<p>In the upcoming 5G-and-beyond era,
ultra-reliable low-latency communication (URLLC) services will be ubiquitous in edge networks. To improve network performance and quality
of service (QoS), URLLC services could be delivered via a sequence of
software-based network functions, also known as service function chains (SFCs).
Towards reliable SFC delivery, it is imperative to incorporate deterministic
fault tolerance during SFC deployment. However, deploying an SFC with deterministic
fault tolerance is challenging because the protection mechanism needs to
consider protection against physical/virtual network failures and hardware/software
failures jointly. Against multiple and diverse failures, this work investigates how
to effectively deliver an SFC in optical edge networks with deterministic fault
tolerance while minimizing wavelength resource consumption. We introduce a
protection augmented graph, called <i>k</i>-connected service function slices
layered graph (KC-SLG), protecting against <i>k</i>-1 fiber link failures and <i>k</i>-1
server failures. We formulate a novel problem called deterministic-fault-tolerant
SFC embedding and propose an effective algorithm, called
most candidate first SF slices layered graph embedding (MCF-SE). MCF-SE employs
two proposed techniques: <i>k</i>-connected network slicing (KC-NS) and <i>k</i>-connected
function slicing (KC-FS). Through thorough mathematical proof, we show that
KC-NS is <i>2</i>-approximate. For KC-FS, we demonstrate that <i>k</i>
= 3 provides the best cost-efficiency. Our experimental results
also show that the proposed MCF-SE achieves deterministic-fault-tolerant
service delivery and performs better than the schemes directly extended from
existing work regarding survivability and average cost-efficiency.</p>
Online Service Function Chain Deployment for Live-Streaming in Virtualized Content Delivery Networks: A Deep Reinforcement Learning Approach