A Load-Balanced Call Admission Controller for IMS Cloud Computing - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Journal Articles IEEE Transactions on Network and Service Management Year : 2016

A Load-Balanced Call Admission Controller for IMS Cloud Computing

Ahmadreza Montazerolghaem
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Alberto Leon-Garcia
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Mahmoud Naghibzadeh
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Farzad Tashtarian
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Abstract

— Network functions virtualization provides opportunities to design, deploy, and manage networking services. It utilizes Cloud computing virtualization services that run on high-volume servers, switches and storage hardware to virtualize network functions. Virtualization techniques can be used in IP Multimedia Subsystem (IMS) cloud computing to develop different networking functions (e.g. load balancing and call admission control). IMS network signaling happens through Session Initiation Protocol (SIP). An open issue is the control of overload that occurs when an SIP server lacks sufficient CPU and memory resources to process all messages. This paper proposes a virtual load balanced call admission controller (VLB-CAC) for the cloud-hosted SIP servers. VLB-CAC determines the optimal " call admission rates " and " signaling paths " for admitted calls along with the optimal allocation of CPU and memory resources of the SIP servers. This optimal solution is derived through a new linear programming model. This model requires some critical information of SIP servers as input. Further, VLB-CAC is equipped with an autoscaler to overcome resource limitations. The proposed scheme is implemented in SAVI (Smart Applications on Virtual Infrastructure) which serves as a virtual testbed. An assessment of the numerical and experimental results demonstrates the efficiency of the proposed work.
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Dates and versions

hal-01597929 , version 1 (29-09-2017)

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Ahmadreza Montazerolghaem, Mohammad Hossein Hossein Yaghmaee, Alberto Leon-Garcia, Mahmoud Naghibzadeh, Farzad Tashtarian. A Load-Balanced Call Admission Controller for IMS Cloud Computing. IEEE Transactions on Network and Service Management, 2016, 13 (4), pp.806 - 822. ⟨10.1109/TNSM.2016.2572161⟩. ⟨hal-01597929⟩
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