Chennoji Sandhya; Mandla Alphonsa; Vankudoth Biksham; T. L. Deepika Roy; Santhosh Kumar Medishetti

Energy and Carbon Emission Aware Task Scheduling in Cloud Computing Using Memetic Optimization Framework

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Author(s)

Chennoji Sandhya ¹ Mandla Alphonsa ² Vankudoth Biksham ³ T. L. Deepika Roy ⁴ Santhosh Kumar Medishetti ⁵

1. Geethanjali College of Engineering and Technology, Hyderabad, Telangana, India

2. Vasavi college of Engineering, Hyderabad, Telangana, India

3. School of Engineering, Department of Data Science, Anurag University, Hyderabad, Telangana, India

4. Koneru Lakshmaiah Education Foundation, Green fields, Guntur, Andhra Pradesh, India

5. Nalla Narasimha Reddy Education Society's Group of Institutions, Hyderabad, Telangana, India

* Corresponding author.

DOI: https://doi.org/10.5815/ijisa.2026.04.05

Received: 8 Jan. 2026 / Revised: 15 Mar. 2026 / Accepted: 21 Jun. 2026 / Published: 8 Aug. 2026

Index Terms

Cloud Computing, Energy Consumption, Carbon Emission, Memetic Algorithm, Task Scheduling, Virtual Machines

Abstract

Minimizing energy consumption and carbon emissions while maintaining system performance is a critical challenge in cloud task scheduling. This paper presents a multi-objective scheduling framework based on a Memetic Algorithm (MA) designed to optimize task-to-VM mapping with respect to energy efficiency, carbon footprint, and throughput. The algorithm employs a weighted fitness function that integrates actual and idle energy usage, simulated time-varying carbon intensity, and task throughput. To enhance solution quality, MA combines global evolutionary operations (selection, crossover, mutation) with local search heuristics that adaptively refine candidate solutions based on workload characteristics and green energy opportunities. The carbon emission model incorporates dynamic emission factors (γ) derived from location- and time-sensitive datasets, reflecting real-world variability in grid carbon intensity. The proposed method is evaluated using the NASA Ames iPSC/860 workload under both low and high resource utilization scenarios. Comparative results demonstrate that the proposed MA approach achieves reduces the carbon emission by 20.2%, minimizes energy consumption by 17.7%, and enhances throughput by 21.2% over conventional techniques such as HDDPGTS and RAPTS, while also ensuring competitive performance in terms of makespan and resource utilization. These improvements underscore the potential of memetic-based hybrid scheduling to support environmentally sustainable and performance-efficient cloud infrastructures. The findings highlight the importance of integrating eco-aware intelligence into task scheduling policies, particularly for mission-critical and energy-intensive cloud applications.

Cite This Paper

Chennoji Sandhya, Mandla Alphonsa, Vankudoth Biksham, T. L. Deepika Roy, Santhosh Kumar Medishetti, "Energy and Carbon Emission Aware Task Scheduling in Cloud Computing Using Memetic Optimization Framework", International Journal of Intelligent Systems and Applications(IJISA), Vol.18, No.4, pp.73-96, 2026. DOI:10.5815/ijisa.2026.04.05

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