Mining Money: the generative capacities of scientific and legal knowledge practices in the making of resource economies

In 1997 a scandal associated with Bre-X, a junior mining firm, and its prospecting activities in Indonesia, exposed to public scrutiny the ways in which mineral exploration firms acquire, assess and report on scientific claims about the natural environment. At stake here was not just how investors understood the provisional nature of scientific knowledge, but also evidence of fraud. Contemporaneous mining scandals not only included the salting of cores, but also unreliable proprietary sample preparation and assay methods, mis-representations of visual field estimates as drilling results and ‘overly optimistic’ geological reports. This paper reports on initiatives taken in the wake of these scandals and prompted by the Mining Standards Task Force (TSE/OSC 1999). For regulators, mandated to increase investor confidence in Canada’s leading role within the global mining industry, efforts focused first and foremost upon identifying and removing sources of error and wilfulness within the production and circulation of scientific knowledge claims. A common goal cross-cutting these initiatives was ‘a faithful representation of nature’ (Daston and Galison 2010), however, as the paper argues, this was manifest in an assemblage of practices governed by distinct and rival regulative visions of science and the making of markets in claims about ‘nature’. These ‘practices of fidelity’, it is argued, can be consequential in shaping the spatial and temporal dynamics of the marketization of nature.

Container-based Cloud Virtual Machine Benchmarking

With the availability of a wide range of cloud Virtual Machines (VMs) it is difficult to determine which VMs can maximise the performance of an application. Benchmarking is commonly used to this end for capturing the performance of VMs. Most cloud benchmarking techniques are typically heavyweight - time consuming processes which have to benchmark the entire VM in order to obtain accurate benchmark data. Such benchmarks cannot be used in real-time on the cloud and incur extra costs even before an application is deployed.

In this paper, we present lightweight cloud benchmarking techniques that execute quickly and can be used in near real-time on the cloud. The exploration of lightweight benchmarking techniques are facilitated by the development of DocLite - Docker Container-based Lightweight Benchmarking. DocLite is built on the Docker container technology which allows a user-defined portion (such as memory size and the number of CPU cores) of the VM to be benchmarked. DocLite operates in two modes, in the first mode, containers are used to benchmark a small portion of the VM to generate performance ranks. In the second mode, historic benchmark data is used along with the first mode as a hybrid to generate VM ranks. The generated ranks are evaluated against three scientific high-performance computing applications. The proposed techniques are up to 91 times faster than a heavyweight technique which benchmarks the entire VM. It is observed that the first mode can generate ranks with over 90% and 86% accuracy for sequential and parallel execution of an application. The hybrid mode improves the correlation slightly but the first mode is sufficient for benchmarking cloud VMs.