Advanced Deep Reinforcement Learning Protocol to Improve Task Offloading for Edge and Cloud Computing

Hashem, Walaa M.; Attia, Radwa; Nashaat, Heba; Rizk, Rawya

doi:10.1007/978-3-031-03918-8_51

Cited by 3 publications

(3 citation statements)

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“…The convergence here is the process of moving closer to the optimal. More precisely, GR, obtained by (19), refers to gain from the optimal weighted execution cost of executing all the channel tasks at time t (𝜓 2 (D 𝑖𝑛 (𝑡), 𝐴(𝑡))) to the minimum weighted execution cost of executing all the channel tasks at time t (𝜓 1 (D 𝑖𝑛 (𝑡), 𝐴(𝑡))) found by the DRL-DO algorithm.…”

Section: Simulation Implementation and Resultsmentioning

confidence: 99%

“…Several centralized offloading algorithms are proposed in [17][18][19] to solve the above problems while considering the total system state. An online predictive offloading algorithm based on DRL and Long Short-Term Memory (LSTM) networks is proposed in [17], it predicts the load of the ES in real time during the model's training phase and allocates the computational resources for the task in advance to substantially increase the convergence speed and accuracy of the DRL algorithm during the offloading process.…”

Section: Related Workmentioning

confidence: 99%

“…In [18], a DRL-based Task Offloading with cloud edge jointly Load Balance Optimization (TOLBO) algorithm is proposed to select the best ES or CS for offloading in order to minimize long-term task latency and energy consumption by jointly considering the requirements of latency and energy-sensitive tasks and the overall load dynamics in the cloud, edge, and end layers. An advanced DRL-based offloading algorithm is proposed in [19], considering the previous processing time for MDs, ESs, and CS, it can generate and store multi-class offloading decisions with the system state together in a database and then training and updating multiple parallel CNNs with a batch of labeled data for executing independent tasks. Despite the rapid convergence and global optimum benefits of centralized techniques, they are not scalable and unable to effectively address application placement issues due to their excessive computation, communication overhead, limited generalizability, and slow flexibility.…”

Section: Related Workmentioning

confidence: 99%

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DRL-Based Distributed Task Offloading Framework in Edge-Cloud Environment

Nashaat,

Hashem,

Rizk

et al. 2024

IEEE Access

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The Internet of Things (IoT), real-time media streaming, and other related technologies have increased due to the rapid development of wireless communication technologies and the enormous growth of computation, storage, and data transmission tasks. Edge-Cloud Computing (ECC) is a technology that can be used to better meet the diverse needs of IoT users, it combines the benefits of Mobile Cloud Computing (MCC) and Mobile Edge Computing (MEC) to meet energy consumption and delay requirements, and achieve more stable and affordable task execution. The most significant challenge in ECC is making realtime task offloading decisions. In order to generate offloading decisions in ECC environments in an efficient and near optimal manner, a Deep Reinforcement Learning (DRL)-based Distributed task Offloading (DRL-DO) framework is proposed. Simulation results demonstrate the accuracy of the DRL-DO framework, it achieves high Gain Ratio (GR) and greatly reduces the energy consumption, response time, while attaining moderate time cost compared with other offloading algorithms.

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Section: Simulation Implementation and Resultsmentioning

confidence: 99%