Abstract

The Internet-of-Things (IoT) and Smart Cities continue to expand at enormous rates. Centralized Cloud architectures cannot sustain the requirements imposed by IoT services. Enormous traffic demands and low latency constraints are among the strictest requirements, making cloud solutions impractical. As an answer, Fog Computing has been introduced to tackle this trend. However, only theoretical foundations have been established and the acceptance of its concepts is still in its early stages. Intelligent allocation decisions would provide proper resource provisioning in Fog environments. In this article, a Fog architecture based on Kubernetes, an open source container orchestration platform, is proposed to solve this challenge. Additionally, a network-aware scheduling approach for container-based applications in Smart City deployments has been implemented as an extension to the default scheduling mechanism available in Kubernetes. Last but not least, an optimization formulation for the IoT service problem has been validated as a container-based application in Kubernetes showing the full applicability of theoretical approaches in practical service deployments. Evaluations have been performed to compare the proposed approaches with the Kubernetes standard scheduling feature. Results show that the proposed approaches achieve reductions of 70% in terms of network latency when compared to the default scheduling mechanism.

Highlights

  • In recent years, the Internet-of-Things (IoT) rapidly started gaining popularity due to the wide adoption of virtualization and cloud technologies

  • Results show that the proposed approaches achieve reductions of 70% in terms of network latency when compared to the default scheduling mechanism

  • Fog Computing provides effective ways to overcome the high demanding requirements introduced by IoT use cases, such as low latency, high energy efficiency and high mobility

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Summary

Introduction

The Internet-of-Things (IoT) rapidly started gaining popularity due to the wide adoption of virtualization and cloud technologies. Setting up a proper Fog-based architecture to support millions of devices and their high demand heterogeneous applications without dismissing the importance of network latency, bandwidth usage and geographic coverage is still a challenge to be addressed in Fog Computing [8]. Linear programming (ILP) formulation for the IoT service placement problem presented in [16] has been deployed on the Kubernetes container orchestration platform, showing the full applicability of theoretical approaches in practical service deployments. Evaluations based on Smart City container-based applications have been performed to compare the performance of the proposed provisioning mechanisms with the standard scheduling feature present in Kubernetes.

Related Work
Open Challenge
Fog-Based Kubernetes Architecture for Smart City Deployments
Kubernetes
Resource Scheduling in Kubernetes
Resource Scheduling Extension in Kubernetes
Evaluation Setup
Scenario Description
ILP Model Configurations
Evaluation Results
Scheduler Execution Time
Scheduler Resource Consumption
Allocation Scheme
Network Latency and Bandwidth
Conclusions

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