PingAn: An Insurance Scheme for Job Acceleration in Geo-distributed Big Data Analytics System
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Geo-distributed data analysis in a cloud-edge system is emerging as a daily demand. Out of saving time in wide area data transfer, some tasks are dispersed to the edge clusters satisfied data locality. However, execution in the edge clusters is less well, due to limited resource, overload interference and cluster-level unreachable troubles, which obstructs the guarantee on the speed and completion of jobs. Synthesizing the impact of cluster heterogeneity and costly inter-cluster data fetch, we expect to make effective copies across clusters for tasks to provide both success and efficiency of the arriving jobs. To this end, we design PingAn, an online insurance algorithm making redundance across-cluster copies for tasks, promising (1+ε)-speed o(1/ε^2+ε)-competitive in sum of the job flowtimes. PingAn shares resource among a part of jobs with an adjustable ε fraction to fit the system load condition and insures for tasks following efficiency-first reliability-aware principle to optimize the effect of copies on jobs' performance. Trace-driven simulations demonstrate that PingAn can reduce the average job flowtimes by at least 14% more than the state-of-the-art speculation mechanisms. We also build PingAn in Spark on Yarn System to verify its practicality and generality. Experiments show that PingAn can reduce the average job completion time by up to 40% comparing to the default Spark execution.
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