Using flash-based solid state drives (SSDs) as main memory has been proposed as a practical solution towards scaling memory capacity for data-intensive applications. However, almost all existing approaches rely on the paging mechanism to move data between SSDs and host DRAM. This inevitably incurs significant performance overhead and extra I/O traffic. Thanks to the byte-addressability supported by the PCIe interconnect and the internal memory in SSD controllers, it is feasible to access SSDs in both byte and block granularity today. Exploiting the benefits of SSD's byte-accessibility in today's memory-storage hierarchy is, however, challenging as it lacks systems support and abstractions for programs. In this paper, we present FlatFlash, an optimized unified memory-storage hierarchy, to efficiently use byte-addressable SSD as part of the main memory. We extend the virtual memory management to provide a unified memory interface so that programs can access data across SSD and DRAM in byte granularity seamlessly. We propose a lightweight, adaptive page promotion mechanism between SSD and DRAM to gain benefits from both the byte-addressable large SSD and fast DRAM concurrently and transparently, while avoiding unnecessary page movements. Furthermore, we propose an abstraction of byte-granular data persistence to exploit the persistence nature of SSDs, upon which we rethink the design primitives of crash consistency of several representative software systems that require data persistence, such as file systems and databases. Our evaluation with a variety of applications demonstrates that, compared to the current unified memory-storage systems, FlatFlash improves the performance for memory-intensive applications by up to 2.3x, reduces the tail latency for latency-critical applications by up to 2.8x, scales the throughput for transactional database by up to 3.0x, and decreases the meta-data persistence overhead for file systems by up to 18.9x. FlatFlash also improves the cost-effectiveness by up to 3.8x compared to DRAM-only systems, while enhancing the SSD lifetime significantly.
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Chameleon: An Adaptive Wear Balancer for Flash Clusters
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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- NSF-PAR ID:
- 10065105
- Date Published:
- Journal Name:
- the 32nd IEEE International Parallel and Distributed Processing Symposium (IPDPS)
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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