Queueing Strategies for Local Overload Control in SIP Server

Garroppo, Rosario G.; Giordano, Stefano; Spagna, Stella; Niccolini, Saverio

doi:10.1109/glocom.2009.5425591

Cited by 23 publications

(24 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…QoS is widely used in various systems as a means to capture the status of running systems. By contrast, in the actual operation of a server system, it is empirically known that the system has nonlinear dynamics and its QoS degrades catastrophically under overloaded conditions, and this has also been confirmed in experiments concerning the performance management of a server system [2,3,[14][15][16]. To summarize, QoS decline occurs because of the property that it can only be observed after an event, such as overload or resource depletion, has already occurred.…”

Section: Problem Of Observing the Dynamics Of Server Systemsmentioning

confidence: 97%

Analysis of the Macroscopic Behavior of Server Systems in the Internet Environment

et al. 2017

View full text Add to dashboard Cite

Elasticity is one of the key features of cloud-hosted services built on virtualization technology. To utilize the elasticity of cloud environments, administrators should accurately capture the operational status of server systems, which changes constantly according to service requests incoming irregularly. However, it is difficult to detect and avoid in advance that operating services are falling into an undesirable state. In this paper, we focus on the management of server systems that include cloud systems, and propose a new method for detecting the sign of undesirable scenarios before the system becomes overloaded as a result of various causes. In this method, a measure that utilizes the fluctuation of the macroscopic operational state observed in the server system is introduced. The proposed measure has the property of drastically increasing before the server system is in an undesirable state. Using the proposed measure, we realize a function to detect that the server system is falling into an overload scenario, and we demonstrate its effectiveness through experiments.

show abstract

Section: Problem Of Observing the Dynamics Of Server Systemsmentioning

confidence: 97%

Analysis of the Macroscopic Behavior of Server Systems in the Internet Environment

et al. 2017

View full text Add to dashboard Cite

show abstract

“…However, if the number of SIP messages in the buffer is greater than k sup or the number is increasing between k inf and k sup , the requests are rejected. In Garroppo et al and Ohta, two separate buffers are intended, one for invite requests and another for non‐invite requests . The non‐invite requests have high priority, and if their buffer becomes empty, the invite requests are processed.…”

Section: Related Workmentioning

confidence: 99%

Overload management with regard to fairness in session initiation protocol networks by holonic multiagent systems

Khazaei

Mozayani

2017

Int J Network Mgmt

View full text Add to dashboard Cite

Summary Session initiation protocol (SIP) is a widely used standard protocol for multimedia applications and IP multimedia subsystems. Internet protocol multimedia subsystem was introduced by the 3GGP signaling foundation as part of a set of next generation network architectures. Despite having useful practical features, SIP does not have suitable mechanisms to handle overload. This challenge creates a sharp drop in quality of service for next generation network users. Because a distributed SIP network is a complex system composed of subsystems interacting with one another, multiagent system is an appropriate method to model network interactions and to solve overload in SIP networks. In this paper, holonic organization is applied to reduce the multiagent system complexity in modeling a large SIP network. Holonic organization is a hierarchical structure in which each holon covers a geographical area of the SIP network at the first level. At the second level, upper‐level holons control the first‐level holons, and so on. Overload control is achieved by communication and the exchange of knowledge between the intelligent holons. Experimental results show that the proposed method prevents overload in the SIP network. The method also increases total throughput, reduces delay, and considers fairness in the SIP network.

show abstract

“…In the literature, SIP messages are prioritized according to their roles during the SIP session establishment, such as higher priority is given to INVITE messages over non‐INVITE messages and vice versa . In Guduru and Usha, the overload control is achieved with a priority queuing model to reduce the retransmissions by giving high priority to retransmitted requests and to avoid transmitting unnecessary retransmissions towards downstream of SIP network.…”

Section: Related Workmentioning

confidence: 99%

“…The local overload control is an internal mechanism that is used to keep the performance of the server in an acceptable level under overload conditions. The mechanism monitors the resources of the server, and a local overload is reported when the queue length [17][18][19][20][21] or CPU load is grater than the prespecified thresholds. 17,22 The queuing delay is also used as an indicator for overload condition.…”

Section: Related Workmentioning

confidence: 99%

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

Yavaş

Hökelek

Günsel

2017

Int J Communication

View full text Add to dashboard Cite

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.

show abstract

Queueing Strategies for Local Overload Control in SIP Server

Cited by 23 publications

References 6 publications

Analysis of the Macroscopic Behavior of Server Systems in the Internet Environment

Analysis of the Macroscopic Behavior of Server Systems in the Internet Environment

Overload management with regard to fairness in session initiation protocol networks by holonic multiagent systems

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

Contact Info

Product

Resources

About