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Elastic Cloud Caches for Accelerating Service-Oriented Computations
Gagan AgrawalOhio State UniversityColumbus, OH
David ChiuWashington State UniversityVancouver, WA
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Cloud Computing
• Pay-As-You-Go Computing‣ Running 1 machine for 10
hours = running 10 machines for 1 hour
• Elasticity‣ Cloud applications can
stretch and contract their resource requirements
• “Infinite resources”
3
Outline
‣Accelerating Data Intensive Services Using the Cloud
•Motivating Application
•Design of an Elastic Cache
‣Performance Evaluation
•Up-Scaling (cache expansion)
•Down-Scaling (cache contraction)
‣Future Work & Conclusion
4
Motivating Application
Data Sources
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Computing & Storage Resources
Geoinformatics Cyber Infrastructure: Lake Erie
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Shared/Proprietary Web Services
= Web Service
Geoinformatics Cyber Infrastructure: Lake Erie
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. . .
Service Interaction with Cyber Infrastructure
Service Infrastructure
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Service Interaction with Cyber Infrastructure
. . .
invoke
results
Service Infrastructure
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Problem: Query Intensive Circumstances
. . .
. . .
. . .
Service Infrastructure
10
Outline
‣Accelerating Data Intensive Services Using the Cloud
•Motivating Application
•Design of an Elastic Cache
‣Performance Evaluation
•Up-Scaling (cache expansion)
•Down-Scaling (cache contraction)
‣Future Work & Conclusion
11
Designing an Elastic Cache
Compute Cloud
. . .
Service Infrastructure
A
B
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Designing an Elastic Cache. .
.
Service Infrastructure
A
BCache
Requests
Inserts
Misses
node = (k mod 2)
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Eventual Overloading. .
.
Service Infrastructure
A
BCache
Requests
Inserts
Misses
node = (k mod 2)
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Scaling up to Meet Demand. .
.
Service Infrastructure
A
B
CacheRequests
Compute Cloud
C
node = (k mod 2)
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Issues with Naive Hashing. .
.
Service Infrastructure
A
B
CacheRequests
node = (k mod 3)
C
How to incorporate node C withleast amount of “disruption?”
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. . .
A
B
75
25
8
Hash Intervals (buckets)
Distributed Hashtables (DHT)
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. . .
A
B
75
25
8
invoke:
service(35)
(35 mod 100) = 35Which proxy has the page?h(k) = (k mod 100)
h(35)
Distributed Hashtables (DHT)
A
B
75
25
8
50 COnly records hashing into (25,50] need to be moved
from A to C!
DHT to Minimize Hash Disruption when Scaling
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That’s Not Completely Elastic
‣What about relaxing the amount of nodes to help save
Cloud save costs?
‣First, we need an eviction scheme
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Exponential Decay Eviction
‣At eviction time:
•A value, , is calculated for each data record in the
evicted slice
• is higher:
- if was accessed more recently
- if was accessed frequently
•If is lower than some threshold, evict
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Outline
‣Accelerating Data Intensive Services Using the Cloud
•Motivating Application
•Design of an Elastic Cache
‣Performance Evaluation
•Up-Scaling (cache expansion)
•Down-Scaling (cache contraction)
‣Future Work & Conclusion
22
Experimental Configuration
• Application‣ Shoreline Extraction‣ Takes 23 seconds to
complete without benefits of cache
‣ Executed on a miss
‣Amazon EC2 Cloud
•Each Cloud node:
- Small Instances (Single core 1.2Ghz, 1.7GB, 32bits)
- Ubuntu Linux
•Caches start out cold
•Data stored in memory only
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Experimental Configuration
‣Our approach exploits an elastic Cloud environment:
‣We compare GBA against statically allocated Cloud
environments:
•2 fixed nodes (static-2)
•4 fixed nodes (static-4)
•8 fixed nodes (static-8)
•Cache overflow --> LRU eviction
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Relative Speedup
Querying Rate: 255 invocations/sec
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Cache Expansion/Migration Times
Querying Rate: 255 invocations/sec
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Experimental Configuration
‣Amazon EC2 Cloud
•Each Cloud node:
- Small Instance (Single core 1.2Ghz, 1.7GB, 32bits)
•Caches start out cold
•Data stored in memory
•When 2 nodes become < 30% capacity, merge
‣Sliding Window Configuration:
•Time Slice: 1 sec
•Size: 100 Time Slices
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Data Eviction: 50/255/50 queries per sec
Sliding Window Size = 100 sec
50 q/sec 255 q/sec 50 q/sec
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Cache Contraction: 50/255/50 queries per sec
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Cache Contraction: 50/255/50 queries per sec
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Experimental Summary
‣Caching Web service results reduces mean execution
times significantly for our application
‣Cloud node allocation is a huge overhead, but the cost
is amortized over average execution times
‣On average, our approach uses less nodes (and thus,
less cost) than statically allocated schemes
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Outline
‣Accelerating Data Intensive Services Using the Cloud
•Motivating Application
•Design of an Elastic Cache
‣Performance Evaluation
•Up-Scaling (cache expansion)
•Down-Scaling (cache contraction)
‣Future Work & Conclusion
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Conclusion
‣We introduced to some challenges in the Cloud:
•Controlling Cost
•Real-time system management (downscaling, upscaling)
‣We saw how the Cloud’s elasticity could be harnessed
to accelerate service-oriented processes
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Future/Current Work
34
Thank you
‣Questions and Comments?
•David Chiu - [email protected]
•Gagan Agrawal - [email protected]
In memory of Prof. Yuri Breitbart
(1940 -- 2010)
