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Proactive & Time-Optimized Data Synopsis Management at the Edge
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DOI：10.1109/TKDE.2020.3021377.png)
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摘要
En 中文
Internet of Things offers the infrastructure for smooth functioning of autonomous context-aware devices being connected towards the Cloud. Edge Computing (EC) relies between the IoT and Cloud providing significant advantages. One advantage is to perform local data processing (limited latency, bandwidth preservation) with real time communication among IoT devices, while multiple nodes become hosts of the collected data (reported by IoT devices). In this work, we provide a mechanism for the exchange of data synopses (summaries of extracted knowledge) among EC nodes that are necessary to give the knowledge on the data present in EC environments. The overarching aim is to intelligently decide on when nodes should exchange data synopses in light of efficient execution of tasks. We enhance such a decision with a stochastic optimization model based on the Theory of Optimal Stopping. We provide the fundamentals of our model and the relevant formulations on the optimal time to disseminate data synopses to network edge nodes. We report a comprehensive experimental evaluation and comparative assessment related to the optimality achieved by our model and the positive effects on EC.
Keyword:
Task analysis
Decision making
Computational modeling
Data models
Cloud computing
Context modeling
Monitoring
Edge computing
data synopsis
optimal stopping theory
network monitoring
context-awareness
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10.4
论文数:
6.8K
被引数:
3.2W
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