Power and Performance Analysis of Persistent Key-Value Stores
With the current rate of data growth, processing needs are becoming difficult to fulfill due to CPU power and energy limitations. Data serving systems and especially persistent key-value stores have become a substantial part of data processing stacks in the data center, providing access to massive a...
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| Main Authors: | , , , , |
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| Format: | Journal Article |
| Language: | English |
| Published: |
31-08-2020
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| Subjects: | |
| Online Access: | Get full text |
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| Summary: | With the current rate of data growth, processing needs are becoming difficult
to fulfill due to CPU power and energy limitations. Data serving systems and
especially persistent key-value stores have become a substantial part of data
processing stacks in the data center, providing access to massive amounts of
data for applications and services. Key-value stores exhibit high CPU and I/O
overheads because of their constant need to reorganize data on the devices. In
this paper, we examine the efficiency of two key-value stores on four servers
of different generations and with different CPU architectures. We use RocksDB,
a key-value that is deployed widely, e.g. in Facebook, and Kreon, a research
key-value store that has been designed to reduce CPU overhead. We evaluate
their behavior and overheads on an ARM-based microserver and three different
generations of x86 servers. Our findings show that microservers have better
power efficiency in the range of 0.68-3.6x with a comparable tail latency. |
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| DOI: | 10.48550/arxiv.2008.13402 |