Statistical Characterization of Business-Critical Workloads Hosted in Cloud Datacenters

• Published in: 2015 15th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing pp. 465 - 474
• Main Authors: Shen, Siqi, Van Beek, Vincent, Iosup, Alexandru
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.05.2015
• Subjects: characterization; Computational modeling; datacenters; Dynamic scheduling; Google; Memory management; Resource management; Servers; workload
• DOI: 10.1109/CCGrid.2015.60

Business-critical workloads -- web servers, mail servers, app servers, etc. -- are increasingly hosted in virtualized data enters acting as Infrastructure-as-a-Service clouds (cloud data enters). Understanding how business-critical workloads demand and use resources is key in capacity sizing, in infrastructure operation and testing, and in application performance management. However, relatively little is currently known about these workloads, because the information is complex -- larges-scale, heterogeneous, shared-clusters -- and because datacenter operators remain reluctant to share such information. Moreover, the few operators that have shared data (e.g., Google and several supercomputing centers) have enabled studies in business intelligence (MapReduce), search, and scientific computing (HPC), but not in business-critical workloads. To alleviate this situation, in this work we conduct a comprehensive study of business-critical workloads hosted in cloud data enters. We collect two large-scale and long-term workload traces corresponding to requested and actually used resources in a distributed datacenter servicing business-critical workloads. We perform an in-depth analysis about workload traces. Our study sheds light into the workload of cloud data enters hosting business-critical workloads. The results of this work can be used as a basis to develop efficient resource management mechanisms for data enters. Moreover, the traces we released in this work can be used for workload verification, modelling and for evaluating resource scheduling policies, etc.