VECTrust

Abstract

The unprecedented growth in edge resources (e.g., scientific instruments, edge servers, sensors) and related data sources has caused a data deluge in scientific application communities. The data processing is increasingly relying on algorithms that utilize machine learning to cope with the heterogeneity, scale, and velocity of the data. At the same time, there is an abundance of low-cost computation resources that can be used for edge-cloud collaborative computing viz., "volunteer edge-cloud (VEC) computing". However, lack of trust in terms of performance, agility, cost, and security (PACS) factors in edge resources is proving to be a barrier for wider adoption of VEC. In this paper, we propose a novel "VECTrust" model for support of trusted resource allocation algorithms in VEC computing environments for scientific data-intensive workflows. Our VECTrust features a two-stage probabilistic model that defines trust of VEC computing cluster resources by considering trustworthiness in metrics relevant to PACS factors. We evaluate our VECTrust model's ability to provide dynamic resource allocation based on PACS factors, while also enhancing edge-cloud trust in a VEC computing testbed. Further, we show that VECTrust is able to create a uniform and robust probability distribution of salient PACS factor related metrics within diverse bioinformatics workflows execution over batches of workflows.