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.
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A Comparative Study of HDD and SSD RAIDs’ Impact on Server Energy Consumption
In the US alone, data centers consumed around $20 billion (200 TWh) yearly electricity in 2016, and this amount doubles itself every five years. Data storage alone is estimated to be responsible for about 25% to 35% of data-center power consumption. Servers in data centers generally include multiple HDDs or SSDs, commonly arranged in a RAID level for better performance, reliability, and availability. In this study, we evaluate HDD and SSD based Linux (md) software RAIDs' impact on the energy consumption of popular servers. We used the Filebench workload generator to emulate three common server workloads: web, file, and mail, and measured the energy consumption of the system using the HOBO power meter. We observed some similarities and some differences in energy consumption characteristics of HDD and SSD RAIDs, and provided our insights for better energy-efficiency. We hope that our observations will shed light on new energy-efficient RAID designs tailored for HDD and SSD RAIDs' specific energy consumption characteristics.
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- Award ID(s):
- 1657296
- NSF-PAR ID:
- 10050305
- Date Published:
- Journal Name:
- 2017 IEEE International Conference on Cluster Computing (CLUSTER)
- Page Range / eLocation ID:
- 625 to 626
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/CLUSTER.2017.103
