Electricity Price Time Series Forecasting in Deregulated Markets Using Recurrent Neural Network Based Approaches

Ph.DDOCTOR OF PHILOSOPH

An Enhanced MapReduce Workload Allocation Tool for Spot Market Resources

When a cloud user allocates a cluster to execute a map-reduce workload, the user must determine the number and type of virtual machine instances to minimize the workload\u27s financial cost. The cloud user may rent on-demand instances at a fixed price or spot instances at a variable price to execute the workload. Although the cloud user may bid on spot virtual machine instances at a reduced rate, the spot market auction may delay the workload\u27s start or terminate the spot instances before the workload completes. The cloud user requires a forecast for the workload\u27s financial cost and completion time to analyze the trade-offs between on-demand and spot instances. While existing estimation tools predict map-reduce workloads\u27 completion times and costs, these tools do not provide spot instance estimates because a spot market auction determines the instance\u27s start time and duration. The ephemeral spot instances impact execution time estimates because the spot market auction forces the map-reduce workloads to use different storage strategies to persist data after the spot instances terminate. The spot market also reduces the existing tools\u27 completion time and cost estimate accuracy because the tool must factor in spot instance wait times and early terminations. This dissertation updated an existing tool to forecast map-reduce workload\u27s monetary cost and completion time based on spot market historical traces. The enhanced estimation tool includes three new enhancements over existing tools. First, the estimation tool models the impact to the execution from new storage strategies. Second, the enhanced tool calculates additional execution time from early spot instance termination. Finally, the enhance tool predicts the workloads wait time and early termination probabilities from historic traces. Based on two historical Amazon EC2 spot market traces, the enhancements reduce the average completion time prediction error by 96% and the average monetary cost prediction error by 99% over existing tools

Evolutionary computation and economic time series forecasting

10.1109/CEC.2007.44244712007 IEEE Congress on Evolutionary Computation, CEC 2007188-19