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Service function chaining (SFC), consisting of a sequence of virtual network functions (VNFs), is the de-facto service provisioning mechanism in VNF-enabled data centers (VDCs). However, for the SFC, the dynamic and diverse virtual machine (VM) traffic must traverse a sequence of VNFs possibly installed at different locations at VDCs, resulting in prolonged network delay, redundant network traffic, and large consumption of cloud resources (e.g., bandwidth and energy). Such adverse effects of the SFC, which we refer to as SFC traffic storm, significantly impede its efficiency and practical implementation.In this paper, we solve the SFC traffic storm problem by proposing AggVNF, a framework wherein the VNFs of an SFC are implemented into one aggregate VNF while multiple instances of aggregate VNFs are available in the VDC. AggVNF adaptively allocates and migrates aggregate VNFs to optimize cloud resources in dynamic VDCs while achieving the load balance of VNFs. At the core of the AggVNF are two graph-theoretical problems that have not been adequately studied. We solve both problems by proposing optimal, approximate, and heuristic algorithms. Using real traffic patterns in Facebook data centers, we show that a) our VNF allocation algorithms yield traffic costs 56.3% smaller than the latest research using the SFC design, b) our VNF migration algorithms yield 84.2% less traffic than the latest research using the SFC design, and c) VNF migration is an effective technique in mitigating dynamic traffic in VDCs, reducing the total traffic cost by up to 24.8%.
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Ant Colony based Online Learning Algorithm for Service Function Chain Deployment
Network Function Virtualization (NFV) emerges as a promising paradigm with the potential for cost-efficiency, manage-convenience, and flexibility, where the service function chain (SFC) deployment scheme is a crucial technology. In this paper, we propose an Ant Colony Optimization (ACO) meta-heuristic algorithm for the Online SFC Deployment, called ACO-OSD, with the objectives of jointly minimizing the server operation cost and network latency. As a meta-heuristic algorithm, ACO-OSD performs better than the state-of-art heuristic algorithms, specifically 42.88% lower total cost on average. To reduce the time cost of ACO-OSD, we design two acceleration mechanisms: the Next-Fit (NF) strategy and the many-to-one model between SFC deployment schemes and ant-tours. Besides, for the scenarios requiring real-time decisions, we propose a novel online learning framework based on the ACO-OSD algorithm, called prior-based learning real-time placement (PLRP). It realizes near real-time SFC deployment with the time complexity of O(n), where n is the total number of VNFs of all newly arrived SFCs. It meanwhile maintains a performance advantage with 36.53% lower average total cost than the state-of-art heuristic algorithms. Finally, we perform extensive simulations to demonstrate the outstanding performance of ACO-OSD and PLRP compared with the benchmarks.
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- Award ID(s):
- 1717731
- PAR ID:
- 10472606
- Publisher / Repository:
- IEEE
- Date Published:
- ISBN:
- 979-8-3503-3414-2
- Page Range / eLocation ID:
- 1 to 10
- Format(s):
- Medium: X
- Location:
- New York City, NY, USA
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/INFOCOM53939.2023.10229012
