Mobile devices can improve their battery life by offloading their tasks to a nearby cloudlet instead of executing tasks on the mobile device. Because mobile devices have low-speed processors, small-size memory, and limited battery. As the mobile devices are moving, they are connected and disconnected from the cloudlets. So, their tasks are offloaded to the new cloudlets and also migrated from one cloudlet to another until the tasks finish their execution. Scheduling these tasks in the cloudlet will reduce the tasks' execution time and the mobile device's power consumption using this proposed new method (AGWO). The GWO algorithm is modified to accept the inputs from a two-dimensional array instead of sequence inputs and search for the prey within the twodimensional array instead of an unknown circle area. This method deals with the arrival time of the task, task size, and big task. The migration of the partially executed task dynamically to other VMs is also examined. This proposed method also reduces the average scheduling delay and increases the percentage of requests executed by the cloudlet than other variations of GWO and other research algorithms.
Mobile applications and tasks are huge and handle huge data. These applications are offloaded and executed in the cloud. Instead of executing in the cloud, the cloudlet which is a small cloud situated near the access point is used for execution. The cloudlet has less number of VMs. When more tasks are offloaded to the cloudlet, there is no VM free as VMs are busy in executing tasks. This cloudlet is full of tasks and all VMs are busy. But nearby another cloudlet may have free VMs. Those cloudlets have free VMs. So the tasks that do not get the VM in the near cloudlet, are offloaded to nearby cloudlets. So all the cloudlets now have tasks for execution and resources are utilized optimally. This survey discusses the various load balancing methods proposed by different researchers and their issues.
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