The architecture and usage modalities of storage systems have been revolutionized by advances in disk drive and networking technologies. Resource management issues occupy a central and increasingly important role in the operation of data centers that must coordinate the operation of large numbers of concurrent devices and serve hundreds of gigabytes of data per second. Such shared storage servers are being proposed as a cost-effective solution for maintaining data repositories. This research effort addresses resource allocation and scheduling issues that arise in multiplexing a shared storage server and seeks to provide isolation and performance guarantees for multiple contending flows that may have different characteristics. The research is developing robust models, algorithms, and mechanisms that enable efficient sharing of server I/O resources while enforcing performance guarantees, in the presence of dynamically varying resource constraints. The results will also have applicability to systems of heterogeneous servers.
The intellectual advance pursued in this research is the systematic investigation of a new set of resource management issues that arise in the shared server environment. It studies, at a fundamental level, the problem of providing fair or differentiated quality of service among diverse concurrent clients (flows) accessing a shared parallel I/O system. The work is developing robust models that incorporate the unique resource constraints and performance responses arising in parallel I/O, developing efficient algorithms for adaptive resource allocation, formal characterization of the solutions with respect to performance and stability, and the development of simulation infrastructure for empirical evaluation of the solutions.
The impact of the research will be more robust and scalable resource management for data centers such as commercial and supercomputing server environments. The research will produce mechanisms to enforce service agreements between the service provider and concurrent clients, reaping the economies of scale of shared resources and the increasing economic benefits of consolidated management. The research will also result in the training and education of graduate students in a growing sector of the information technology industry.
