Subscription Normalization for Effective Content-Based Messaging

Efficient subscription summarization and event matching is key to the scalability of content-based publish/subscribe networks (CPSNs). Current summarization and event matching mechanisms based on subscription subsumption induce heavy event processing load on brokers degrading the performance of CPSNs especially under high rates of churn, i.e., addition, deletion, or modification of subscriptions. Yet, many modern CPS applications such as location-based services or algorithmic trading inherently rely on high frequency subscription changes. This paper describes Beretta, a dynamic CPSN which sustains high throughput and low event-propagation latencies even under a high frequency of subscription changes. Beretta leverages strong event typing and represents all subscriptions in a normalized form as combinations of value intervals and set inclusions without compromising on expressiveness. Beretta’s “split and subsume” broker algorithm reduces the complexity of matching an event from $O(K\,N)$ to $O(K\,\log \,N + |result|)$, with $N$ being the number of subscriptions for the event type and $K$ the number of its attributes. Event types and normalization are exploited to split subscriptions into predicates on individual event types and attributes and to efficiently regroup these in segment trees and hash maps which yield excellent subsumption properties and support attribute-wise split filtering during event matching. Normalization enables the systematic introduction of parameters into subscriptions to support both parametric and structural updates. This paper also empirically demonstrates the performance improvements due to our techniques through realistic algorithmic trading and highway traffic monitoring benchmarks.