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Found 13 papers, 6 papers with code
Karasu: A Collaborative Approach to Efficient Cluster Configuration for Big Data Analytics
However, big data analytics jobs across users can share many common properties: they often operate on similar infrastructure, using similar algorithms implemented in similar frameworks.
FedZero: Leveraging Renewable Excess Energy in Federated Learning
Federated Learning (FL) is an emerging machine learning technique that enables distributed model training across data silos or edge devices without data sharing.
Probabilistic Time Series Forecasting for Adaptive Monitoring in Edge Computing Environments
With increasingly more computation being shifted to the edge of the network, monitoring of critical infrastructures, such as intermediate processing nodes in autonomous driving, is further complicated due to the typically resource-constrained environments.
Perona: Robust Infrastructure Fingerprinting for Resource-Efficient Big Data Analytics
Choosing a good resource configuration for big data analytics applications can be challenging, especially in cloud environments.
Magpie: Automatically Tuning Static Parameters for Distributed File Systems using Deep Reinforcement Learning
Distributed file systems are widely used nowadays, yet using their default configurations is often not optimal.
On the Potential of Execution Traces for Batch Processing Workload Optimization in Public Clouds
With the growing amount of data, data processing workloads and the management of their resource usage becomes increasingly important.
Enel: Context-Aware Dynamic Scaling of Distributed Dataflow Jobs using Graph Propagation
Distributed dataflow systems like Spark and Flink enable the use of clusters for scalable data analytics.
Evaluation of Load Prediction Techniques for Distributed Stream Processing
We identify three use-cases and formulate requirements for making load predictions specific to DSP jobs.
Bellamy: Reusing Performance Models for Distributed Dataflow Jobs Across Contexts
First, a general model is trained on all the available data for a specific scalable analytics algorithm, hereby incorporating data from different contexts.
Predicting Medical Interventions from Vital Parameters: Towards a Decision Support System for Remote Patient Monitoring
Constant patient monitoring enables better medical treatment as it allows practitioners to react on time and provide the appropriate treatment.
Learning Dependencies in Distributed Cloud Applications to Identify and Localize Anomalies
Operation and maintenance of large distributed cloud applications can quickly become unmanageably complex, putting human operators under immense stress when problems occur.
Chiron: Optimizing Fault Tolerance in QoS-aware Distributed Stream Processing Jobs
However, this is an expensive operation which impacts negatively on the overall performance of the system and manually optimizing fault tolerance for specific jobs is a difficult and time consuming task.
Distributed, Parallel, and Cluster Computing
Effectively Testing System Configurations of Critical IoT Analytics Pipelines
Distributed stream processing has become key to analyzing data generated by these connected devices and improving our ability to make decisions.
Distributed, Parallel, and Cluster Computing
