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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SSD-Insider: Internal Defense of Solid-State Drive against Ransomware with Perfect Data Recovery
Ransomware is a malware that encrypts victim's data, where the decryption key is released after a ransom is paid by the data owner to the attacker. Many ransomware attacks were reported recently, making anti-ransomware a crucial need in security operation, and an issue for the security community to tackle. In this paper, we propose a new approach to defending against ransomware inside NAND flash-based SSDs. To realize the idea of defense-inside-SSDs, both a lightweight detection technique and a perfect recovery algorithm to be used as a part of SSDs firmware should be developed. To this end, we propose a new set of lightweight behavioral features on ran-somware's overwriting pattern, which are invariant across various ransomwares. Our features rely on observing the block I/O request headers only, and not the payload. For perfect and instant recovery, we also propose using the delayed deletion feature of SSDs, which is intrinsic to NAND flash. To demonstrate their feasibility, we implement our algorithms atop an open-channel SSD as a working prototype called SSD-Insider. In experiments using eight real-world and two in-house ransomwares with various background applications running, SSD-Insider achieved a detection accuracy 0% FRR/FAR in most scenarios, and only 5% FAR when heavy overwriting resembling ransomware's data wiping occurs. SSD-Insider detects ransomware activity within 10s, and recovers instantly an infected SSD within 1s with 0% data loss. The additional software overheads incurred by the SSD-Insider is just 147 ns and 254 ns for 4-KB reads and writes, respectively, which is negligible considering NAND chip latency (50-1000 μs).
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- NSF-PAR ID:
- 10084234
- Date Published:
- Journal Name:
- 38th IEEE International Conference on Distributed Computing Systems, ICDCS 2018
- Page Range / eLocation ID:
- 875 to 884
- 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/ICDCS.2018.00089
