A Mean Field Game Model for Timely Computation Offloading in Edge Computing Systems
The core message of this article is to model the computation offloading problem in multi-access edge computing (MEC) systems as a non-cooperative game and employ the mean-field game framework to derive a decentralized algorithm that optimizes the tradeoff between power consumption and information freshness (age of information) for the IoT devices.
Digitaler Zwilling-unterstützte Reinforcement-Learning-Methode für ressourcenbewusstes Microservice-Offloading in Edge Computing
Eine digitaler Zwilling-unterstützte Deep-Reinforcement-Learning-Methode (DTDRLMO) wird entwickelt, um die dynamische Ressourcenverfügbarkeit bei der gemeinsamen Optimierung von Microservice-Offloading und Bandbreitenzuweisung in Collaborative Edge Computing (CEC) Netzwerken zu adressieren.
X-HEEP: Offene, konfigurierbare und erweiterbare RISC-V-Mikrocontrollerplattform für die Erforschung von Ultra-Low-Power-Edge-Beschleunigern
Die X-HEEP-Plattform bietet eine offene, konfigurierbare und erweiterbare Lösung für die Integration von Ultra-Low-Power-Edge-Beschleunigern.
X-HEEP: An Open-Source, Configurable and Extendible RISC-V Microcontroller for Ultra-Low-Power Edge Accelerators
Heterogeneous architectures with custom accelerators enhance energy efficiency in edge computing.
Sind Unikernels bereit für Serverless am Edge?
Unikernels zeigen vielversprechende Leistung für Edge FaaS, trotz einiger Einschränkungen.
Unikernels for Serverless on the Edge: Performance Evaluation and Comparison
Unikernels show promise for edge FaaS environments, offering advantages in cold start efficiency and memory usage.
Edge Computing Enabled Real-Time Video Analysis via Adaptive Spatial-Temporal Semantic Filtering
Proposing a novel edge computing system for real-time video analysis with adaptive spatial-temporal semantic filtering to maximize processing rate and accuracy.
Lessons Learned from Building Edge Software System Testbeds: Insights and Challenges
Building edge software testbeds presents challenges and insights for researchers and practitioners.
Optimal Forwarding and Computation Offloading for Service Chain Tasks
Optimizing forwarding and computation offloading in edge computing networks for service chain tasks is crucial for minimizing network costs.
Resource-efficient Parallel Split Learning in Heterogeneous Edge Computing
Efficiently accelerating federated learning on resource-constrained edge devices through adaptive model partitioning and bandwidth allocation.
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