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Improved salp swarm algorithm based optimization of mobile task offloading.

Aishwarya R¹, Mathivanan G¹

¹Department of Computer Science and Engineering, Sathyabama Institute of Science and Technology, Jeppiaar Nagar, Chennai, Tamil Nadu, India.

Peerj. Computer Science

|June 26, 2025

Summary

This study introduces an improved salp swarm algorithm for mobile edge computing (MEC) to optimize task offloading. The ISSA-MAOA technique minimizes energy consumption, memory usage, and task delays for better mobile application performance.

Keywords:

Cloud computing Mobile application offloading Mobile edge computing Salp swarm algorithm Task offloading

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Area of Science:

Computer Science
Artificial Intelligence
Mobile Computing

Background:

Mobile devices and IoT require significant computation for applications like real-time video processing and augmented reality.
Limited energy and computational power on edge devices can lead to high latency and energy consumption for intensive tasks.
Mobile Edge Computing (MEC) offers a solution by offloading complex computations to edge servers for faster processing.

Purpose of the Study:

To address the limitations of concurrent task execution and optimize task offloading in MEC environments.
To minimize energy consumption and response time for mobile applications by finding an optimal task offloading range.
To introduce an improved salp swarm algorithm-based Mobile Application Offloading Algorithm (ISSA-MAOA) for efficient task allocation.

Main Methods:

Developed an improved salp swarm algorithm (ISSA) to intelligently allocate computing tasks between mobile devices and edge servers.
Implemented the ISSA-MAOA technique to concurrently minimize energy consumption, memory usage, and task completion delays.
Focused on evolutionary algorithms for resolving multi-objective optimization problems in task offloading.

Main Results:

The ISSA-MAOA technique effectively optimizes task allocation in MEC, leading to reduced energy consumption and latency.
Demonstrated the capability of the improved salp swarm algorithm in enhancing mobile cloud computing (MCC) frameworks.
Achieved concurrent minimization of energy, memory, and delay for offloading tasks.

Conclusions:

The proposed ISSA-MAOA provides a more efficient and sustainable solution for offloading tasks in mobile applications.
The research contributes to improved resource management and enhanced user interactions in MEC environments.
This approach leads to significant improvements in overall efficiency for mobile cloud computing.