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Ibrahim A. Elgendy
Researcher at Harbin Institute of Technology
Publications - 40
Citations - 1029
Ibrahim A. Elgendy is an academic researcher from Harbin Institute of Technology. The author has contributed to research in topics: Computation offloading & Cloud computing. The author has an hindex of 9, co-authored 30 publications receiving 334 citations. Previous affiliations of Ibrahim A. Elgendy include Menoufia University.
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Adaptive Energy-Aware Algorithms for Minimizing Energy Consumption and SLA Violation in Cloud Computing
Rahul Yadav,Weizhe Zhang,Omprakash Kaiwartya,Prabhat Ranjan Singh,Ibrahim A. Elgendy,Yu-Chu Tian +5 more
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.
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Joint computation offloading and task caching for multi-user and multi-task MEC systems: reinforcement learning-based algorithms
Ibrahim A. Elgendy,Ibrahim A. Elgendy,Weizhe Zhang,Hui He,Brij B. Gupta,Brij B. Gupta,Ahmed A. Abd El-Latif +6 more
TL;DR: This study proposes an offloading model for a multi-user MEC system with multi-task, and an equivalent form of reinforcement learning is created where the state spaces are defined based on all possible solutions and the actions are defined on the basis of movement between the different states.
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Resource allocation and computation offloading with data security for mobile edge computing
TL;DR: A multiuser resource allocation and computation offloading model with data security to address the limitations of mobile users and IoT devices and can significantly improve the performance of the entire system compared with local execution and full offloading schemes.
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Secure and Optimized Load Balancing for Multitier IoT and Edge-Cloud Computing Systems
Weizhe Zhang,Ibrahim A. Elgendy,Mohamed Hammad,Abdullah M. Iliyasu,Xiaojiang Du,Mohsen Guizani,Ahmed A. Abd El-Latif +6 more
TL;DR: An integrated model of load balancing, CO and security is formulated as a problem whose goal is to decrease the time and energy demands of the system.
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Efficient and Secure Multi-User Multi-Task Computation Offloading for Mobile-Edge Computing in Mobile IoT Networks
TL;DR: An efficient and secure multi-user multi-task computation offloading model with guaranteed performance in latency, energy, and security for mobile-edge computing and can scale well for large-scale IoT networks.