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Modeling Interference for Apache Spark Jobs
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1016/j.future.2013.12.036openalex-snapshot
Performance evaluation of NoSQL big-data applications using multi-formalism models
inferredcites →https://doi.org/10.1109/ccgrid.2014.101openalex-snapshot
MIMP: Deadline and Interference Aware Scheduling of Hadoop Virtual Machines
inferredcites →https://doi.org/10.1109/cloud.2012.25openalex-snapshot
A Performance Interference Model for Managing Consolidated Workloads in QoS-Aware Clouds
inferredcites →https://doi.org/10.1109/cloud.2013.107openalex-snapshot
Performance Modeling of MapReduce Jobs in Heterogeneous Cloud Environments
inferredcites →https://doi.org/10.1109/cloud.2014.14openalex-snapshot
IO Performance Interference among Consolidated n-Tier Applications: Sharing Is Better Than Isolation for Disks
inferredcites →https://doi.org/10.1109/hpcc-css-icess.2015.246openalex-snapshot
Performance Prediction for Apache Spark Platform
inferredcites →https://doi.org/10.1109/icdcs.2012.65openalex-snapshot
PREPARE: Predictive Performance Anomaly Prevention for Virtualized Cloud Systems
inferredcites →https://doi.org/10.1109/icdcs.2013.31openalex-snapshot
HybridMR: A Hierarchical MapReduce Scheduler for Hybrid Data Centers
inferredcites →https://doi.org/10.1109/icdcsw.2014.26openalex-snapshot
Automated Modeling of I/O Performance and Interference Effects in Virtualized Storage Systems
inferredcites →https://doi.org/10.1109/ipdps.2015.36openalex-snapshot
Resource and Deadline-Aware Job Scheduling in Dynamic Hadoop Clusters
inferredcites →https://doi.org/10.1109/mascots.2015.35openalex-snapshot
CloudScope: Diagnosing and Managing Performance Interference in Multi-tenant Clouds
inferredcites →https://doi.org/10.1109/tpds.2015.2405552openalex-snapshot
Hadoop Performance Modeling for Job Estimation and Resource Provisioning
inferredcites →https://doi.org/10.1109/tsc.2011.7openalex-snapshot
Optimization of Resource Provisioning Cost in Cloud Computing
inferredcites →https://doi.org/10.1145/2462902.2462904openalex-snapshot
Interference and locality-aware task scheduling for MapReduce applications in virtual clusters
inferredcites →https://doi.org/10.1145/2463676.2467800openalex-snapshot
Performance and resource modeling in highly-concurrent OLTP workloads
inferredcites →https://doi.org/10.1145/2668930.2688047openalex-snapshot
Enhancing Performance Prediction Robustness by Combining Analytical Modeling and Machine Learning
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.1007/978-3-319-68066-8_13openalex-snapshot
Client-Side Scheduling Based on Application Characterization on Kubernetes
inferred← citeshttps://doi.org/10.1007/s00607-022-01056-7openalex-snapshot
Scalability and performance analysis of BDPS in clouds
inferred← citeshttps://doi.org/10.1007/s10586-017-1466-3openalex-snapshot
Design and implementation of an analytical framework for interference aware job scheduling on Apache Spark platform
inferred← citeshttps://doi.org/10.1007/s10619-020-07286-yopenalex-snapshot
A gray-box modeling methodology for runtime prediction of Apache Spark jobs
inferred← citeshttps://doi.org/10.1016/j.future.2020.03.058openalex-snapshot
Sequence-to-sequence models for workload interference prediction on batch processing datacenters
inferred← citeshttps://doi.org/10.1016/j.jpdc.2020.10.010openalex-snapshot
Efficient Performance Prediction for Apache Spark
inferred← citeshttps://doi.org/10.1109/access.2021.3098426openalex-snapshot
Diaspore: Diagnosing Performance Interference in Apache Spark
inferred← citeshttps://doi.org/10.1109/cloud.2017.119openalex-snapshot
Understanding the Influence of Configuration Settings: An Execution Model-Driven Framework for Apache Spark Platform
inferred← citeshttps://doi.org/10.1109/cloud.2017.20openalex-snapshot
Hierarchical Spark: A Multi-Cluster Big Data Computing Framework
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/hase.2019.00039openalex-snapshot
Modeling and Verifying Spark on YARN Using Process Algebra
inferred← citeshttps://doi.org/10.1109/icccn.2017.8038381openalex-snapshot
AutoPath: Harnessing Parallel Execution Paths for Efficient Resource Allocation in Multi-Stage Big Data Frameworks
inferred← citeshttps://doi.org/10.1109/icdew.2019.00-23openalex-snapshot
Gray Box Modeling Methodology for Runtime Prediction of Apache Spark Jobs
inferred← citeshttps://doi.org/10.1109/ispa/iucc.2017.00052openalex-snapshot
iSpot: Achieving Predictable Performance for Big Data Analytics with Cloud Transient Servers
inferred← citeshttps://doi.org/10.1109/tpds.2018.2873397openalex-snapshot
Cost-Effective Cloud Server Provisioning for Predictable Performance of Big Data Analytics
inferred← citeshttps://doi.org/10.1109/tsusc.2024.3359325openalex-snapshot
CloudProphet: A Machine Learning-Based Performance Prediction for Public Clouds
inferred← citeshttps://doi.org/10.1145/3167132.3167150openalex-snapshot
Data locality and VM interference aware mitigation of data skew in hadoop leveraging modern portfolio theory
inferred← citeshttps://doi.org/10.1145/3514221.3517892openalex-snapshot
Juggler: Autonomous Cost Optimization and Performance Prediction of Big Data Applications
inferred← citeshttps://doi.org/10.14778/3681954.3681990openalex-snapshot
Agile-Ant: Self-Managing Distributed Cache Management for Cost Optimization of Big Data Applications
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