Ultra-Reliable Distributed Cloud Network Control with End-to-End Latency Constraints

TL;DR

This paper develops a novel control framework for distributed cloud networks that guarantees strict end-to-end latency deadlines for real-time AgI services, while minimizing operational costs.

Contribution

It introduces a new queuing system and control algorithms that ensure delay constraints are met in highly distributed cloud/edge networks, a significant advancement over existing average-delay-focused methods.

Findings

Proposed control policy outperforms existing algorithms in simulations.

Guarantees strict end-to-end deadlines for AgI services.

Effectively balances latency guarantees with cost minimization.

Abstract

We are entering a rapidly unfolding future driven by the delivery of real-time computation services, such as industrial automation and augmented reality, collectively referred to as AgI services, over highly distributed cloud/edge computing networks. The interaction intensive nature of AgI services is accelerating the need for networking solutions that provide strict latency guarantees. In contrast to most existing studies that can only characterize average delay performance, we focus on the critical goal of delivering AgI services ahead of corresponding deadlines on a per-packet basis, while minimizing overall cloud network operational cost. To this end, we design a novel queuing system able to track data packets' lifetime and formalize the delay-constrained least-cost dynamic network control problem. To address this challenging problem, we first study the setting with average capacity (or resource budget) constraints, for which we characterize the delay-constrained stability region and design a near-optimal control policy leveraging Lyapunov optimization theory on an equivalent virtual network. Guided by the same principle, we tackle the peak capacity constrained scenario by developing the reliable cloud network control (RCNC) algorithm, which employs a two-way optimization method to make actual and virtual network flow solutions converge in an iterative manner. Extensive numerical results show the superior performance of the proposed control policy compared with the state-of-the-art cloud network control algorithm, and the value of guaranteeing strict end-to-end deadlines for the delivery of next-generation AgI services.