A Resource Management Algorithm for Virtual Machine Migration in Vehicular Cloud Computing

In recent years,vehicular cloud computing(VCC)has gained vast attention for providing a variety of services by creating virtual machines(VMs).These VMs use the resources that are present in modern smart vehicles.Many studies reported that some of these VMs hosted on the vehicles are overloaded,whereas others are underloaded.As a circumstance,the energy consumption of overloaded vehicles is drastically increased.On the other hand,underloaded vehicles are also drawing considerable energy in the underutilized situation.Therefore,minimizing the energy consumption of the VMs that are hosted by both overloaded and underloaded is a challenging issue in the VCC environment.The proper and efcient utilization of the vehicle’s resources can reduce energy consumption signicantly.One of the solutions is to improve the resource utilization of underloaded vehicles by migrating the over-utilized VMs of overloaded vehicles.On the other hand,a large number of VM migrations can lead to wastage of energy and time,which ultimately degrades the performance of the VMs.This paper addresses the issues mentioned above by introducing a resource management algorithm,called resource utilization-aware VM migration(RU-VMM)algorithm,to distribute the loads among the overloaded and underloaded vehicles,such that energy consumption is minimized.RU-VMM monitors the trend of resource utilization to select the source and destination vehicles within a predetermined threshold for the process of VM migration.It ensures that any vehicles’resource utilization should not exceed the threshold before or after the migration.RU-VMM also tries to avoid unnecessary VM migrations between the vehicles.RU-VMM is extensively simulated and tested using nine datasets.The results are carried out using three performance metrics,namely number of nal source vehicles(nfsv),percentage of successful VM migrations(psvmm)and percentage of dropped VM migrations(pdvmm),and compared with threshold-based algorithm(i.e.,threshold)and cumulative sum(CUSUM)algorithm.The comparisons show that the RU-VMM algorithm performs better than the existing algorithms.RU-VMM algorithm improves 16.91%than the CUSUM algorithm and 71.59%than the threshold algorithm in terms of nfsv,and 20.62%and 275.34%than the CUSUM and threshold algorithms in terms of psvmm.

Waste Cooking Oil as a Fuel Source for Diesel Engines

Almost all cities and towns in Papua New Guinea are producing tonnes of waste vegetable oils annually,mainly from industrial deep fryers in potato processing plants,snack food factories,fast food restaurants,and institutional dinning facilities.These waste vegetable oils are directed to waterways,rivers,and finally into the ocean which destroys the ocean shores and damages the environment.With increasing population,not only the demand for cooking oil will increase but also the environmental problems caused by the waste cooking oil.Most brands of cooking oil that is used in Papua New Guinea are from locally produced palm oil.Palm oil consists mainly of triglycerides made up of a range of fatty acids and contains other minor constituents,such as free fatty acids and non-glyceride components.This composition determines the oil’s chemical and physical characteristics.This is an attempt to improve the waste vegetable oil’s chemical and physical characteristics that will allow the oil to be used as an energy source and at the same time reduce the associated environmental problems.It has been observed that the waste cooking oil can be converted into a useful energy source using the transesterification process.The converted fuel has been tested and found its performance to be equivalent to petroleum diesel.