.png){kind=icon}
Return
Big Data Workflows: Locality-Aware Orchestration Using Software Containers
.png){kind=icon}
OA
AI
DOI：10.3390/s21248212.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
The emergence of the edge computing paradigm has shifted data processing from centralised infrastructures to heterogeneous and geographically distributed infrastructures. Therefore, data processing solutions must consider data locality to reduce the performance penalties from data transfers among remote data centres. Existing big data processing solutions provide limited support for handling data locality and are inefficient in processing small and frequent events specific to the edge environments. This article proposes a novel architecture and a proof-of-concept implementation for software container-centric big data workflow orchestration that puts data locality at the forefront. The proposed solution considers the available data locality information, leverages long-lived containers to execute workflow steps, and handles the interaction with different data sources through containers. We compare the proposed solution with Argo workflows and demonstrate a significant performance improvement in the execution speed for processing the same data units. Finally, we carry out experiments with the proposed solution under different configurations and analyze individual aspects affecting the performance of the overall solution.
Keywords:
big data workflows
orchestration
data locality
software containers
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
3.5
Papers:
7.2W
Citations:
20.9W
Organization
View more organizations
Cited Papers
ShareSaveShareSaveThe euBusinessGraph ontology: A lightweight ontology for harmonizing basic company information.png)
SEMANTIC WEBOAAI
SEMANTIC WEBIF2.9
OAAIShareSaveShareSaveShareSaveShareSaveShareSave
View more