A Network Congestion control Protocol (NCP)

The transmission control protocol (TCP) which is the dominant congestion control protocol at the transport layer is proved to have many performance problems with the growth of the Internet. TCP for instance results in throughput degradation for high bandwidth delay product networks and is unfair for flows with high round trip delays. There have been many patches and modifications to TCP all of which inherit the problems of TCP in spite of some performance improve- ments. On the other hand there are clean-slate design approaches of the Internet. The eXplicit Congestion control Protocol (XCP) and the Rate Control Protocol (RCP) are the prominent clean slate congestion control protocols. Nonetheless, the XCP protocol is also proved to have its own performance problems some of which are its unfairness to long flows (flows with high round trip delay), and many per-packet computations at the router. As shown in this paper RCP also makes gross approximation to its important component that it may only give the performance reports shown in the literature for specific choices of its parameter values and traffic patterns. In this paper we present a new congestion control protocol called Network congestion Control Protocol (NCP). We show that NCP can outperform both TCP, XCP and RCP in terms of among other things fairness and file download times.unpublishe

Distinct counting with a self-learning bitmap

Counting the number of distinct elements (cardinality) in a dataset is a fundamental problem in database management. In recent years, due to many of its modern applications, there has been significant interest to address the distinct counting problem in a data stream setting, where each incoming data can be seen only once and cannot be stored for long periods of time. Many probabilistic approaches based on either sampling or sketching have been proposed in the computer science literature, that only require limited computing and memory resources. However, the performances of these methods are not scale-invariant, in the sense that their relative root mean square estimation errors (RRMSE) depend on the unknown cardinalities. This is not desirable in many applications where cardinalities can be very dynamic or inhomogeneous and many cardinalities need to be estimated. In this paper, we develop a novel approach, called self-learning bitmap (S-bitmap) that is scale-invariant for cardinalities in a specified range. S-bitmap uses a binary vector whose entries are updated from 0 to 1 by an adaptive sampling process for inferring the unknown cardinality, where the sampling rates are reduced sequentially as more and more entries change from 0 to 1. We prove rigorously that the S-bitmap estimate is not only unbiased but scale-invariant. We demonstrate that to achieve a small RRMSE value of $\epsilon$ or less, our approach requires significantly less memory and consumes similar or less operations than state-of-the-art methods for many common practice cardinality scales. Both simulation and experimental studies are reported.Comment: Journal of the American Statistical Association (accepted