Thesis Topic Details

Topic ID:
Generic Undo Framework for Cloud Management
Ingo Weber
Research Area:
Cloud Computing, Distributed Systems, Artificial Intelligence
Associated Staff
Hiroshi Wada
Topic Details
R & D
Group Suitable:
COMP9322 or COMP9423
With the advent of cloud computing and related developments, more and more capabilities become available as APIs. For instance, instead of ordering a new server, waiting a few weeks, and installing the new server in one.s network, nowadays a few API calls suffice to get hold of a new server in a public cloud. While such powerful APIs can provide enormous increases in productivity and time-to-solution, they open new possibilities significant mishaps . e.g., if an administrator inadvertently deletes a virtual disk, all of the contained data is irrecoverably lost. In essence, many administrators operate without a safety net.

In our work, we investigate the undoability of changes. On the one hand, we can check which operations can be undone, and under which circumstances. On the other hand, if undo is required, we can find a sequence of operations that brings a system back to a previously defined, desirable state: a checkpoint. Both techniques make use of Artificial Intelligence (AI) planning, and have been published - see below.

This particular topic addresses the problem of how to best extend the current undo approach and tool into a generic undo framework for cloud management. The current undo tool addresses specific resource types, mostly part of Amazon Web Services EC2. The existing work needs to be extended into a controller of a general-purpose undo framework, which can be easily extended through plugins. These plugins could provide integrated checkpointing / undo across various systems, e.g., internal configuration management of applications, snapshotting and restoring virtual harddrives, software-defined networks, etc.

In the context of this work, there are numerous open topics for future research, see
/ other topics in the database supervised by me.

Previous publications:
- Ingo Weber, Hiroshi Wada, Alan Fekete, Anna Liu and Len Bass. Supporting undoability in systems operations. USENIX Large Installation System Administration Conference (LISA), 2013.
- Ingo Weber, Hiroshi Wada, Alan Fekete, Anna Liu and Len Bass. Automatic undo for cloud management via AI planning. Workshop on Hot Topics in System Dependability, 2012.
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.
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