Resilient Execution of Data-triggered Applications on Edge, Fog and Cloud Resources

TL;DR

This paper presents a novel framework for deploying and reliably executing data pipelines across edge, fog, and cloud resources in IoT environments, ensuring cost-effective and resilient data processing.

Contribution

It introduces a declarative application model and a resilient scheduling strategy that guarantees dataflow completion within deadlines across diverse IoT resources.

Findings

Demonstrates cost-effectiveness and resilience through extensive experiments

Achieves scalable data pipeline execution on over 100 virtual IoT resources

Outperforms baseline scheduling strategies in reliability and cost

Abstract

Internet of Things (IoT) is leading to the pervasive availability of streaming data about the physical world, coupled with edge computing infrastructure deployed as part of smart cities and 5G rollout. These constrained, less reliable but cheap resources are complemented by fog resources that offer federated management and accelerated computing, and pay-as-you-go cloud resources. There is a lack of intuitive means to deploy application pipelines to consume such diverse streams, and to execute them reliably on edge and fog resources. We propose an innovative application model to declaratively specify queries to match streams of micro-batch data from stream sources and trigger the distributed execution of data pipelines. We also design a resilient scheduling strategy using advanced reservation on reliable fogs to guarantee dataflow completion within a deadline while minimizing the execution cost. Our detailed experiments on over 100 virtual IoT resources and for $\approx 10k$ task executions, with comparison against baseline scheduling strategies, illustrates the cost-effectiveness, resilience and scalability of our framework.