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AdaptNF: Adaptive service chain scheduling with stateless migration and NF consolidation

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摘要 The combination of network function virtualization and software-defined networking allows various network functions to process flows according to their characteristics and requirements.Due to the highly dynamic nature of the workload,the network infrastructure needs to properly schedule the underlying resources in order to respond to workload changes in a timely manner.However,the existing NFV platform lacks a comprehensive solution for how to scale under workload variation,which may seriously hurt the overall system performance.To improve the scalability of the NFV platform and ensure consistent high performance under dynamic workloads,we propose AdaptNF,a novel NFV platform designed to support a combination of course-grained and fine-grained resource scheduling strategies.To deal with resource imbalance,which is the essential scheduling problem that leads to insufficient NFV performance,AdaptNF adopts a novel algorithm that can efficiently balance the workload among multiple network function instances through stateless flow migration.Our controlled experiments show that the AdaptNF scheme can optimize resource allocation and ensure outstanding performance after scaling.In terms of network throughput and latency,AdaptNF significantly improves the performance of the underlying NFV platform.
出处 《Digital Communications and Networks》 SCIE CSCD 2023年第2期462-472,共11页 数字通信与网络(英文版)
基金 supported by the Guangdong Province Key Area R&D Program under grant No.2018B010113001 National Key Research and Development Program of China under Grant No.2018YFB1804704 National Natural Science Foundation of China under grant No.61902171 the Shenzhen Key Lab of Software Defined Networking under grant No.ZDSYS20140509172959989.
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