Managing server energy and operational costs in hosting centers
SIGMETRICS '05 Proceedings of the 2005 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
Energy-aware server provisioning and load dispatching for connection-intensive internet services
NSDI'08 Proceedings of the 5th USENIX Symposium on Networked Systems Design and Implementation
A scalable, commodity data center network architecture
Proceedings of the ACM SIGCOMM 2008 conference on Data communication
Dcell: a scalable and fault-tolerant network structure for data centers
Proceedings of the ACM SIGCOMM 2008 conference on Data communication
Statistical profiling-based techniques for effective power provisioning in data centers
Proceedings of the 4th ACM European conference on Computer systems
Autonomic mix-aware provisioning for non-stationary data center workloads
Proceedings of the 7th international conference on Autonomic computing
Monalytics: online monitoring and analytics for managing large scale data centers
Proceedings of the 7th international conference on Autonomic computing
ElasticTree: saving energy in data center networks
NSDI'10 Proceedings of the 7th USENIX conference on Networked systems design and implementation
Network traffic characteristics of data centers in the wild
IMC '10 Proceedings of the 10th ACM SIGCOMM conference on Internet measurement
Augmenting data center networks with multi-gigabit wireless links
Proceedings of the ACM SIGCOMM 2011 conference
Data Centers in the Cloud: A Large Scale Performance Study
CLOUD '12 Proceedings of the 2012 IEEE Fifth International Conference on Cloud Computing
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Data centers, hosted either on-site or by a third party, have become a dominant computing platform. Here, we focus on the usage patterns of in-production data centers that are hosted by a third party and serve several corporate customers. We characterize the data center workload and concentrate especially on the temporal evolution of utilization of basic resource components. We especially focus on the autonomic aspect of this characterization as it can be used to identify how loads across components change in order to identify conditions that can trigger resource reallocation toward better workload management. To this end, we focus on the resource demands of six distinct corporate customers on two specific data centers, highlight the workload diversity across these customers, and especially focus on how resources are used in time scales that range from minutes to days, weeks, and months. This study fills an important gap in our understanding on how data center resources are used and provides helpful insights for the development of autonomous resource management in multi-tenant data centers.