A Migratable User-Level Process Package for PVM

Shared, multi-user, workstation networks are characterized by unpredictable variability in system load. Further, the concept of workstation ownership is typically present. For efficient and unobtrusive computing in such environments, applications must not only overlap their computation with communication but also redistribute their computations adaptively based on changes in workstation availability and load. Managing these issues at application level leads to programs that are difficult to write and debug. In this paper, we present a system that manages this dynamic multi-processor environment while exporting a simple message-based programming model of a dedicated, distributed memory multiprocessor to applications. Programmers are thus insulated from the many complexities of the dynamic environment at the same time are able to achieve the benefits of multi-threading, adaptive load distribution and unobtrusive computing. To support the dedicated multi-processor model efficiently, the system defines a new kind of virtual processor called User-Level Process (ULP) that can be used to implement efficient multi-threading and application-transparent migration. The viability of ULPs is demonstrated through UPVM, a prototype implementation of the PVM message passing interface using ULPs. Typically, existing PVM programs written in Single Program Multiple Data (SPMD) style need only be re-compiled to use this package. The design of the package is presented and the performance analyzed with respect to both micro-benchmarks and some complete PVM applications. Finally, we discuss aspects of the ULP package that affect its portability and its support for heterogeneity, application transparency, and application debugging

LoadBuilder: a Tool for Generating and Modeling Workloads in Distributed Workstation Environments

This report presents LoadBuilder, a distributed environment designed to provide a portable way of empirically studying the effects of various kinds of workloads in local area networks of heterogeneous workstations. This tool is especially intended to build distributed experimentations including composite workload setting, statistics collecting and performance evaluations. The statistical analysis of the experimental results will help the dynamic load balancing designer to select the most meaningful indicators out of the plethora available on workstations, to establish behavior models of the workstations, to exhibit critical workload thresholds and finally, to establish his own set of meaningful workload indicators. In the following, we first describe the architecture of the environment. Then, we present and discuss the algorithms used to build (a) synthetic workloads, (b) statistics collectors and (c) measurement procedures

Effective task assignment strategies for distributed systems under highly variable workloads

Heavy-tailed workload distributions are commonly experienced in many areas of distributed computing. Such workloads are highly variable, where a small number of very large tasks make up a large proportion of the workload, making the load very hard to distribute effectively. Traditional task assignment policies are ineffective under these conditions as they were formulated based on the assumption of an exponentially distributed workload. Size-based task assignment policies have been proposed to handle heavy-tailed workloads, but their applications are limited by their static nature and assumption of prior knowledge of a task's service requirement. This thesis analyses existing approaches to load distribution under heavy-tailed workloads, and presents a new generalised task assignment policy that significantly improves performance for many distributed applications, by intelligently addressing the negative effects on performance that highly variable workloads cause. Many problems associated with the modelling and optimisations of systems under highly variable workloads were then addressed by a novel technique that approximated these workloads with simpler mathematical representations, without losing any of their pertinent original properties. Finally, we obtain advance queuing metrics (such as the variance of key measurements like waiting time and slowdown that are difficult to obtain analytically) through rigorous simulation