VECFlex: Reconfigurability and Scalability for Trustworthy Volunteer Edge-Cloud supporting Data-intensive Scientific Computing
VECFlex: Reconfigurability and Scalability for Trustworthy Volunteer Edge-Cloud supporting Data-intensive Scientific Computing
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DOI：
10.1109/ucc56403.2022.00027
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发表时间：
2022-12
期刊：
影响因子：
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通讯作者：
Mauro Lemus Alarcon;Minh Nguyen;Ashish Pandey;S. Debroy;P. Calyam
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文献类型：
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作者：
Mauro Lemus Alarcon;Minh Nguyen;Ashish Pandey;S. Debroy;P. Calyam
尽管“志愿者边缘云”（VEC）计算已成为近期科学计算的新范式，但由于异构志愿者资源的抽象和缺乏信任，他们无法满足工作流程的能力，因此受到广泛的采用。绩效和安全要求。资源行为建模以其可用性，应用安全策略的灵活性以及任务执行时间的一致性来对资源的可信度进行排名。从大量不同的资源中，我们使用AWS测试台评估了Vecflex的性能，该测试床证明了Vecflex完全满足工作流程的安全要求的能力和$ \ sim $ \ sim $ 2倍提高工作流执行延迟。
Although ‘‘volunteer edge-cloud’’ (VEC) computing has emerged as a new paradigm for scientific computing in recent times, wider adoption is being hindered due to the abundance of heterogeneous volunteer resources and lack of trust in their ability to satisfy workflows’ performance and security requirements. In this paper, we propose the VECFlex, a flexible resource management framework that can reconFigure resource security policies to meet workflow requirements and efficiently allocate resources from a large pool with disparate configurations. It employs Reinforcement Learning (RL)-driven resource behavioral modeling to rank the trustworthiness of resources in terms of their availability, flexibility of applying security policies, and consistency of task execution times. VECFlex also applies a security-based modified Particle Swarm optimization (PSO) scheduler to allocate optimal resources to workflow tasks from a large pool of disparate resources. We evaluate the performance of VECFlex using an AWS testbed that demonstrates VECFlex’s ability to fully satisfy workflows’ security requirements and $\sim$2x improvement in workflow execution latency.