Conclusions and Future Work

This chapter presented our initial study toward the design and implementa- tion of a tool that can provide latency-guarantee for MapReduce jobs in the presence of failures. The tool is a combination of two strategies which aim at masking the latency effect of failures at different levels. First, the tool attempts to mask the latency-effect in each single parallel job that suffers the failure. When the first strategy fails, the second strategy is applied to mask the latency by increasing resources for other parallel jobs in the next phases of the DAG jobs.

For each strategy we discussed a completion time estimation model. In future work, we use this model to build an inverse problem, with which we can reactively obtain estimates of the resource allocation for MapReduce jobs given their deadlines, after failure occurrence.

We believe that our solutions will result in a better overall resource uti- lization for MapReduce jobs frameworks.

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