The cloud computing environment enables cloud users to execute their applications in remote data centers. Many of these applications are considered to be highly complex in nature, requiring parallel processing capabilities. Parallel job scheduling techniques, mainly focus on improving throughput or the information processed by the cloud center in a given interval of time and reducing average task waiting time. For a data center that deals with parallel jobs, it is required to design an optimal scheduler resulting in minimal utilization of memory rate for scheduling each job. In this paper, we present M/M/M Queuing System and Load Optimized (QS-LO) model for parallel job scheduling in multiple cloud centers. The QS-LO model is designed as two algorithms, namely parallel job scheduling and load balancing with aiming at improving the throughput rate and reducing the average task waiting time in multiple cloud centers. We perform experimental analysis using benchmarks and synthetic datasets to measure the performance of the proposed algorithm. The experimental results are compared with the existing parallel job schedulers in terms of the job assigned, throughput memory utilization rate and scheduler time interval. The experimental results show that the QS-LO model is able to improve the throughput rate and also reduce the average task waiting time when compared to the state-of-the-art works.

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Cloud computing is one of the active research areas in High Performance Computing. It helps to share the resources globally in a distributed manner. In this paper, hybrid ACO with ANN is designed to ensure the best and the secured VM consolidation process. Initially, the objectives constraints for power consumption, resource deterioration and the SLA parameters are modelled for the Physical Machines and Virtual Machines. The selection of VMs is explored by the concept of Ant Colony Optimization that select the best VMs by the predefined SLA parameters. Also, the eavesdropping attack is also modulated in the cloud shared environment. The proposed hybrid ACO with ANN is implemented in CloudSim and it's compared with the honeybee with GA and PSO with GA. The simulation results have proved the efficiency of the VMs consolidation process with the security constraints in terms of time-related on uptime and downtime of the servers.

The ‘Canteen Food Ordering and Managing System’ digitalize the operations inside a canteen. Initially the user must create an account, and then he can make use of the services. A menu will be provided from which the customer can choose the desired item then he can make payment online. In the proposed system the paper works will be less. The users can also make use of the virtual queue technique by which they can avoid long queues. The digitalization of the canteen system will help the management to provide a best-in-class service to the customers and which will also contribute in better time management. At last, the customers can also share their feedback through the website according to which the management can improve their services. Thus the online system will be helpful for both customers and the canteen.