Towards Latency Optimization in Hybrid Service Function Chain Composition and Embedding
| Autor: | Danyang Zheng, Xiaojun Cao, Xueting Liao, Chengzong Peng, Ling Tian, Guangchun Luo |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Network Functions Virtualization
business.industry Computer science Distributed computing 05 social sciences 050801 communication & media studies 020206 networking & telecommunications Cloud computing 02 engineering and technology Service provider 0508 media and communications 0202 electrical engineering electronic engineering information engineering Embedding Latency (engineering) business |
| Zdroj: | INFOCOM |
| DOI: | 10.1109/infocom41043.2020.9155529 |
| Popis: | In Network Function Virtualization (NFV), to satisfy the Service Functions (SFs) requested by a customer, service providers will composite a Service Function Chain (SFC) and embed it onto the shared Substrate Network (SN). For many latency-sensitive and computing-intensive applications, the customer forwards data to the cloud/server and the cloud/server sends the results/models back, which may require different SFs to handle the forward and backward traffic. The SFC that requires different SFs in the forward and backward directions is referred to as hybrid SFC (h-SFC). In this paper, we, for the first time, comprehensively study how to optimize the latency in Hybrid SFC composition and Embedding (HSFCE). When each substrate node provides only one unique SF, we prove the NP-hardness of HSFCE and propose the first 2-approximation algorithm to jointly optimize the processes of h-SFC construction and embedding, which is called Eulerian Circuit based Hybrid SFP optimization (EC-HSFP). When a substrate node provides various SFs, we extend EC-HSFP and propose the efficient Betweenness Centrality based Hybrid SFP optimization (BC-HSFP) algorithm. Our extensive simulations and analysis show that EC-HSFP can hold the 2-approximation, while BC-HSFP outperforms the algorithms directly extended from the state-of-art techniques by an average of 20%. |
| Databáze: | OpenAIRE |
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