Enhanced First-fit Decreasing Algorithm for Energy-aware Job Scheduling in Cloud

With the emergence of many new data centers around the globe, large-scale commercial and scientific applications executed in the cloud call for efficient cloud resource management strategies to save energy without compromising the performance. According to the statistics from the Data Centre Dynamic (DCD) organization, the expected energy consumption by computer servers would increase by 19% in 2013 compared with 2012. Such a trend may continue for many years. Moreover, the estimated energy consumption of computers in the U.S. was about 2% out of the total electrical consumption in 2010, which makes IT industry the second largest pollution contributor after aviation. In this paper, a novel approach for scheduling, sharing and migrating Virtual Machines (VMs) for a bag of cloud tasks is designed and developed to reduce energy consumption within certain execution time and high system throughput. This approach is derived from an Enhanced First Fit Decreasing (EFFD) algorithm combined with our VM reuse strategy. Furthermore, a virtual machine migration method is introduced to dynamically monitor the cloud situation for necessary migration. Our simulation results using the open source CloudReport show that EFFD with our VM reuse strategy could gain a higher resource utilization rate and lower energy consumption than regular Greedy, Round Robin (RR) and FDD without VM reuse.