An Automated Pipeline for Advanced Fault Tolerance in Edge Computing Infrastructures
Θεόδωρος Θεοδωρόπουλος, Antonios Makris, John Violos, Konstantinos Tserpes
Abstract
The very fabric of Edge Computing is intertwined with the necessity to be able to orchestrate and manage a huge number of heterogeneous computational resources. On top of that, the rather demanding Quality of Service (QoS) requirements of Internet of Things (IoT) applications that run on these resources, dictate that it is essential to establish robust Fault Tolerance mechanisms. These mechanisms should be able to guarantee that the requirements will be upheld regardless of any potential changes in task production rate. To that end, we suggest an Automated Pipeline for Advanced Fault Tolerance (APAFT) that consists of various components that are designed to operate as functional blocks of an automated closed-control loop. Furthermore, the suggested pipeline is able to carry out the various Horizontal Scaling operations in a proactive manner. These Proactive Scaling capabilities are achieved via the use of a dedicated Deep Learning (DL)-based component that is able to perform multi-step prediction. Our work aims to introduce a number of mechanisms that are able to leverage the benefits that are provided by the multi-step format in a more refined manner. Having access to information regarding multiple future instances allows us to design automated resource orchestration strategies that cater to the specific characteristics of each type of computational node that is part of the Edge Infrastructure.