Diagnosing performance overheads in the xen virtual machine environment
Proceedings of the 1st ACM/USENIX international conference on Virtual execution environments
pMapper: power and migration cost aware application placement in virtualized systems
Proceedings of the 9th ACM/IFIP/USENIX International Conference on Middleware
Minimizing data center cooling and server power costs
Proceedings of the 14th ACM/IEEE international symposium on Low power electronics and design
Understanding data center traffic characteristics
ACM SIGCOMM Computer Communication Review
Efficient resource provisioning in compute clouds via VM multiplexing
Proceedings of the 7th international conference on Autonomic computing
Server workload analysis for power minimization using consolidation
USENIX'09 Proceedings of the 2009 conference on USENIX Annual technical conference
Power and Performance Modeling in a Virtualized Server System
ICPPW '10 Proceedings of the 2010 39th International Conference on Parallel Processing Workshops
Power management of online data-intensive services
Proceedings of the 38th annual international symposium on Computer architecture
Clearing the clouds: a study of emerging scale-out workloads on modern hardware
ASPLOS XVII Proceedings of the seventeenth international conference on Architectural Support for Programming Languages and Operating Systems
Reliability-aware power management for parallel real-time applications with precedence constraints
IGCC '11 Proceedings of the 2011 International Green Computing Conference and Workshops
IEEE Spectrum
Risk Aware Provisioning and Resource Aggregation Based Consolidation of Virtual Machines
CLOUD '12 Proceedings of the 2012 IEEE Fifth International Conference on Cloud Computing
Energy-Efficient Virtual Machine Replication and Placement in a Cloud Computing System
CLOUD '12 Proceedings of the 2012 IEEE Fifth International Conference on Cloud Computing
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Server consolidation plays a key role to mitigate the continuous power increase of datacenters. The recent advent of scale-out applications (e.g., web search, MapReduce, etc.) necessitate the revisit of existing server consolidation solutions due to distinctively different characteristics compared to traditional high-performance computing (HPC), i.e., user interactive, latency critical, and operations on large data sets split across a number of servers. This paper presents a power saving solution for datacenters that especially targets the distinctive characteristics of the scale-out applications. More specifically, we take into account correlation information of core utilization among virtual machines (VMs) in server consolidation to lower actual peak server utilization. Then, we utilize this reduction to achieve further power savings by aggressively-yet-safely lowering the server operating voltage and frequency level. We have validated the effectiveness of the proposed solution using 1) multiple clusters of real-life scale-out application workloads based web search and 2) utilization traces obtained from real datacenter setups. According to our experiments, the proposed solution provides up to 13.7% power savings with up to 15.6% improvement of Quality-of-Service (QoS) compared to existing correlation-aware VM allocation schemes for datacenters.