Service traffic engineering: Avoiding link overloads in service chains

Mebarkia, Khalil; Zsoka, Zoltan

doi:10.1109/jcn.2019.000007

Cited by 8 publications

(1 citation statement)

References 20 publications

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“…To optimize the above SFC deployment problem and find a near-optimal solution in a reasonable time, many scholars have established the mathematical model of SFC deployment [4][5][6][7][8][9][10][11][12][13], and designed heuristic solutions (individual-based [14][15][16][17][18][19], populationbased [20][21][22][23], and others [24][25][26][27][28][29][30][31][32][33]). However, most of these models ignore the constraints of computer architecture on hardware resource allocation; that is, VNF cannot be instantiated with resources of any size, but need to meet certain specification constraints.…”

Section: Introductionmentioning

confidence: 99%

Heuristic Strategy of Service Function Chain Deployment Based on N-Base Continuous Digital Coding in Network Function Virtualization Environment

Liu

2022

Electronics

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As a novel network architecture, network function virtualization (NFV) greatly improves the flexibility and scalability of service provision. Deploying the service function chain (SFC) in an NFV environment needs to coordinate the instantiation of the virtual network function descriptor, network function embedding, and traffic steering, which also improves the complexity of the problem. Although heuristic algorithms are widely used to optimize this problem, due to the lack of complete SFC deployment solution space and digital coding scheme, the time complexity of the algorithm is difficult to meet the requirements. Therefore, this paper studies the digital coding scheme of the heuristic SFC deployment to improve time efficiency without reducing performance. Firstly, we model the SFC deployment as a 0–1 integer linear programming, considering the above factors, and then design a continuous digital solution space construction scheme based on N-Base coding (NBACO-SS) to optimize the above problems. NBACO-SS uses integer size and carry to map the complex SFC deployment to simple digital coding, evaluates the continuity of solution space through the Manhattan distance, and optimizes the continuity through the ant colony algorithm. Based on NBACO-SS, we reconstruct two heuristic algorithms to solve the SFC deployment problem. Experimental results demonstrate that NBACO-SS can improve the time efficiency by 20% without reducing the total network service traffic.

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