Post-graduate theses
Current Record: 85 of 833
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Identifier |
000441652 |
Title |
Balancing Garbage Collection vs I/O Amplification using hybrid Key-Value Placement in LSM-based Key-Value Stores |
Alternative Title |
Ισορροπώντας το κόστος της ανάκτησης χώρου και του αυξημένου I/O σε συστήματα κλειδιού-τιμής βασισμένα στο LSM δέντρο με την υβριδική τοποθέτηση ζευγαριών κλειδιού-τιμής |
Author
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Ξανθάκης, Γιώργος Ι.
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Thesis advisor
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Μπίλας, Άγγελος
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Reviewer
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Μαγκούτης, Κωνσταντίνος
Πρατικάκης, Πολύβιος
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Abstract |
Key-value (KV) separation is a technique that introduces randomness in the I/O access
patterns to reduce I/O amplification in LSM-based key-value stores for fast storage
devices (NVMe). KV separation has a significant drawback that makes it less attractive:
Delete and especially update operations that are important in modern workloads result
in frequent and expensive garbage collection (GC) in the value log.
In this thesis, we design and implement Parallax, which proposes hybrid KV placement
that reduces GC overhead significantly and maximizes the benefits of using a log. We first
model the benefits of KV separation for different KV pair sizes. We use this model to
classify KV pairs in three categories small, medium, and large. Then, Parallax uses different
approaches for each KV category: It always places large values in a log and small values in
place. For medium values it uses a mixed strategy that combines the benefits of using a
log and eliminates GC overhead as follows: It places medium values in a log for all but the
last few (typically one or two) levels in the LSM structure, where it performs a full
compaction, merges values in place, and reclaims log space without the need for GC.
We evaluate Parallax against RocksDB that places all values in place and BlobDB that
always performs KV separation. We find that Parallax increases throughput by up to 12.4x
and 17.83x, decreases I/O amplification by up to 27.1x and 26x, and increases CPU
efficiency by up to 18.7x and 28x respectively, for all but scan-based YCSB workloads.
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Language |
English |
Subject |
Fast storage devices |
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KV separation |
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LSM tree |
Issue date |
2021-07-30 |
Collection
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School/Department--School of Sciences and Engineering--Department of Computer Science--Post-graduate theses
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Type of Work--Post-graduate theses
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Permanent Link |
https://elocus.lib.uoc.gr//dlib/2/2/4/metadata-dlib-1628230369-224748-24010.tkl
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Views |
765 |