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Found 12 papers, 9 papers with code
CILP: Co-simulation based Imitation Learner for Dynamic Resource Provisioning in Cloud Computing Environments
CILP leverages a neural network as a surrogate model to predict future workload demands with a co-simulated digital-twin of the infrastructure to compute QoS scores.
DeepFT: Fault-Tolerant Edge Computing using a Self-Supervised Deep Surrogate Model
The emergence of latency-critical AI applications has been supported by the evolution of the edge computing paradigm.
DRAGON: Decentralized Fault Tolerance in Edge Federations
Edge Federation is a new computing paradigm that seamlessly interconnects the resources of multiple edge service providers.
MetaNet: Automated Dynamic Selection of Scheduling Policies in Cloud Environments
Task scheduling is a well-studied problem in the context of optimizing the Quality of Service (QoS) of cloud computing environments.
SplitPlace: AI Augmented Splitting and Placement of Large-Scale Neural Networks in Mobile Edge Environments
This makes the problem of deploying such large-scale neural networks challenging in resource-constrained mobile edge computing platforms, specifically in mission-critical domains like surveillance and healthcare.
CAROL: Confidence-Aware Resilience Model for Edge Federations
To address this, we present a confidence aware resilience model, CAROL, that utilizes a memory-efficient generative neural network to predict the Quality of Service (QoS) for a future state and a confidence score for each prediction.
TranAD: Deep Transformer Networks for Anomaly Detection in Multivariate Time Series Data
Efficient anomaly detection and diagnosis in multivariate time-series data is of great importance for modern industrial applications.
GOSH: Task Scheduling Using Deep Surrogate Models in Fog Computing Environments
Advances like deterministic surrogate models, deep neural networks (DNN) and gradient-based optimization allow low energy consumption and response times to be reached.
MCDS: AI Augmented Workflow Scheduling in Mobile Edge Cloud Computing Systems
Workflow scheduling is a long-studied problem in parallel and distributed computing (PDC), aiming to efficiently utilize compute resources to meet user's service requirements.
PreGAN: Preemptive Migration Prediction Network for Proactive Fault-Tolerant Edge Computing
Building a fault-tolerant edge system that can quickly react to node overloads or failures is challenging due to the unreliability of edge devices and the strict service deadlines of modern applications.
HUNTER: AI based Holistic Resource Management for Sustainable Cloud Computing
The worldwide adoption of cloud data centers (CDCs) has given rise to the ubiquitous demand for hosting application services on the cloud.
Generative Optimization Networks for Memory Efficient Data Generation
In standard generative deep learning models, such as autoencoders or GANs, the size of the parameter set is proportional to the complexity of the generated data distribution.
