  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.
