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Geo-distributed data analysis in a cloud-edge system is emerging as a dailydemand. Out of saving time in wide area data transfer, some tasks are dispersedto the edges. However, due to limited computing, overload interference andcluster-level unreachable troubles, efficient execution in the edges is hard,which obstructs the guarantee on the efficiency and reliability of jobs.Launching copies across clusters can be an insurance on a task's completion.Considering cluster heterogeneity and accompanying remote data fetch, clusterselection of copies affects execution quality, as different insuring plansdrive different revenues. For providing On-Line-Real-Time analysis results, asystem needs to insure the geo-distributed resource for the arriving jobs. Ourchallenge is to achieve the optimal revenue by dynamically weighing the gainsdue to insurance against the loss of occupying extra resource for insuring.  To this end, we design PingAn, an online insurance algorithm promising$(1+\varepsilon)\!\!-\!speed \o(\frac{1}{\varepsilon^2+\varepsilon})\!\!-\!competitive$ in sum of the jobflowtimes via cross-cluster copying for tasks. PingAn shares resource among theanterior fraction of jobs with the least unprocessed datasize and the fractionis adjustable to fit the system load condition. After sharing, PingAnconcretely insures for tasks following efficiency-first reliability-awareprinciple to optimize the revenue of copies on jobs' performance. Trace-drivensimulations demonstrate that PingAn can reduce the average job flowtimes by atleast $14\%$ than the state-of-the-art speculation mechanisms. We also buildPingAn in Spark on Yarn System to verify its practicality and generality.Experiments show that PingAn can reduce the average job flowtimes by up to$40\%$ comparing to the default Spark execution.

PingAn: An Insurance Scheme for Job Acceleration in Geo-distributed Big Data Analytics System