1. Cloud Resilience System
By: Arij Alfaidi

2. Main Goal
To make the “context switching time” between two VMs shortest.
If one server is not working the other server should cover it up.
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aalfaidi/CRS

3. Main Idea
Adopting mirror processes (cloud service) on both Virtual Machines, and context switcher process (cloud server), splitting large file R/W operations into small sized cloud block R/W operations.
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4. Cloud Server ( Data Broker )
Cloud Service
Cloud Service
Cloud Server ( Data Broker )
Cloud Client
Client PC
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5. Main commands supported by the cloud clientNO
Cloud Client
Description 1
Help
List supported shell commands and their usage 2
List
List filenames that the cloud system kept
If a special filename is given as parameter, its size will be returned 3
Get
Download a file from the cloud system to local storage of the client machine 4
Put
Upload a file to the cloud system from the local storage of the client machine 5
Quit
Quit remote file manager shell
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6. Data flow diagram of the Cloud system
LIST, INFO, READ, WRITE request
(Type, Size, Offset, Filename)
Client
Shell >
Request
Relay the requests, resort the results and return it to the client
If INFO/READ operation returned, skip other cloud machines.
Cloud machine IP list
Server
Accept the request and treat it with file operations
Return result or error code if fails
Request
Request
Service1
Service2
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7. Request packet data structure shared in the cloud system
Type
Size
Offset
Path name
LIST : ‘L’
INFO: ‘I’
GET : ’R’
PUT : ’W’
4096 8
<= 4096
As file operation
“empty”
File name 1B 4B 8B
Max? 240B
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8. Implementation constraints
Cloud client, server, and services are using TCP socket for safe communication.
Each socket client has time constraints about 2~6 seconds.
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9. Testing
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10. References
Abhinav Mishra and Anil Kumar, “Context Switching In Clouds: Analysis And Enhancement”, International Journal of Engineering Research & Technology (IJERT) Vol. 2 Issue 7, July
David Linthicum, “Build a cloud redundancy strategy using multiple providers”
Luqun Li, “An Optimistic Differentiated Service Job Scheduling System for Cloud Computing Service Users and Providers”, 2009 IEEE, DOI /MUE
Reese, G., “Cloud Application Architectures: Building Applications and Infrastructure in the cloud (Theory in Practice)”, O„Reilly Media, 1st Ed., 2009.
Xing Pu, Ling Liu, YiduoMei, SankaranSivathanu, YounggyunKoh, CaltonPu, “Understanding Performance Interference of I/O Workload in Virtualized Cloud Environments”, 2010 IEEE DOI /CLOUD
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