PrEdICT: Predictive dictionary maintenance for message compression in publish/subscribe

Data usage is a significant concern, particularly in smartphone applications, M2M communications and for Internet of Things (IoT) applications. Messages in these domains are often exchanged with a backend infrastructure using publish/subscribe (pub/sub). Shared dictionary compression has been shown to reduce data usage in pub/sub networks beyond that obtained using well-known techniques, such as DEFLATE, gzip and delta encoding, but such compression requires manual configuration, which increases the operational complexity. To address this challenge, we design a new dictionary maintenance algorithm called PreDict that adjusts its operation over time by adapting its parameters to the message stream and that amortizes the resulting compression-induced bandwidth overhead by enabling high compression ratios. PreDict observes the message stream, takes the costs specific to pub/sub into account and uses machine learning and parameter fitting to adapt the parameters of dictionary compression to match the characteristics of the streaming messages continuously over time. The primary goal is to reduce the overall bandwidth of data dissemination without any manual parameterization. PreDict reduces the overall bandwidth by 72.6% on average. Furthermore, the technique reduces the computational overhead by ≈ 2× for publishers and by ≈ 1.4× for subscribers compared to the state of the art using manually selected parameters. In challenging configurations that have many more publishers (10k) than subscribers (1), the overall bandwidth reductions are more than 2× higher than that obtained by the state of the art.