  Large batch jobs such as Deep Learning, HPC and Spark require far more
computational resources and higher cost than conventional online service. Like
the processing of other time series data, these jobs possess a variety of
characteristics such as trend, burst, and seasonality. Cloud providers offer
short-term instances to achieve scalability, stability, and cost-efficiency.
Given the time lag caused by joining into the cluster and initialization,
crowded workloads may lead to a violation in the scheduling system. Based on
the assumption that there are infinite resources and ideal placements available
for users to require in the cloud environment, we propose a predictive
autoscaler to provide an elastic interface for the customers and overprovision
instances based on the trained regression model. We contribute to a method to
embed heterogeneous resource requirements in continuous space into discrete
resource buckets and an autoscaler to do predictive expand plans on the time
series of resource bucket counts. Our experimental evaluation of the production
resources usage data validates the solution and the results show that the
predictive autoscaler relieves the burden of making scaling plans, avoids long
launching time at lower cost and outperforms other prediction methods with
fine-tuned settings.
