Abstract
Load balancing effectively distributes network load and balances the load during the scheduling and allocation
process. Hence various load balancing techniques in task scheduling and resource allocation along with VM
migration has been presented previously but they have a heavy load on some VM and violate cloud service level
agreement with a single point of failure. Therefore, a novel Intelligent PSO-based Feedback Controller has been
proposed with regulated Scheduling, Allocation, and VM migration to perform optimal load balancing. In this
proposed technique, a novel Intelligent Weighted filtering based PSO Approach is used to reduce computation
time during task scheduling and resource allocation. This approach uses a multi-objective PSO algorithm with
Pareto dominance to achieve high quality of service, throughput, scalability, low response time, and optimal
bilateral transposed conv filtering. Moreover, during VM migration existing techniques result in service level
agreement violations owing to inefficient VM placement among PMs. To overcome these issues, a Double Deep Q
proximal model with a feedback controller has been proposed. The double weight set in the offline and online
updating process in the decision model maintains a smooth service level agreement with the cloud. Also,
centralized and decentralized controller algorithm fails with a single point of failure and coordination issue in
complicated situations with instruction mixing of processes. Finally, the conditional GAN feedback controller has
been used to eliminate a single point of failure with high fault tolerance, low energy consumption and migration
time.
process. Hence various load balancing techniques in task scheduling and resource allocation along with VM
migration has been presented previously but they have a heavy load on some VM and violate cloud service level
agreement with a single point of failure. Therefore, a novel Intelligent PSO-based Feedback Controller has been
proposed with regulated Scheduling, Allocation, and VM migration to perform optimal load balancing. In this
proposed technique, a novel Intelligent Weighted filtering based PSO Approach is used to reduce computation
time during task scheduling and resource allocation. This approach uses a multi-objective PSO algorithm with
Pareto dominance to achieve high quality of service, throughput, scalability, low response time, and optimal
bilateral transposed conv filtering. Moreover, during VM migration existing techniques result in service level
agreement violations owing to inefficient VM placement among PMs. To overcome these issues, a Double Deep Q
proximal model with a feedback controller has been proposed. The double weight set in the offline and online
updating process in the decision model maintains a smooth service level agreement with the cloud. Also,
centralized and decentralized controller algorithm fails with a single point of failure and coordination issue in
complicated situations with instruction mixing of processes. Finally, the conditional GAN feedback controller has
been used to eliminate a single point of failure with high fault tolerance, low energy consumption and migration
time.
| Original language | English |
|---|---|
| Journal | Sustainable Computing: Informatics and Systems |
| Publication status | Published - 1 Jan 2024 |