SAEA: A security-aware and energy-aware task scheduling strategy by Parallel Squirrel Search Algorithm in cloud environment

The rapid growth of networking technologies resulted in the execution of an extensive data-centric task, which needs the critical quality of service by cloud data centers. The task scheduling problem is difficult to attain an optimal solution, so we use the Squirrel Search Algorithm to approximate the optimal solution. Traditional scheduling algorithms attempt to reduce execution time without taking into account the energetic cost and security issues. In this scheme, a fuzzy-based task scheduling (SAEA) algorithm is developed which closely combines energy cost, makespan, degree of imbalance, and security levels for multi-objective optimization scheduling problems. In addition, SAEA tries to find a high-quality knowledge base that accurately describes the fuzzy system by parallel squirrels search algorithm (PSSA). The automatic design of a fuzzy rule-based system is currently attracting the interest due to the inherently dynamic nature and the typical complex search spaces of cloud. Extensive experiments prove that SAEA algorithm obtains superior performances in energy cost around 45% compared with MGA and has a better result in terms of total execution time, makespan, degree of imbalance, and security value than other similar scheduling algorithms under high load condition.

Automated summary

The study developed a fuzzy-based task scheduling (SAEA) algorithm to address the difficult task scheduling problem, incorporating energy cost, makespan, degree of imbalance, and security levels for multi-objective optimization. The experiment results showed that SAEA algorithm outperforms other similar scheduling algorithms in terms of energy cost reduction and overall performance under high load conditions.