Middleware systems for volunteer computing convert a set of computers that is large and diverse (in terms of hardware, software, availability, reliability, and trustworthiness) into a unified computing resource. This involves a number of scheduling policies and parameters, which have a large impact on the throughput and other performance metrics. How can we study and refine these policies? Experimentation in the context of a working project is problematic, and it is difficult to accurately model complex middleware in a conventional simulator. Instead, we use an approach in which the policies being studied are “emulated”, using parts of the actual middleware. In this paper we describe EmBOINC, an emulator based on the BOINC middleware system. EmBOINC simulates a population of volunteered clients (including heterogeneity, churn, availability, and reliability) and emulates the BOINC server components. After describing the design of EmBOINC and its validation, we present three case studies in which the impact of different scheduling policies are quantified in terms of throughput, latency, and starvation metrics.

Internet based volunteer computing projects such as SETI@home are currently restricted to performing coarse grained, embarrassingly parallel master-worker style tasks. This is partly due to the “pull” nature of task distribution in volunteer computing environments, where workers request tasks from the master rather than the master assigning tasks to arbitrary workers. In this paper we propose algorithms for computing batches of medium grained tasks with deadlines in pull-style volunteer computing environments. We develop models of unreliable workers based on analysis of trace data from an actual volunteer computing project. These models are used to develop algorithms for task distribution in volunteer computing systems with a high probability of meeting batch deadlines. We develop algorithms for perfectly reliable workers, computation-reliable workers and unreliable workers. Finally, we demonstrate the effectiveness of the algorithms through simulations using traces from actual volunteer computing environments.