Abstract
A Data Processing Network (DPN) streams massive volumes of data collected and stored by the network to multiple processing units to compute desired results in a timely fashion. Due to ever-increasing traffic, distributed cache nodes can be deployed to store hot data and rapidly deliver them for consumption. However, prior work on caching policies has primarily focused on the potential gains in network performance, e.g., cache hit ratio and download latency, while neglecting the impact of cache on data processing and consumption.In this paper, we propose a novel framework, DeepChunk, which leverages deep Q-learning for chunk-based caching in DPN. We show that cache policies must be optimized for both network performance during data delivery and processing efficiency during data consumption. Specifically, DeepChunk utilizes a model-free approach by jointly learning limited network, data streaming, and processing statistics at runtime and making cache update decisions under the guidance of powerful deep Q-learning. It enables a joint optimization of multiple objectives including chunk hit ratio, processing stall time, and object download time while being self-adaptive under the time-varying workload and network conditions. We build a prototype implementation of DeepChunk with Ceph, a popular distributed object storage system. Our extensive experiments and evaluation demonstrate significant improvement, i.e., 43% in total reward and 39% in processing stall time, over a number of baseline caching policies.