Corpus ID: 199552217

uPredict: A User-Level Profiler-Based Predictive Framework for Single VM Applications in Multi-Tenant Clouds

  title={uPredict: A User-Level Profiler-Based Predictive Framework for Single VM Applications in Multi-Tenant Clouds},
  author={Hamidreza Moradi and Wei Wang and Amanda Fernandez and Dakai Zhu},
Most existing studies on performance prediction for virtual machines (VMs) in multi-tenant clouds are at system level and generally require access to performance counters in Hypervisors. In this work, we propose uPredict, a user-level profiler-based performance predictive framework for single-VM applications in multi-tenant clouds. Here, three micro-benchmarks are specially devised to assess the contention of CPUs, memory and disks in a VM, respectively. Based on measured performance of an… Expand
uPredict: A User-Level Profiler-Based Predictive Framework in Multi-Tenant Clouds
UPredict, a user-level profiler-based performance predictive framework for single-VM applications in multitenant clouds, is proposed and a smart load-balancing scheme powered by uPredict is presented and can effectively reduce the execution and turnaround times of the considered application by 19% and 10%, respectively. Expand
DiHi: Distributed and Hierarchical Performance Modeling of Multi-VM Cloud Running Applications
  • Hamidreza Moradi, Wei Wang, Dakai Zhu
  • Computer Science
  • 2020 IEEE 22nd International Conference on High Performance Computing and Communications; IEEE 18th International Conference on Smart City; IEEE 6th International Conference on Data Science and Systems (HPCC/SmartCity/DSS)
  • 2020
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DeepDive: Transparently Identifying and Managing Performance Interference in Virtualized Environments
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Predicting Cloud Performance for HPC Applications: A User-Oriented Approach
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CherryPick: Adaptively Unearthing the Best Cloud Configurations for Big Data Analytics
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