A Profit Optimizing Strategy for Congestion Control in Signaling Networks

Document type: Conference Papers
Peer reviewed: Yes
Full text:
Author(s): Stefan Pettersson, Åke Arvidsson
Title: A Profit Optimizing Strategy for Congestion Control in Signaling Networks
Conference name: Bangkok Regional International Teletraffic Seminar
Year: 1995
Pagination: paper no. 39
City: Bangkok
Organization: Blekinge Institute of Technology
Department: Dept. of Telecommunications and Mathematics (Institutionen för telekommunikation och matematik)
Dept. of Telecommunications and Mathematics S-371 79 Karlskrona
+46 455 780 00
Authors e-mail: stefanp@itm.hk-r.se
Language: English
Abstract: Congestion control in the signaling system number 7 (SS7) is a necessity to fulfil the
requirements of a telecommunication network that satisfy customers’ requirements on
quality of service. Heavy network load is an important source of customer dissatisfaction
as congested networks result in deteriorated quality of service.
With the introduction of a Congestion Control Mechanism (CCM), that annihilates
service sessions with a predicted completion time greater than the maximum allowed com-pletion
time for the session, network performance improve dramatically. Annihilation of
already delayed sessions let other sessions benefit and increase the overall network
This paper investigates the possibilities of using a decision theoretic approach that
base the decision of annihilation on the average loss attached to each of the two possible
actions, i.e. annihilate or not. Attributes are attached to each session describing the out-come
of any performed CCM action, e.g. the economic loss connected with the annihila-tion
of a session. The attributes are also used to calculate the network loss for a given
network load.
The results in this paper indicate that the decision theoretic approach can decrease the
network loss up to 40% for the improved CCM (ICCM) compared to an ordinary CCM.
Subject: Telecommunications\Overload Control in Intelligent Networks
Keywords: Overload control, Intelligent networks
Note: This article is written under the Project "Overload Control in Intelligent Networks"