Cutting Throughput on the Edge:App-Aware Placement in Fog Computing

TL;DR

This paper addresses application placement in fog computing using a greedy algorithm to optimize microservice deployment at the edge, reducing latency and communication bottlenecks for IoT applications.

Contribution

It introduces a scalable greedy placement algorithm for microservice-based applications in fog computing, validated through extensive numerical and real-world testing.

Findings

The greedy algorithm achieves near-optimal performance.

The solution scales well with increasing application numbers.

Real implementation confirms effectiveness and scalability.

Abstract

Fog computing extends cloud computing technology to the edge of the infrastructure to let IoT applications access objects' data with reduced latency, location awareness and dynamic computation. By displacing workloads from the central cloud to the edge devices, fog computing overcomes communication bottlenecks avoiding raw data transfer to the central cloud, thus paving the way for the next generation IoT-based applications. In this paper we study scheduling and placement of applications in fog computing, which is key to ensure profitability for the involved stakeholders. We consider a scenario where the emerging microservice architecture allows for the design of applications as cascades of coupled microservice modules. It results into a mixed integer non linear problem involving constraints on both application data flows and computation placement. Due to the complexity of the original problem, we resort to a simplified version, which is further solved using a greedy algorithm. This algorithm is the core placement logic of the FogAtlas platform, a fog computing platform based on existing virtualization technologies. Extensive numerical results validate the model and the scalability of the proposed solution, showing it attains performance close to the optimal solution and, in our real implementation, it scales well with respect to the number of served applications.