Improved slime mold algorithm boosts efficiency in e-commerce cloud data migration

As e-commerce platforms grow ever more reliant on cloud computing, efficiency and sustainability have come to the fore as urgent pressures on development. A study published in the International Journal of Reasoning-based Intelligent Systems has introduced an innovative approach to the problem based on a slime mold algorithm (SMA). The work could improve both performance and energy efficiency for e-commerce systems.

At the core of the work is the development of BOSMA—the Balanced Optimization Slime Mold Algorithm. The SMA is a heuristic optimization technique inspired by the natural behavior of slime molds.

Slime molds are useful models for algorithms because they excel at finding efficient paths through complex environments and adapting to changing conditions. Moreover, they do so without any central control system. They can explore their surroundings by sending out multiple tendrils, pseudopodia, in different directions, adjusting their shape and connections in response to feedback such as nutrient availability or obstacles.

This decentralized, probabilistic behavior helps maintain a balance between exploring new possibilities and refining promising ones. By translating these adaptive behaviors into mathematical rules, researchers have designed algorithms that solve complex computational problems, such as network routing, task scheduling, and data migration, where the goal is to find efficient solutions in large, dynamic, and uncertain search spaces.

BOSMA addresses the issues that have become apparent with the simple SMAs by integrating two key enhancements. First, the researchers have added a balanced optimization operator, which fine-tunes the algorithm’s balance between exploration (seeking out new possible solutions) and exploitation (refining known good ones).

The second enhancement is the addition of a stochastic difference variance operator, which injects some randomness into the search process and so helps avoid the kind of early convergence on an inferior solution to which simpler SMAs are prone. Together, these modifications make BOSMA more agile and more efficient in solving problems.

In this latest research, the team uses BOSMA to tackle the data migration problem. The transfer of data between different cloud systems or storage environments is complex, especially in high-volume e-commerce operations. If data migration is not optimized, then energy costs and so economic costs rise.

BOSMA recruits mobile devices and edge computing terminals to reduce the load on cloud systems. By dynamically adjusting for communication delays and other operational constraints, BOSMA thus reassigns tasks to whichever processing environment delivers the greatest energy saving.