  Benchmark suites are significant for evaluating various aspects of Cloud
services from a holistic view. However, there is still a gap between using
benchmark suites and achieving holistic impression of the evaluated Cloud
services. Most Cloud service evaluation work intended to report individual
benchmarking results without delivering summary measures. As a result, it could
be still hard for customers with such evaluation reports to understand an
evaluated Cloud service from a global perspective. Inspired by the boosting
approaches to machine learning, we proposed the concept Boosting Metrics to
represent all the potential approaches that are able to integrate a suite of
benchmarking results. This paper introduces two types of preliminary boosting
metrics, and demonstrates how the boosting metrics can be used to supplement
primary measures of individual Cloud service features. In particular, boosting
metrics can play a summary Response role in applying experimental design to
Cloud services evaluation. Although the concept Boosting Metrics was refined
based on our work in the Cloud Computing domain, we believe it can be easily
adapted to the evaluation work of other computing paradigms.
