Dynamic Resource Provisioning on Fog Landscapes

Abstract EN

A huge amount of smart devices which have capacity of computing, storage, and communication to each other brings forth fog computing paradigm.

Fog computing is a model in which the system tries to push data processing from cloud servers to “near” IoT devices in order to reduce latency time.

The execution orderings and the deployed places of services make significant effect on the overall response time of an application.

Beside new research directions in fog computing, e.g., fog-cloud collaboration, service scalability, fog scalability, mobile fog computing, fog federation, trade-off between energy consumption and communication efficiency, duration of storing data locally, storage security and communication security, and semantic-aware fog computing, the service deployment problem is one of the attractive research fields of fog computing.

The service deployment is a multiobjective optimization problem; there are so many proposed solutions for various targets, such as response time, communication cost, and energy consumption.

In this paper, we focus on the optimization problem which minimizes the overall response time of an application with awareness of network usage and server usage.

Then, we have conducted experiments on two service deployment strategies, called cloudy and foggy strategies.

We analyze numerically the overall response time, network usage, and server usage of those two strategies in order to prove the effectiveness of our proposed foggy service deployment strategy.