Proceedings of the 39th Annual IEEE/ACM International Symposium on Microarchitecture
ACM SIGARCH Computer Architecture News
Making scheduling "cool": temperature-aware workload placement in data centers
ATEC '05 Proceedings of the annual conference on USENIX Annual Technical Conference
Power provisioning for a warehouse-sized computer
Proceedings of the 34th annual international symposium on Computer architecture
No "power" struggles: coordinated multi-level power management for the data center
Proceedings of the 13th international conference on Architectural support for programming languages and operating systems
Power capping: a prelude to power shifting
Cluster Computing
Optimal power allocation in server farms
Proceedings of the eleventh international joint conference on Measurement and modeling of computer systems
Joint optimization of idle and cooling power in data centers while maintaining response time
Proceedings of the fifteenth edition of ASPLOS on Architectural support for programming languages and operating systems
On the energy (in)efficiency of Hadoop clusters
ACM SIGOPS Operating Systems Review
A Counter Architecture for Online DVFS Profitability Estimation
IEEE Transactions on Computers
Identifying the optimal energy-efficient operating points of parallel workloads
Proceedings of the International Conference on Computer-Aided Design
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We propose techniques for power budgeting in data centers, where a large power budget is allocated among the servers and the cooling units such that the aggregate performance of the entire center is maximized. Maximizing the performance for a given power budget automatically maximizes the energy efficiency. We first propose a method to partition the total power budget among the cooling and computing units in a self-consistent way, where the cooling power is sufficient to extract the heat of the computing power. Given the computing power budget, we devise an optimal computing budgeting technique based on knapsack-solving algorithms to determine the power caps for the individual servers. The optimal computing budgeting technique leverages a proposed on-line throughput predictor based on performance counter measurements to estimate the change in throughput of heterogeneous workloads as a function of allocated server power caps. We set up a simulation environment for a data center, where we simulate the air flow and heat transfer within the center using computational fluid dynamic simulations to derive accurate cooling estimates. The power estimates for the servers are derived from measurements on a real server executing heterogeneous workload sets. Our budgeting method delivers good improvements over previous power budgeting techniques.