Parallelism-aware Service Function Chaining and Embedding for 5G Networks

The ultra-fast speed and massive capacity in 5G networks push huge amounts of data to networks. With network function virtualization, these data will go through multiple service functions (SFs) and big data processing/analysis. As a result, the processing delay from such SFs and data processing/anal...

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Bibliographic Details
Published in:Proceedings - International Conference on Computer Communications and Networks pp. 1 - 9
Main Authors: Zheng, Danyang, Peng, Chengzong, Liao, Xueting, Cao, Xiaojun
Format: Conference Proceeding
Language:English
Published: IEEE 01.07.2021
Subjects:
5G mobile communication
Big Data
Computational modeling
Delays
Integer programming
Network function parallelism
Network function virtualization
Parallel processing
Service function chaining
Service function chaining and embedding
ISSN:2637-9430
Online Access:Get full text
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Summary:The ultra-fast speed and massive capacity in 5G networks push huge amounts of data to networks. With network function virtualization, these data will go through multiple service functions (SFs) and big data processing/analysis. As a result, the processing delay from such SFs and data processing/analysis can significantly impact the delivery of latency-sensitive services. To reduce the processing delay, network function parallelism techniques are introduced to allow multiple SFs running parallelly for the same request. In this work, we study how to apply network function parallelism into SF chaining and embedding to optimize the latency. When physical nodes have unlimited computing resource, we propose the mixed integer programming based parallelism-aware SFC optimization (MIP-PS) algorithm. Our analysis proves the proposed MIP-PS is integer-approximation. When physical nodes have limited computing resource, we propose the latency factor based parallelism-aware SFC optimization (LF-PS) algorithm. Our extensive simulations demonstrate that our proposed schemes outperform the approaches extended directly from the existing work.
ISSN:2637-9430
DOI:10.1109/ICCCN52240.2021.9522271