Abstract
Spot-instances(SI) is an auction-based pricing scheme used by cloud providers. It allows users to place bids for spare computing instances and rent them at a substantially lower price compared to the fixed on-demand price. This inexpensive computational power is at the cost of availability, because a spot instance can be revoked whenever the spot market price exceeds the bid. Therefore, SI has become an attractive option for applications without requiring real-time availability constraints, such as the batch jobs in different application domains, including big data analytics, scientific computing, and deep learning. For batch jobs, service interruptions and execution delays can be tolerated as long as their service quality is gauged by an execution deadline. Hence, this paper aims to develop a static bidding strategy for minimizing the monetary cost of a batch job with hard deadline constraints. We formulate the problem as a Markov chain process and use Dynamic Programming to find the optimal bid in polynomial time. Experiments conducted on real workloads from Amazon Spot Instance historical prices show that our proposed strategy successfully outperformed two state-of-art dynamic bidding strategies (Amazing, DBA), and several deadline agnostic static bidding strategies with lower cost.