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Service-oriented virtual network deployment is based on statistical resourcedemands of different services, while data traffic from each service fluctuatesover time. In this paper, a delay-aware flow migration problem for embeddedservices is studied to meet end-to-end (E2E) delay requirement withtime-varying traffic. A non-convex multi-objective mixed integer optimizationproblem is formulated, addressing the trade-off between maximum load balancingand minimum reconfiguration overhead due to flow migrations, under processingand transmission resource constraints and QoS requirement constraints. Sincethe original problem is non-solvable in optimization solvers due to unsupportedtypes of quadratic constraints, it is transformed to a tractable mixed integerquadratically constrained programming (MIQCP) problem. The optimality gapbetween the two problems is proved to be zero, so we can obtain the optimum ofthe original problem through solving the MIQCP problem with somepost-processing. Numerical results are presented to demonstrate theaforementioned trade-off, as well as the benefit from flow migration in termsof E2E delay performance guarantee.

Delay-Aware Flow Migration for Embedded Services in 5G Core Networks