Schahram Dustdar
6 papers in the PaperMetrix corpus
Papers by this author
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Smart Fabric - An Infrastructure-Agnostic Artifact Topology Deployment Framework
2015
The cloud computing paradigm enables the development of applications that can elastically react to changes in their environment by autonomously provisioning and releasing infrastructure resources. However, current applications need to be specifically tailored to a …
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Running Industrial Workflow Applications in a Software-Defined Multicloud Environment Using Green Energy Aware Scheduling Algorithm
2020 · IEEE Transactions on Industrial Informatics
Industry 4.0 have automated the entire manufacturing sector (including technologies and processes) by adopting Internet of Things and cloud computing. To handle the workflows from Industrial Cyber-Physical systems, more and more data centers have been …
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A Privacy Preserving System for AI-assisted Video Analytics
2021
The emerging Edge computing paradigm facilitates the deployment of distributed AI-applications and hardware, capable of processing video data in real time. AI-assisted video analytics can provide valuable information and benefits for parties in various domains. …
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Architectural Vision for Quantum Computing in the Edge-Cloud Continuum
2023
Quantum processing units (QPUs) are currently exclusively available from cloud vendors. However, with recent advancements, hosting QPUs will soon be possible everywhere. Existing work has yet to draw from research in edge computing to explore …
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Blockchain-Based Zero Trust on the Edge
2023
Internet of Things (IoT) devices pose significant security challenges due to their heterogeneity (i.e., hardware and software) and vulnerability to extensive attack surfaces. Today's conventional perimeter-based systems use credential- based authentication (e.g., username/password, certificates, etc.) …
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Federated Domain Generalization: A Survey
2025 · Proceedings of the IEEE
Machine learning (ML) typically relies on the assumption that training and testing distributions are identical and that data are centrally stored for training and testing. However, in real-world scenarios, distributions may differ significantly, and data …