Paul Barham

The design and implementation of an operating system to support distributed multimedia applications

Support for multimedia applications by general purpose computing platforms has been the subject of considerable research. Much of this work is based on an evolutionary strategy in which small changes to existing systems are made. The approach adopted is to start ab initio with no backward compatibility constraints. This leads to a novel structure for an operating system. The structure aims to decouple applications from one another and to provide multiplexing of all resources, not just the CPU, at a low level. The motivation for this structure, a design based on the structure, and its implementation on a number of hardware platforms is described.

Devices on the desk area network

The desk area network was proposed as an architecture suitable for a multimedia workstation. This paper describes how the architecture has evolved and the demonstration workstation that has been constructed. >

Measurement-based resource allocation for multimedia applications

Modern networks are now capable of guaranteeing a consistent quality of service (QoS) to multimedia traffic streams. A number of major operating system vendors are also working hard to extend these guarantees into the end-system. In both cases, however, there remains the problem of determining a service rate sufficient to ensure the desired quality of service. Source modeling is not a sustainable approach in the network case and it is even less feasible to model the demands of multimedia applications. The ESPRIT measure project is successfully using on-line measurement and estimation to perform resource allocation for bursty traffic in ATM networks. In this paper we consider the applicability of the same theory to resource allocation in a multimedia operating system which offers QoS guarantees to its applications.

Efficient Data-parallel Computing on Small Heterogeneous Clusters

Cluster-based data-parallel frameworks such as MapReduce, Hadoop, and Dryad are increasingly popular for a large class of compute-intensive tasks. Such systems are designed for large-scale clusters, and employ several techniques to decrease the run time of jobs in the presence of failures, slow machines, and other effects. In this paper, we apply Dryad to smaller-scale, “ad-hoc” clusters such as those formed by aggregating the servers and workstations in a small office. We first show that, while Dryad’s greedy scheduling algorithm performs well at scale, it is significantly less optimal in a small (5-10 machine) cluster environment where nodes have widely differing performance characteristics. We further show that in such cases, performance models of dataflow operators can be constructed which predict runtimes of vertex processes with sufficient accuracy to allow a more intelligent planner to achieve significant performance gains for a variety of jobs, and we show how to efficiently construct such models. Our system enhances the DryadLINQ data-parallel language compiler with a planner/optimizer implemented using constraint programming, and can exploit our operator models to significantly enhance the performance of parallel jobs on ad-hoc clusters.