CoDeC: A Cost-Effective and Delay-Aware SFC Deployment

Service Function Chain (SFC) provides an end-to-end service by processing traffic flow through a series of Virtual Network Functions (VNFs) in a specific order. Satisfying user's demands (e.g., end-to-end delay) on one hand and minimizing the cost of SFC deployment in terms of energy and resour...

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Bibliographic Details
Published in:IEEE eTransactions on network and service management 2020-06, Vol.17 (2), p.793-806
Main Authors: Tashtarian, Farzad, Zhani, Mohamed Faten, Fatemipour, Bita, Yazdani, Delaram
Format: Article
Language:English
Subjects:
Algorithms
Codec
Codecs
Computer simulation
Delay
Delays
Energy distribution
Heuristic methods
Integer programming
Linear programming
Minimum cost
Mirrors
Mixed integer
network function virtualization
NF distribution
NF parallelism
Noise measurement
Optimization
Parallel processing
Resource allocation
Resource management
SFC
Traffic flow
Virtual networks
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Summary:Service Function Chain (SFC) provides an end-to-end service by processing traffic flow through a series of Virtual Network Functions (VNFs) in a specific order. Satisfying user's demands (e.g., end-to-end delay) on one hand and minimizing the cost of SFC deployment in terms of energy and resource on the other hand, introduces VNFs placement as a crucial issue that is receiving significant attention by researchers. To address this problem and boost the performance of SFC, different techniques such as Network Function (NF) distribution, NF parallelism and optimal resource allocation have been utilized. Applying these mechanisms imposes other costs which must be taken into account by network providers. In this paper, we introduce CoDeC as a Cost-effective and Delay-aware resource allocation approach. By having user defined end-to-end threshold and using aforementioned mechanisms, CoDeC tries to place the requested VNFs with the minimum cost of deployment, distribution, parallelism and energy. Therefore, we formulate the addressed problem in form of Mixed Integer linear Programming (MILP) model. We then show that the problem is NP-complete and suffers from high time complexity in large-scale scenarios. Thus, a heuristic algorithm is introduced to determine a near-optimal solution in a reasonable amount of time. Our simulation results show that CoDeC achieves better performance in term of cost and acceptance rate compared to using each mechanism individually.
ISSN:1932-4537
1932-4537
DOI:10.1109/TNSM.2019.2949753