  With the emergence of large-scale data-intensive high-performance
applications, new I/O challenges appear in the efficient management of
petabytes of information in High-Performance Computing (HPC) environments. Data
management environments must meet the performance needs of such applications,
represented by various Quality-of-Service (QoS) metrics such as desired
bandwidth, response time guarantee, and resource utilization. Traditional
high-performance management platforms are facing considerable challenges
regarding flexibility, as well as the need to address a variety of QoS metrics
and constraints. To tackle these challenges, a Software-Defined approach is
considered promising, and various prototypes have already been deployed in
Cloud-based data centers. In this paper, we investigate the idea of utilizing a
software-defined approach to provide I/O QoS provisioning for HPC applications.
We identify the key challenges towards the high degree of concurrency and
variation in HPC platforms, and propose a series of novel designs into the
general software-defined approach in order to deliver our goal. Specifically,
we introduced a borrowing-based strategy and a new M-LWDF algorithm based on
traditional token-bucket algorithms to assure a fair and efficient utilization
of resources for HPC applications. Due to the lack of software-defined
frameworks in current HPC platform, we evaluated our framework through
simulation. The experimental results show that our strategies make a
significant improvement upon the general HPC frameworks and lead to clear
performance gain for HPC applications.
