Task Scheduling Based on Multi-level Hashing and HRRN in cloud computing
Lei Zeng, Jiawei Sun, Jianhao Ma, Qi Liu · 2021 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech) · 2021
Task scheduling plays a crucial role in the cloud data center. An excellent job scheduling system can not only bring a high quality of user service, but also improve the resource utilization rate of the cloud service providers. This paper focuses on makespan and response time in task scheduling to improve quality of service. Under the framework of packing problem, this paper proposes a heterogeneous data center task scheduling algorithm based on multi-level hashing and High Response Ratio Next. Considering the different computing performance of physical hosts in heterogeneous data centers and the different resource demands of tasks, multi-level hashing is adopted to map the tasks to different queues waiting for execution, which can improve the speed of task allocation. When the tasks are waiting to be executed, the high response ratio next algorithm is executed in each queue to improve the service quality. Simulation is conducted by CloudSim, and the experimental results show that the proposed method is better than First-Come-First-Served (FCFS) and Shortest-Job-First (SJF) in the optimization of response time and makespan.