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Configuration space complexity makes the big-data software systems hard to configure well. Consider Hadoop, with over nine hundred parameters, developers often just use the default configurations provided with Hadoop distributions. The opportunity costs in lost performance are significant. Popular learning-based approaches to auto-tune software does not scale well for big-data systems because of the high cost of collecting training data. We present a new method based on a combination of Evolutionary Markov Chain Monte Carlo (EMCMC)} sampling and cost reduction techniques tofind better-performing configurations for big data systems. For cost reduction, we developed and experimentally tested and validated two approaches: using scaled-up big data jobs as proxies for the objective function for larger jobs and using a dynamic job similarity measure to infer that results obtained for one kind of big data problem will work well for similar problems. Our experimental results suggest that our approach promises to improve the performance of big data systems significantly and that it outperforms competing approaches based on random sampling, basic genetic algorithms (GA), and predictive model learning. Our experimental results support the conclusion that our approach strongly demonstrates the potential toimprove the performance of big data systems significantly and frugally.
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Ignem: Upward Migration of Cold Data in Big Data File Systems
This paper investigates whether migrating cold data can yield significant speedup for big data jobs that run on modern big data file systems. Our work is motivated by two observations. First, improving the input stage of a job can provide significant speedup because many jobs spend a large part of their execution reading inputs. The second observation is that the inputs for many jobs are cold. Common techniques that aim to keep hot data in memory do not benefit these jobs. We analyze the Google production cluster trace data and find that the key ingredients for effectively migrating cold data do exist in such production environments. Encouraged by our findings, we design and implement Ignem, a framework for migrating cold data in big data file systems. We evaluate Ignem in a series of experiments and show that it provides significant speedup for both small and large jobs. Specifically, Hive queries are accelerated by up to 34%; the mean job duration in a tracedriven workload is reduced by 12% and the task duration by nearly 40%; other standalone jobs such as sort and wordcount also improve similarly by up to 30%.
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- Award ID(s):
- 1718980
- PAR ID:
- 10058538
- Date Published:
- Journal Name:
- 8th IEEE International Conference on Distributed Computing Systems (ICDCS 2018)
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
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