The optimization and transesterification of soybean oil with methanol in the presence of sodium hydroxide as a catalyst was investigated. A low-temperature transesterification process was selected to make the transesterification process more energy efficient. To further improve the production of biodiesel, the experimental design was carried out with the Box-Behnken method. The results were analysed using the response surface methodology. A model was developed to correlate the performance of biodiesel with the parameters of the process, such as the molar ratio, the concentration of the catalyst and the reaction time. The influence of the reaction variables, including; The molar ratio of oil (6: 1–12: 1), temperature (50° C) and catalyst concentration (1–2% by weight) and residence time (30–60 minutes) on the transesterification reaction of the methyl ester of Fatty acid (FAME) were studied. A biodiesel yield of 80.86% with the molar ratio (8:1) was reached using NaOH as catalyst (1.8) in 34 minutes at a temperature of 50° C. It was observed that the catalyst concentration, the reaction time and the molar ratio had a significant effect on the yield of soybean biodiesel.
Scheduling in cloud environment is a big challenge, it has two flavors in cloud environment one to schedule the placement of the virtual machines (VM) and second is the placement of cloudlet or tasks in the right virtual machine for the fast execution. In first type of scheduling to save the energy in the DataCenter it is always a good idea to re-arrange the running VM’s on the underlying physical machines, so the underload physical machine can go to sleep to save energy. So, the assignment of the VM from the underloaded physical machine to other is a challenge. In second the placement of cloudlet to the VM for execution, the decision to VM is crucial. In DataCenter, the underlying environment is heterogeneous, it is a challenge to use all the VM which are now old and new high-end specs VM. So, balancing the tasks assignment is challenging. In the proposed work the placement of the tasks is taken up, the tasks are picked- up for execution on the FCFS basis and our algorithm assigns all the tasks to the VM which is providing the minimum execution time. It calculates the time of execution from all the VM available and calculates the time to finish the previous assigned or under execution tasks to find the minimum execution time. We will see the algorithm working in load scenarios.
Cloud Computing offers efficient computing with Pay-as-you-go models. It is now easy for consumer to start with- out need of initial setup, which saves a lot of infrastructure cost. As consumers are subscribing to the cloud, the load is increasing on the data centers, thus data centers are in need for more resources and more power. And all this process is increasing the carbon footprint and polluting environment. Now the time has come when we require efficiency in term of power. We really need to look for mechanism how the power can me be managed to be more efficient. This paper suggests the Green Architecture Framework and also suggests to use of Dynamic Voltage Frequency Scaling (DVFS) as per the load requirement which results in better energy efficiency
Keywords: Power Efficiency, Energy Efficiency, Green Cloud, DVFS, VM Migration
Cloud is a bag full of resources. Using cloud services at an optimal level is required as now cloud is primary technology for deployment over Internet. This is indeed a practice to make use of things efficiently to make cloud a better place. Cloud is providing all computing resources that one may need to compile tasks, but efficiently using of resources can increase the power to accommodate more con- sumers and also consumer can save on cost for the services subscribed. This paper provides a mechanism to increase or decrease the subscription as per the use.
Keywords Cloud computing Resource Optimisation