A Novel Energy and Communication Aware Scheduling on Green Cloud Computing

The rapid growth of service-oriented and cloud computing has created large-scale data centres worldwide. Modern data centres’ operating costs mostly come from back-end cloud infrastructure and energy consumption. In cloud computing, extensive communication resources are required. Moreover, cloud applications require more bandwidth to transfer large amounts of data to satisfy end-user requirements. It is also essential that no communication source can cause congestion or bag loss owing to unnecessary switching buffers. This paper proposes a novel Energy and Communication (EC) aware scheduling (EC-scheduler) algorithm for green cloud computing, which optimizes data centre energy consumption and traffic load. The primary goal of the proposed EC-scheduler is to assign user applications to cloud data centre resources with minimal utilization of data centres. We first introduce a Multi-Objective Leader Salp Swarm (MLSS) algorithm for task sorting, which ensures traffic load balancing, and then an Emotional Artificial Neural Network (EANN) for efficient resource allocation. EC-scheduler schedules cloud user requirements to the cloud server by optimizing both energy and communication delay, which supports the lower emission of carbon dioxide by the cloud server system, enabling a green, unalloyed environment. We tested the proposed plan and existing cloud scheduling methods using the GreenCloud simulator to analyze the efficiency of optimizing data centre energy and other scheduler metrics. The EC-scheduler parameters Power Usage Effectiveness (PUE), Data Centre Energy Productivity (DCEP), Throughput, Average Execution Time (AET), Energy Consumption, and Makespan showed up to 26.738%, 37.59%, 50%, 4.34%, 34.2%, and 33.54% higher efficiency, respectively, than existing state of the art schedulers concerning number of user applications and number of user requests.