NAND flash-based Solid State Devices (SSDs) offer
the desirable features of high performance, energy efficiency,
and fast growing capacity. Thus, the use of SSDs is increasing in
distributed storage systems. A key obstacle in this context is that
the natural unbalance in distributed I/O workloads can result in
wear imbalance across the SSDs in a distributed setting. This, in
turn can have significant impact on the reliability, performance,
and lifetime of the storage deployment. Extant load balancers
for storage systems do not consider SSD wear imbalance when
placing data, as the main design goal of such balancers is to
extract higher performance. Consequently, data migration is
the only common technique for tackling wear imbalance, where
existing data is moved from highly loaded servers to the least
loaded ones.
In this paper, we explore an innovative holistic approach,
Chameleon, that employs data redundancy techniques such as
replication and erasure-coding, coupled with endurance-aware
write offloading, to mitigate wear level imbalance in distributed
SSD-based storage. Chameleon aims to balance the wear among
different flash servers while meeting desirable objectives of:
extending life of flash servers; improving I/O performance; and
avoiding bottlenecks. Evaluation with a 50 node SSD cluster
shows that Chameleon reduces the wear distribution deviation
by 81% while improving the write performance by up to 33%.
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Reference-Counter Aware Deduplication in Erasure-Coded Distributed Storage System
In modern distributed storage systems, space efficiency and system reliability are two major concerns. As a result, contemporary storage systems often employ data deduplication and erasure coding to reduce the storage overhead and provide fault tolerance, respectively. However, little work has been done to explore the relationship between these two techniques. In this paper, we propose Reference-counter Aware Deduplication (RAD), which employs the features of deduplication into erasure coding to improve garbage collection performance when deletion occurs. RAD wisely encodes the data according to the reference counter, which is provided by the deduplication level and thus reduces the encoding overhead when garbage collection is conducted. Further, since the reference counter also represents the reliability levels of the data chunks, we additionally made some effort to explore the trade-offs between storage overhead and reliability level among different erasure codes. The experiment results show that RAD can effectively improve the GC performance by up to 24.8% and the reliability analysis shows that, with certain data features, RAD can provide both better reliability and better storage efficiency compared to the traditional Round- Robin placement.
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- NSF-PAR ID:
- 10100342
- Date Published:
- Journal Name:
- 2018 IEEE International Conference on Networking, Architecture and Storage (NAS)
- Page Range / eLocation ID:
- 1 to 10
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/NAS.2018.8515697
