In this paper, we proposed a methodology using Kubernetes clustered on-site edge servers with external clouds to provide computational offloading functionality for resource-limited private edge servers. This methodology enables additional functionalities without changing hardware infrastructures for industrial areas such as manufacturing systems. We devised a compute-intensive task scheduling algorithm using real-time CPU usage information of Kubernetes cluster to determine computation offloading decision. The purpose of the experiment is to compare overall performance between on-site edge only cluster and external cloud offloading cluster. The experiment scenario contains complex simulation problem which selects optimal tollgate for congested traffic situation. The result of experiment shows the proposed CollabOffloading methodology reduces entire execution time of simulations.
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J. Kim et. al, "Trends in Lightweight Kernel for Many core Based High-Performance Computing", Electronics and Telecommunications Trends. Vol. 32, No. 4, 2017, KOGL Type 4: Source Indication + Commercial Use Prohibition + Change Prohibition
J. Sim et.al, “the Fourth Industrial Revolution and ICT – IDX Strategy for leading the Fourth Industrial Revolution”, ETRI Insight, 2017, KOGL Type 4: Source Indication + Commercial Use Prohibition + Change Prohibition
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