Application Component Placement in NFV-based Hybrid Cloud/Fog Systems

TL;DR

This paper addresses the challenge of optimally placing application components in NFV-based hybrid cloud/fog systems, considering non-deterministic application graphs with complex sub-structures, to minimize latency and cost.

Contribution

It introduces a novel ILP model for placing components of non-deterministic application graphs in hybrid cloud/fog environments, extending prior work limited to deterministic graphs.

Findings

ILP model effectively minimizes makespan and cost

Evaluation shows promising results in small-scale scenarios

Highlights the importance of handling non-deterministic graphs

Abstract

Applications are sets of interacting components that can be executed in sequence, in parallel, or by using more complex constructs such as selections and loops. They can, therefore, be modeled as structured graphs with sub-structures consisting of these constructs. Fog computing can reduce the latency induced by distant clouds by enabling the deployment of some components at the edge of the network (i.e., closer to end-devices) while keeping others in the cloud. Network Functions Virtualization (NFV) decouples software from hardware and enables an agile deployment of network services and applications as Virtual Network Functions (VNFs). In NFV settings, efficient placement algorithms are required to map the structured graphs representing the VNF Forwarding Graphs (VNF-FGs) onto the infrastructure of the hybrid cloud/fog system. Only deterministic graphs with sequence and parallel sub-structures have been considered thus to date. However, several real-life applications do require non-deterministic graphs with sub-structures as selections and loops. This paper focuses on application component placement in NFV-based hybrid cloud/fog systems, with the assumption that the graph representing the application is non-deterministic. The objective is to minimize an aggregated weighted function of makespan and cost. The problem is modeled as an Integer Linear Programming (ILP) and evaluated over small-scale scenarios using the CPLEX optimization tool.