QoS-aware middleware for cluster-based servers to support interactive and resource-adaptive applications

Advances in commodity processor and network technologies have made cluster-based servers very attractive for supporting a large number of interactive applications (such as visualization and data mining) in the domains of Grid computing and distributed computing. These applications involve accesses to huge amounts of data within the servers and heavy computations on the accessed data before sending out the results to the clients. The interactive nature of these applications requires some kind of QoS support (such as guarantees on response time) from the underlying server. Unfortunately, the current generation cluster-based servers with the popular interconnect (Gigabit Ethernet, Myrinet, or Quadrics) do not provide any kinds of QoS support. Fortunately, many of these applications are resource-adaptive, i.e., application parameters can be changed to suit user demands and available system resources. To solve these problems, a new QoS-aware middleware layer is proposed in this paper for cluster-based servers with Myrinet interconnect. The middleware is built on top of a simple NIC-based rate control scheme that provides proportional bandwidth allocation. Three major components of the middleware (profiler, QoS translator, and resource allocator), their functionalities, designs, and the associated algorithms are presented. These components work together to execute a requested job in a predictable manner with an efficient allocation of system resources while exploiting the resource-adaptive property of the application. The complete middleware is designed, developed, and implemented on a Myrinet cluster. It is evaluated for two visualization applications: polygon rendering and ray-tracing. Experimental evaluations demonstrate that the proposed QoS framework enables multiple interactive and resource-adaptive applications to be executed in a predictable manner while keeping the allocation of system resources efficient. It is shown that the QoS-aware middleware helps applications to obtain response times within 7% of the expected times, compared to increases of up to 117% in the absence of any QoS support.