# Constructing Reliable Computing Environments on Top of Amazon EC2 Spot Instances +
## 2 Related Work  
Scientific applications and experiments are usually composed of a certain number of tasks, with various computational and data needs. Such experiments were initially addressed with dedicated high-performance computing (HPC) infrastructures, and new capabilities (e.g., selection of most appropriate set of machines meeting the applications requirements) have been lately introduced with grid computing [18]. In turn, clouds offer many technical and economic advantages over other distributed computing models. By using cloud-based services, scientists have easy access to large distributed infrastructures, with the ability to scale up and down the computing resources according to the application's needs, available in a pay-as-you-go manner. From the scientists point of view, cloud computing provides many resources they need, when they need them, and for as long as they need them. In this regard, commercial clouds such as Amazon EC2 have been the target of relevant research over the last few years towards the execution of scientific applications. Much of the research conducted has focused particularly on developing effective fault-tolerant and robust workflow scheduling algorithms that use spot and on-demand resources to schedule workflow tasks in an effort to minimize the execution cost of the workflow and at the same time satisfy the deadline constraint. This is the case of [PERSON] et al. [13], who have proposed an adaptive, just-in-time scheduling algorithm for scientific workflows. The presented scheduling algorithm, essential critical path task replication (ECPTR), uses both spot and on-demand instances and consolidates resources to further minimize execution time and monetary cost. Fault tolerance regarding eventual SI interruption is achieved by means of replication of tasks. Based on extensive simulations, the authors have shown the effectiveness and robustness of proposed heuristics in scheduling scientific workflow tasks with minimal makespan and monetary cost. Replication of tasks is a fault tolerance technique based on redundancy, but previous studies [19] have shown that it increases monetary costs due to the additional consumption of resources. [PERSON] et al. [12] have addressed related problems, namely: (1) SIs' unreliability caused by the fluctuations of the bidding prices (i.e., a SI may be terminated at any time when the bidding price is lower than the spot price); and (2) the appropriate number of spot and on-demand resources in terms of users' requirements. Aiming at minimizing the total renting cost under deadline constraints, the authors propose to construct schedules for workflows with both non-preemptive and preemptive tasks on spot block and on-demand instances. The idle time three step block-based algorithm (ITB) determines the block time for SIs and improves the task-to-instance mapping. Based on sets of experiments, the authors demonstrate the effectiveness of the proposed algorithm in reducing monetary cost on average, compared to scheduling strategieswith only on-demand instances. Unfortunately, Amazon EC2 spot block instances represent a more expensive alternative than SIs, which gives us room to exploit SIs rented in the form of a fleet in order to decrease monetary costs with tasks execution. Additionally, today's SIs' unreliability is caused mainly by scarcity of EC2 resources, since spot prices tend to be relatively stable now. [PERSON] et al. (2020) have developed a probabilistic framework named Dyna for the scheduling of scientific workflows aimed at minimizing the monetary cost while satisfying their probabilistic deadline guarantees. The authors also presented a hybrid instance configuration refinement mechanism of spot and on-demand instances for price dynamics. The Dyna framework was deployed on Amazon EC2 and experiments were carried out The authors concluded that their solution is able to achieve lower monetary cost than the state-of-the-art approaches while accurately meeting users' probabilistic requirements.