Cloud computing offers a variable-cost payment scheme that allows cloud customers to specify the price they are willing to pay for renting spot instances to run their applications at much lower costs than fixed payment schemes, and depending on the varying demand from cloud customers, cloud platforms could revoke spot instances at any time. To alleviate the effect of spot instance revocations, applications often employ different fault-tolerance mechanisms to minimize or even eliminate the lost work for each spot instance revocation. However, these fault-tolerance mechanisms incur additional overhead related to application completion time and deployment cost. We propose a novel cloud market-based approach that leverages cloud spot market features to provision spot instances without employing fault-tolerance mechanisms to reduce the deployment cost and completion time of applications. We evaluate our approach in simulations and use Amazon spot instances that contain jobs in Docker containers and realistic price traces from EC2 markets. Our simulation results show that our approach reduces the deployment cost and completion time compared to approaches based on faulttolerance mechanisms.

One of the major advantages of cloud spot instances in cloud computing is to allow stakeholders to economically deploy their applications at much lower costs than that of other types of cloud instances. In exchange, spot instances are often exposed to revocations (i.e., terminations) by cloud providers. With spot instances becoming pervasive, terminations have become a part of the normal behavior of cloud-based applications; thus, these applications may be left in an incorrect state leading to certain bugs. Unfortunately, these applications are not designed or tested to deal with this behavior in the cloud environment, and as a result, the advantages of cloud spot instances could be significantly minimized or even entirely negated. We propose a novel solution to automatically find these bugs and locate their causes in the source code. We evaluate our solution using 10 popular open-source applications. The results show that our solution not only finds more instances and different types of these bugs compared to the random approach, but it also locates the causes of these bugs to help developers improve the design of the shutdown process and is more efficient in finding instances of these bugs since it interposes at the system call layer.

One of the major advantages of cloud spot instances in cloud computing is to allow stakeholders to economically deploy their applications at much lower costs than that of other types of cloud instances. In exchange, spot instances are often exposed to revocations (i.e., terminations) by cloud providers. With spot instances becoming pervasive, terminations have become a part of the normal behavior of cloud-based applications; thus, these applications may be left in an incorrect state leading to certain bugs. Unfortunately, these applications are not designed or tested to deal with this behavior in the cloud environment, and as a result, the advantages of cloud spot instances could be significantly minimized or even entirely negated. We propose a novel solution to automatically find these bugs and locate their causes in the source code. We evaluate our solution using 10 popular open-source applications. The results show that our solution not only finds more instances and different types of these bugs compared to the random approach, but it also locates the causes of these bugs to help developers to improve the design of the shutdown process for cloud-based applications.

One of the main benefits of cloud computing is to enable customers to deploy their applications on a cloud infrastructure that provisions resources (e.g., memory) to these applications on as-needed basis. Unfortunately, certain workloads can cause customers to pay for resources that are provisioned to, but not fully used by their applications, and as a result their performances then deteriorate beyond some acceptable thresholds and the benefits of cloud computing may be significantly reduced or even completely obliterated. We propose a novel approach to automatically discover these workloads to stress test elastic resource provisioning for cloud-based applications. We experimented with four non-trivial applications on the Microsoft Azure cloud to determine how effectively and efficiently our approach explores a very large space of the workload parameters’ values. The results show that our approach discovers the first irregular workload faster in the search space of over $$10^{40}$$ input combinations compared to the random approach, and it discovers more irregular workloads that result in much higher costs and performance degradations for applications in the cloud.