A statistical approach to predictive detection
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Self-Similar Network Traffic and Performance Evaluation
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Process cruise control: event-driven clock scaling for dynamic power management
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Xen and the art of virtualization
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Memory resource management in VMware ESX server
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Resource overbooking and application profiling in shared hosting platforms
OSDI '02 Proceedings of the 5th symposium on Operating systems design and implementationCopyright restrictions prevent ACM from being able to make the PDFs for this conference available for downloading
Mitigating Amdahl's Law through EPI Throttling
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A performance-conserving approach for reducing peak power consumption in server systems
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Ensemble-level Power Management for Dense Blade Servers
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Power provisioning for a warehouse-sized computer
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Live migration of virtual machines
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No "power" struggles: coordinated multi-level power management for the data center
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Power routing: dynamic power provisioning in the data center
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Exploiting inter-sequence correlations for program behavior prediction
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Underprovisioning backup power infrastructure for datacenters
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Energy-saving self-configuring networked data centers
Computer Networks: The International Journal of Computer and Telecommunications Networking
Energy-credit scheduler: An energy-aware virtual machine scheduler for cloud systems
Future Generation Computer Systems
Power-aware optimization for heterogeneous multi-tier clusters
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Analysis of virtual machine live-migration as a method for power-capping
The Journal of Supercomputing
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Current capacity planning practices based on heavy over-provisioning of power infrastructure hurt (i) the operational costs of data centers as well as (ii) the computational work they can support. We explore a combination of statistical multiplexing techniques to improve the utilization of the power hierarchy within a data center. At the highest level of the power hierarchy, we employ controlled underprovisioning and over-booking of power needs of hosted workloads. At the lower levels, we introduce the novel notion of soft fuses to flexibly distribute provisioned power among hosted workloads based on their needs. Our techniques are built upon a measurement-driven profiling and prediction framework to characterize key statistical properties of the power needs of hosted workloads and their aggregates. We characterize the gains in terms of the amount of computational work (CPU cycles) per provisioned unit of power Computation per Provisioned Watt (CPW). Our technique is able to double the CPWoffered by a Power Distribution Unit (PDU) running the e-commerce benchmark TPC-W compared to conventional provisioning practices. Over-booking the PDU by 10% based on tails of power profiles yields a further improvement of 20%. Reactive techniques implemented on our Xen VMM-based servers dynamically modulate CPU DVFS states to ensure power draw below the limits imposed by soft fuses. Finally, information captured in our profiles also provide ways of controlling application performance degradation despite overbooking. The 95th percentile of TPC-W session response time only grew from 1.59 sec to 1.78 sec--a degradation of 12%.