Virtualized data centers usually consist of heterogeneous servers which have different specifications (performance). Though there are usually a number of unused servers with different performance in such heterogeneous data centers, conventional DVFS (Dynamic Voltage and Frequency Scaling)-based DTM (Dynamic Thermal Management) techniques do not exploit the unused servers to cool down hot servers. In this paper, we propose a novel DTM technique which adaptively exploits external computing resources (unused servers with different performance) as well as internal computing resources (unused CPU cores in the server) available in heterogeneous data centers. When the temperature of a CPU core in a server exceeds a pre-defined thermal threshold, our proposed technique first identifies memory intensiveness and usage of VMs (Virtual Machines). Depending on the memory intensiveness and usage of VMs, our technique adaptively employs the following three methods: 1) a method that migrates a VM to another server with different performance, 2) a method that migrates VMs among CPU cores in the server, and 3) a DVFS-based method. In our experiments, our proposed technique improves performance by 9.6% and saves system-wide EDP by 12.9%, on average (by up to 17.1% and 24.5%, respectively), compared to a conventional DVFS-based DTM technique, satisfying thermal constraints.

SUMMARY: We present a user-friendly, cloud-based, data analysis pipeline for the deconvolution of pooled screening data. This tool, CRISPRcloud, serves a dual purpose of extracting, clustering and analyzing raw next generation sequencing files derived from pooled screening experiments while at the same time presenting them in a user-friendly way on a secure web-based platform. Moreover, CRISPRcloud serves as a useful web-based analysis pipeline for reanalysis of pooled CRISPR screening datasets. Taken together, the framework described in this study is expected to accelerate development of web-based bioinformatics tool for handling all studies which include next generation sequencing data. AVAILABILITY AND IMPLEMENTATION: http://crispr.nrihub.org. CONTACT: [email protected]. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.