Online VNF Chaining and Scheduling with Prediction: Optimality and Trade-Offs

For NFV systems, the key design space includes the function chaining for network requests and resource scheduling for servers. The problem is challenging since NFV systems usually require multiple (often conflicting) design objectives and the computational efficiency of decision making with limited information. Besides, the limits and benefits of predictive scheduling to NFV systems still remain unexplored. In this paper, we propose POSCARS, an efficient, distributed, and online algorithm that achieves a tunable trade-off between various system metrics with stability guarantee, while exploiting the power of predictive scheduling. Using randomized load balancing techniques, we propose three variants of POSCARS to further reduce sampling overheads. Theoretical analysis and trace-driven simulations show that POSCARS and its variants require only mild-value of future information to achieve a near- optimal average system cost while effectively shortening the average request response time.