Problem: Task Scheduling in Cloud Computing Suppose you are given N tasks and M virtual machines (VMs). Each task has a specified size (unit: million instructions) and deadline (unit: seconds). Each VM also has a main characteristic called processing speed (unit: million instructions per second). The task scheduling problem can be defined as follows: Mapping tasks to VMs in such a way that the completion time of the last task, i.e., Makespan, is optimized while meeting the deadline of the tasks. If the deadline of a task is not met, that task must be rejected. Solve and implement this problem using the following algorithms: A) An efficient heuristic algorithm b) Genetic Algorithm (GA) Finally, put the results of these two algorithms in the attached Excel file so that we can compare them with each other. In order to increase the reliability of the results, please run each algorithm 10 times and report the average, maximum and minimum values. - Choose the size of the tasks randomly from the range [1000-1000]. - The deadline of the tasks should be chosen randomly from the interval [10-60]. - The processing speed of virtual machines should be randomly selected from the range [2000- 8000]. - First scenario: consider the number of tasks from 50 to 300 with steps of 50 and the number of VMs equal to 15. - Second scenario: Consider the number of VMs from 5 to 30 with steps of 5 and the number of tasks equal to 200. - For the genetic algorithm, consider the initial population size equal to 20 and the number of iterations equal to 100. - Set the rest of the required parameters arbitrarily and experimentally. - You can do the implementation with any desired programming language.

Number of Tasks
50	100	150	200	250	300
14.70	22.04	36.87	39.91	44.61	57.45
4.13	7.67	22.50	7.15	15.66	31.12
3.44	5.14	15.26	16.94	12.40	17.38
11.99	31.48	35.45	54.75	50.08	78.03
3.47	34.41	17.29	31.94	27.37	28.73
3.75	11.57	13.78	18.30	17.24	37.82