outline

Evaluating Caching and Storage Options on the Amazon Web Services

Cloud

Gagan Agrawal, Ohio State University - Columbus, OH

David Chiu, Washington State University - Vancouver, WA

Presented by

Smita Vijayakumar, Juniper Networks

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Outline

‣ Introduction to Cloud Computing

‣Background on AWS and Motivation

‣Cost and Performance Evaluation

‣Conclusion

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Cloud Computing Paradigm

Cloud “Utility” Providers:Amazon AWS, Azure, Cloudera,

Google App Engine

Consumers:Companies, labs, schools, et al.

4


Algorithms& Data


Google App Engine


5


Algorithms& Data


Google App Engine


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Algorithms& Data


Google App Engine


ProcessedResults

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Promises of Cloud Computing

Allows us to consolidate

machines and outsource

computation and storage

Pay-as-you-go Computing

“Infinite” compute resources and storage

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Outline




‣Conclusion

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A Motivating Example

‣A service-oriented system that answers queries

from a similar domain

‣ Intermediate and

final results can be

cached and reused

for future queries

‣Often present in

workflow

applications

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Leveraging the Cloud for Storage

‣Store and Cache Intermediate and Final Results in the

Cloud

‣The Cloud has many options for data storage

• Memory

• Disks

• Network Disks

• Highly Available Persistent Storage

‣There are several tradeoffs in each option

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Amazon Web Services (AWS)

‣A Case study: AWS has emerged as one of the most

widely used Cloud platform

‣We consider caching and storage performance in three

AWS Services:

• Elastic Compute Cloud (EC2) Machine instances

• Simple Storage Service (S3)

• Elastic Block Storage (EBS)

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AWS Services: EC2

‣Elastic Compute Cloud (EC2)

• Access to virtualized machines with varying capabilities

(e.g., CPU cores, memory, disk space) depending on

price.

Instance Type CPU Memory Disk I/O

Small 1 virtual core 1.7GB 160GB medium

XLarge 4 virtual cores(x 2 compute units ea)

15.0GB 1.7TB high

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AWS Services: EBS

‣Elastic Block Storage (EBS)

• Persisted network disks.

• Must be mounted onto EC2 machine before use.

• Users must initially specify a fixed size and format to

appropriate file system.

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AWS Services: S3

‣Simple Storage Service (S3)

• Simple FTP-style API: GET, PUT, etc.

• Highly available, reliable, and durable storage (but

slower)

• “Infinite capacity”

• Not required to be used with EC2 machines.

• Very inexpensive in terms of costs.

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Costs of AWS Services

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Tradeoffs Per Application and Service

‣Caching in-core (EC2-Memory)

• Fast, but expensive

• Small, may need extra logic to coordinate set of EC2

nodes

• Data is volatile

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‣Caching on local disk (EC2-Disk)

• Much slower than memory

• Much more space

• Data is still volatile

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‣Caching on Elastic Block Store (EC2-EBS)

• Possibly slower than disk

• Volume size is initially configured by application users

• Data is persisted

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‣Caching on S3

• Slowest option, but most reliable

• No bound on size

• Data is persisted

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Outline




‣Conclusion

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Experimental Application

‣Geospatial Application: Land Elevation Change

• In general, 2 large matrices (DEM files) are retrieved, and their

difference is returned

‣500 unique requests

‣Requests are issued randomly

‣Eviction not considered (we assume cache/storage configuration

is being used to store all results)

Smita

Do we need this line?

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Performance

‣We use 4 different DEM data sizes to test performance:

• 1KB, 1MB, 5MB, 50MB

‣This means a full cache would hold

• 500KB, 500MB, 2.5GB, 25GB

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1KB DEM Size

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1MB DEM Size

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5MB DEM Size

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50MB DEM Size

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Cost Analysis

‣We next assess the costs versus the performance

‣Performance is being measured as relative speedup over

the baseline DEM process execution, shown in Table 2

‣We project costs and speedup over 2000 and 200000

requests

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Monthly Costs for Volatile Cache (1MB)

200000 I/O Requestsoutside of AWS


Cost per unit speedup is low when requests are high.

I/O costs are still low because of small data size

3.5 3.26 3.6 3.6 267 28 347 180.5Speedup

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Monthly Costs for Volatile Cache (50MB)



Costs are now dominated by I/O due to large data size

In terms of performance, makes more sense to use xlarge for large data size

2.9 3.3 16.05 31.66Speedup

small instance makes better economic sense for small number of requests

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Monthly Costs for Persistent Cache (1MB)



S3 makes better economic sense than EBS-based instances

3.4 3.62 3.58 30 13.6 134Speedup

S3 performance is comparable for a cache with small I/O requests

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Monthly Costs for Persistent Cache (50MB)



Interesting - Even with low cost of S3, it still makes sense to use xlarge when I/O requests are high

2.59 2.74 3.19 6.4 11.09 22.66Speedup

S3 still comparable, and makes better economic sense than EBS-based instances

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Outline




‣Conclusion

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Summary (1)

‣For smaller data (<= 5MB)

• If request rate is low: Use small instance on-disk

• If request rate is high: Use small instance in-memory

• Although I/O is slow, the cost of using small instance is

very low

‣ If persistence is needed,

• Use S3, and avoid EBS

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Summary (2)

‣For larger data (>= 50MB and large cache sizes)

• Use xlarge instances

• Higher I/O rates

• Larger memory and disk capacity

‣EBS may be considered in conjunction to XLarge

instances for persistence

‣ If performance is not an issue, but persistence and

costs are, use S3

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Conclusion

‣Cloud offers many viable options for data storage and

caching

‣We evaluated the cost-performance tradeoffs of these

various options, and determined a roadmap for making

clear decisions on resource usage

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Thank you

‣Questions and Comments?

• David Chiu - [email protected]

• Gagan Agrawal – [email protected]

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