Bandwidth and Latency Model for DHT Based Peer-to-Peer Networks under Variable Churn

Kumar, P. Sreenivasa; Sridhar, G.; Sridhar, V.

doi:10.1109/icw.2005.31

Cited by 10 publications

(5 citation statements)

References 7 publications

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“…The construction method of parameter k as formula (3) can be used to design Churn model or adjust other Churn models in different network environments. For example in formula (3), N represents the scale of network, W represents the busy degree of network, and B represents the stabilization of network. Of course, we can also use other factors in Table I according to different research needs.…”

Section: Methods To Improve the Present P2p Churn Modelmentioning

confidence: 99%

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Multi‐factors oriented study of P2P Churn

Yang

Zhang

et al. 2009

Int J Communication

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SUMMARYThe dynamics of peers, namely Churn, is an inherent property of peer-to-peer (P2P) systems and is critical to their design and evaluation. Although every excellent P2P protocol has some solution to this issue, studies on Churn are still seldom. This paper studies various factors related to Churn, and uses them to analyze and evaluate P2P protocols. Prior researches on Churn are all based on the P2P network factors in Churn environment, and their difference is whether to use these factors as predecessor references to build Churn analytical models or as successor references to test the models. According to this difference, this paper first divides various factors into two categories: impacting Churn and affected by Churn. There is a causal relationship between these two categories. Factors impacting Churn are cause, and the factors affected by Churn are effect. In this paper, we use this causality to simulate and analyze P2P Churn. Cause is used as the input data and effect is used as the output result. Second, based on the classification of Churn factors, we present a performance evaluation framework and two comparing models. Based on the framework and models, we simulate and analyze three P2P protocols and get some useful results such as the performance of these protocols under Churn, the advantage of Chord over others, and the most important factors impacting Churn. Finally, we present a method to improve recent P2P Churn models by adding some influence factors.

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Section: Methods To Improve the Present P2p Churn Modelmentioning

confidence: 99%

“…Previous Churn-related studies can be mainly divided into three categories. The first is to directly acquire and analyze Churn correlative factors [1][2][3][4], such as the arrival and departure time of peers. The second category builds Churn models based on some simulation or analysis study of P2P system [5,6].…”

Section: Introductionmentioning

confidence: 99%

Multi‐factors oriented study of P2P Churn

Yang

Zhang

et al. 2009

Int J Communication

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“…The mesh devices of WMN are less dynamic in nature, which are running an overlay application for resource sharing and discovery. The Chord overlay performs well under low churn and less dynamic scenarios with respect to the latency of lookup messages 48 Fog nodes container service: An execution environment is needed to run microservices in the participating fog nodes.…”

Section: Proposed Frameworkmentioning

confidence: 99%

Wireless‐Fog Mesh: A framework for in‐network computing of microservices in semipermanent smart environments

2020

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Large gathering events such as open air rock concerts and religious gatherings require a semipermanent communication and computation infrastructure for providing IoT-based smart solutions. In these places, the installation of wired network is not a cost-effective solution to provide connectivity. Wireless mesh network (WMN) presents itself as a quickly deployable and dismantable solution to provide wireless connectivity to IoT devices. In this paper, we have proposed a "wireless-fog mesh" computing framework for installation of microservices in network devices of WMN. This requires continuous monitoring of residual resources of devices and associated network characteristics. Our proposed fog controller exploits underutilized resources of mesh devices and network metrics for mapping a microservice to a fog node. This controller uses a DHT-based overlay of fog nodes (also acting as distributed MQTT broker) for disseminating resource profiles of fog nodes. The network metrics like path cost, routing loads, number of stations, and delay are used to implement a policy-based FNS algorithm for electing a fog node to run microservice. We have designed and implemented a testbed that consists of 10 OpenWrt-based Raspberry Pi3 node to form a WMN. We have deployed various microservices such as MQTT broker, air quality, web servers, annd video surveillance in fog nodes (mesh routers and access points) to test the FNS algorithm. The results establish that the FNS algorithm significantly outperforms the random node selection (RNS) algorithm and advocates the applicability of our wireless-fog mesh framework for semipermanent use cases.

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“…EDFs are related to peer-to-peer systems for which there is plenty of literature, however, mostly focused on analyzing the performance and scalability of distributed hashing and routing algorithms (e.g., [1,20,25]). To the best of our knowledge, no techniques exist in the literature for automated model extraction of EDFs and we are not aware of any performance modeling case studies of EDFs.…”

Section: Related Workmentioning

confidence: 99%

Automated Simulation-Based Capacity Planning for Enterprise Data Fabrics

Kounev¹,

Bender²,

Brosig³

et al. 2011

Proceedings of the 4th International ICST Conference on Simulation Tools and Techniques

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Enterprise data fabrics are gaining increasing attention in many industry domains including financial services, telecommunications, transportation and health care. Providing a distributed, operational data platform sitting between application infrastructures and back-end data sources, enterprise data fabrics are designed for high performance and scalability. However, given the dynamics of modern applications, system sizing and capacity planning need to be done continuously during operation to ensure adequate quality-ofservice and efficient resource utilization. While most products are shipped with performance monitoring and analysis tools, such tools are typically focused on low-level profiling and they lack support for performance prediction and capacity planning. In this paper, we present a novel case study of a representative enterprise data fabric, the Gem-Fire EDF, presenting a simulation-based tool that we have developed for automated performance prediction and capacity planning. The tool, called Jewel, automates resource demand estimation, performance model generation, performance model analysis and results processing. We present an experimental evaluation of the tool demonstrating its effectiveness and practical applicability.

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Bandwidth and Latency Model for DHT Based Peer-to-Peer Networks under Variable Churn

Cited by 10 publications

References 7 publications

Multi‐factors oriented study of P2P Churn

Multi‐factors oriented study of P2P Churn

Wireless‐Fog Mesh: A framework for in‐network computing of microservices in semipermanent smart environments

Automated Simulation-Based Capacity Planning for Enterprise Data Fabrics

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