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Found 8 papers, 2 papers with code
Edge-MultiAI: Multi-Tenancy of Latency-Sensitive Deep Learning Applications on Edge
We propose an efficient NN model management framework, called Edge-MultiAI, that ushers the NN models of the DL applications into the edge memory such that the degree of multi-tenancy and the number of warm-starts are maximized.
FELARE: Fair Scheduling of Machine Learning Tasks on Heterogeneous Edge Systems
The challenge is to allocate user requests for different ML applications on the Heterogeneous Edge Computing Systems (HEC) with respect to both the energy and latency constraints of these systems.
Exploring the Impact of Virtualization on the Usability of the Deep Learning Applications
Deep Learning-based (DL) applications are becoming increasingly popular and advancing at an unprecedented pace.
SAED: Edge-Based Intelligence for Privacy-Preserving Enterprise Search on the Cloud
We propose Smartness At Edge (SAED mechanism that offers intelligence in the form of semantic and personalized search at the edge tier while maintaining privacy of the search on the cloud tier.
SensPick: Sense Picking for Word Sense Disambiguation
Word sense disambiguation (WSD) methods identify the most suitable meaning of a word with respect to the usage of that word in a specific context.
Analyzing the Performance of Smart Industry 4.0 Applications on Cloud Computing Systems
To make Industry 4. 0 robust, solution architects and researchers need to understand the behavior of DNN-based applications and capture the stochasticity exists in their inference times.
Privacy-Preserving Clustering of Unstructured Big Data for Cloud-Based Enterprise Search Solutions
To overcome the hindrance in clustering encrypted big data, we propose privacy-preserving clustering schemes for three forms of unstructured encrypted big datasets, namely static, semi-dynamic, and dynamic datasets.
ClustCrypt: Privacy-Preserving Clustering of Unstructured Big Data in the Cloud
One common approach to address the concerns is client-side encryption where data is encrypted on the client machine before being stored in the cloud.
Cryptography and Security Distributed, Parallel, and Cluster Computing
