Recovery analysis of the DOCSIS protocol after service disruption

Sdralia, V.; Tzerefos, P.; Smythe, C.

doi:10.1109/11.979375

Cited by 6 publications

(3 citation statements)

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“…In [10] and [13] it was introduced a subtly different approach, namely the persistent ranging algorithm (PRA), and showed that it performs better that the one used in DOCSIS in case of a power outage event.…”

Section: Persistent Ranging Algorithmmentioning

confidence: 99%

“…Issues related to the DOCSIS CATV protocol have been studied over the last few years [4], [5], [6], [11], [14], [15], including service disruption events such as large scale power outages [10], [12], [13]. In case of service disruption the connection between the Head-End (HE) and the subscriber Cable Modem (CM) is terminated.…”

Section: Introductionmentioning

confidence: 99%

“…programming language [13] were developed. These were then used to determine the cable network's recovery time, defined as the number of ranging opportunities required for the ranging of all previously active CMs with the HE.…”

Section: Introductionmentioning

confidence: 99%

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Optimized recovery of DOCSIS networks using reserved persistent ranging

Sdralia

GLOBECOM'01. IEEE Global Telecommunications Conference (Cat. No.01CH37270)

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Power outage events can severely disrupt the digital services over Community Antenna Television (CATV) networks. This paper proposes two schemes for reserved persistent initial ranging of the Data Over Cable Service Interface Specification (DOCSIS) protocol, namely priority and cluster ranging. Both schemes restrain the number of CMs that can compete simultaneously for the acquisition of the upstream channel and minimize the number of idle opportunities leading to faster recovery of the network. Their employment involves the introduction of a new field in the MAP message and a new Cable Modem (CM) parameter in the configuration file downloaded from the Cable Modem Termination System (CMTS). Our results show an absolute reduction in the network's recovery time of up to 42% with an average of 38%.

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Section: Persistent Ranging Algorithmmentioning

confidence: 99%