Data Center Power Oversubscription with a Medium Voltage Power Plane and Priority-Aware Capping

Sakalkar, Varun; Kontorinis, Vasileios; Landhuis, David; Li, Shaohong; Ronde, Darren De; Blooming, T.M.; Ramesh, Anand; Kennedy, J. J.; Malone, Christopher G.; Clidaras, Jimmy; Ranganathan, Parthasarathy

doi:10.1145/3373376.3378533

Cited by 25 publications

(6 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Consequently, enabling power savings at high machine utilization is critical since that is when the peak power requirements must be met. To fit within the available power budget, servers may have to be limited in their power usage or even shut off by power capping [6,22,35]. Avoiding these extremely user-impacting events leads to better user experience, can enable higher power over-subscription, and saves on capital expenses by delaying the building of new data centers.…”

Section: Why Focus On High Utilization?mentioning

confidence: 99%

Leveraging application classes to save power in highly-utilized data centers

Kaffes

Sbîrlea

Lin

et al. 2020

Proceedings of the 11th ACM Symposium on Cloud Computing

View full text Add to dashboard Cite

Data center energy consumption has become an increasingly significant contributor both to greenhouse emissions and costs. To increase utilization of individual hosts and improve efficiency, most modern data centers co-locate workloads belonging to different application classes, some being latency-sensitive (LS) and others best-effort (BE) which are more tolerant to performance variation. It is therefore necessary to design mechanisms that reduce power consumption even in the resulting high-utilization environment, while preserving LS task performance. Moreover, the abundance of different workloads and the security implications of public cloud make mechanisms that rely on extensive knowledge of workload characteristics or on application-exported metrics challenging to deploy. We present PACT, Per Application Class Turbo Controller, a system that leverages two novel mechanisms to reduce power consumption even in highly-utilized data centers. By treating applications like opaque boxes that do not need to provide application-specific performance signals, the first mechanism, Turbo Control, reduces power consumption by decreasing the operating frequency and throttling only BE tasks, without affecting performance-sensitive LS tasks. We identify the shortcomings of Turbo Control and increase its effectiveness by introducing CPUJailing, a mechanism that allocates different sets of cores to LS and BE applications. We deploy PACT (Turbo Control + CPUJailing) in production * Kostis Kaffes was an intern at Google during this work. † Christos Kozyrakis was partly at Google during this work.

show abstract

Section: Why Focus On High Utilization?mentioning

confidence: 99%

Leveraging application classes to save power in highly-utilized data centers

Kaffes

Sbîrlea

Lin

et al. 2020

Proceedings of the 11th ACM Symposium on Cloud Computing

View full text Add to dashboard Cite

show abstract

“…The data center adopts the method of data center power over-provisioning [2][3][4] to improve the power utilization rate of the data center. Assuming that the rated power of the rack is 800W and the peak power of the server is 50W, in this case we can deploy 16 servers.…”

Section: Introductionmentioning

confidence: 99%

DCMigrationALG: A Power-Aware Data Center Container Migration Algorithm

Zou,

Feng,

Tang

et al. 2023

J. Phys.: Conf. Ser.

View full text Add to dashboard Cite

In order to make full use of the power capacity of the data center when deploying servers in the data center, the deployment method of data center power over-provisioning is adopted. By using the hypothetical power capacity of the data center instead of the actual power capacity to deploy servers, we can deploy more servers to improve power utilization and space utilization. However, in some cases, when the servers under the same power supply component reach a higher power level at the same time, the real-time power of the power supply component will exceed the rated power, affecting service operation and causing heavy losses. In response to the above problems, we propose DCMigrationALG: a power-aware data center container migration algorithm. This algorithm uses container migration to migrate the containers under the power supply component to other power supply components, thereby reducing the real-time power of the power supply component and ensuring power security. We conduct experiments in a real computer room environment to prove the effectiveness of our algorithm.

show abstract

Section: Introductionmentioning

confidence: 99%

“…Now, the global annual expenditure on data centers exceeds 200 billion dollars. 2 The huge power consumption has brought high costs to data center operators and has harmed the environment.Therefore, it is imminent to promote the energy efficiency of data centers. Data centers can save power through advanced technologies [such as dynamic power management (DPM) 3 and power napping 4 ] and strategies [such as task scheduling and virtual machines (VMs) consolidation 5,6 ].…”

mentioning

confidence: 99%

Data characteristics aware prediction model for power consumption of data center servers

Shen

Zhou

Zhang

et al. 2022

Concurrency and Computation

View full text Add to dashboard Cite

Summary Due to the rapid increase in the number and scale of data centers, the information and communication technology (ICT) equipment in data centers consumes an enormous amount of power. A power prediction model is therefore essential for decision‐making optimization and power management of ICT equipment. However, it is difficult to predict the power consumption of data centers accurately due to the complex power patterns and nonlinear interdependencies among components. Existing methods either rely on standard formulas, or simply treat it as time series, both leading to poor power prediction accuracy. To overcome those limitations, in this article, we present a systematic power prediction framework called characteristic aware attention‐augmented deep learning‐based prediction method. In particular, we first analyze the different power consumption series to illustrate their different temporal characteristics. Second, we perform different data processing for the corresponding characteristics of power series samples. Third, we propose an accurate and efficient neural network model to predict future power consumption with the pretreated data. The experimental results show that the proposed model is able to achieve superior prediction accuracy.

show abstract

Data Center Power Oversubscription with a Medium Voltage Power Plane and Priority-Aware Capping

Cited by 25 publications

References 28 publications

Leveraging application classes to save power in highly-utilized data centers

Leveraging application classes to save power in highly-utilized data centers

DCMigrationALG: A Power-Aware Data Center Container Migration Algorithm

Data characteristics aware prediction model for power consumption of data center servers

Contact Info

Product

Resources

About