Online VNF Chaining and Predictive Scheduling: Optimality and Trade-offs

TL;DR

This paper introduces POSCARS, an online predictive scheduling scheme for NFV systems that balances multiple objectives, achieves near-optimal performance with limited future info, and guarantees queue stability.

Contribution

It presents a novel online predictive scheduling framework that decouples complex optimization into manageable sub-problems with tunable trade-offs and low overhead.

Findings

Near-optimal system cost with limited future information

Ultra-low request response time

Flexible trade-offs among system metrics

Abstract

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 real-time decision making with limited information. Furthermore, the benefits of predictive scheduling to NFV systems still remain unexplored. In this paper, we propose POSCARS, an efficient predictive and online service chaining and resource scheduling scheme that achieves tunable trade-offs among various system metrics with queue stability guarantee. Through a careful choice of granularity in system modeling, we acquire a better understanding of the trade-offs in our design space. By a non-trivial transformation, we decouple the complex optimization problem into a series of online sub-problems to achieve the optimality with only limited information. By employing randomized load balancing techniques, we propose three variants of POSCARS to reduce the overheads of decision making. Theoretical analysis and simulations show that POSCARS and its variants require only mild-value of future information to achieve near-optimal system cost with an ultra-low request response time.