The 5th generation mobile network (5G) is expected to support numerous services with versatile quality of service (QoS) requirements such as high data rates and low end-to-end (E2E) latency. It is widely agreed that E2E latency can be significantly reduced by moving content/computing capability closer to the network edge. However, since the edge nodes (i.e., base stations) have limited computing capacity, mobile network operators shall make a decision on how to provision the computing resources to the services in order to make sure that the E2E latency requirement of the services are satisfied while the network resources (e.g., computing, radio, and transport network resources) are used in an efficient manner. In this work, we employ integer linear programming (ILP) techniques to formulate and solve a joint user association, service function chain (SFC) placement, and resource allocation problem where SFCs, composed of virtualized service functions (VSFs), represent user requested services that have certain E2E latency and data rate requirements. Specifically, we compare three variants of an ILP-based algorithm that aim to minimize E2E latency of requested services, service provisioning cost, and VSF migration frequency, respectively. We then propose a heuristic in order to address the scalability issue of the ILP-based solutions. Simulations results demonstrate the effectiveness of the proposed heuristic algorithm.

Latency-Aware Service Function Chain Placement in 5G Mobile Networks

Roberto Riggio
2019

Abstract

The 5th generation mobile network (5G) is expected to support numerous services with versatile quality of service (QoS) requirements such as high data rates and low end-to-end (E2E) latency. It is widely agreed that E2E latency can be significantly reduced by moving content/computing capability closer to the network edge. However, since the edge nodes (i.e., base stations) have limited computing capacity, mobile network operators shall make a decision on how to provision the computing resources to the services in order to make sure that the E2E latency requirement of the services are satisfied while the network resources (e.g., computing, radio, and transport network resources) are used in an efficient manner. In this work, we employ integer linear programming (ILP) techniques to formulate and solve a joint user association, service function chain (SFC) placement, and resource allocation problem where SFCs, composed of virtualized service functions (VSFs), represent user requested services that have certain E2E latency and data rate requirements. Specifically, we compare three variants of an ILP-based algorithm that aim to minimize E2E latency of requested services, service provisioning cost, and VSF migration frequency, respectively. We then propose a heuristic in order to address the scalability issue of the ILP-based solutions. Simulations results demonstrate the effectiveness of the proposed heuristic algorithm.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11566/291338
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