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Understanding the Influence of Configuration Settings: An Execution Model-Driven Framework for Apache Spark Platform
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1109/cloud.2014.43openalex-snapshot
Performance Modeling to Divide Performance Interference of Virtualization and Virtual Machine Combination
inferredcites →https://doi.org/10.1109/cloud.2016.0063openalex-snapshot
Modeling Interference for Apache Spark Jobs
inferredcites →https://doi.org/10.1109/cloud.2016.0069openalex-snapshot
CSMiner: An Automated Tool for Analyzing Changes in Configuration Settings across Multiple Versions of Large Scale Cloud Software
inferredcites →https://doi.org/10.1109/hpcc-css-icess.2015.246openalex-snapshot
Performance Prediction for Apache Spark Platform
inferredcites →https://doi.org/10.1145/2786805.2786845openalex-snapshot
Performance-influence models for highly configurable systems
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.1007/978-3-030-38961-1_1openalex-snapshot
SPM: Modeling Spark Task Execution Time from the Sub-stage Perspective
inferred← citeshttps://doi.org/10.1007/978-3-031-25049-1_11openalex-snapshot
A Deterministic Model to Predict Execution Time of Spark Applications
inferred← citeshttps://doi.org/10.1007/978-3-031-40289-0_26openalex-snapshot
SPOAHA: Spark Program Optimizer Based on Artificial Hummingbird Algorithm
inferred← citeshttps://doi.org/10.1007/s00607-022-01056-7openalex-snapshot
Scalability and performance analysis of BDPS in clouds
inferred← citeshttps://doi.org/10.1016/j.aej.2018.03.006openalex-snapshot
A Machine Learning Approach for Predicting Execution Time of Spark Jobs
inferred← citeshttps://doi.org/10.1016/j.jpdc.2020.10.010openalex-snapshot
Efficient Performance Prediction for Apache Spark
inferred← citeshttps://doi.org/10.1016/j.jss.2021.111028openalex-snapshot
Tuning configuration of apache spark on public clouds by combining multi-objective optimization and performance prediction model
inferred← citeshttps://doi.org/10.1109/bdcat50828.2020.00012openalex-snapshot
A Survey: Benchmarking and Performance Modelling of Data Intensive Applications
inferred← citeshttps://doi.org/10.1109/cicn56167.2022.10008282openalex-snapshot
Performance Evaluation of Machine Learning Models on Apache Spark: An Empirical Study
inferred← citeshttps://doi.org/10.1109/cloud.2018.00038openalex-snapshot
Towards Selecting Best Combination of SQL-on-Hadoop Systems and JVMs
inferred← citeshttps://doi.org/10.1109/cloud.2018.00059openalex-snapshot
Towards Automatic Tuning of Apache Spark Configuration
inferred← citeshttps://doi.org/10.1109/tcc.2024.3437484openalex-snapshot
Sparkle: Deep Learning Driven Autotuning for Taming High-Dimensionality of Spark Deployments
inferred← citeshttps://doi.org/10.1145/3284557.3284563openalex-snapshot
A Study of Reconfigurable Accelerators for Cloud Computing
inferred← citeshttps://doi.org/10.1145/3368474.3368482openalex-snapshot
Exploiting Spark for HPC Simulation Data
inferred← citeshttps://doi.org/10.1145/3375998.3376039openalex-snapshot
Spark Performance Optimization Analysis in Memory Tuning On GC Overhead for Big Data Analytics
inferred← citeshttps://doi.org/10.32920/ryerson.14651544openalex-snapshot
Performance Evaluation of a Big Data Application on Apache Spark
inferred← citeshttps://doi.org/10.32920/ryerson.14651544.v1openalex-snapshot
Performance Evaluation of a Big Data Application on Apache Spark
inferred← citeshttps://doi.org/10.5821/dissertation-2117-344364openalex-snapshot
Learning workload behaviour models from monitored time-series for resource estimation towards data center optimization
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