OpenNebula, a cloud platform handles a variety of leases employing scheduler, Haizea and majority of them are deadline-sensitive in real time. As existing Backfilling AHP model for deadline-sensitive lease scheduling suffers from lease rejection and do not scrutinize the estimations for waiting leases. In our proposed work, to overcome this pitfall we have devised Hungarian-Genetic Algorithm (HGA). Time Estimations for leases are performed using optimized Hungarian Algorithm to optimally render resources to available leases but it executes boundlessly. Thus, it’s blended with Genetic Algorithm to set bounds to it by utilizing fitness function. Output of HGA is a scheduling structure with optimal lease combination which consumes minimum time. Finally HGA is compared with Backfilling AHP model and HGA schedules greater quota of leases and minimizes lease ostracism comparatively. Also proposed model works fine on increasing number of leases as computational time is not directly proportional to number of leases scheduled.

Deadline sensitive, Resource allocation, Leases, Lease scheduling, Cloud computing

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