Rahul Yadav

TL;DR: Three adaptive models, namely, gradient descent-based regression (Gdr), maximize correlation percentage (MCP), and bandwidth-aware selection policy (Bw), that can significantly minimize energy consumption and SLA violation are proposed.

TL;DR: This paper addresses the challenges and provides an Energy-efficient dynamic Computation Offloading and resources allocation Scheme (ECOS) to minimize energy consumption and service latency and proposes a heuristic approach to solve the resource allocation problem between the vehicular node and selected user tasks for energy-latency tradeoff.

TL;DR: Adapt heuristic algorithms, namely least medial square regression for overloaded host detection and minimum utilization prediction for VM selection from overloaded hosts are proposed, reducing CDC energy consumption with minimal SLA.

TL;DR: This paper proposes Computation Offloading using Reinforcement Learning (CORL) scheme to minimize latency and energy consumption, and shows the benefits of the proposed scheme in terms of saving energy, minimizing latency, and maximum utilization of node resources in edge-enabled sensor networks.

TL;DR: An adaptive heuristics energy-aware algorithm is proposed, which creates an upper CPU utilization threshold using recent CPU utilization history to detect overloaded hosts and dynamic VM selection algorithms to consolidate the VMs from overloaded or underloaded host.