MSST16 Session 4: Spotlight on Flash memory and Solid-State Drives

Adaptive policies for balancing performance and lifetime of mixed SSD arrays through workload sampling

high-end SSD: cache

Low-end SSD: main storage 

1 high-end SSD cache for 3 low-end SSD: high-end SSD life is 1.47 years versus low-end 6.34 years assuming LRU cache policy

problem: high-end SSD cache can wear out faster than low-end SSDs main storage 

approach: balance the performance and lifetime at the same time 

metric: optimize latency over lifetime (less is better) 

selective caching policies ---> decide cache policy based on request size and hotness 

REAL: A Retention Error Aware LDPC Decoding Scheme to Improve NAND Flash Read Performance 

error correction codes: BCH, LDPC

Analytic models for flash-based SSD performance when subject to trimming 

SSD structures: N blocks, b pages per block, unit of data exchange is a page, page has 3 possible states: eras, valid or invalid

data can only be wrtten on pages in erase state

erase can be performed on whole block only 

assume j valid pages on a victim block with probability pj,

write amplification A equals 

A = b/(b - sum(j*pj)) 

prior work: mostly assumes uniform random writes and Rosenblum(hot/cold) workloads 

               exact (closed form) results when N -> infinity 

                  1. greedy is optimzed under random writes, d-choices close to optimal (for d as small as 10) 

                     2. increaseing hotness worsens WA in case of single WF (as no hot/cold data sepeartion takes place) 

                     3. Double WF (seperates writes triggered by hot and GC): WA decreases with hotness (as partial hot/cold data separation takes place) 

                      However, they all assume no trimming

How do we model trim behavior? 

      

Main takeaway: 

trimming results in effective load (utilization)

Reducing Write Amplification of Flash Storage through Cooperative Data Management with NVM

write amplification and GC causes SSD performance fluctuation 

in traditional systems, all live pages need to be copied to another block whiling erasing

however, CDM skips coping 

"removable" state: can be erased if the data needed to be copied into 

issue 1: consistency ---> file system needs to be modified 

issue 2: communication overhead -> events in cache and storage should be notified to each other synchronously --> use NVM-e to piggyback 

NV-cache as in-storage cache

evaluation: 

CDM reduces write-amplification by 20x, improves response time as well

Exploiting Latency Variation for Access Conflict Reduction of NAND Flash Memory

motivation: 

ECC complexity, ECC capability and read speed tradeoffs: high sensing level means preciser memory and higher ECC capability 

program size and write speed tradeoff: 

process variable and retention variation leed to speed variation 

hotness-aware write scheduling: 

retention aware read scheduling

write: size-based predicted hotness

read: 

evaluation: 

MSST'16 session 3 Store More, Longer, and for Less: Deduplication and Archival Systems

A Long Term User-Centric Analysis of Deduplication Patterns

study a dataset of 21 months, 1 snapshot per user per day

tracer.filesystems.org 

a lot of small files (< 1M), but a few large files consume most of the space

in general, small files achieve higher deduplication ratio than large files 

per-user deduplication ratio, redundancy (across users),  differs a lot  

Lazy Exact Deduplication

postpone disk lookups (fingerprints lookup) until we can do them in a batch

Sorted Deduplication: How to Process Thousands of Backup Streams

requirement is changing: a few large streams ---> many streams (e.g., cloud backup)

Effects of Prolonged Media Usage and Long-term Planning on Archival Systems

preserving data for ~100 to ~1000 years

question: 

when do you retire/replace media?

how long do you plan for?

Failure scenarios: device failures and economic failure 

1. should media be used past their manufacture suggested service life or warranty period? 

    (for archival data disk might last longer) 

have a model to model the purchase, maintaining and retiring phase to calculate cost 

MSST'16: It's never too fast: storage performance enhancements

Pfimbi: Accelerating Big Data Jobs Through Flow-Controlled Data Replication

in HDFS, synchronous replication (in pipeline) has performance bottlenecks, and seldomly helps application performance
   - only 2% of data was read within 5mins of being written
 
So do asynchronous replication
Need to use flow control to manage congestion as well

ManyLogs: Improved CMR/SMR Disk Bandwidth and Faster Durability with Scattered Logs

problem: small durable writes severely impacts bandwidth of other users (e.g., sequential reader) 

in this case, data journaling outperforms ordered journaling!

Ordered journaling: efficient for large writes

data journaling:efficient for small writes (less seeks) 

previous work: adaptive journaling (ATC'05) 

many logs, small writes to the nearest log (to the current head)  

where to put logs on the disk? reserve 10MB for every platter (?)

checkpointing: lazy instead of every 5 seconds for many-logs