An Energy-Saving Task Scheduling Model via Greedy Strategy under Cloud Environment

Cloud computing, an emerging computing paradigm, has been widely concerned due to its high scalability and availability. An essential stage of cloud is resource management. Currently, the existing research about technology two prevalent disadvantages: energy consumption low utilization. Considering greedy scheduling effective strategy for management in particularly improving utilization reducing consumption, we consider heterogeneous characteristics resources save datacenter when tasks are fundamental element datacenter. Meanwhile, granular a complex problem-solving through granulation method. Thus, introduce theory into task propose based on different information granules, dividing three types (i.e., CPU, memory, hybrid type). Finally, assign various strategies with characteristics. All numerical experiments CloudSim platform show that our method significant effects optimization practical algorithm.