Dynamic Resource Provisioning on Fog Landscapes

Abstract

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.

A systematic review on resource provisioning in fog computing

“…Fog scalability, service scalability, fog federation, fog mobile computing, data storing rate, energy consumption trade‐off storage security, fog‐cloud collaboration, communication security, communication efficiency, communication security, deployment problem, and sematic‐aware fog computing issues enchanted the research field. Hoang‐Nam et al 77 considered optimization issues on fog landscapes in dynamic resource provisioning. Fog computing supports data processing from the cloud to IoT devices with low latency.…”

Resource allocation for fog computing based on software-defined networks

“…Gu et al [16] considered the integration of fog computing and medical cyber-physical devices, and have proposed an algorithm for jointly optimize base station association, task distribution, and virtual machine placement to minimize the cost of this network. Pham-Nguyen and Tran-Minh [17] considered the service deployment problem as a multi-objective optimization that minimizes the overall response time of an application. Huang et al [18] have proposed the task offloading problem in IoT-based fog computing with deep reinforcement learning in single -nodes task graphs.…”

Toward intelligent resource management in dynamic Fog Computing‐based Internet of Things environment with Deep Reinforcement Learning: A survey

“…Pham‐Nguyen and Tran 34 focused on two deployment strategies known as cloudy and foggy strategies to solve multiobjective optimization problems considering overall response time and network server usage as targets. It took three types of application: sequence, parallel, and heterogeneous and conducted two experiments for each type.…”