Speaker:
恐神 貴行 (Takayuki Osogami) 氏
IBM Research
Schedule:
23th May, 2011
17:00 – 18:00
Place:
tau gallery
Title:
Optimizing systems with more reliable performance metrics
より信頼できる指標に基づくシステム性能の最適化
Abstract:
The system of measuring the performance of a Web system using a workload generator can be modeled as a closed interactive system. In such a system, the throughput and the mean response time are related by the response time law. However, we find that a measured throughput and a corresponding measured mean response time can have significantly different accuracy. As a result, one metric may be more reliable than the other to identify the better of two given configurations of a Web system, which is an important problem that appears frequently in practice. Using simulation, we derive rules of thumb that characterize when throughput is more reliable than mean response time. Also, we explain these rules of thumb analytically. Specifically, we refine the response time law using the central limit theorem and formally define the asymptotic reliability of an estimator of a metric. Using these analytical frameworks, we provide insights into when and why one metric is more reliable than the other.
負荷生成器を用いてウェブシステムの性能を測定するシステムは, 閉インタラクティブ系でモデル化できる. そのようなシステムにおいては,スループットと平均応答時間の2つの性能指標は応答時間の法則で関連付けられる. ところが,これらの2つの性能指標の測定誤差は大きく異なることを示す. これにより, ウェブシステムの複数の設定からより良い設定を特定する際に,一方の性能指標が他方の性能指標よりも信頼できることがある. シミュレーションにより,どのような条件でスループットが平均応答時間より信頼できるのかについて, 経験則を導く. また, 中心極限定理により応答時間の法則を精密化し,性能指標の推定値の漸近的信頼性を形式的に定義することで, 経験則を解析的に説明し, 直感的な理解を与える.
Biography:
Takayuki Osogami is an advisory researcher at IBM Research – Tokyo. He received his Ph.D. in Computer Science from Carnegie Mellon University in August 2005, and a B.Eng. degree in Electronic Engineering from the University of Tokyo in 1998. His research interests include building mathematical tools for analyzing and optimizing stochastic models. A primary focus of his current research is optimization of stochastic models over multiple periods with a consideration of risks.
日本アイ・ビー・エム（株）東京基礎研究所アドバイザリー・リサーチャー. 確率モデルの解析や最適化のための数理的アプローチの研究に従事.
最近は特にリスクを考慮した多期間の最適化技術に注力. 2005年カーネギーメロン大学計算機科学科博士課程修了.
1998年東京大学電子工学科卒業.
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Materials:
http://www.sfc.wide.ad.jp/seminar/wp-content/uploads/2011/05/osogami.pdf