Data-Driven Resource Shaping for Compute Clusters

Nowadays, data-centers are largely under-utilized because resource allocation is based on reservation mechanisms which ignore actual resource utilization. Indeed, it is common to reserve resources for peak demand, which may occur only for a small portion of the application life time. As a consequence, cluster resources often go under-utilized. In this work, we propose a mechanism that improves cluster utilization, thus decreasing the average turnaround time, while preventing application failures due to contention in accessing finite resources such as RAM. Our approach monitors resource utilization and employs a data-driven approach to resource demand forecasting, featuring quantification of uncertainty in the predictions. Using demand forecast and its confidence, our mechanism modulates cluster resources assigned to running applications, and reduces the turnaround time by more than one order of magnitude while keeping application failures under control. Thus, tenants enjoy a responsive system and providers benefit from an efficient cluster utilization

Descemet membrane air-bubble separation in donor corneas

We describe a technique to obtain Descemet-endothelium disks from donors. To detach Descemet membrane, an air bubble was introduced in the deep stroma of human donor corneas mounted on an artificial chamber. In Group A (n = 5), the bubble was left inflated. In Group B (n = 4), the bubble was deflated immediately after the membrane was detached. In Group C (n = 7), the Descemet-endothelium disk was trephined and separated from the stroma after the bubble was deflated. All tissues were stored at 4\ub0C. Descemet detachment was achieved in 89% of the tissues. After 48 hours, the mean endothelial loss was 83% \ub1 10% (SD), 15% \ub1 11%, and 3% \ub1 3% in the 3 groups, respectively. With this technique, Descemet-endothelium disks were obtained without significant alterations in the endothelial layer