Reuse of the works of others has become common practice on the Internet and has formed the basis for collaboration in some online communities. However, some works are reused much more frequently than others. In this article we build a quantitative model that explains which factors are most salient in determining the likelihood that an author's work will be reused. Controlling for common factors, such as the work's popularity, we show that the probability of reuse depends on (a) the degree of derivativity of the work in question, (b) the specific ways in which it derives meaning from other works (intertextuality), (c) the audience's preferential attachment to authors of high fecundity, and (d) the author's social embededness in networks of reuse. We use trace data from an online community that was built for the purpose of demonstrating the ability of open sharing and reuse to spur collaboration and innovation in music. Although our model is designed for broad applicability, we explain that the size and direction of the effects reported in this paper may vary, when reuse is performed with other media or for different purposes.

The proliferation of online social networks has created intense interest in studying their nature and revealing information of interest to the end user. At the same time, such revelation raises privacy concerns. Existing research addresses this problem following an approach popular in the database community: a model of data privacy is defined, and the data is rendered in a form that satisfies the constraints of that model while aiming to maximize some utility measure. Still, these is no consensus on a clear and quantifiable utility measure over graph data. In this paper, we take a different approach: we define a utility guarantee, in terms of certain graph properties being preserved, that should be respected when releasing data, while otherwise distorting the graph to an extend desired for the sake of confidentiality. We propose a form of data release which builds on current practice in social network platforms: A user may want to see a subgraph of the network graph, in which that user as well as connections and affiliates participate. Such a snapshot should not allow malicious users to gain private information, yet provide useful information for benevolent users. We propose a mechanism to prepare data for user view under this setting. In an experimental study with real data, we demonstrate that our method preserves several properties of interest more successfully than methods that randomly distort the graph to an equal extent, while withstanding structural attacks proposed in the literature.

Cycle-harvesting is a significant part of the Grid computing landscape. However, creating commercial service contracts based on resources made available by cycle-harvesting is a significant challenge: the characteristics of the harvested resources are inherently stochastic; and secondly, in a commercial environment, purchasers can expect providers to optimize against the quality of service (QoS) definitions. The essential point for creating commercially valuable QoS definitions is to guarantee a set of statistical parameters for each contract instance. Here we describe an appropriate QoS definition, Hard Statistical QoS (HSQ), and show how this can be implemented using a hybrid stochastic-deterministic system. We analyze algorithm behavior analytically using a distribution free approach versus the expected proportion of deterministic resources required for an HSQ specification. We conclude that commercial service contracts based on cycle-harvested resources are viable both from a conceptual point of view and quantitatively.

Contemporary computing systems, especially large-scale systems such as Grids promise ultra-fast ubiquitous utility computing, always available at the flip of a switch. A major unresolved issue is the organization and efficient usage of such infrastructure in a commercial context where several entities compete for shared resources. This has long been resolved for conventional utility resources such as gas and electricity through commoditization, a variety of market designs, customization, and decision support for the resulting portfolios of assets and commitments. The paper reviews the state of Grid commercialization and compares it to the commercialization of conventional resources. We draw specific lessons for commercialized Grids and detail them as architecture requirements at each level of the architecture stack. We provide an example to illustrate the benefits of commercialized resources in terms of the financial clarity it brings to decisions for different user groups, namely application users and IT managers.

The objective of this paper is to test whether network failures will have serious economic consequences for participants in the newly developing bandwidth markets. We measure economic consequences by looking at changes in bandwidth prices, in value-at-risk (VAR) and in conditional-value-at-risk (CVAR). Bandwidth markets may be particularly sensitive to network failures because bandwidth is a non-storable commodity and because alternative paths with equivalent quality of service (QoS) are perfect substitutes. Thus a local failure can affect alternative equivalent paths and this in turn can have a knock-on effect. We found that for a realistic large-scale market topology if there are, say, four failures per link per year, half of which are long enough to affect the market, then VAR is increased by 30%; and CVAR by 40%. This is true even with a spike size (/spl times/3) that is modest compared to observations in electricity markets (/spl times/10 to /spl times/100). For participants with capacity positions in such a market these consequences are likely to be serious. Thus if failures occur at this rate their consequence must be included in planning. Even at low failure intensities, the network itself acts as a significant amplifier and thus cannot be neglected. This amplification is itself a true emergent phenomenon of a large-scale bandwidth market.