We consider the problem of selecting the best feasible system with constraints on multiple secondary performance measures. We develop fully sequential indifference-zone procedures to solve this problem that guarantee a nominal probability of correct selection. In addition, we address two issues critical to the efficiency of these procedures: namely, the allocation of error between feasibility determination and selection of the best system, and the use of Common Random Numbers. We provide a recommended error allocation as a function of the number of constraints, supported by an experimental study and an approximate asymptotic analysis. The validity and efficiency of the new procedures with independent and CRN are demonstrated through both analytical and experimental results.
discussion of when F I B and MSS satisfy Assumptions 3 and 4, respectively. Then, as CMS satisfies Assumptions 2, 3, and 4, we apply Lemma 1 and the fact that β 1 = β 2 /s, and obtain that
Redundancy is an important measure of an operation’s ability to withstand planned or unplanned system failures. While this concept is commonly used in power systems, redundancy can be extended to data center cooling systems, as well. We propose a rack-based redundancy metric for cooling performance that is similar in nomenclature to metrics for power systems, but also captures the local nature of data center cooling. This paper will explain how to compute this metric for general data center layouts and show how cooling redundancy can influence design choices when used in combination with typical measures of cooling coverage: inlet temperature and Capture Index.
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