  Provenance is the derivation history of information about the origin of data
and processes. For a highly dynamic system such as the cloud, provenance must
be effectively detected to be used as proves to ensure accountability during
digital forensic investigations. This paper proposes active-threaded provenance
cognition algorithms that ensure effective and high speed detection of
provenance information in the activity layer of the cloud. The algorithms also
support encapsulation of the provenance information on specific targets.
Performance evaluation of the proposed algorithms reveal mean delay of 8.198
seconds that is below the pre-defined benchmark of 10 seconds. Standard
deviation and cumulative frequencies for delays are found to be 1.434 and 45.1%
respectively.
