Resource-Aware-Energy-Efficient-Workflow-Scheduling-in-Cloud-Infrastructure

Implementation in python for the paper:

M. S. Kumar, I. Gupta and P. K. Jana, "Resource-Aware Energy Efficient Workflow Scheduling in Cloud Infrastructure," 2018 International Conference on Advances in Computing, Communications and Informatics (ICACCI), Bangalore, 2018, pp. 293-299, doi: 10.1109/ICACCI.2018.8554707.

• Link to Paper

• for speedup, EET = max{total execution time if all task run on only vm[i] for i in range(vm_count)}

• to use cost function, give(set) vm_cost array explicitly

• otherwise cost_2 function will calculate cost

• for more optimum result(i.e. less energy consumption and more vm utilisation), tune budget and deadline parameters

• in evergy_vf function, I have taken random values for operating frequency and voltage, take a fixed value.

• all the dataset should be in 'Dataset' folder

• dataset contain number of task and number of vms in first line, then ETR matrix with [task_count * vm_count] dimension,

• then it contain 2-D communication cost array(adjacency matrix) to tell about task dependency for DAG creation.

• Demo

  • Task to schedule

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  • Implementation result of my code

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Additional metrics used:

• cost calculation:

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• energy consumption BusyIdle:

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• energy consumption VolatageFrequency:

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• load balancing factor

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• load balancing RTL

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• vm utilisation

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• speedup

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