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D. Fernando, M. A. Rodriguez, and R. Buyya are with the Quantum Cloud Computing and Distributed Systems (qCLOUDS) Laboratory, School of Computing and Information Systems, The University of Melbourne, Parkville, Australia
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Achieve up to 50% energy savings and 80% latency reduction in edge-based object detection by intelligently balancing load across heterogeneous devices, even with a minor accuracy trade-off.
Overcome the scalability bottleneck of GNN-based root cause analysis in edge computing by cascading subnetworks over clustered service graphs, achieving near-constant inference latency without sacrificing accuracy.
