This paper describes our implementation and experience of incorporating computer science concepts into a team-taught, first-year interdisciplinary course for prospective science majors at the University of Richmond. The course integrates essential concepts from each of five STEM disciplines: biology, chemistry, computer science, mathematics, and physics. Including computer science in this course faces three primary challenges: few of the students have any CS background; the time devoted to CS instruction is reduced compared to a traditional introductory CS course; and the spirit of the course requires the CS material to be highly integrated with the other disciplines. Here we discuss our experience from three-plus years of offering the course and its impact on the major/minor pool of students in our own discipline.

This paper describes a proof of concept for introducing iPods and iPodLinux into a one-semester introductory undergraduate operating systems course. iPodLinux is a version of the Linux operating system modified to run on iPods. We added a project to our course in which the students modified the iPodLinux kernel, and we supplemented lectures by discussing specifics of the Linux implementation as they relate to general operating systems concepts. We feel the course was much improved by these additions, with no substantive omission of regular material. Student response was very enthusiastic, and we feel the new material enhanced their course experience by providing a component that was empowering and helped to further improve their knowledge and skills.

Volunteer distributed computations utilize spare processor cycles of personal computers that are connected to the Internet. The related computation integrity concerns are commonly addressed by assigning tasks redundantly. Aside from the additional computational costs, a significant disadvantage of redundancy is its vulnerability to colluding adversaries. This paper presents a tunable redundancy-based task distribution strategy that increases resistance to collusion while significantly decreasing the associated computational costs. Specifically, our strategy guarantees a desired cheating detection probability regardless of the number of copies of a specific task controlled by the adversary. Though not the first distribution scheme with these properties, the proposed method improves upon existing strategies in that it requires fewer computational resources. More importantly, the strategy provides a practical lower bound for the number of redundantly assigned tasks required to achieve a given detection probability

Traditionally, scheduling in high-end parallel systems focuses on how to minimize the average job waiting time and on how to maximize the overall system utilization. Despite the development of scheduling strategies that aim at maximizing system utilization, parallel supercomputing traces that span long time periods indicate that such systems are mostly underutilized. Much of the time there is simply not enough load to keep the system fully utilized, although time periods do exist where system utilization levels peak at nearly 95%. In this paper, we propose a new family of scheduling policies that aims at minimizing power consumption and cooling costs by selectively choosing to power down (or put in "sleep" mode) parts of the system during periods of low load. Our goal is the development of a scheduling mechanism that adaptively adjusts the number of processors to the offered load while meeting predefined service-level agreements (SLAs). This scheduling mechanism uses online simulation, i.e., lightweight simulation modules that can execute while the system and its scheduler are in operation, and can guide resource provisioning in parallel systems. Detailed experimentation using traces from the Parallel Workloads Archive indicates that the proposed online mechanism is a viable alternative to conserve energy while meeting performance-based SLAs.

The past few years have seen the development of distributed computing platforms designed to utilize the spare processor cycles of a large number of personal computers attached to the Internet in an effort to generate levels of computing power normally achieved only with expensive supercomputers. Such large scale distributed computations running in untrusted environments raise a number of security concerns, including the potential for intentional or unintentional corruption of computations, and for participants to claim credit for computing that has not been completed. This paper presents two strategies for hardening selected applications that utilize such distributed computations. Specifically, we show that carefully seeding certain tasks with precomputed data can significantly increase resistance to cheating (claiming credit for work not computed) and incorrect results. Similar results are obtained for sequential tasks through a strategy of sharing the computation of N tasks among K>N nodes. In each case, the associated cost is significantly less than the cost of assigning tasks redundantly.

We focus on non-FCFS job scheduling policies for parallel systems that allow jobs to backfill, i.e., to move ahead in the queue, given that they do not delay certain previously submitted jobs. Consistent with commercial schedulers that maintain multiple queues where jobs are assigned according to the user-estimated duration, we propose a self-adapting backfilling policy that maintains multiple job queues to separate short from long jobs. The proposed policy adjusts its configuration parameters by continuously monitoring the system and quickly reacting to sudden fluctuations in the workload arrival pattern and/or severe changes in resource demands. Detailed performance comparisons via simulation using actual supercomputing, traces from the parallel workload archive indicate that the proposed policy consistently outperforms traditional backfilling.