Model-free control for distributed stream data processing using deep reinforcement learning

TL;DR: A literature review of the application of RL to a variety of fields, including robotics and autonomous control, communication and networking, natural language processing, games and self-organized system, scheduling management and configuration of resources, and computer vision is presented.

TL;DR: This paper proposes an adaptive resource allocation approach for microservice workflow system based on recent advances in reinforcement learning and demonstrates that it outperforms existing resource allocation approaches with read-world emulated workflows.

TL;DR: In this article , the authors formulate the job scheduling problem of a cloud-deployed Spark cluster and propose a novel reinforcement learning (RL) model to accommodate the SLA objectives.

TL;DR: A deep reinforcement learning framework, named as DeepAPP, which learns a model-free predictive neural network from historical app usage data and reduces the prediction time of the state-of-the-art by 6.58× is presented.

TL;DR: This book presents dozens of algorithms and implementation examples, all in pseudo-code and suitable for use in real-world, large-scale data mining projects, and provides a comprehensive, practical look at the concepts and techniques you need to get the most out of real business data.

TL;DR: In practice, this paper finds that MillWheel's unique combination of scalability, fault tolerance, and a versatile programming model lends itself to a wide variety of problems at Google.

TL;DR: This work proposes a method to choose the initial state to obtain uniqueness and to remove transients at both ends in forward-backward filtering.

TL;DR: The combination of high-level abstractions for parallel data and computation, deferred evaluation and optimization, and efficient parallel primitives yields an easy-to-use system that approaches the efficiency of hand-optimized pipelines.