San Francisco
June 30 - July 2, 2014

Spark Summit 2014 brought the Apache Spark community together on June 30- July 2, 2014 at the The Westin St. Francis in San Francisco. It featured production users of Spark, Shark, Spark Streaming and related projects.


Spark Summit 2014
Distributed Reinforcement Learning for Electricity Market Bidding with Spark
Dr. Vijay Srinivas Agneeswaran, Dr. Vishnuteja Nanduri (Impetus)

A majority of the electricity in the U.S. is traded in independent system operator (ISO) based wholesale markets. ISO-based markets typically function in a two-step settlement process with day-ahead (DA) financial settlements followed by physical real-time (spot) market settlements for electricity. In this work, we focus on obtaining equilibrium bidding strategies for electricity generators in DA markets. Electricity prices in DA markets are determined by the ISO, which matches competing supply offers from power generators with demand bids from load serving entities. Since there are multiple generators competing with one another to supply power, this can be modeled as a competitive Markov decision problem, which we solve using a reinforcement learning approach. For power networks of realistic sizes, the state-action space could explode, making the RL procedure computationally intensive. This has motivated us to solve the above problem over Spark. The talk provides the following takeaways:

1. Modeling the day-ahead market as a Markov decision process
2. Code sketches to show the markov decision process solution over Spark and Mahout over Apache Tez
3. Performance results comparing Mahout over Apache Tez and Spark.

Dr. Vijay Srinivas Agneeswaran has a Bachelor’s degree in Computer Science & Engineering from SVCE, Madras University (1998), an MS (By Research) from IIT Madras in 2001 and a PhD from IIT Madras (2008). He was a post-doctoral research fellow in the LSIR Labs, Swiss Federal Institute of Technology, Lausanne (EPFL) for a year. He has spent the last seven years creating intellectual property and building products in the big data area in Oracle, Congizant and Impetus, where he is now Director, Big Data Labs. He has worked on PMML support for Spark/Storm and is building a product for a role-based fine-grained access control inside of Hadoop YARN. He is a professional member of the ACM and the IEEE for the last 8+ years. He has filed patents with US and European patent office’s (with two issued US patents) and published in leading journals and conferences, including IEEE transactions. His research interests include distributed systems – cloud, grid, peer-to-peer computing as well as machine learning for Big-Data and other emerging technologies.

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