Volunteer-based grid computing resources are characteristically volatile and frequently become unavailable due to the autonomy that owners maintain over them. This resource volatility has significant influence on the applications the resources host. Availability predictors can forecast unavailability, and can provide schedulers with information about reliability, which helps them make better scheduling decisions when combined with information about speed and load. This paper studies using this prediction information for deciding when to replicate jobs. In particular, our predictors forecast the probability that a job will complete uninterrupted, and our schedulers replicate those jobs that are least likely to do so. Our strategies outperform other comparable replication strategies, as measured by improved make span and fewer redundant operations. We define a new ``replication efficiency" metric, and demonstrate that our availability predictor can provide information that allows our schedulers to be more efficient than the most closely related replication strategy for a variety of loads in a trace-based grid simulation. We demonstrate that under low load conditions, our techniques come within 6% of the makespan improvement of a previously proposed replication technique while creating 76.8% fewer replicas and under higher loads, can improve makespan marginally while creating 72.5% fewer replicas.

Parallel computing on volatile distributed resources requires schedulers that consider job and resource characteristics. We study unconventional computing environments containing devices spread throughout a single large organization. The devices are not necessarily typical general purpose machines; instead, they could be processors dedicated to special purpose tasks (for example printing and document processing), but capable of being leveraged for distributed computations. Harvesting their idle cycles can simultaneously help resources cooperate to perform their primary task and enable additional functionality and services. A new burstiness metric characterizes the volatility of the high-priority native tasks. A burstiness-aware scheduling heuristic opportunistically introduces grid jobs (a lower priority workload class) to avoid the higher-priority native applications, and effectively harvests idle cycles. Simulations based on real workload traces indicate that this approach improves makespan by an average of 18.3% over random scheduling, and comes within 7.6% of the theoretical upper bound.

The frequent and volatile unavailability of volunteer-based grid computing resources challenges grid schedulers to make effective application placements. The manner in which host resources become unavailable will have different effects on different applications, depending on their runtime and their ability to be checkpointed or replicated. A multi-state availability model can help improve scheduling performance by capturing the various ways a resource may be available or unavailable to the grid. This paper uses a multi-state model and describes several prediction techniques to forecast resource transitions into the model's states. We analyze the accuracy of our predictors, which outperform existing approaches. Our approach increases the accuracy of multi-state resource availability prediction, and enables better application placement decisions that reduce the number of operations lost due to resource failure and eviction, decreases application makespan, or both.