This invention discloses a cross-platform
big data resource optimization method and
system based on
cloud computing, relating to the fields of
cloud computing and
big data technology. It includes: S1, collecting raw indicator data from different cloud service providers, different
virtualization technologies, and different
big data frameworks, and simultaneously extracting descriptive information corresponding to each raw indicator data. The descriptive information includes the indicator name, resource type, statistical
caliber, aggregation method, and collection source. This invention constructs a unified resource indicator semantic
library by collecting raw indicators and descriptive information from multiple sources to achieve indicator semantic
parsing and mapping, eliminating semantic
ambiguity between indicators on different platforms; it compares and synchronously calibrates the time of each monitoring source by marking and calibrating the time base; it unifies the indicator
time step through sampling feature normalization
processing, and then constructs accurate task-level resource profiles with coverage, consistency, and integrity labels according to task instances and stages.