An Online Mechanism for Purchasing IaaS Instances and Scheduling Pleasingly Parallel Jobs in Cloud Computing Environments
Bingbing Zheng, Li Pan, Shijun Liu, Lu Wang · 2019
Nowadays, many users select to outsource their job executions to service clouds. These users often have heterogeneous demands while they dynamically arrive at the clouds. For reducing the costs and risks, lots of service cloud operators purchase on-demand instances from public IaaS clouds and provide professional services elastically to users. However, without knowing the future information, it is hard for cloud operators to optimally determine the instance purchasing as well as job scheduling and pricing schemes. In order to achieve maximum social welfare, this paper targets to design an auction mechanism which executes in an online fashion for service clouds, with unique features of job-oriented users, pleasingly parallel jobs and soft deadline constraints. Such a mechanism ought to run in polynomial time, provide truthfulness guarantee, satisfy individual rationality and budget balance, and achieve competitive social welfare. Nevertheless, when designing mechanisms there are a few significant challenges, including the difficulty for finding optimal solution, the strategic behaviours of selfish users with private information and the online arrivals of users. Facing these challenges, we leverage the idea of proportional sharing and propose an online mechanism which is proven to achieve all desired properties. The efficiency of the proposed mechanism is validated by both theoretical analysis and extensive simulations which use both synthetic data and Google's job traces.