Scheduling Decisions: Data Locality and Multitenancy

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Covers the Spark runtime architecture, including RDDs, transformations, actions, and caching for performance optimization.

Introduces the Simplex algorithm for solving flow problems and handling negative cost cycles.

Covers the linear programming approach to reinforcement learning, focusing on its applications and advantages in solving Markov decision processes.

Covers deriving basic linear program representation, finding solutions, and exploring optimality.

Covers advanced data wrangling techniques using Hadoop, focusing on Hive and HBase integration.

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Explores dynamic programming with the Steinitz sequence to optimize solutions efficiently.

Covers the process of solving Linear Programs (LPs) using the simplex method.

Explores finding the initial Basic Feasible Solution (BFS) in a linear program.

Covers Farkas' lemma, exploring the relationship between linear programs and the conditions for its validity.

Explores the complexities of scheduling in distributed computing frameworks, emphasizing data locality optimization and multitenancy strategies.

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