Daniel Ellard

File classification in self-* storage systems

To tune and manage themselves, file and storage systems must understand key properties (e.g., access pattern, lifetime, size) of their various files. This paper describes how systems can automatically learn to classify the properties of files (e.g., read-only access pattern, short-lived, small in size) and predict the properties of new files, as they are created, by exploiting the strong associations between a files properties and the names and attributes assigned to it. These associations exist, strongly but differently, in each of four real NFS environments studied. Decision tree classifiers can automatically identify and model such associations, providing prediction accuracies that often exceed 90%. Such predictions can be used to select storage policies (e.g., disk allocation schemes and replication factors) for individual files. Further, changes in associations can expose information about applications, helping autonomic system components distinguish growth from fundamental change.

On the design of a new CPU architecture for pedagogical purposes

Ant-32 is a new processor architecture designed specifically to address the pedagogical needs of teaching many subjects, including assembly language programming, machine architecture, compilers, operating systems, and VLSI design. This paper discusses our motivation for creating Ant-32 and the philosophy we used to guide our design decisions and gives a high-level description of the resulting design.

Ant architecture: an architecture for CS1

A central goal of high-level programming languages, such as those we use to teach introductory computer science courses, is to provide an abstraction that hides the complexity and idiosyncrasies of computer hardware. Although programming languages are effective at achieving this goal, certain properties of computer hardware cannot be hidden, or are useful for students to know about. As a consequence, many of the greatest conceptual challenges for beginning programmers arise from a lack of understanding of the basic properties of the hardware upon which computer programs execute. To address this problem, we have developed a simple virtual machine called ANT for use in our introductory computer science (CS1) curriculum. ANT is designed to be simple enough that a CS1 student can quickly understand it, while at the same time providing an accurate model of many important properties of computer hardware. After two years of experience with ANT in our CS1 course, we believe it is a valuable tool for helping young students understand how programs and data are represented in a computer system.