A performance methodology for commercial servers

  • Authors:

  • S. R. Kunkel;R. J. Eickemeyer;M. H. Lipasti;T. J. Mullins;B. O'Krafka;H. Rosenberg;S. P. VanderWiel;P. L. Vitale;L. D. Whitley

  • Affiliations:

  • IBM Server Group, Rochester, Minnesota;IBM Server Group, Rochester, Minnesota;University of Wisconsin at Madison, Madison, Wisconsin;IBM Server Group, Rochester, Minnesota;Sun Microsystems, Austin, Texas;Sun Microsystems, Burlington, Massachusetts;IBM Server Group, Rochester, Minnesota;IBM Server Group, Rochester, Minnesota;IBM Server Group, Rochester, Minnesota

  • Venue:
  • IBM Journal of Research and Development
  • Year:
  • 2000

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Abstract

This paper discusses a methodology for analyzing and optimizing the performance of commercial servers. Commercial server workloads are shown to have unique characteristics which expand the elements that must be optimized to achieve good performance and require a unique performance methodology. The steps in the process of server performance optimization are described and include the following: 1. Selection of representative commercial workloads and identification of key characteristics to be evaluated. 2. Collection of performance data. Various instrumentation techniques are discussed in light of the requirements placed by commercial server workloads on the instrumentation. 3. Creation of input data for performance models on the basis of measured workload information. This step in the methodology must overcome the operating environment differences between the instance of the measured system under test and the target system design to be modeled. 4. Creation of performance models. Two general types are described: high-level models and detailed cycle-accurate simulators. These types are applied to model the processor, memory, and I/O system. 5. System performance optimization. The tuning of the operating system and application software is described. Optimization of performance among commercial applications is not simply an exercise in using traces to maximize the processor MIPS. Equally significant are items such as the use of probabilities to reflect future workload characteristics, software tuning, cache miss rate optimization, memory management, and I/O performance. The paper presents techniques for evaluating the performance of each of these key contributors so as to optimize the overall performance and cost/performance of commercial servers.