Performance analysis of BIO Inspired Load Balancing Algorithms in Cloud Computing
Title: Improving the Resource Management in Cloud Computing through multi metric Load Balancing using BIO inspired algorithms
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Scenario 1: ACO Algorithm Implementation:
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Step 1: Initially, we create a cloud-sim environment with n number of users, n number of data centers, n number of virtual machines, n number of brokers and n number of cloud service providers.
Step 2: Next, we collect and preprocess the Bitbrains dataset
Step 3: Next we implement the ACO Algorithm for load balancing process
Step 4: Finally we generate graph for
4.1: Response Time(s) vs no of Tasks
4.2: Makespan Time(s) vs no of Tasks
4.3: execution time(s) vs Number of VMs used
4.4: Degree of Imbalance vs Iterations
4.5: Number of VM migrations vs no of Tasks
4.6: CPU utilization(%) vs Time(s)
4.7: Resource Utilization(%) vs no of Tasks
4.8: Processing Speed(s) vs no of Tasks
4.9: Execution Cost(%) vs no of Tasks
4.10: Scalability(%) vs no of users
4.11: SLA violation rate(%) vs no of users
Scenario 2: PSO Algorithm basic Implementation:
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Step 1: Initially, we create a cloud-sim environment with n number of users, n number of data centers, n number of virtual machines, n number of brokers and n number of cloud service providers.
Step 2: Next, we collect and preprocess the Bitbrains dataset
Step 3: Next we implement the PSO basic Algorithm for load balancing process
Step 4: Finally we generate graph for
4.1: Response Time(s) vs no of Tasks
4.2: Makespan Time(s) vs no of Tasks
4.3: execution time(s) vs Number of VMs used
4.4: Degree of Imbalance vs Iterations
4.5: Number of VM migrations vs no of Tasks
4.6: CPU utilization(%) vs Time(s)
4.7: Resource Utilization(%) vs no of Tasks
4.8: Processing Speed(s) vs no of Tasks
4.9: Execution Cost(%) vs no of Tasks
4.10: Scalability(%) vs no of users
4.11: SLA violation rate(%) vs no of users
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