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Caching is widely known to be an effective method for improving I/O performance by storing frequently used data on higher speed storage components. However, most existing studies that focus on caching performance evaluate fairly small files populating a relatively small cache. Few reports are available that detail the performance of traditional cache replacement policies on extremely large caches. Do such traditional caching policies still work effectively when applied to systems with petabytes of data? In this paper, we comprehensively evaluate the performance of several cache policies, which include First-In-First-Out (FIFO), Least Recently Used (LRU) and Least Frequently Used (LFU), on the global satellite imagery distribution application maintained by the U.S. Geological Survey (USGS) Earth Resources Observation and Science Center (EROS). Evidence is presented suggesting traditional caching policies are capable of providing performance gains when applied to large data sets as with smaller data sets. Our evaluation is based on approximately three million real-world satellite images download requests representing global user download behavior since October 2008.
Citation preview
Performance Evaluation of Traditional Caching Policies on
A Large System with Petabytes of Data
Texas State University, TX, USA1
Texas A&M University-Kingsville2
Auburn University, AL, USA3
04/08/2023
Ribel Fares1, Brian Romoser1, Ziliang Zong1, Mais Nijim2 and Xiao Qin3
Presented at the 7th IEEE International Conference on Networking, Architecture, and Storage (NAS2012)
2
Motivation
04/08/2023
• Large-scale data processing • High-performance storage systems
3
High-Performance Clusters
04/08/2023
• The Architecture of a Cluster
Client Network switch
Computing Nodes
Storage subsystems (or Storage Area Network)Internet
Head Node
4
Techniques for High-Performance Storage Systems
• Caching • Prefetching• Active Storage• Parallel Processing
5
Do traditional caching policies still work effectively?
Over 4 petabytes of satellite imagery available.
More than 3 million image requests since 2008.
Earth Resources Observation and Science (EROS) Center
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EROS Data Center - System Workflow
datadata
datadata
USGS / EROS Storage System
Type Model CapacityHardwar
eBus
Interface
1Sun/Oracle
F5100 100 TB SSD SAS/FC
2 IBM DS3400 1 PB HDD SATA
3 Sun/Oracle T10K 10 PB Tape Infiniband
The FTP server from which users download images is of type 1.
The USGS / EROS Distribution System
• Each cache miss costs 20–30 minutes of processing time.
USGS / EROS Log File
•Landsat
L
•ETM+ sensor
E
•Satellite designation
7
•WRS path
004•W
RS row
063
•Acquisition year
2006
•Acquisition day of year
247
•Capture station
ASN
•Version
00
Observation 1
• Top 9 aggressive users account for 18% of all requests.
• A second log file was created by removing requests made by the top 9 aggressive users.
Observation 2
• Duplicate images within 7 days were removed from the log file.
0.00%
5.00%
10.00%
15.00%
20.00%
25.00%
30.00%
35.00%
0 2 4 6 8 10 12 14 16
Dup
licat
e Re
ques
t Per
cent
age
Time Window (Days)
Caching Algorithms
• FIFO: First entry in cache gets removed first.
• LRU: Least recently requested image removed first.
• LFU: Least popular image removed first.
Case StudiesSimulation Number Cache Policy Cache Size (TB) Top 9 Aggressive
Users
1 FIFO 30 Included
2 FIFO 30 Not Included
3 FIFO 60 Included
4 FIFO 60 Not Included
5 LRU 30 Included
6 LRU 30 Not Included
7 LRU 60 Included
8 LRU 60 Not Included
9 LFU 30 Included
10 LFU 30 Not Included
11 LFU 60 Included
12 LFU 60 Not Included
Simulation Assumptions/Restrictions
• When cache server reaches 90% capacity, images will be removed according to adopted cache policy until server load is reduced down to 45%.
• Images are assumed to be processed instantaneously.
• A requested image can not be removed from the server before 7 days.
Results – Hit Rates ofDiffering Cache Replacement Policies
Oct-08
Dec-08
Feb-09
Apr-09
Jun-09
Aug-09
Oct-09
Dec-09
Feb-10
Apr-10
Jun-10
Aug-10
Oct-10
Dec-10
Feb-11
Apr-11
Jun-11
Aug-11
Oct-11
0.15
0.25
0.35
0.45
0.55
0.65
Hit Rates: 60TB – Aggressive Users Included
LRU Hit RateLFU Hit RateFIFO Hit Rate
First Clean Up
Monthly hit ratiosAggressive users excluded
Monthly hit ratiosAggressive users excluded
Results – Impact of Inclusion ofAggressive Users
FIFO LRU LFU
30 TB 0.32661 0.345919 0.339515
60 TB 0.438536 0.457727 0.454811
0.025
0.075
0.125
0.175
0.225
0.275
0.325
0.375
0.425
0.475
30 TB
60 TB
With Aggressive Users
Hit R
ate
Results – Impact of Exclusion ofAggressive Users
FIFO LRU LFU
30 TB 0.319171 0.332741 0.345208
60 TB 0.430349 0.449621 0.45871
0.025
0.075
0.125
0.175
0.225
0.275
0.325
0.375
0.425
0.475
30 TB
60 TB
No Aggressive Users
Hit R
ate
Conclusion & Future Work
• LRU and LFU initiate cache clean-up at similar points.
• Aggressive users destabilize monthly hit rates
• LFU was least affected by the inclusion of aggressive users.
Conclusion & Future Work cont’d.
• LRU and LFU methods improve FIFO as expected.
• However, improvements are on the weaker side.
• Global user behaviors should be further investigated to design more complex caching and/or prefetching strategies.
22
Summary
• Data-Intensive Processing– EROS (Earth Resources Observation and Science)
Data Center– visEROS
• Improving I/O Performance– Prefetching– Active Storage– Parallel Processing
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The VisEROS Project – Motivation
• 2M downloads from the EROS data center.• No existing visualization tools available to utilize
these data• Need a tool to:– Monitor user download behaviors– Show the current global download “hot spots”– Demonstrate the actual usage of EROS data– Optimize the storage system performance– Improve the satellite image distribution service
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The VisEROS Prototype
Generated by VisEROS
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This project is supported by
the U.S. National Science Foundation
No. 0917137
Download the presentation slideshttp://www.slideshare.net/xqin74
Google: slideshare Xiao Qin
27
Many Thanks!
04/08/2023
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