Optimized Cloud-Edge Task Allocation and Load Balancing Strategy

Optimized Cloud-Edge Task Allocation and Load Balancing Strategy

Saharsha B P Dept.of.CS&E PESITM

Shimogga,India saharsha160@gmail.com

Sumanth S V Dept.of.CS&E PESITM Shimogga,India sumanthsv19@gmail.com

Sumkha Gowda Dept.of.CS&E PESITM

Shimogga,India Sumukagowda177@gmail.com

Supreeth E Dept.of.CS&E PESITM

Shimogga,India

supreeth59@gmail.com

Abstract- The rapid evolution of distributed systems has necessitated innovative approaches for managing task deployment and achieving load balancing in cloudedge environments. This paper addresses the prevalent issue of data overload at cloud computing centers and edge nodes by introducing a Joint Cloud-Edge Task Distribution (JCETD) model. Utilizing pruning algorithms and deep reinforcement learning, the proposed approach ensures efficient resource utilization and optimized task allocation. Key contributions include minimizing computational complexity and enhancing system performance through strategic pruning and adaptive learning techniques.

Keywords- Distributed systems, cloud-edge environments, task deployment, load balancing, data congestion, cloud computing centers, edge nodes, JCETD model, pruning algorithms, deep reinforcement learning, resource optimization, task allocation, computational overhead, system efficiency, adaptive learning methods.