Spanning Tree or Gossip for Aggregation: A Comparative Study

Nyers, Lehel; Jelasity, Márk

doi:10.1007/978-3-319-09873-9_32

Cited by 3 publications

(4 citation statements)

References 22 publications

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“…Generally, it is identified that PS outperforms PP except for several scenarios. In addition, the application of these algorithms to estimating network size is discussed in [30,34,[37][38][39][40][41][42], but a comprehensive analysis is provided in none of them. In our research, it is identified that there is no significant difference between these two algorithms (PS performs a bit better), their precision is high in general, and they require fewer messages for consensus than RG and GG.…”

Section: Discussionmentioning

confidence: 99%

“…Thus, there is a lack of papers concerned with an analysis of RG, GG, and BG for network size estimation. The two other chosen algorithms (i.e., PS and PP) for this purpose are briefly discussed in [30,34,[37][38][39][40][41][42]. However, in none of these papers, a comprehensive analysis of these algorithms applied to estimating the network size is provided.…”

Section: Related Workmentioning

confidence: 99%

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Comparative Study of Distributed Consensus Gossip Algorithms for Network Size Estimation in Multi-Agent Systems

Kenyeres

2021

Future Internet

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Determining the network size is a critical process in numerous areas (e.g., computer science, logistic, epidemiology, social networking services, mathematical modeling, demography, etc.). However, many modern real-world systems are so extensive that measuring their size poses a serious challenge. Therefore, the algorithms for determining/estimating this parameter in an effective manner have been gaining popularity over the past decades. In the paper, we analyze five frequently applied distributed consensus gossip-based algorithms for network size estimation in multi-agent systems (namely, the Randomized gossip algorithm, the Geographic gossip algorithm, the Broadcast gossip algorithm, the Push-Sum protocol, and the Push-Pull protocol). We examine the performance of the mentioned algorithms with bounded execution over random geometric graphs by applying two metrics: the number of sent messages required for consensus achievement and the estimation precision quantified as the median deviation from the real value of the network size. The experimental part consists of two scenarios—the consensus achievement is conditioned by either the values of the inner states or the network size estimates—and, in both scenarios, either the best-connected or the worst-connected agent is chosen as the leader. The goal of this paper is to identify whether all the examined algorithms are applicable to estimating the network size, which algorithm provides the best performance, how the leader selection can affect the performance of the algorithms, and how to most effectively configure the applied stopping criterion.

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Section: Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Comparative Study of Distributed Consensus Gossip Algorithms for Network Size Estimation in Multi-Agent Systems

Kenyeres

2021

Future Internet

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“…This study is a significantly extended and improved version of our previous conference publication .…”

Section: Introductionmentioning

confidence: 92%

A comparative study of spanning tree and gossip protocols for aggregation

Nyers

Jelasity

2015

Concurrency and Computation

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Distributed aggregation queries like average and sum can be implemented in different paradigms like gossip and hierarchical approaches. In the literature, these two paradigms are routinely associated with stereotypes such as "trees are fragile and complicated" and "gossip is slow and expensive". However, a closer look reveals that these statements are not backed up by systematic studies. A fair and informative comparison is clearly needed. However, this is a hard task because the performance of protocols from the two paradigms depends on different subtleties of the environment and the implementation of the protocols. We tackle this problem by carefully designing the comparison study. We use state-of-the-art algorithms and propose the problem of monitoring the network size in the presence of churn as the ideal problem for comparing very different paradigms for global aggregation. Our simulation study helps us identify the most important factors that differentiate between gossip and spanning tree aggregation: the time needed to compute a truly global output, the properties of the underlying topology, and sensitivity to dynamism. We demonstrate the effect of these factors in different practical topologies and scenarios. Our results help us to choose the right protocol in the light of the topology and dynamism patterns. IntroductionFully distributed aggregation is an important problem where we wish to execute queries such as sum, average, minimum, or maximum over unreliable networks (sensor networks, physical networks of routers, overlay networks, etc.), in which no central servers are directly accessible.At least two paradigms are known that solve this problem. The first one is the gossip approach where algorithms were proposed to achieve large degrees of robustness. Gossip protocols do not rely on fixed topologies: nodes exchange information with random neighbors to implement a diffusion-like computation pattern, and as a result the system converges to a state where all the nodes know the query result. From the literature, here we just focus on the adaptive approaches. In [1], the authors propose the restarting technique to convert any one-shot algorithm into an adaptive one. Apart from restarting, other approaches have been proposed that focus on error correction through some form of bookkeeping at the nodes [2][3][4][5].The second paradigm is hierarchical aggregation, which is a popular method in sensor networks [6]. It was also proposed for general process groups [7]. Tree-based aggregation remained unpopular in some areas like peer-to-peer networks due to the widely held assumptions about its lack of robustness. There are a few notable exceptions however: the Astrolabe framework [8], which is in fact only a virtual tree with completely unstructured gossip communication patterns behind it; the GAP protocol and its variants [5, 9-11] that actually build a spanning tree over a distributed network; and PRISM [12], a hierarchical approach that is built on top of a distributed hashtable, with a focus on dete...

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“…The work in [29] studied global data aggregation in spanning trees and gossip-based schemes. The study concludes that gossip-based approaches are adaptive and efficient for dynamic topologies while spanning trees are preferable in stable topologies.…”

Section: Related Workmentioning

confidence: 99%

Agreement in Epidemic Data Aggregation

Ayiad

Fatta

2017

2017 IEEE 23rd International Conference on Parallel and Distributed Systems (ICPADS)

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Computing and spreading global information in large-scale distributed systems pose significant challenges when scalability, parallelism, resilience and consistency are demanded. Epidemic protocols are a robust and scalable computing and communication paradigm that can be effectively used for information dissemination and data aggregation in a fully decentralised context where each network node requires the local computation of a global synopsis function. Theoretical analysis of epidemic protocols for synchronous and static network models provide guarantees on the convergence to a global target and on the consistency among the network nodes. However, practical applications in real-world networks may require the explicit detection of both local convergence and global agreement (consensus). This work introduces the Epidemic Consensus Protocol (ECP) for the determination of consensus on the convergence of a decentralised data aggregation task. ECP adopts a heuristic method to locally detect convergence of the aggregation task and stochastic phase transitions to detect global agreement and reach consensus. The performance of ECP has been investigated by means of simulations and compared to a tree-based Three-Phase Commit protocol (3PC). Although, as expected, ECP exhibits total communication costs greater than the optimal tree-based protocol, it is shown to have better performance and scalability properties; ECP can achieve faster convergence to consensus for large system sizes and inherits the intrinsic decentralisation, fault-tolerance and robustness properties of epidemic protocols.

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Spanning Tree or Gossip for Aggregation: A Comparative Study

Cited by 3 publications

References 22 publications

Comparative Study of Distributed Consensus Gossip Algorithms for Network Size Estimation in Multi-Agent Systems

Comparative Study of Distributed Consensus Gossip Algorithms for Network Size Estimation in Multi-Agent Systems

A comparative study of spanning tree and gossip protocols for aggregation

Agreement in Epidemic Data Aggregation

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