Optimal Dynamic Cloud Network Control

TL;DR

This paper develops distributed online algorithms for optimal service delivery in cloud networks, balancing cost and delay through dynamic flow control and resource allocation.

Contribution

It introduces a family of Lyapunov-based algorithms that stabilize queues and optimize cost-delay tradeoffs in distributed cloud networks.

Findings

Algorithms achieve near-minimum cost with controlled delay.

Quadratic metric minimization improves cost-delay tradeoff.

Enhanced algorithms with distance bias improve delay without affecting throughput.

Abstract

Distributed cloud networking enables the deployment of a wide range of services in the form of interconnected software functions instantiated over general purpose hardware at multiple cloud locations distributed throughout the network. We consider the problem of optimal service delivery over a distributed cloud network, in which nodes are equipped with both communication and computation resources. We address the design of distributed online solutions that drive flow processing and routing decisions, along with the associated allocation of cloud and network resources. For a given set of services, each described by a chain of service functions, we characterize the cloud network capacity region and design a family of dynamic cloud network control (DCNC) algorithms that stabilize the underlying queuing system, while achieving arbitrarily close to minimum cost with a tradeoff in network delay. The proposed DCNC algorithms make local decisions based on the online minimization of linear and quadratic metrics obtained from an upper bound on the Lyapunov drift-plus-penalty of the cloud network queuing system. Minimizing a quadratic vs. a linear metric is shown to improve the cost-delay tradeoff at the expense of increased computational complexity. Our algorithms are further enhanced with a shortest transmission-plus-processing distance bias that improves delay performance without compromising throughput or overall cloud network cost. We provide throughput and cost optimality guarantees, convergence time analysis, and extensive simulations in representative cloud network scenarios.