skip to main content
US FlagAn official website of the United States government
dot gov icon
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
https lock icon
Secure .gov websites use HTTPS
A lock ( lock ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.


Title: Towards Server-Level Power Monitoring in Data Centers Using Single-Point Voltage Measurement
Server-level power monitoring in data centers can significantly contribute to its efficient management. Nevertheless, due to the cost of a dedicated power meter for each server, most data center power management only focuses on UPS or cluster-level power monitoring. In this paper, we propose a low-cost novel power monitoring approach that uses only one sensor to extract power consumption information of all servers. We utilize the conducted electromagnetic interference of server power supplies to measure its power consumption from non-intrusive single-point voltage measurement. Using a pair of commercial grade Dell PowerEdge servers, we demonstrate that our approach can estimate each server's power consumption with ~3% mean absolute percentage error.  more » « less
Award ID(s):
2152357
PAR ID:
10450645
Author(s) / Creator(s):
; ; ;
Date Published:
Journal Name:
In Proceedings of the 20th ACM Conference on Embedded Networked Sensor Systems (SenSys '22)
Page Range / eLocation ID:
855 to 856
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. Server-level power monitoring in data centers can significantly contribute to its efficient management. Nevertheless, due to the cost of a dedicated power meter for each server, most data center power management only focuses on UPS or cluster-level power monitoring. In this paper, we propose a low-cost novel power monitoring approach that uses only one sensor to extract power consumption information of all servers. We utilize the conducted electromagnetic interference (EMI) of server power supplies to measure their power consumption from non-intrusive single-point voltage measurements. We present a theoretical characterization of conducted EMI generation in server power supply and its propagation through the data center power network. Using a set of ten commercial-grade servers (six Dell PowerEdge and four Lenovo ThinkSystem), we demonstrate that our approach can estimate each server's power consumption with less than ~7% mean absolute error. 
    more » « less
  2. Datacenter capacity is growing exponentially to satisfy the increasing demand for many emerging computationally-intensive applications, such as deep learning. This trend has led to concerns over datacenters’ increasing energy consumption and carbon footprint. The most basic prerequisite for optimizing a datacenter’s energy- and carbon-efficiency is accurately monitoring and attributing energy consumption to specific users and applications. Since datacenter servers tend to be multi-tenant, i.e., they host many applications, server- and rack-level power monitoring alone does not provide insight into the energy usage and carbon emissions of their resident applications. At the same time, current application-level energy monitoring and attribution techniques are intrusive: they require privileged access to servers and necessitate coordinated support in hardware and software, neither of which is always possible in cloud environments. To address the problem, we design WattScope, a system for non-intrusively estimating the power consumption of individual applications using external measurements of a server’s aggregate power usage and without requiring direct access to the server’s operating system or applications. Our key insight is that, based on an analysis of production traces, the power characteristics of datacenter workloads, e.g., low variability, low magnitude, and high periodicity, are highly amenable to disaggregation of a server’s total power consumption into application-specific values. WattScope adapts and extends a machine learning-based technique for disaggregating building power and applies it to server- and rack-level power meter measurements that are already available in data centers. We evaluate WattScope’s accuracy on a production workload and show that it yields high accuracy, e.g., often 10% normalized mean absolute error, and is thus a potentially useful tool for datacenters in externally monitoring application-level power usage. 
    more » « less
  3. We consider a large-scale parallel-server loss system with an unknown arrival rate, where each server is able to adjust its processing speed. The objective is to minimize the system cost, which consists of a power cost to maintain the servers' processing speeds and a quality of service cost depending on the tasks' processing times, among others. We draw on ideas from stochastic approximation to design a novel speed scaling algorithm and prove that the servers' processing speeds converge to the globally asymptotically optimum value. Curiously, the algorithm is fully distributed and does not require any communication between servers. Apart from the algorithm design, a key contribution of our approach lies in demonstrating how concepts from the stochastic approximation literature can be leveraged to effectively tackle learning problems in large-scale, distributed systems. En route, we also analyze the performance of a fully heterogeneous parallel-server loss system, where each server has a distinct processing speed, which might be of independent interest. 
    more » « less
  4. We present Whisper, a system for privacy-preserving collection of aggregate statistics. Like prior systems, a Whisper deployment consists of a small set of non-colluding servers; these servers compute aggregate statistics over data from a large number of users without learning the data of any individual user. Whisper's main contribution is that its server-to-server communication cost and its server-side storage costs scale sublinearly with the total number of users. In particular, prior systems required the servers to exchange a few bits of information to verify the well-formedness of each client submission. In contrast, Whisper uses silently verifiable proofs, a new type of proof system on secret-shared data that allows the servers to verify an arbitrarily large batch of proofs by exchanging a single 128-bit string. This improvement comes with increased client-to-server communication, which, in cloud computing, is typically cheaper (or even free) than the cost of egress for server-to-server communication. To reduce server storage, Whisper approximates certain statistics using small-space sketching data structures. Applying randomized sketches in an environment with adversarial clients requires a careful and novel security analysis. In a deployment with two servers and 100,000 clients of which 1% are malicious, Whisper can improve server-to-server communication for vector sum by three orders of magnitude while each client's communication increases by only 10%. 
    more » « less
  5. In typical data centers, the servers and IT equipment are cooled by air and almost half of total IT power is dedicated to cooling. Hybrid cooling is a combined cooling technology with both air and water, where the main heat generating components are cooled by water or water-based coolants and rest of the components are cooled by air supplied by CRAC or CRAH. Retrofitting the air-cooled servers with cold plates and pumps has the advantage over thermal management of CPUs and other high heat generating components. In a typical 1U server, the CPUs were retrofitted with cold plates and the server tested with raised coolant inlet conditions. The study showed the server can operate with maximum utilization for CPUs, DIMMs, and PCH for inlet coolant temperature from 25–45 °C following the ASHRAE guidelines. The server was also tested for failure scenarios of the pumps and fans with reducing numbers of fans and pumps. To reduce cooling power consumption at the facility level and increase air-side economizer hours, the hybrid cooled server can be operated at raised inlet air temperatures. The trade-off in energy savings at the facility level due to raising the inlet air temperatures versus the possible increase in server fan power and component temperatures is investigated. A detailed CFD analysis with a minimum number of server fans can provide a way to find an operating range of inlet air temperature for a hybrid cooled server. Changes in the model are carried out in 6SigmaET for an individual server and compared to the experimental data to validate the model. The results from this study can be helpful in determining the room level operating set points for data centers housing hybrid cooled server racks. 
    more » « less