The Network Effects of Prefetching

Prefetching has been shown to be an effective technique for reducing user perceived latency in distributed systems. In this paper we show that even when prefetching adds no extra traffic to the network, it can have serious negative performance effects. Straightforward approaches to prefetching increase the burstiness of individual sources, leading to increased average queue sizes in network switches. However, we also show that applications can avoid the undesirable queueing effects of prefetching. In fact, we show that applications employing prefetching can significantly improve network performance, to a level much better than that obtained without any prefetching at all. This is because prefetching offers increased opportunities for traffic shaping that are not available in the absence of prefetching. Using a simple transport rate control mechanism, a prefetching application can modify its behavior from a distinctly ON/OFF entity to one whose data transfer rate changes less abruptly, while still delivering all data in advance of the user's actual requests

Contamination Estimation via Convex Relaxations

Identifying anomalies and contamination in datasets is important in a wide variety of settings. In this paper, we describe a new technique for estimating contamination in large, discrete valued datasets. Our approach considers the normal condition of the data to be specified by a model consisting of a set of distributions. Our key contribution is in our approach to contamination estimation. Specifically, we develop a technique that identifies the minimum number of data points that must be discarded (i.e., the level of contamination) from an empirical data set in order to match the model to within a specified goodness-of-fit, controlled by a p-value. Appealing to results from large deviations theory, we show a lower bound on the level of contamination is obtained by solving a series of convex programs. Theoretical results guarantee the bound converges at a rate of $O(\sqrt{\log(p)/p})$, where p is the size of the empirical data set.Comment: To appear, ISIT 201

Artefact collecting: creating or destroying the archaeological record?

This paper examines some of the arguments used by archaeologists in favour of collaborating useful for archaeological research and is a form of public engagement with archaeology. It takes as a case study records of 48 600 medieval artefacts removed from archaeological contexts by artefact hunters and recorded by the Portable Antiquities Scheme in England and Wales. The past and potential uses of these records as an archaeological source are objectively reviewed, together with an assessment of the degree to which they provide mitigation of the damage caused to the otherwise unthreatened archaeological record. It is concluded that, although information can be obtained by studying records of findspots of addressed artefacts such as coins, in general the claims made in support of professional archaeological collaboration with this kind of activity prove to be false.This paper examines some of the arguments used by archaeologists in favour of collaborating useful for archaeological research and is a form of public engagement with archaeology. It takes as a case study records of 48 600 medieval artefacts removed from archaeological contexts by artefact hunters and recorded by the Portable Antiquities Scheme in England and Wales. The past and potential uses of these records as an archaeological source are objectively reviewed, together with an assessment of the degree to which they provide mitigation of the damage caused to the otherwise unthreatened archaeological record. It is concluded that, although information can be obtained by studying records of findspots of addressed artefacts such as coins, in general the claims made in support of professional archaeological collaboration with this kind of activity prove to be false