Modeling and design of a Session Initiation Protocol overload control algorithm

Hong, Yang; Huang, Changcheng; Yan, James

doi:10.1177/0037549712443920

Cited by 9 publications

(6 citation statements)

References 35 publications

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“…The mechanism monitors the resources of the server, and a local overload is reported when the queue length or CPU load is grater than the prespecified thresholds . The queuing delay is also used as an indicator for overload condition . When the local overload is detected, the conventional approach rejects request messages with 503 (Service Unavailable) response messages or just drops them without sending any response.…”

Section: Related Workmentioning

confidence: 99%

“…To model r ( n ), retransmissions can be decomposed into subcomponents, as it is proposed in Hong et al (see Figure ), such that

r_{j} false(n false) = \sum_{i = 1}^{j + 1} r_{j i} false(n false); 1 \leq j \leq 6 .

As shown in Figure , subcomponents are divided into 3 groups. Groups 1 and 2 consist of redundant retransmissions, while group 3 consists of nonredundant retransmissions.…”

Section: Fluid‐flow Queuing Model For Prsmmentioning

confidence: 99%

“…In this section, the performance of an overloaded SIP server equipped with the PRSM has been evaluated using the fluid‐flow model. To demonstrate the scalability improvement of the PRSM, the conventional SIP design using the fluid‐flow model has also been evaluated. The evaluation is done numerically by implementing the fluid‐flow model in Java and using the time slot of 50 ms. We have developed the simulation tool to evaluate the effect of SIP retransmission mechanism on the SIP server under various scenarios as well as to evaluate the performance of the PRSM.…”

Section: Performance Evaluationmentioning

confidence: 99%

“…The server capacity and link loss rate are set to fixed parameters, and hence, the model does not consider retransmissions because of excessive queuing delays. A discrete time fluid‐flow model to evaluate the impact of SIP retransmission mechanism on the SIP server overloading is described in Hong et al Their formulation considers excessive queuing delays as the main reason for retransmissions and defines fluid‐flow models to capture the dynamic behavior of SIP retransmission mechanism of a single server with infinite buffer and tandem servers with finite buffer …”

Section: Introductionmentioning

confidence: 99%

“…A discrete time fluid-flow model to evaluate the impact of SIP retransmission mechanism on the SIP server overloading is described in Hong et al 6,7 Their formulation considers excessive queuing delays as the main reason for retransmissions and defines fluid-flow models to capture the dynamic behavior of SIP retransmission mechanism of a single server with infinite buffer 6 and tandem servers with finite buffer. 7 In our previous works, 8,9 we developed a priority-based SIP-server request scheduling mechanism (PRSM) as a new overload prevention approach by diminishing the negative effect of redundant retransmissions. The main idea is to identify retransmitted messages before queuing and to provide strict priority for the original incoming SIP requests over the retransmissions.…”

Section: Introductionmentioning

confidence: 99%

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On fluid‐flow modeling of priority based request scheduling for finite buffer SIP server

Yavaş

Hökelek

Günsel

2017

Int J Communication

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Summary With the widespread deployments of voice‐over‐internet protocol services, the existing session initiation protocol (SIP) design cannot scale up for large network sizes. Events triggering a demand burst or a server slowdown can cause SIP server overload, overload propagation, and crash, thus bringing down the whole SIP network. Since the SIP retransmission mechanism exacerbates the overload condition, existing models created for a stable SIP system cannot be effectively used to analyze an overloaded server. In this paper, we propose a fluid‐flow model to characterize the behavior of the finite buffer SIP server equipped with priority‐based request scheduling mechanism (PRSM). The model for the PRSM uses primary and secondary queues for the original request messages and the retransmitted requests, respectively. The performance metrics, namely, the failed call attempts and the response delay from sending INVITE request until receiving a 100‐Trying response, are derived using the arrival time‐slot tracking and the removal processes of the proposed fluid‐flow model. We conducted test cases under the heavy traffic conditions, where the overload is caused by bulk and bursty arrivals or server slowdown. The numerical results closely match with the simulation results for all experiments, indicating that the proposed model can accurately capture the dynamic behavior of an SIP server with the PRSM. The experiments demonstrate that the number of failed call attempts is close to 0 and the mean response delay is kept constant around 175 ms for the PRSM when the buffer size is higher than 1K while both metrics are significantly higher for the conventional SIP.

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Section: Related Workmentioning

confidence: 99%

“…To model r ( n ), retransmissions can be decomposed into subcomponents, as it is proposed in Hong et al (see Figure ), such that

r_{j} false(n false) = \sum_{i = 1}^{j + 1} r_{j i} false(n false); 1 \leq j \leq 6 .

As shown in Figure , subcomponents are divided into 3 groups. Groups 1 and 2 consist of redundant retransmissions, while group 3 consists of nonredundant retransmissions.…”