Subject: [P2PSIP] New Version Notification for draft-maenpaa-p2psip-self-tuning-01

From: Jouni Mäenpää <[email-masked]>
Date: Mon, 26 Oct 2009 12:45:22 +0200

Hi,

We have just submitted a new version of the draft "A Self-tuning
Distributed Hash Table (DHT) for REsource LOcation And Discovery
(RELOAD)". The initial revision of the draft was presented in IETF 74 in
San Francisco. The most important change is that instead of defining a
new topology plugin for RELOAD as the previous version did, the new
version describes how the default topology plugin of RELOAD can be
extended to support self-tuning. In addition, the new version no longer
defines any load balancing mechanisms.

Abstract:

REsource LOcation And Discovery (RELOAD) is a peer-to-peer (P2P)
signaling protocol that provides an overlay network service.  Peers
in a RELOAD overlay network collectively run an overlay algorithm to
organize the overlay, and to store and retrieve data.  This document
describes how the default topology plugin of RELOAD can be extended
to support self-tuning, that is, to adapt to changing operating
conditions such as churn and network size.

A URL for this Internet-Draft is:
http://www.ietf.org/id/draft-maenpaa-p2psip-self-tuning-01.txt

Comments are welcome!

Regards,
Jouni Maenpaa

-------------------------------

A new version of I-D, draft-maenpaa-p2psip-self-tuning-01.txt has been
successfuly submitted by Jouni Maenpaa and posted to the IETF repository.

Filename:	 draft-maenpaa-p2psip-self-tuning
Revision:	 01
Title:		 A Self-tuning Distributed Hash Table (DHT) for REsource
LOcation And Discovery (RELOAD)
Creation_date:	 2009-10-26
WG ID:		 Independent Submission
Number_of_pages: 20

Abstract:
REsource LOcation And Discovery (RELOAD) is a peer-to-peer (P2P)
signaling protocol that provides an overlay network service.  Peers
in a RELOAD overlay network collectively run an overlay algorithm to
organize the overlay, and to store and retrieve data.  This document
describes how the default topology plugin of RELOAD can be extended
to support self-tuning, that is, to adapt to changing operating
conditions such as churn and network size.