uPredict: A User-Level Profiler-Based Predictive Framework for Single VM Applications in Multi-Tenant Clouds
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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 application and micro-benchmarks, the application and VM-specific predictive models can be derived by exploiting various regression and neural network based techniques. These models can then be used to predict the application's performance using the in-situ profiled resource contention with the micro-benchmarks. We evaluated uPredict extensively with representative benchmarks from PARSEC, NAS Parallel Benchmarks and CloudSuite, on both a private cloud and two public clouds. The results show that the average prediction errors are between 9.8 17 contention, while the errors are within 4 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
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