VECTrust: trusted resource allocation in volunteer edge-cloud computing workflows
VECTrust: trusted resource allocation in volunteer edge-cloud computing workflows
复制标题
DOI：
10.1145/3468737.3494099
复制
发表时间：
2021-12
期刊：
影响因子：
--
通讯作者：
Ashish Pandey;P. Calyam;S. Debroy;Songjie Wang;Mauro Lemus Alarcon
中科院分区：
文献类型：
--
作者：
Ashish Pandey;P. Calyam;S. Debroy;Songjie Wang;Mauro Lemus Alarcon
边缘资源（如科学仪器、边缘服务器、传感器）和相关数据源的空前增长导致了科学应用社区的数据泛滥。数据处理越来越依赖于利用机器学习来处理数据的异质性、规模和速度的算法。同时，有大量的低成本计算资源可用于边缘云协同计算，即“志愿边缘云计算”。然而，在边缘资源的性能、敏捷性、成本和安全性（PACS）因素方面缺乏信任，这被证明是VEC广泛采用的障碍。在本文中，我们提出了一种新的“VECTrust”模型，用于支持科学数据密集型工作流的VEC计算环境中的可信资源分配算法。我们的VECTrust具有两阶段概率模型，该模型通过考虑与PACS因素相关的度量的可信度来定义VEC计算集群资源的信任。我们评估了VECTrust模型基于PACS因素提供动态资源分配的能力，同时也增强了VEC计算测试平台中的边缘云信任。此外，我们表明，VECTrust能够在不同的生物信息学工作流程中创建一个统一的、鲁棒的显著PACS因素相关指标的概率分布。
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.