TY - GEN
T1 - Supporting Undoability in Systems Operations
AU - Weber, Ingo
AU - Wada, Hiroshi
AU - Fekete, Alan
AU - Liu, Anna
AU - Bass, Len
N1 - Publisher Copyright:
© LISA 2013.
PY - 2013
Y1 - 2013
N2 - When managing cloud resources, many administrators operate without a safety net. For instance, inadvertently deleting a virtual disk results in the complete loss of the contained data. The facility to undo a collection of changes, reverting to a previous acceptable state, is widely recognized as valuable support for dependability. In this paper, we consider the particular needs of the system administrators managing API-controlled resources, such as cloud resources on the IaaS level. In particular, we propose an approach which is based on an abstract model of the effects of each available operation. Using this model, we check to which degree each operation is undoable. A positive outcome of this check means a formal guarantee that any sequence of calls to such operations can be undone. A negative outcome contains information on the properties preventing undoability, e.g., which operations are not undoable and why. At runtime we can then warn the user intending to use an irreversible operation; if undo is possible and desired, we apply an AI planning technique to automatically create a workflow that takes the system back to the desired earlier state. We demonstrate the feasibility and applicability of the approach with a prototypical implementation and a number of experiments.
AB - When managing cloud resources, many administrators operate without a safety net. For instance, inadvertently deleting a virtual disk results in the complete loss of the contained data. The facility to undo a collection of changes, reverting to a previous acceptable state, is widely recognized as valuable support for dependability. In this paper, we consider the particular needs of the system administrators managing API-controlled resources, such as cloud resources on the IaaS level. In particular, we propose an approach which is based on an abstract model of the effects of each available operation. Using this model, we check to which degree each operation is undoable. A positive outcome of this check means a formal guarantee that any sequence of calls to such operations can be undone. A negative outcome contains information on the properties preventing undoability, e.g., which operations are not undoable and why. At runtime we can then warn the user intending to use an irreversible operation; if undo is possible and desired, we apply an AI planning technique to automatically create a workflow that takes the system back to the desired earlier state. We demonstrate the feasibility and applicability of the approach with a prototypical implementation and a number of experiments.
UR - http://www.scopus.com/inward/record.url?scp=85094809286&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85094809286
T3 - 27th Large Installation System Administration Conference, LISA 2013
SP - 75
EP - 87
BT - 27th Large Installation System Administration Conference, LISA 2013
PB - USENIX Association
T2 - 27th Large Installation System Administration Conference, LISA 2013
Y2 - 3 November 2013 through 8 November 2013
ER -