Cloud computing provides a facility to access an unlimited pool of resources based on pay-per-use. This work addresses the problems of "job scheduling and resource allocation", which is to schedule and allocate virtual resources to achieve high resource utilization to meet user’s needs with Quality of Service (QoS) parameter. The scheduling is a Non-deterministic Polynomial time (NP) - complete problem. This scheduling and resource allocation concepts are executed under the service of Infrastructure as a Service (IaaS) and the hybrid cloud will use for the deployment model. The scheduling process is concentrated on the objective of minimized time and cost. To perform an optimum schedule the genetic algorithm and bacterial foraging optimization algorithm were hybridized. The fitness function of the genetic algorithm is hybridized with Bacterial Foraging Optimization (BFO) algorithm. The simulation of the environment is using CloudSim tool. Tasks will be represented as instances in [login to view URL] proposed hybrid algorithm efficiently divides the job into tasks with less execution time and minimum delay. Resource allocation is the process of allocating jobs to resources (CPU speed, RAM size, and bandwidth) in the virtual machines. Optimum allocation of a task to VM is an important process. The mutation and crossover operation of the genetic algorithm is combined with the BFO algorithm. Resource allocation parameters are execution time, makespan and throughput. Reliability is one of the important parameter, which is not yet concentrated in previous works. So, the final phase of the research proposal will be to address the reliability measure of resource utilization. The reliability of the proposed model will be analyzed using Design of Experiments (DoE) tool.
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