Goodbye to Fixed Bandwidth Reservation: Job Scheduling with Elastic Bandwidth Reservation in Clouds
Haiying Shen, Lei Yu, Liuhua Chen, Zhuozhao Li · 2016
The shared nature of cloud network infrastructures causes unpredictable network performance, which may degrade the performance of these applications. Recently, several works propose to explicitly reserve the network bandwidth in the cloud with virtual network abstraction models, which pre-specify the network bandwidth between virtual machines (VMs) for a tenant job. However, the pre-specification fails to exploit the elastic feature of the bandwidth resource (i.e., more reserved bandwidth within no-elongation threshold bandwidth leads to shorter job execution time and vice versa) in job scheduling. It is difficult for ordinary tenants (without specialized network knowledge) to estimate the exact needed bandwidth. In this paper, we propose a new cloud job scheduler, in which each tenant only needs to specify job deadline and each job's reserved bandwidth is elastically determined by leveraging the elastic feature to maximize the total job rewards, which represent the worth of successful completion by deadlines. Finally, the scheduler tries to reduce the execution time of each job. It also jointly considers the computational capacity of VMs and reserved VM bandwidth in job scheduling. Using trace-driven and real cluster experiments, we show the efficiency and effectiveness of our job scheduler in comparison with other scheduling strategies.