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Predator — An experience guided configuration optimizer for Hadoop MapReduce
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
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Planning as heuristic search
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Efficient Global Optimization of Expensive Black-Box Functions
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A Practical Performance Model for Hadoop MapReduce
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Traffic management and network control using collaborative on-line simulation
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Autotuning Configurations in Distributed Systems for Performance Improvements Using Evolutionary Strategies
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Automatic configuration of internet services
inferredcites →https://doi.org/10.1145/1272998.1273020openalex-snapshot
Automatic configuration of internet services
inferredcites →https://doi.org/10.1145/1639562.1639581openalex-snapshot
Experience transfer for the configuration tuning in large scale computing systems
inferredcites →https://doi.org/10.1145/2188286.2188323openalex-snapshot
Apache hadoop performance-tuning methodologies and best practices
inferredcites →https://doi.org/10.1145/781027.781052openalex-snapshot
A recursive random search algorithm for large-scale network parameter configuration
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A smart hill-climbing algorithm for application server configuration
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Profiling, what-if analysis, and cost-based optimization of MapReduce programs
inferredcites →https://doi.org/10.21276/ijre.2018.5.5.4openalex-snapshot
MapReduce: Simplified Data Processing on Large Cluster
inferredcites →https://doi.org/10.5860/choice.33-1577openalex-snapshot
Artificial intelligence: a modern approach
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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Performance comparison between Hadoop and Spark frameworks using HiBench benchmarks
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Cross-Scenario Performance Modelling for Big Data Ecosystems
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Managing Dynamicity in SoS
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Apache Hadoop Yarn MapReduce Job Classification Based on CPU Utilization and Performance Evaluation on Multi-cluster Heterogeneous Environment
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Customized Parameter Configuration Framework for Performance Tuning in Apache Hadoop
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PTSSBench: a performance evaluation platform in support of automated parameter tuning of software systems
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Assessing and optimizing the performance impact of the just-in-time configuration parameters - a case study on PyPy
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MapReduce Parallel Programming Model: A State-of-the-Art Survey
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Investigating Automatic Parameter Tuning for SQL-on-Hadoop Systems
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Performance Tuning of MapReduce Jobs Using Surrogate-based Modeling
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Automated Configuration Parameter Classfication Model for Hive Query Plan on the Apache Yarn
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Co-Tuning of Cloud Infrastructure and Distributed Data Processing Platforms
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Comparative study between Hadoop and Spark based on Hibench benchmarks
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A self-tuning system based on application Profiling and Performance Analysis for optimizing Hadoop MapReduce cluster configuration
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Analyzing & optimizing hadoop performance
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Auto-Tuning Spark Configurations Based on Neural Network
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Apache Hadoop Yarn Parameter configuration Challenges and Optimization
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Performance evaluation and tuning for MapReduce computing in Hadoop distributed file system
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Performance optimization of Hadoop cluster using linux services
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On Machine Learning-based Stage-aware Performance Prediction of Spark Applications
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In the Quest of Trade-off between Job Parallelism and Throughput in Hadoop: A Stochastic Learning Approach for Parameter Tuning on the Fly
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HYPPO: A Hybrid, Piecewise Polynomial Modeling Technique for Non-Smooth Surfaces
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Auto-tuned Hadoop MapReduce for ECG analysis
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Exploiting Hadoop Topology in Virtualized Environments
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Automating the Configuration of MapReduce: A Reinforcement Learning Scheme
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Optimizing the Performance-Related Configurations of Object-Relational Mapping Frameworks Using a Multi-Objective Genetic Algorithm
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A Survey on Automatic Parameter Tuning for Big Data Processing Systems
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The Weights Can Be Harmful: Pareto Search versus Weighted Search in Multi-objective Search-based Software Engineering
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A Cloud-Friendly Communication-Optimal Implementation for Strassen's Matrix Multiplication Algorithm
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Towards an Ontology-Based Semantic Approach to Tuning Parameters to Improve Hadoop Application Performance
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An Empirical Performance Analysis on Hadoop via Optimizing the Network Heartbeat Period
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