Optimization algorithms for energy-efficient data centers
Hendrik F. Hamann
InterPACK 2013
Partitioning is effective in avoiding expensive shuffling operations. However, it remains a significant challenge to automate this process for Big Data analytics workloads that extensively use user defined functions (UDFs), where sub-computations are hard to be reused for partitionings compared to relational applications. In addition, functional dependency that is widely utilized for partitioning selection is often unavailable in the unstructured data that is ubiquitous in UDF-centric analytics. We propose the Lachesis system, which represents UDF-centric workloads as workflows of analyzable and reusable sub-computations. Lachesis further adopts a deep reinforcement learning model to infer which sub-computations should be used to partition the underlying data. This analysis is then applied to automatically optimize the storage of the data across applications to improve the performance and users’ productivity.
Hendrik F. Hamann
InterPACK 2013
Khaled A.S. Abdel-Ghaffar
IEEE Trans. Inf. Theory
Matthias Kaiserswerth
IEEE/ACM Transactions on Networking
Minkyong Kim, Zhen Liu, et al.
INFOCOM 2008