Khalid, O., Maljevic, I., Anthony, R., Petridis, Miltiadis, Parrot, K. and Schulz, M. (2009) Dynamic scheduling of virtual machines running HPC workloads in scientific grids In: 3rd International Conference on New Technologies, Mobility and Security (NTMS), 2009, Cairo, 20-23 December, 2008.Full text not available from this repository.
The primary motivation for uptake of virtualization has been resource isolation, capacity management and resource customization allowing resource providers to consolidate their resources in virtual machines. Various approaches have been taken to integrate virtualization in to scientific Grids especially in the arena of High Performance Computing (HPC) to run grid jobs in virtual machines, thus enabling better provisioning of the underlying resources and customization of the execution environment on runtime. Despite the gains, virtualization layer also incur a performance penalty and its not very well understood that how such an overhead will impact the performance of systems where jobs are scheduled with tight deadlines. Since this overhead varies the types of workload whether they are memory intensive, CPU intensive or network I/O bound, and could lead to unpredictable deadline estimation for the running jobs in the system. In our study, we have attempted to tackle this problem by developing an intelligent scheduling technique for virtual machines which monitors the workload types and deadlines, and calculate the system over head in real time to maximize number of jobs finishing within their agreed deadlines.
|Item Type:||Contribution to conference proceedings in the public domain ( Full Paper)|
|Subjects:||G000 Computing and Mathematical Sciences > G400 Computing|
|DOI (a stable link to the resource):||10.1109/NTMS.2009.5384725|
|Faculties:||Faculty of Science and Engineering > School of Computing, Engineering and Mathematics > Computational Intelligence|
|Depositing User:||editor cmis|
|Date Deposited:||18 Jan 2012 11:28|
|Last Modified:||01 Apr 2015 14:47|
Actions (login required)
Downloads per month over past year