An investigation of the Internet’s IP-layer connectivity

Tang, Li; Li, Jun; Li, Yanda; Shenker, Scott

doi:10.1016/j.comcom.2008.12.015

Cited by 7 publications

(4 citation statements)

References 20 publications

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“…One of the main issues to consider when designing and implementing a distributed software architecture is node reachability [20]. When a node in a distributed system cannot be reached at a given time, it is usually very difficult to know the specific cause: it may be due to hardware failure, software failure, network congestion, network partition, etc.…”

Section: Distributed Software Architecturesmentioning

confidence: 99%

Failover and takeover contingency mechanisms for network partition and node failure

López

Castro

Cabrero

2012

Proceedings of the Eleventh ACM SIGPLAN Workshop on Erlang Workshop

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Proper definition of suitable mechanisms to cope with network partition and to recover from node failure are among the most common problems when designing and implementing a fault-tolerant distributed system. The concern is even more serious when the different scenarios could not be predicted beforehand and are detected once the system is at deployment stage.There are a number of decisions that can be made when choosing the right contingency mechanisms to deal with these distribution-bounded problems. The factors that must be taken into account include not only the technology in use, the node layout, the message protocol and the properties of the messages to be exchanged, certain desired/demanded features such as latency, bandwidth,. . . but also the communications network reliability, and even the hardware where the system is running on.In this paper we present ADVERTISE, a distributed system for advertisement transmission to on-customer-home set-top boxes (STBs) over a Digital TV network (iDTV) of a cable operator. We use this system as a case study to explain how we addressed the aforementioned problems, and present a set of good practices that can be extrapolated to comparable systems.

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Section: Distributed Software Architecturesmentioning

confidence: 99%

Failover and takeover contingency mechanisms for network partition and node failure

López

Castro

Cabrero

2012

Proceedings of the Eleventh ACM SIGPLAN Workshop on Erlang Workshop

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“…Packet loss is proven to be one of the main quality impairment factors stemming from measurements in service provider networks [24] and potentially limiting the perspective of VoIP from becoming widely deployed. Furthermore, it has been shown that packet losses mostly appear in bursts [25,26], exhibiting strong long-term dependency. Burst losses decrease PLC's capability to conceal subsequent losses.…”

Section: Packet Loss Pattern Analysis and Modelingmentioning

confidence: 99%

Packet dispersion strategy evaluation from the perspective of packet loss pattern and VoIP quality

Ninkovic¹,

Gajin²,

Reljin³

2016

COMSIS J

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The appearance of burst packet losses and its devastating effect on Voice over IP (VoIP) service have imposed a requirement for the implementation of loss recovery mechanisms to address VoIP quality during periods when high packet loss is exhibited. Existing loss recovery mechanisms are dependent on end point capabilities, whereas Quality of service (QoS) routing protocols suffer from complexity and scalability issues. In this paper, we examine packet dispersion's ability to address burst losses and provide a computational model, which is verified using real network testing. A study has been carried out to investigate the effect of different packet dispersion strategies on burst losses, which clearly shows dispersion's qualitative superiority over single path routing. Furthermore, an analytical approach is proposed resulting in quality estimation obtained by individual strategies. Practical evaluation has shown that each strategy copes differently with various burst scenarios in order to maximize VoIP quality.Keywords: burst loss, Markov model, packet dispersion, voice over IP, quality of service.

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“…Thus, providing availability first requires understanding failure: how long are failures, what causes them, and how well are they masked? This is particularly true for networks, which have been increasingly identified as the leading cause of end-to-end service disruption [2,9,15,24,30], as they exhibit complex failure modes.…”

Section: Introductionmentioning

confidence: 99%

California fault lines

Turner

Levchenko

Snoeren

et al. 2010

Proceedings of the ACM SIGCOMM 2010 Conference

107

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Of the major factors affecting end-to-end service availability, network component failure is perhaps the least well understood. How often do failures occur, how long do they last, what are their causes, and how do they impact customers? Traditionally, answering questions such as these has required dedicated (and often expensive) instrumentation broadly deployed across a network.We propose an alternative approach: opportunistically mining "low-quality" data sources that are already available in modern network environments. We describe a methodology for recreating a succinct history of failure events in an IP network using a combination of structured data (router configurations and syslogs) and semi-structured data (email logs). Using this technique we analyze over five years of failure events in a large regional network consisting of over 200 routers; to our knowledge, this is the largest study of its kind.

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An investigation of the Internet’s IP-layer connectivity

Cited by 7 publications

References 20 publications

Failover and takeover contingency mechanisms for network partition and node failure

Failover and takeover contingency mechanisms for network partition and node failure

Packet dispersion strategy evaluation from the perspective of packet loss pattern and VoIP quality

California fault lines

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