Timothy Wood

TL;DR: In this paper, a pipelined data replication method for disaster recovery has been proposed, where the replicated first data is a replica of the first data in the primary processing environment.

TL;DR: This work proposes two schedulers: one in the virtualization layer designed to minimize interference on high priority interactive services, and one inThe Hadoop framework that helps batch processing jobs meet their own performance deadlines.

TL;DR: Seagull is described, a system designed to facilitate cloud bursting by determining which applications can be transitioned into the cloud most economically, and automating the movement process at the proper time, and reducing cloud costs by more than 45% when bursting to the cloud.

TL;DR: In this article, the authors present a method for optimizing a prediction of resource usage of multiple applications running in a virtual environment, comprising: providing a predetermined set of benchmarks, executing the predetermined set, and executing the set, in a native hardware system in which the application natively resides; collecting first traces of first resource utilization metrics in the native HPC system based on the execution of the predetermined HPC benchmarks in the HPC environment; collecting second traces of second 10 HPC metrics, in the virtual environment.

TL;DR: This work proposes Matrix, a novel performance and resource management system that ensures the desired performance of an application achieved on a VM, and utilizes machine learning methods clustering models with probability estimates to predict the performance of new workloads in a virtualized environment, choose a suitable VM type, and dynamically adjust the resource configuration of a virtual machine on the fly.