LSTM-RNN Based Efficient Execution System For Compute And Data Intensive Mobile Applications In The Edges

• Uma Natarajan,
• Anitha Ramachandran,

Vol. 19, No. 1, January 31, 2025
10.3837/tiis.2025.01.002, Download Paper (Free):

• Compute Intensive
• computation offloading
• Data-Intensive
• Edge Communication

Abstract

Mobile computing faces significant challenges, including limited processing power, battery life, and memory capacity, which hinder the performance of modern applications. This research aims to optimize mobile computing by introducing an offloading system tailored to enhance the performance of devices like smart phones and tablets. The system's key feature is predicting a mobile device's next visitation location, crucial for effective offloading. Future location prediction employs a modified LSTM (Long Short-Term Memory) Recurrent Neural Network model. The system, inclusive of Mobile Communication Manager, Edge Communication Manager, and Decision Engine, dynamically makes offloading decisions based on CPU usage, execution time, energy consumption, and memory usage. In evaluating the Decision Engine algorithm, practical experiments involve a source and four edge devices, measuring task processing latency, completion time, CPU utilization, memory usage, and energy consumption. Opting for a resource-rich edge for face recognition results in a notable reduction in processing time (177ms) and lower CPU utilization (22%) compared to the source device (2049ms, 75% CPU utilization). Practical experiments affirm the Decision Engine's efficacy in optimal offloading across diverse mobile applications.

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