Flashing Up the Storage Layer I. Koltsidas, S. D. Viglas (U of Edinburgh), VLDB 2008 Shimin Chen Big...

Flashing Up the Storage Layer

I. Koltsidas, S. D. Viglas (U of Edinburgh), VLDB 2008

Shimin ChenBig Data Reading Group

Motivation: Flash Disks: 64GB – 128GB SSDs available as

of Feb’08 Intel announced 80GB SSDs

Flash disks vs. magnetic disks Same I/O interface: logical 512B sectors No mechanical latency, I/O asymmetry, erase-before-

write: Random reads 10X faster than magnetic disks Random writes 10X slower than magnetic disks, esp

MLC

Exploit flash disks for storage?

Architecture

Flash disk as a cache for magnetic disk? Suboptimal for database workloads

because of write inefficiency

Flash disk and magnetic disk on the same level (This Paper)

ProblemStatement

Page migrations (Storage Manager)

Workload prediction Self-tuning

Page replacement (Buffer Manager)

Outline

Introduction Page placement Page replacement Experimental study Conclusion

Model

Random read/write costs of flash and magnetic disks Page migration decision is always made when a page

is in buffer pool Migration cost == write cost

The ideas are not new. The novel thing here is that logical I/Os are served by buffer pool. Only part of them are seen physically.

r, w: the cost of the current disk; r’, w’: the cost of the other disk

pg.C: a counter per page – the accumulated cost difference

Conservativeness Migration operation only after the cost

of migrating to and back Only physical operations on pages

3-competitive to optimal offline algorithm

Properties

Not conservative on migrations Based on logical operations

Hybrid Algorithm Idea:

Consider both physical and logical operations

More weight on physical ones

If a file has n pages, and b pages are cached in the buffer pool, then Prob_miss = 1 – b/n

Outline

Introduction Page placement Page replacement Experimental study Conclusion

Eviction Cost

Evicting a page: Dirty page incurs write cost Fetching a page back in the future

incurs read cost Cost:

Buffer Pool Organization

Sorted on timestamp

Sorted on cost of eviction

LRU

Impact of λ As λ increases:

Time segment decreases Cost segment increases Disk pages increases, flash pages decreases

Flash pages are evicted first, typically only found in time segment

Let Hm be the increase of disk hit rate, Mf be the increase of flash miss rate So we want

Outline

Introduction Page placement Page replacement Experimental study Conclusion

Experimental Setup Implementation:

Buffer manager, storage manager, B+trees for storing data

Machine: 2.26GHz Pentium4, 1.5GB RAM Debian linux, kernel 2.6.21 Two magnetic disks (300GB Maxtor

DiamondMax) 1 SSD (Samsung MLC 32GB) Data is stored on 1 disk + 1 SSD (both raw

devices)

Experimental Setup Cont’d Capacity of either disk is enough to hold all

data Metadata for files, pages, page mappings, and

free space are not modeled

B+tree is 140MB large, scattered across 1.4GB address space

Buffer pool is 20MB large

Raw Performance: 1 million 4KB random accesses

Impact of Using Both Disks

Conservative + LRU Query mix: read-only, write-only,

read/write Each set of queries executed 15

times

Read-Only

Write-Only

Mixed

Page Placement Algorithms

Infrequently changing workload

Frequently changing workload

Buffer Pool Replacement

Conclusion

Flash disk vs. magnetic disk Page migration and placement Page replacement Can be applied to databases and

file systems (?)

Outline

Introduction Page placement Page replacement Experimental study Conclusion