Institutional Repositories, Policies, and Disruption

For many librarians, institutional repositories (IRs) promised significant change for academic libraries. We envisioned enlarging collection development scope to include locally produced scholarship and an expansion of library services to embrace scholarly publication and distribution. However, at the University of Rochester, as at many other institutions, this transformational technology was introduced in the conservative, controlled manner associated with stereotypical librarian culture, and so these expected changes never materialized. In this case study, we focus on the creation of our institutional repository (a potentially disruptive technology) and how its success was hampered by our organizational culture, manifested as a lengthy and complicated set of policies. In the following pages, we briefly describe our repository project, talk about our original policies, look at the ways those policies impeded our project, and discuss the disruption of those policies and the benefits in user uptake that resulted.

Litigation-Generated Science: Why Should We Care?

BACKGROUND: In a 1994 Ninth Circuit decision on the remand of Daubert v. Merrell Dow Pharmaceuticals, Inc., Judge Alex Kosinski wrote that science done for the purpose of litigation should be subject to more stringent standards of admissibility than other science. OBJECTIVES: We analyze this proposition by considering litigation-generated science as a subset of science involving conflict of interest. DISCUSSION: Judge Kosinski's formulation suggests there may be reasons to treat science involving conflict of interest differently but raises questions about whether litigation-generated science should be singled out. In particular we discuss the similar problems raised by strategically motivated science done in anticipation of possible future litigation or otherwise designed to benefit the sponsor and ask what special treatment, if any, should be given to science undertaken to support existing or potential future litigation. CONCLUSION: The problems with litigation-generated science are not special. On the contrary, they are very general and apply to much or most science that is relevant and reliable in the courtroom setting.

On the Emergence of Highly Variable Distributions in the Autonomous System Topology

Recent studies have noted that vertex degree in the autonomous system (AS) graph exhibits a highly variable distribution [15, 22]. The most prominent explanatory model for this phenomenon is the Barabási-Albert (B-A) model [5, 2]. A central feature of the B-A model is preferential connectivity—meaning that the likelihood a new node in a growing graph will connect to an existing node is proportional to the existing node’s degree. In this paper we ask whether a more general explanation than the B-A model, and absent the assumption of preferential connectivity, is consistent with empirical data. We are motivated by two observations: first, AS degree and AS size are highly correlated [11]; and second, highly variable AS size can arise simply through exponential growth. We construct a model incorporating exponential growth in the size of the Internet, and in the number of ASes. We then show via analysis that such a model yields a size distribution exhibiting a power-law tail. In such a model, if an AS’s link formation is roughly proportional to its size, then AS degree will also show high variability. We instantiate such a model with empirically derived estimates of growth rates and show that the resulting degree distribution is in good agreement with that of real AS graphs.

How Well Can TCP Infer Network State?

The Transmission Control Protocol (TCP) has been the protocol of choice for many Internet applications requiring reliable connections. The design of TCP has been challenged by the extension of connections over wireless links. We ask a fundamental question: What is the basic predictive power of TCP of network state, including wireless error conditions? The goal is to improve or readily exploit this predictive power to enable TCP (or variants) to perform well in generalized network settings. To that end, we use Maximum Likelihood Ratio tests to evaluate TCP as a detector/estimator. We quantify how well network state can be estimated, given network response such as distributions of packet delays or TCP throughput that are conditioned on the type of packet loss. Using our model-based approach and extensive simulations, we demonstrate that congestion-induced losses and losses due to wireless transmission errors produce sufficiently different statistics upon which an efficient detector can be built; distributions of network loads can provide effective means for estimating packet loss type; and packet delay is a better signal of network state than short-term throughput. We demonstrate how estimation accuracy is influenced by different proportions of congestion versus wireless losses and penalties on incorrect estimation.

Connection Scheduling in Web Servers

Under high loads, a Web server may be servicing many hundreds of connections concurrently. In traditional Web servers, the question of the order in which concurrent connections are serviced has been left to the operating system. In this paper we ask whether servers might provide better service by using non-traditional service ordering. In particular, for the case when a Web server is serving static files, we examine the costs and benefits of a policy that gives preferential service to short connections. We start by assessing the scheduling behavior of a commonly used server (Apache running on Linux) with respect to connection size and show that it does not appear to provide preferential service to short connections. We then examine the potential performance improvements of a policy that does favor short connections (shortest-connection-first). We show that mean response time can be improved by factors of four or five under shortest-connection-first, as compared to an (Apache-like) size-independent policy. Finally we assess the costs of shortest-connection-first scheduling in terms of unfairness (i.e., the degree to which long connections suffer). We show that under shortest-connection-first scheduling, long connections pay very little penalty. This surprising result can be understood as a consequence of heavy-tailed Web server workloads, in which most connections are small, but most server load is due to the few large connections. We support this explanation using analysis.