Rapid Processing of Synthetic
Seismograms using Windows Azure
Cloud
Vedaprakash Subramanian, Liqiang Wang
Department of Computer Science
University of Wyoming
En-Jui Lee, Po Chen
Department of Geology and Geophysics
University of Wyoming
1
Introduction
• Scientific applications
– Large amount of computing resources
– Massive storage for datasets
• Traditionally Handled by HPC Clusters
– But Cost-ineffective
• Claim: Cloud Computing is a better substitute
2
… more
• Conduct numerical simulation of synthetic
seismograms
• Implemented a system on Azure cloud
– to generate synthetic seismograms on the fly
based on user queries
– to deliver them in real-time
3
Synthetic Seismograms
• Seismogram is a record of the ground shaking
• Synthetic seismograms are generated by
solving seismic wave – equation
• Method is rapid centroid moment tensor
(CMT) inversion method
– Based on 3D velocity model
• To increase efficiency, we store Receiver Green
Tensors strain fields
– Generated at the receiver’s location
4
… more
• The input parameters for this wave-equation are
– latitude, longitude and depth of the earthquake
locations
– strike, dip, and rake (source parameters)
5
… more
Why synthetic seismograms is
important?
• Helps to map the Earth’s internal structure
• Locate and measure the size of different seismic sources
• For realistic interpretation of structures
• Seismic Hazard Analysis
6
Windows Azure
7
Windows Azure
• Windows Azure is Platform as a service
architecture
• Provides
– Scalable cloud operating system
– Data storage system
• User controls the hosted application
• User cannot control the underlying
infrastructure
8
Service Architecture
• Azure service consists of :
– Web role
• For web application
• For user interface
– Worker role
• For generalized development
• For background processing
• Roles are virtual machines
• Say, 2 instances of worker role = 2 virtual
machines running the code of worker role
Storage Abstractions
• Blobs
• Tables
• Queues
• Drives
• Our system uses only first three
Blob Storage
• Blobs
– Interface for storing files
– Two types namely Page blobs and Block blobs
– Containers for grouping
Account
Container
Blob
File1 txt
California
Geo
Texas
File2 txt
File1 txt
Table Storage
• Tables
– Structured storage
– Consists set of entities, which contain a set of
properties
– Partition key and Row key
Account
Table
Customers
Geo
Photos
Entity
Name = …
Email = …
Name = …
Email = …
Photo Id = …
Date = …
Queue Storage
• Queues
– Reliable storage and delivery of messages
– Communication between roles
Account
Queue
Message
ID = ……
Geo
Orders
ID = …
Load Balancing
• Azure master system
– Automatically load balance based on the partition
key
• Partition key for various storage abstractions
– Blobs – Container Name + Blob Name
– Entities – Table Name + Partition Key
– Messages – Queue Name
14
Implementation of the System
15
Overview of the System
• The architecture of the system :
– Web Role
• User interface
– Job Manager
• Coordinate the computation
• Monitor the system
User
Computation
Input Queue
Request Queue
Computation
Output Queue
Windows Azure Storage (Blob, Table, Queue)
… more
– Computation Worker Role
• Computation stuff
– 3 Azure Queues
• Communication interface between the roles
User
Computation
Input Queue
Request Queue
Computation
Output Queue
Windows Azure Storage (Blob, Table, Queue)
Web Role
User
• Receive the user input
• Place the request as message in request queue
Request Queue
Windows Azure Storage (Blob, Table, Queue)
Job Manager
•
•
•
•
Retrieve the message from request queue
Read the job
Divide the job into sub-jobs
Place the sub-jobs as message in inputcomputation queue
Computation
Input Queue
Request Queue
Computation
Output Queue
Windows Azure Storage (Blob, Table, Queue)
Coordinate the Computation
•
•
•
•
•
Num of computation worker roles : 5
Num of CPU cores in each instance : 8
Input : 1000 source locations
Num of queue messages : 1000 / 8 = 125
125 queue messages served by 5 computation
worker roles
• Performance gain factor (Theoretical)
= Num of CPU cores * Num of instances
= 8 * 5 = 40
Inside Computation Worker Role
• Retrieve the message
• Process the sub-jobs in parallel using .NET Task
Parallel Library
• Write the result
• Send message stating job completed
Computation
Input Queue
Computation
Output Queue
Windows Azure Storage (Blob, Table, Queue)
Monitor System Response
• Based on the response time of the message
• Threshold response time is 2ms
• If exceeds
– Allocate a new instance of computational worker
role
– Maintain its detail
… more
• De-allocate if
– any new instance has been allocated
– and its lifetime > one hour
– and no message in the queue
Monitor System Response
Request to allocate VM
msg
msg
msg
Computation Input Queue
Distributed File System
msg
Job Manager
• Allocation & de-allocation are asynchronous
• So, mutual exclusion lock are enforced
between
– Job assignment
– De-allocation of an instance
Data Storage
• Seismic wave observations stored as
data file
Point (40’59’’N, 122’7’’W)
4059-1227
• Each data file is represented by its
own latitude and longitude
• So, blob name = latitude + longitude
• For grouping the blobs, the region of
California is divided into blocks
– based on the seismic wave observation
stations
26
… more
• Currently 4096 stations = 4096
blocks
35’25’’N, 119’3’’W
35’25’’N, 116’47’’W
• Each block is characterized by
range of latitude and longitude
34’51’’N, 119’3’’W
• So, block identification number =
range of latitude + longitude
34’51’’N, 116’47’’W
3525-3451-1193-11647
• Container name = identification
number
27
… more
• Divide California region
into 16 parts
• 1 Table for 1 part
• Store list of blocks in the
part into the table
• Helps in better retrieval
28
Data Query Algorithm
• Locate blob corresponding to the given point
– Retrieve the container name and blob name
• Retrieve container name from the table
– Locate the table
– Do a linear search inside the table
• Blob name = latitude + longitude
29
Data Query Algorithm
41’43’’N, 124’6’’W
41’43’’N, 120’43’’W
40’85’’N, 123’56’’W
40’85’’N, 122’52’’W
40’59’’N, 122’7’’W
Point
40’27’’N, 123’56’’W
39’11’’N, 124’6’’W
40’27’’N, 122’52’’W
39’11’’N, 122’52’’W
40’27’’N, 124’6’’W
39’11’’N, 120’43’’W
30
Experiment
31
Experiment
• Performance was evaluated on
– various configurations and number of instances of
computational worker role
– datasets from different number of seismic wave
observation
32
Performance Measurement –
Execution Time
Single Worker (4 core)
Execution Time (seconds)
1000
900
Four Worker (4 core)
800
Single worker (4 core) +
TPL
Four Worker (4 core) + TPL
700
600
500
Two worker (8 core) + TPL
400
300
200
100
0
0 100 200 300 400 500 600 700 800 900 1000
Number of stations
33
Conclusion
34
Conclusion
• Implemented the system on Windows Azure
• Hence Cloud is a better substitute
• Future Work :
– Add Seismic Hazard Analysis feature to the system
35
Thanks
36