On optimization of simulation execution on Amazon EC2 spot market

Abstract

Large scale simulations require considerable amounts of computing power and often cloud services are utilized to perform them. In such settings the execution costs can be significantly decreased through the use of the Amazon spot price market. Its downside is that Amazon can interrupt the user’s computations when her bid price is too low. This poses a problem in finding an on-line bidding algorithm that balances the computation cost and the simulation experiment completion time. We identify key drivers governing the spot prices on Amazon EC2 and using these insights propose an adaptive bidding strategy that simultaneously minimizes the computation cost and the delays due to computation termination. We show that bidding close to a spot price and dynamically switching between instances is a strategy that is efficient and simple to implement in practice. In the paper we present a simulator of the EC2 spot pricing mechanism. The simulator can be easily used to develop and test other bidding strategies on Amazon spot price market.