Thesis Topic Details

Topic ID:
3519
Title:
Predicting Success Likelihood of Cloud Operations
Supervisor:
Ingo Weber
Research Area:
Cloud Computing, Distributed Systems, Business Process Management
Associated Staff
Assessor:
Len Bass
Topic Details
Status:
Active
Type:
R & D
Programs:
CS CE SE
Group Suitable:
Yes
Industrial:
Yes
Pre-requisites:
COMP9322 or COMP9423
Description:
With developments such as virtualization and cloud computing, system operation (such as installation, deployment, upgrading) has become a significantly more complex task: an operator might be responsible for thousands of machines, which are built and connected in ever more complex ways. Therefore it is important to support operators to make sure that, e.g., an upgrade process is executing correctly and has the desired result.

Our work thus is concerned with (i) discovering how processes are executed for log files, and (ii) making sure a running process corresponds to the correct execution. Initial works of ours have been published - see below.

Predicting success likelihood in this context is envisaged as follows: from partial executions of a process, e.g. when done manually by an operator, we want to be able to tell the operator how likely he is to achieve his goal. This may help to prevent hours of unnecessary work or major outages.

In the context of this work, there are numerous open topics for future research, see
http://ssrg.nicta.com.au/students/theses.pml#ug-IW
/ other topics in the database supervised by me.

Previous publication:
- Sherry Xu, Ingo Weber, Hiroshi Wada, Len Bass, Liming Zhu and Steve Teng. Detecting Cloud Provisioning Errors Using an Annotated Process Model. 2nd Workshop on Secure and Dependable Middleware for Cloud Monitoring and Management, pp. 6, Beijing, China, December, 2013.
Comments:
Students will work closely with senior researchers at National ICT Australia (NICTA) in a very friendly, diverse team environment. Suitable for students interested in software design, architecture, and practical industry development methods.
Students will be exposed to latest cloud technologies and advanced methods from business process management / process mining.
Past Student Reports
 
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