High-end servers that can be partitioned into logical subsystems and repartitioned on the fly are now becoming available. This development raises the possibility of reconfiguring distributed systems online to optimize for dynamically changing workloads. This paper presents one approach to solving

are changing the workloads of Web servers, but that major improvements to that architecture are still necessary. In particular, we uncover evidence that a better consistency mechanism is required for World-Wide Web caches.

Automated tools for understanding application behavior and its changes during the application life-cycle are essential for many performance analysis and debugging tasks. Application performance issues have an immediate impact on customer experience and satisfaction. A sudden slowdown of enterprise

HIGHLIGHTS • Functional near-infrared spectroscopy brain sensing is feasible for use in long duration (3 h) tasks. • Hemodynamic response was diminished during the middle of a long duration, low task load simulation when engagement and priming were lowest. • fNIRS did not detect a change in workload, but did

HIGHLIGHTS • Functional near-infrared spectroscopy brain sensing is feasible for use in long duration (3 h) tasks. • Hemodynamic response was diminished during the middle of a long duration, low task load simulation when engagement and priming were lowest. • fNIRS did not detect a change in workload, but did

Configuring redundant disk arrays is a black art. To configure an array properly, a system administrator must understand the details of both the array and the workload it will support. Incorrect understanding of either, or changes in the workload over time, can lead to poor performance. We present

The production environment for analytical data management applications is rapidly changing. Many enterprises are shifting away from deploying their analytical databases on high-end proprietary machines, and moving towards cheaper, lower-end, commodity hardware, typically arranged in a shared

To contribute to systems that reason about human attention, our work empirically demonstrates how a user’s mental workload changes during task execution. We conducted a study where users performed interactive, hierarchical tasks while mental workload was measured through the use of pupil size

for reducing the overhead. Users register continuous queries with precision requirements at the central stream processor, which installs filters at remote data sources. The filters adapt to changing conditions to minimize stream rates while guaranteeing that all continuous queries still receive

coefficients to better conform to the actual load conditions. Under this model, periodic tasks can intentionally change their execution rate to provide different quality of service and the other tasks can automatically adapt their periods to keep the system underloaded. The proposed model can also be used