“Delay-Constrained Task Offloading and Resource Optimization in Edge-Cloud Networks’’
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“Delay-Constrained Task Offloading and Resource Optimization in Edge-Cloud Networks’’
Authors:
Mr. Mula Mahender*1, M.Hithesh*2, G.Aswitha*3, Nithin Goud*4,
*1Associate Professor, CSE (AI & ML), ACE Engineering College, Hyderabad, India.
*2,3,4 Students of Department CSE (AI & ML), ACE Engineering College, Hyderabad, India.
ABSTRACT-Delay-Constrained Task Offloading and Resource Optimization in Edge-Cloud Networks A joint optimization model is presented to reduce task latency and energy expenditure while satisfying application deadlines. The problem is cast as a mixed-integer nonlinear program (MINLP), and a hybrid solution integrating deep reinforcement learning and convex optimization is formulated to handle its complexity. This paper is extremely applicable to latency-sensitive IoT, augmented reality, and smart transportation applications. Our method is shown through extensive simulations to significantly reduce latency and save energy compared to conventional methods. In comparison to random or cloud-only approaches, our model guarantees timely completion of delay-sensitive tasks while optimizing resource usage throughout the network. This framework has great potential for facilitating faster, greener, and smarter future computing services in hybrid edge-cloud networks.
Keywords: Edge Computing, Cloud Computing, Task Offloading, Delay Constraints.
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