A Survey on Deep Reinforcement Learning for Data Processing and Analytics

In the age of big data, data processing and analytics are fundamental, ubiquitous, and crucial to many organizations which undertake a digitalization journey to improve and transform their businesses and operations. Data analytics typically entails other key operations such as data acquisition, data cleansing, data integration, modeling, etc., before insights could be extracted. Big data can unleash significant value creation across many sectors such as health care and retail[56]. However, the complexity of data (e.g., high volume, high velocity, and high variety) presents many challenges in data analytics and hence renders the difficulty in drawing meaningful insights. To tackle the challenge and facilitate the data processing and analytics efficiently and effectively, a lot of algorithms and techniques have been designed and numerous learning systems have also been developed by researchers and practitioners such as Spark MLlib[63], and Rafiki[104]. To support fast data processing and accurate data analytics, a huge number of algorithms rely on rules that are developed based on human knowledge and experience. For example, Shortest-job-first is a scheduling algorithm that chooses the job with the smallest execution time for the next execution. However, without fully exploiting characteristics of the workload, it can achieve inferior performance compared to DRL-based scheduling algorithm [58].