Energy Efficient Workflow Management System for Workload Distribution and Scheduling in Cloud Data Centers

Cloud computing is deemed appropriate for enterprises because to its adaptability and the delivery of digital amenities through the Internet. The cloud offers virtually unlimited computing resources on request, devoid of initial expenses or extended agreements, allowing enterprises to fulfil their needs for computing more cost-effectively. Several aspects are impacted by the platform's efficacy, including QoS, overload, the lifespan, energy usage, reliability, and adaptability. Data centers significantly contribute to carbon emissions, hence impacting universal carbon footprints. In consideration of delivering an environmentally conscious approach to society, different aspects that can facilitate the attainment of carbon neutrality and optimal sustainability were examined. Throughout the data centre's lifetime, our research indicated that a sustainable approach to incoming workflow was necessary. This research examined numerous bio-inspired strategies to identify the most effective method for load balancing in geographically distributed cloud data centers. The four unique algorithms-ACO, Improved ACO, Firefly, and Improved Firefly-are assessed concerning their energy usage, the frequency of VM migrations, and number of SLA violations. Enhanced Firefly is distinguished as the superior algorithm in all metrics, exhibiting the lowest energy use, the fewest VM migrations, and least SLA violations. ACO demonstrates its lowest efficiency for energy usage and virtual machine migrations, although shows modest service level agreement performance. The improved ACO offers a thorough enhancement in all areas relative to the original ACO. Firefly, despite its energy efficiency, compromises service quality by elevating SLA violations.