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Network congestion control: mechanisms for congestion avoidance and recovery

Published:18 December 2011Publication History

ABSTRACT

The increase in usage of smart phones and extension of wireless technology to non-human interactions (e.g., Machine to machine communications) has made avoidance and recovery from congestion a priority, both for network operators and network vendors. With the advent of 4G, which provides higher data rate and new age applications like video, user plane congestion in Radio and backhaul is possible. With applications like machine to machine communications, the signaling load on the network can also increase exponentially. Therefore both User plane and Control Plane congestion needs to be addressed.

The current mechanisms for Congestion Control rely on statically allocating different priorities for UEs. This allows calls from high priority UEs to be allowed in the network, during disasters. However any call made by the UE is considered as high priority. This is not sufficient as non-necessary data service may hog the bandwidth during emergency blocking important services. In this paper we discuss dynamic priority for individual calls. Specifically, in this paper we discuss the solutions using back off timer mechanism based on the device type and the priority of the access to recover from congestion scenario.

The control and user plane congestion in the core network can be alleviated by usage of intelligent node selection and mobility using DNS based mechanism. This helps to distribute traffic to lightly loaded nodes, thereby alleviating congestion in a single node. This is especially useful when there is regional congestion due to some unforeseen events like natural disasters.

The user plane congestion can occur in the radio or in the backhaul. The radio congestion is caused due to high number of users trying to access the network which causes most requests to be rejected. We propose a solution in which the rejection can be based on the subscription profile and type of application. This allows only high priority applications to access the network during time of disaster while rejecting all low priority accesses even from high priority user. During non-disaster time, the mechanism has the advantage of allowing the revenue of the operators to be maximized and also fair resources to be provided to everybody.

We focus on 3GPP Network as an example to demonstrate the usage of these mechanisms.

References

  1. 3GPP TS 23.401: "General Packet Radio Service (GPRS) enhancements for Evolved Universal Terrestrial Radio Access Network (E-UTRAN) access".Google ScholarGoogle Scholar
  2. 3GPP TS 36.331: "Evolved Universal Terrestrial Radio Access (E-UTRA); Radio Resource Control (RRC); Protocol specification".Google ScholarGoogle Scholar
  3. IETF RFC 3401: "Dynamic Delegation Discovery System (DDDS) Part One: The Comprehensive DDDS".Google ScholarGoogle Scholar
  4. 3GPP TS 29.303: "Domain Name System Procedures; Stage 3"Google ScholarGoogle Scholar
  5. 3GPP TS 23.203: "Policy Control and Charging architecture".Google ScholarGoogle Scholar
  6. 3GPP TS 29.274: "3GPP Evolved Packet System. Evolved GPRS Tunnelling Protocol for EPS (GTPv2)".Google ScholarGoogle Scholar
  7. 3GPP TS 24.301: "Non-Access-Stratum (NAS) protocol for Evolved Packet System (EPS); Stage 3".Google ScholarGoogle Scholar
  8. IETF RFC 3403: " Dynamic Delegation Discovery System (DDDS) Part Three: The Domain Name System (DNS) Database".Google ScholarGoogle Scholar
  9. IETF RFC 2782: "A DNS RR for specifying the location of services (DNS SRV)".Google ScholarGoogle Scholar
  10. IETF RFC 3958: "Domain-Based Application Service Location Using SRV RRs and the Dynamic Delegation Discovery Service (DDDS)".Google ScholarGoogle Scholar

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  1. Network congestion control: mechanisms for congestion avoidance and recovery

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      cover image ACM Conferences
      ACWR '11: Proceedings of the 1st International Conference on Wireless Technologies for Humanitarian Relief
      December 2011
      517 pages
      ISBN:9781450310116
      DOI:10.1145/2185216

      Copyright © 2011 ACM

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      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 18 December 2011

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