# Presentation: 013562

## FreeLoader: Scavenging Desktop Storage Resources for Scientific Data

- Sudharshan Vazhkudai,1 Xiaosong Ma,1,2 Vincent Freeh,2 Jonathan Strickland,2 Nandan Tammineedi,2 and Stephen Scott 1
- 1 Oak Ridge National Laboratory
- 2 North Carolina State University
- SC|05 Technical Paper Presentation
- Session: Storage and Data
- November 17, 2005
- Seattle, WA

## Outline

- Problem space
- Desktop storage scavenging for scientific data
- FreeLoader architecture
- FreeLoader performance in a user’s HPC setting
- Philosophizing...
- Wrap up on a funny note!

## Problem Domain

**Problem Domain**

- Data Deluge
- Experimental facilities: SNS, LHC (PBs/yr)
- Observatories: sky surveys, world-wide telescopes
- Simulations from NLCF end-stations
- Internet archives: NIH GenBank (serves 100 gigabases of sequence data)
- Typical user access traits on large scientific data
- Download remote datasets using favorite tools
- FTP, GridFTP, hsi, wget
- Shared interest among groups of researchers
- A Bioinformatics group collectively analyze and visualize a sequence database for a few days: Locality of interest!
- Often times, discard original datasets after interest dissipates

## So, what’s the problem with this story?

- Wide-area data movement is full of pitfalls
- Sever bottlenecks, BW/latency fluctuations
- GridFTP-like tuned tools not widely available
- Popular Internet repositories still served through modest transfer tools!
- User applications are often latency intolerant
- e.g., real-time viz rendering of a TerraServer map from Microsoft on ORNL’s tiled display!
- Why can’t we address this with the current storage landscape?
- Shared storage: Limited quotas
- Dedicated storage: SAN storage is a non-trivial expense! (4TB disk array ~ $40K)
- Local storage: Usually not enough for such large datasets
- Archive in mass storage for future accesses: High latency
- Upshot
- Retrieval rates significantly lower than local I/O or LAN throughput

## Is there a silver lining at all? (Desktop Traits)

- Desktop Capabilities better than ever before
- Space usage to Available storage ratio is significantly low in academic and industry settings
- Increasing numbers of workstations online most of the time
- At ORNL-CSMD, ~ 600 machines are estimated to be online at any given time
- At NCSU, > 90% availability of 500 machines
- Well-connected, secure LAN settings
- A high-speed LAN connection can stream data faster than local disk I/O

## Desktop Storage Scavenging?

- FreeLoader
- Imagine Condor for storage
- Harness the collective storage potential of desktop workstations ~ Harnessing idle CPU cycles
- Increased throughput due to striping
- Split large datasets into pieces, *Morsels*, and stripe them across desktops
- Scientific data trends
- Usually write-once-read-many
- Remote copy held elsewhere
- Primarily sequential accesses
- Data trends + LAN-Desktop Traits + user access patterns make collaborative caches using storage scavenging a viable alternative!

## Old wine in a new bottle?

- Key strategies derived from “best practices” across a broad range of storage paradigms...
- Desktop Storage Scavenging from P2P systems
- Striping, parallel I/O from parallel file systems
- Caching from cooperative Web caching
- And, applied to scientific data management for
- Access locality, aggregating I/O, network bandwidth and data sharing
- Posing new challenges and opportunities: heterogeneity, striping, volatility, donor impact, cache management and availability

## FreeLoader Environment

## FreeLoader Architecture

- Lightweight UDP
- Scavenger device: metadata bitmaps, morsel organization
- Morsel service layer
- Monitoring and Impact control

- Global free space management
- Metadata management
- Soft-state registrations
- Data placement
- Cache management
- Profiling

## Testbed and Experiment setup

- FreeLoader installed in a user’s HPC setting
- GridFTP access to NFS
- GridFTP access to PVFS
- hsi access to HPSS
- Cold data from tapes
- Hot data from disk caches
- wget access to Internet archive

## Comparing FreeLoader with other storage systems

## Client Access-pattern Aware Striping

- Uploading client likely to access more frequently
- So, let’s try to optimize data placement for him!
- Overlap network I/O with local I/O
- What is the optimal *local:remote* data ratio?
- Model

## Striping Parameters

## Client-side Filters

## Computation Impact

## Network Activity Test

## Disk-intensive Task

## Impact Control

## Philosophizing...

- What the scavenged storage “is not”:
- Not a file system, not a replacement to high-end storage
- Not intended for wide-area resource integration
- What it “is”:
- Low-cost, best-effort storage cache for scientific data sources
- Intended to facilitate
- Transient access to large, read-only datasets
- Data sharing within administrative domain
- To be used in conjunction with higher-end storage systems

## Slide 20