A Latency-driven Availability Assessment for Multi-Tenant Service Chains

TL;DR

This paper introduces a novel latency-driven availability assessment method for multi-tenant service chains using containerized network functions, combining multi-state system modeling, queueing theory, and an optimized computational approach.

Contribution

It presents a new combined modeling and computational framework for evaluating availability and latency in complex containerized service chains, addressing multi-tenancy and resource sharing.

Findings

Effective availability assessment for containerized service chains.

Validated approach with a containerized IP Multimedia Subsystem example.

Optimized computation method reduces complexity in availability evaluation.

Abstract

Nowadays, most telecommunication services adhere to the Service Function Chain (SFC) paradigm, where network functions are implemented via software. In particular, container virtualization is becoming a popular approach to deploy network functions and to enable resource slicing among several tenants. The resulting infrastructure is a complex system composed by a huge amount of containers implementing different SFC functionalities, along with different tenants sharing the same chain. The complexity of such a scenario lead us to evaluate two critical metrics: the steady-state availability (the probability that a system is functioning in long runs) and the latency (the time between a service request and the pertinent response). Consequently, we propose a latency-driven availability assessment for multi-tenant service chains implemented via Containerized Network Functions (CNFs). We adopt a multi-state system to model single CNFs and the queueing formalism to characterize the service latency. To efficiently compute the availability, we develop a modified version of the Multidimensional Universal Generating Function (MUGF) technique. Finally, we solve an optimization problem to minimize the SFC cost under an availability constraint. As a relevant example of SFC, we consider a containerized version of IP Multimedia Subsystem, whose parameters have been estimated through fault injection techniques and load tests.