Honolulu, HI, USA USA
June 24, 2012 to June 29, 2012
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/CLOUD.2012.87
With the advancement of virtualization technologies and the benefit of economies of scale, industries are seeking scalable IT solutions, such as data centers hosted either in-house or by a third party. Data center availability, often via a cloud setting, is ubiquitous. Nonetheless, little is known about the in-production performance of data centers, and especially the interaction of workload demands and resource availability. This study fills this gap by conducting a large scale survey of in-production data center servers within a time period that spans two years. We provide in-depth analysis on the time evolution of existing data center demands by providing a holistic characterization of typical data center server workloads, by focusing on their basic resource components, including CPU, memory, and storage systems. We especially focus on seasonality of resource demands and how this is affected by different geographical locations. This survey provides a glimpse on the evolution of data center workloads and provides a basis for an economics analysis that can be used for effective capacity planning of future data centers.
Servers, Resource management, Capacity planning, Time series analysis, Economics, Bandwidth, Focusing, capacity planing, datacenter, performance analysis
Robert Birke, Lydia Y. Chen, Evgenia Smirni, "Data Centers in the Cloud: A Large Scale Performance Study", CLOUD, 2012, 2013 IEEE Sixth International Conference on Cloud Computing, 2013 IEEE Sixth International Conference on Cloud Computing 2012, pp. 336-343, doi:10.1109/CLOUD.2012.87