A Prediction Approach to Define Checkpoint Intervals in Spot Instances

Cloud computing providers have started offering their idle resources in the form of virtual machines (VMs) without availability guarantees. Know as transient servers, these VMs can be revoked at any time without user intervention. Spot instances are transient servers offered by Amazon at lower prices than regular dedicated servers. A market model was used to create a bidding scenario for cloud users of servers without service reliability guarantees, where prices changed dynamically over time based on supply and demand. To prevent data loss, the use of fault tolerance techniques allows the exploration of transient resources. This paper proposes a strategy that addresses the problem of executing a distributed application, like bag-of-tasks, using spot instances. We implemented a heuristic model that uses checkpoint and restore techniques, supported by a statistical model that predicts time to revocation by analyzing price changes and defining the best checkpoint interval. Our experiments demonstrate that by using a bid strategy and the observed price variation history, our model is able to predict revocation time with high levels of accuracy. We evaluate our strategy through extensive simulations, which use the price change history, simulating bid strategies and comparing our model with real time to revocation events. Using instances with considerable price changes, our results achieve an 94% success with standard deviation of 1.36. Thus, the proposed model presents promising results under realistic working conditions.