MespaConfig: Memory-Sparing Configuration Auto-Tuning for Co-Located In-Memory Cluster Computing Jobs

Distributed in-memory computing frameworks usually have lots of parameters (e.g., the buffer size of shuffle) to form a configuration for each execution. A well-tuned configuration can bring large improvements of performance. However, to improve resource utilization, jobs are often share the same cl...

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