As university-level distance learning programs become more and more popular, and software engineering courses incorporate eXtreme Programming (XP) into their curricula, certain challenges arise when teaching XP to students who are not physically co-located. In this paper, we present the results of a three-year study of such an online software engineering course targeted to graduate students, and describe some of the specific challenges faced, such as students' aversion to aspects of XP and difficulties in scheduling. We discuss our findings in terms of the course's educational objectives, and present suggestions to other educators who may face similar situations.

As users interact with the world and their peers through their computers, it is becoming important to archive and later search the information that they have viewed. We present DejaView, a personal virtual computer recorder that provides a complete record of a desktop computing experience that a user can playback, browse, search, and revive seamlessly. DejaView records visual output, checkpoints corresponding application and file system state, and captures displayed text with contextual information to index the record. A user can then browse and search the record for any visual information that has been displayed on the desktop, and revive and interact with the desktop computing state corresponding to any point in the record. DejaView combines display, operating system, and file system virtualization to provide its functionality transparently without any modifications to applications, window systems, or operating system kernels. We have implemented DejaView and evaluated its performance on real-world desktop applications. Our results demonstrate that DejaView can provide continuous low-overhead recording without any user noticeable performance degradation, and allows browsing, search and playback of records fast enough for interactive use.

We have created ZapC, a novel system for transparent coordinated checkpoint-restart of distributed network applications on commodity clusters. ZapC provides a thin visualization layer on top of the operating system that decouples a distributed application from dependencies on the cluster nodes on which it is executing. This decoupling enables ZapC to checkpoint an entire distributed application across all nodes in a coordinated manner such that it can he restarted from the checkpoint on a different set of cluster nodes at a later time. ZapC checkpoint-restart operations execute in parallel across different cluster nodes, providing faster checkpoint-restart performance. ZapC uniquely supports network state in a transport protocol independent manner, including correctly saving and restoring socket and protocol state for both TCP and UDP connections. We have implemented a ZapC Linux prototype and demonstrate that it provides low visualization overhead and fast checkpoint-restart times for distributed network applications without any application, library, kernel, or network protocol modifications

The high cost of operating large computing installations has motivated a broad interest in reducing the need for human intervention by making systems self-managing. This paper explores the extent to which control theory can provide an architectural and analytic foundation for building self-managing systems. Control theory provides a rich set of methodologies for building automated self-diagnosis and self-repairing systems with properties such as stability, short settling times, and accurate regulation. However, there are challenges in applying control theory to computing systems, such as developing effective resource models, handling sensor delays, and addressing lead times in effector actions. We propose a deployable testbed for autonomic computing (DTAC) that we believe will reduce the barriers to addressing research problems in applying control theory to computing systems. The initial DTAC architecture is described along with several problems that it can be used to investigate.

We applied second-order blind identification (SOBI), an independent component analysis method, to MEG data collected during cognitive tasks. We explored SOBI's ability to help isolate underlying neuronal sources with relatively poor signal-to-noise ratios, allowing their identification and localization. We compare localization of the SOBI-separated components to localization from unprocessed sensor signals, using an equivalent current dipole modeling method. For visual and somatosensory modalities, SOBI preprocessing resulted in components that can be localized to physiologically and anatomically meaningful locations. Furthermore, this preprocessing allowed the detection of neuronal source activations that were otherwise undetectable. This increased probability of neuronal source detection and localization can be particularly beneficial for MEG studies of higher-level cognitive functions, which often have greater signal variability and degraded signal-to-noise ratios than sensory activation tasks.