Characterizing Performance in Virtualized Execution

Workload execution in virtualized machines is rapidly becoming commonplace while support for self-virtualization is already available in mass market processors. From a performance analysis and prediction standpoint, virtualization introduces new sources of uncertainty in reasoning about the factors that impact overall system performance and efficiency. This paper describes a general methodology for profiling the vertical software and hardware stack of a virtualized platform. A key contribution of this paper is an instrumentation approach that enforces alignment between measurements of potentially correlated factors, so that statistical analysis techniques such as regression modeling can be used effectively against the data. The paper shows, through the use of preliminary examples, how one can use such instrumentation to obtain decompositions of the CPI (cycles-per-instruction) metric for two workloads when each is executed first on native hardware and then in an identically configured virtual machine

[1]  Jeffrey Casazza,et al.  Redefining Server Performance Characterization for Virtualization Benchmarking , 2006 .

[2]  Matthias Hauswirth,et al.  Automating vertical profiling , 2005, OOPSLA '05.

[3]  Sally A. McKee,et al.  Efficiently exploring architectural design spaces via predictive modeling , 2006, ASPLOS XII.

[4]  Lei He,et al.  Micro-architecture Performance Estimation by Formula , 2005, SAMOS.

[5]  Charles Yount,et al.  Using Model Trees for Computer Architecture Performance Analysis of Software Applications , 2007, 2007 IEEE International Symposium on Performance Analysis of Systems & Software.

[6]  Charles Yount,et al.  On the Comparison of Regression Algorithms for Computer Architecture Performance Analysis of Software Applications , .

[7]  Miss A.O. Penney.  (b) , 1974, The New Yale Book of Quotations.

[8]  Leo Breiman,et al.  Classification and Regression Trees , 1984 .

[9]  Ludmila Cherkasova,et al.  XenMon: QoS Monitoring and Performance Profiling Tool , 2005 .

[10]  Gil Neiger,et al.  Intel virtualization technology , 2005, Computer.

[11]  Tal Garfinkel,et al.  Virtual machine monitors: current technology and future trends , 2005, Computer.

[12]  Ole Agesen,et al.  A comparison of software and hardware techniques for x86 virtualization , 2006, ASPLOS XII.

[13]  Jeanna Neefe Matthews,et al.  Quantifying the performance isolation properties of virtualization systems , 2007, ExpCS '07.

[14]  Srihari Makineni,et al.  Characterization of network processing overheads in Xen , 2006, First International Workshop on Virtualization Technology in Distributed Computing (VTDC 2006).

[15]  Kapil Vaswani,et al.  A Predictive Performance Model for Superscalar Processors , 2006, 2006 39th Annual IEEE/ACM International Symposium on Microarchitecture (MICRO'06).

[16]  David M. Brooks,et al.  Accurate and efficient regression modeling for microarchitectural performance and power prediction , 2006, ASPLOS XII.

[17]  Chandra Krintz,et al.  Evaluating the Performance Impact of Xen on MPI and Process Execution For HPC Systems , 2006, First International Workshop on Virtualization Technology in Distributed Computing (VTDC 2006).

[18]  Ludmila Cherkasova,et al.  Measuring CPU Overhead for I/O Processing in the Xen Virtual Machine Monitor , 2005, USENIX ATC, General Track.

[19]  Jack J. Dongarra,et al.  A Portable Programming Interface for Performance Evaluation on Modern Processors , 2000, Int. J. High Perform. Comput. Appl..

[20]  Amer Diwan,et al.  The DaCapo benchmarks: java benchmarking development and analysis , 2006, OOPSLA '06.