ICTACT Journal on Communication Technology

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Multi-Agent Independent Non-Cooperative Reinforcement Learning for Load Balancing in Cloud Heterogeneous Networks


Affiliations
1 Department of Electronics and Communication Engineering, IK Gujral-Punjab Technical University, India
2 Department of Electronics and Communication Engineering, DAV Institute of Engineering and Technology, India

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Cloud services are now seeing significant advancements and have witnessed a growing demand. Hence, implementing Load Balancing is necessary to enhance resource usage by effectively distributing workload among numerous Virtual Machines (VMs). The present research aims to address task scheduling challenges and achieve efficient load balancing for all VMs by implementing a novel noncooperative load balancing algorithm called Multi-agent Independent Deep Q Networks (MAIDQN-LB) in cloud computing heterogeneous networks. The list of tasks is passed to MAIDQN-LB, which will search for a list of VM to be allocated, maintaining the load of all VMs. This procedure facilitates the identification of optimized VMs, the allocation of workloads based on the optimal solution derived from the analysis and optimize the performance parameters. The performance analysis considers essential parameters, including makespan time, average turnaround time, average response time, degree of imbalance (DI), task rejection rate (TRR), and convergence loss. The findings indicate that MAIDQN-LB demonstrates superior performance compared to the current system, exhibiting enhancements of 1.82% and 0.05% regarding DI and TRR.
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  • Multi-Agent Independent Non-Cooperative Reinforcement Learning for Load Balancing in Cloud Heterogeneous Networks

Abstract Views: 103  | 

Authors

Tanu Kaistha
Department of Electronics and Communication Engineering, IK Gujral-Punjab Technical University, India
Kiran Ahuja
Department of Electronics and Communication Engineering, DAV Institute of Engineering and Technology, India

Abstract


Cloud services are now seeing significant advancements and have witnessed a growing demand. Hence, implementing Load Balancing is necessary to enhance resource usage by effectively distributing workload among numerous Virtual Machines (VMs). The present research aims to address task scheduling challenges and achieve efficient load balancing for all VMs by implementing a novel noncooperative load balancing algorithm called Multi-agent Independent Deep Q Networks (MAIDQN-LB) in cloud computing heterogeneous networks. The list of tasks is passed to MAIDQN-LB, which will search for a list of VM to be allocated, maintaining the load of all VMs. This procedure facilitates the identification of optimized VMs, the allocation of workloads based on the optimal solution derived from the analysis and optimize the performance parameters. The performance analysis considers essential parameters, including makespan time, average turnaround time, average response time, degree of imbalance (DI), task rejection rate (TRR), and convergence loss. The findings indicate that MAIDQN-LB demonstrates superior performance compared to the current system, exhibiting enhancements of 1.82% and 0.05% regarding DI and TRR.