Optimization in Mobile Cloud Computing - IT Assignment Help

Assignment Task

Working title 
Energy based Task Scheduling Optimization in Mobile Cloud Computing
Introduction and justification 
Optimization of energy usage has been a major challenge in cloud computing and this study  focuses on simulation of the real load conditions to find the optimality factors. Thus the decisions  are enhanced in this process and leads to efficiency improvement as both the on load and the off  load conditions are taken into consideration. Resource monitor and automated planner /  schedulers help in management of several smart devices as the works are broken and scheduled  only based on the battery level and the current energy usage conditions. Since several smart  devices are integrated in this process using the common cloud applications, the process of  deployment of the right genetic algorithm helps in monitoring the devices. Thus, majority of the  high battery consuming processes are performed at the servers and lap-tops with higher battery  capacity. The live assessment of the battery conditions helps in distribution of the jobs based on  the current capacity leading to optimal energy usage.  The three level feedback based multiple level queues are recommended multi-level queuing  model deployed for the high battery consuming multimedia applications and services. As most  of the multimedia applications drain the battery much faster, it is essential to plan the division of task based on the battery capacity and energy consumed by the MM applications / tasks. The high  battery consuming tasks are scheduled and placed in queues leading to high battery and high CPU  devices. Whereas the lower battery consuming tasks are scheduled to the low battery smart  devices, and the average towards the smart phones or iPads with average battery values. Regular  update of the current battery value of the device in the energy tables in central cloud servers leads to accurate assessment of the devices with better optimal capacities. Breaking certain complicated  tasks into several sub-tasks and splitting them among the nodes based on assessment of their  energy consumption values are possible. But, the major issues in real-time tasks are lack of  accuracy in prediction of the amount of energy values consumed. As most of the tasks are new  and accuracy of the prediction leads to better scheduling to the appropriate queues. Though,  over  80% of the tasks are predictable in nature, the major challenge is in assessing the complexity of  the 20% of the jobs which are new and have no past histories of resource consumption.  Therefore clustering of the nodes based upon the battery levels, computational power and the  resource availability helps in formulation of a better optimal model. 
 

    
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