The performance of the execution of an analytical workload critically impacts the speed at which companies are able to react to market changes. In the era of Big Data, it is imperative that large, complex analytics are executed in a timely manner. In this paper, we propose a method to analyze the data access pattern of analytical workloads on large datasets to identify optimal data partitioning and replication strategies. This, in turn, helps the already existing query optimization components of modern data management systems.
Adaptive replication strategy in highly distributed data management systems
Bottoni S.;Trombetta A.;
2022-01-01
Abstract
The performance of the execution of an analytical workload critically impacts the speed at which companies are able to react to market changes. In the era of Big Data, it is imperative that large, complex analytics are executed in a timely manner. In this paper, we propose a method to analyze the data access pattern of analytical workloads on large datasets to identify optimal data partitioning and replication strategies. This, in turn, helps the already existing query optimization components of modern data management systems.
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