期刊
2022 IEEE 46TH ANNUAL COMPUTERS, SOFTWARE, AND APPLICATIONS CONFERENCE (COMPSAC 2022)
卷 -, 期 -, 页码 1153-1158出版社
IEEE
DOI: 10.1109/COMPSAC54236.2022.00181
关键词
Scheduling; Adaptation; Computing Continuum; Fog and Edge computing; Resources management
资金
- Horizon 2020 Programme
The Computing Continuum, including Cloud, Fog, and Edge systems, provides resource-as-a-service for Internet applications with different requirements. However, automating the resource management of Big Data pipelines across the Computing Continuum presents challenges. Traditional resource management strategies are not suitable for dynamic data processing pipelines, resulting in inefficient scheduling and complex deployment. To address this, we propose a scheduling and adaptation approach implemented as a software tool, enabling domain experts to actively participate in Big Data pipeline adaptation.
The Computing Continuum, covering Cloud, Fog, and Edge systems, promises to provide on-demand resource-as-a-service for Internet applications with diverse requirements, ranging from extremely low latency to high-performance processing. However, eminent challenges in automating the resources management of Big Data pipelines across the Computing Continuum remain. The resource management and adaptation for Big Data pipelines across the Computing Continuum require significant research effort, as the current data processing pipelines are dynamic. In contrast, traditional resource management strategies are static, leading to inefficient pipeline scheduling and overly complex process deployment. To address these needs, we propose in this work a scheduling and adaptation approach implemented as a software tool to lower the technological barriers to the management of Big Data pipelines over the Computing Continuum. The approach separates the static scheduling from the run-time execution, empowering domain experts with little infrastructure and software knowledge to take an active part in the Big Data pipeline adaptation. We conduct a feasibility study using a digital healthcare use case to validate our approach. We illustrate concrete scenarios supported by demonstrating how the scheduling and adaptation tool and its implementation automate the management of the lifecycle of a remote patient monitoring, treatment, and care pipeline.
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