Understanding failure response in service discovery systems

Dabrowski, Christopher; Mills, Kevin L.; Quirolgico, Stephen

doi:10.1016/j.jss.2006.11.017

Cited by 8 publications

(5 citation statements)

References 31 publications

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“…The research described in [15] presents an alternative approach to extend the limit of Zeroconf beyond the local link. Here, the robustness of existing discovery mechanism is evaluated under increasing failure intensity.…”

Section: Scenario 3 Results: Timely Service Discovery Of All Servimentioning

confidence: 99%

“…To create a Tahoe-LAFS Introducer on a node, basic configuration parameters are introduced (the grid name, the node name and optionally 15 https://www.tahoe-lafs.org/trac/tahoe-lafs if the default value is inadequate for some reasons the port the service will be listening to). As soon as the service is started, if it is marked as public, it is announced to the rest of Cloudy nodes.…”

Section: Real Use Case Examplementioning

confidence: 99%

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TROBADOR: Service Discovery for Distributed Community Network Micro-Clouds

Selimi

Freitag

Centelles

et al. 2015

2015 IEEE 29th International Conference on Advanced Information Networking and Applications

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Community networks are decentralized communication networks built and operated by citizens, for citizens. We consider service discovery for clouds in such community networks. The currently employed solutions for service discovery are static and are not able to follow the dynamics in cloud service provision. We propose a novel service discovery mechanism based on the common Linux tool Avahi combined with TincVPN, in which the VPN allows Avahi to reach nodes beyond the local link. The concept of the micro-cloud is introduced which contains the nodes reached with the extended broadcast domain of Avahi. We evaluate the performance of the proposed service discovery mechanism in a real community cloud deployment consisting of 25 geographically distributed nodes. In experiments with different settings and number of services, we measure the number of services discovered as a function of the discovery time. Our results show that while a client eventually discovers all the services, a significant time is needed to achieve a complete service discovery. The proposed mechanism therefore seems appropriate for the case of community clouds with many replicas of a sought service, where the fast discovery of just a few providers satisfies the requirements of a client. By applying standard Linux tools, an advantage of our solution is that it is by default available on most Linux distribution, which allows our solution to be easily used for real deployments.

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Section: Scenario 3 Results: Timely Service Discovery Of All Servimentioning

confidence: 99%

Section: Real Use Case Examplementioning

confidence: 99%

TROBADOR: Service Discovery for Distributed Community Network Micro-Clouds

Selimi

Freitag

Centelles

et al. 2015

2015 IEEE 29th International Conference on Advanced Information Networking and Applications

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“…The research described in [12] is closely related to the topic of this paper. Here, the robustness of existing discovery mechanisms is evaluated under increasing failure intensity.…”

Section: Related Workmentioning

confidence: 95%

Experimental responsiveness evaluation of decentralized service discovery

Dittrich

Salfner

2010

2010 IEEE International Symposium on Parallel &Amp; Distributed Processing, Workshops and PHD Forum (IPDPSW)

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Service discovery is a fundamental concept in service networks. It provides networks with the capability to publish, browse and locate service instances. Service discovery is thus the precondition for a service network to operate correctly and for the services to be available. In the last decade, decentralized service discovery mechanisms have become increasingly popular. Especially in ad-hoc scenarios -such as ad-hoc wireless networks -they are an integral part of auto-configuring service networks. Albeit the fact that auto-configuring networks are increasingly used in application domains where dependability is a major issue, these environments are inherently unreliable. In this paper, we examine the dependability of decentralized service discovery. We simulate service networks that are automatically configured by Zeroconf technologies. Since discovery is a timecritical operation, we evaluate responsiveness -the probability to perform some action on time even in the presence of faults -of domain name system (DNS) based service discovery under influence of packet loss. We show that responsiveness decreases significantly already with moderate packet loss and becomes practicably unacceptable with higher packet loss.

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“…Pervasive environments can have many service providers providing services with similar functionality but possessing different criteria. The different criteria [2] that influence the service selection in pervasive environments are availability [3], cost [4], reliability [5,6], capability [3], mobility [7], responsiveness [8], trust [3,9] and locality [7]. Users, who require a service, will be requested to provide their preferences for the criteria.…”

Section: Introductionmentioning

confidence: 99%