Optimal Cloud Orchestration Model of Containerized Task Scheduling Strategy Using Integer Linear Programming: Case Studies of IoTcloudServe@TEIN Project

Abstract

As a playground for cloud computing and IoT networking environment, IoTcloudServe@TEIN has been established in the Trans-Eurasia Information Network (TEIN). In the IoTcloudServe@TEIN platform, a cloud orchestration for conducting the flow of IoT task demands is imperative for effectively improving performance. In this paper, we propose the model of optimal containerized task scheduling in cloud orchestration that maximizes the average payoff from completing tasks within the whole cloud system with different levels of cloud hierarchies. Based on integer linear programming, the model can take into account demand requirement and resource availability in terms of storage, computation, network, and splittable task granularity. To show the insights obtainable from the proposed model, the edge-core cluster of IoTcloudServe@TEIN and its peer-to-peer federated cloud scenario with OF@TEIN+ are numerically experimented and herein reported. To evaluate the model’s performance, payoff level and task completion time are considered by comparing with a well-known round-robin scheduling algorithm. The proposed ILP model can be a guideline for the cloud orchestration in IoTcloudserve@TEIN because of the lower task completion time and the higher payoff level especially upon the large demand growth, which is the major operation range of concerns in practice. Moreover, the proposed model illustrates mathematically the significance of implementing cloud architecture with refined splittable task granularity via the light-weighted container technology that has been used as the basis for IoTcloudServe@TEIN clustering design.