Declarative Transport: No More Transport Protocols to Design, Only Policies to Specify

Transport protocols are an integral part of the inter-process communication (IPC) service used by application processes to communicate over the network infrastructure. With almost 30 years of research on transport, one would have hoped that we have a good handle on the problem. Unfortunately, that is not true. As the Internet continues to grow, new network technologies and new applications continue to emerge putting transport protocols in a never-ending flux as they are continuously adapted for these new environments. In this work, we propose a clean-slate transport architecture that renders all possible transport solutions as simply combinations of policies instantiated on a single common structure. We identify a minimal set of mechanisms that once instantiated with the appropriate policies allows any transport solution to be realized. Given our proposed architecture, we contend that there are no more transport protocols to design—only policies to specify. We implement our transport architecture in a declarative language, Network Datalog (NDlog), making the specification of different transport policies easy, compact, reusable, dynamically configurable and potentially verifiable. In NDlog, transport state is represented as database relations, state is updated/queried using database operations, and transport policies are specified using declarative rules. We identify limitations with NDlog that could potentially threaten the correctness of our specification. We propose several language extensions to NDlog that would significantly improve the programmability of transport policies.

Is a Detector Only Good for Detection?

A common design of an object recognition system has two steps, a detection step followed by a foreground within-class classification step. For example, consider face detection by a boosted cascade of detectors followed by face ID recognition via one-vs-all (OVA) classifiers. Another example is human detection followed by pose recognition. Although the detection step can be quite fast, the foreground within-class classification process can be slow and becomes a bottleneck. In this work, we formulate a filter-and-refine scheme, where the binary outputs of the weak classifiers in a boosted detector are used to identify a small number of candidate foreground state hypotheses quickly via Hamming distance or weighted Hamming distance. The approach is evaluated in three applications: face recognition on the FRGC V2 data set, hand shape detection and parameter estimation on a hand data set and vehicle detection and view angle estimation on a multi-view vehicle data set. On all data sets, our approach has comparable accuracy and is at least five times faster than the brute force approach.