continuity of various geometric maps, including the Voronoi diagram as a function of its generators, the location of a centroid as a function of a set, and the widely known multicenter function studied in facility location problems. Third, we prove two convergence theorems for dynamical systems on metric spaces described by deterministic and stochastic switches.

Cooperative Convex Optimization in Networked Systems: Augmented Lagrangian Algorithms With Directed Gossip Communication

We study distributed optimization in networked systems, where nodes cooperate to find the optimal quantity of common interest, x=x^\star. The objective function of the corresponding optimization problem is the sum of private (known only by a node,) convex, nodes' objectives and each node imposes a private convex constraint on the allowed values of x. We solve this problem for generic connected network topologies with asymmetric random link failures with a novel distributed, decentralized algorithm. We refer to this algorithm as AL-G (augmented Lagrangian gossiping,) and to its variants as AL-MG (augmented Lagrangian multi neighbor gossiping) and AL-BG (augmented Lagrangian broadcast gossiping.) The AL-G algorithm is based on the augmented Lagrangian dual function. Dual variables are updated by the standard method of multipliers, at a slow time scale. To update the primal variables, we propose a novel, Gauss-Seidel type, randomized algorithm, at a fast time scale. AL-G uses unidirectional gossip communication, only between immediate neighbors in the network and is resilient to random link failures. For networks with reliable communication (i.e., no failures,) the simplified, AL-BG (augmented Lagrangian broadcast gossiping) algorithm reduces communication, computation and data storage cost. We prove convergence for all proposed algorithms and demonstrate by simulations the effectiveness on two applications: l_1-regularized logistic regression for classification and cooperative spectrum sensing for cognitive radio networks.

Causal Information Transmission Protocol Between Publishers and Subscribers for Overlapping Groups

Communication infrastructure of wireless sensor networks significantly tends to depend on application requirements. Gossip communication is becoming one of the promising solutions for addressing its scalability problems in providing information propagation functionality based on the P (publish)/S (subscribe) paradigm. In particular, despite the importance of both guaranteeing message delivery order required and supporting overlapping groups in sensor networks, there exist little research works on development of gossip-style dissemination protocols to satisfy all these requirements. In this paper, we present the latest causal information transmission protocol between publishers and subscribers for overlapping groups. In this protocol, sensor leaders as publishers might guarantee consistently causally ordered message delivery among themselves by aggregating causality information. On the other hand, only the latest causal information piggybacked on each multicast message is transmitted from publishers to subscribers through gossip-style dissemination. Its scalability feature might be highly suitable for the area of the applications requiring only the minimum meaningful information.