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The low cost of resource ownership and flexibility have led users to increasingly port their applications to the clouds. To fully realize the cost benefits of cloud services, users usually need to reliably know the execution performance of their applications. However, due to the random performance fluctuations experienced by cloud applications, the black box nature of public clouds and the cloud usage costs, testing on clouds to acquire accurate performance results is extremely difficult. In this paper, we present a novel cloud performance testing methodology called PT4Cloud. By employing non-parametric statistical approaches of likelihood theory and the bootstrap method, PT4Cloud provides reliable stop conditions to obtain highly accurate performance distributions with confidence bands. These statistical approaches also allow users to specify intuitive accuracy goals and easily trade between accuracy and testing cost. We evaluated PT4Cloud with 33 benchmark configurations on Amazon Web Service and Chameleon clouds. When compared with performance data obtained from extensive performance tests, PT4Cloud provides testing results with 95.4% accuracy on average while reducing the number of test runs by 62%. We also propose two test execution reduction techniques for PT4Cloud, which can reduce the number of test runs by 90.1% while retaining an average accuracy of 91%. We compared our technique to three other techniques and found that our results are much more accurate. 

Several recent studies have investigated the virtual machine (VM) provisioning problem for requests with time constraints (deadlines) in cloud systems. These studies typically assumed that a request is associated with a single execution time when running on VMs with a given resource demand. In this paper, we consider modern applications that are normally implemented with generic frameworks that allow them to execute with various numbers of threads on VMs with different resource demands. For such applications, it is possible for the users to specify multiple execution options (MEOs) for a request where each execution option is represented by a certain number of VMs with some resources to run the application and its corresponding execution time. We investigate the problem of virtual machine provisioning for such time-sensitive requests with MEOs in resource-constrained clouds. By incorporating the MEOs of requests, we propose several novel and flexible VM provisioning schemes that carefully balance resource usage efficiency, input workloads and request deadlines with the objective of achieving higher resource utilization and system benefits. We evaluated the proposed MEO-aware schemes on various workloads with both benchmark requests and synthetic requests. The results show that our MEO-aware algorithms outperform the state-of-the-art schemes that consider only a single execution option of requests by serving up to 38% more requests and achieving up to 27% more benefits. 

Several recent studies have investigated the virtual machine (VM) provisioning problem for requests with time constraints (deadlines) in cloud systems. These studies typically assumed that a request is associated with a single execution time when running on VMs with a given resource demand. In this paper, we consider modern applications that are normally implemented with generic frameworks that allow them to execute with various numbers of threads on VMs with different resource demands. For such applications, it is possible for the users to specify multiple execution options (MEOs) for a request where each execution option is represented by a certain number of VMs with some resources to run the application and its corresponding execution time. We investigate the problem of virtual machine provisioning for such time-sensitive requests with MEOs in resource-constrained clouds. By incorporating the MEOs of requests, we propose several novel and flexible VM provisioning schemes that carefully balance resource usage efficiency, input workloads and request deadlines with the objective of achieving higher resource utilization and system benefits. We evaluated the proposed MEO-aware schemes on various workloads with both benchmark requests and synthetic requests. The results show that our MEO-aware algorithms outperform the state-of-the-art schemes that consider only a single execution option of requests by serving up to 38% more requests and achieving up to 27% more benefits. 

challenging due to many intrinsic and external non-deterministic factors. Existing RnR systems achieve significant progress in terms of performance overhead, but none targets the in-situ setting, in which replay occurs within the same process as the recording process. Also, most existing work cannot achieve identical replay, which may prevent the reproduction of some errors. This paper presents iReplayer, which aims to identically replay multithreaded programs in the original process (under the “in-situ” setting). The novel in-situ and identical replay of iReplayer makes it more likely to reproduce errors, and allows it to directly employ debugging mechanisms (e.g. watchpoints) to aid failure diagnosis. Currently, iReplayer only incurs 3% performance overhead on average, which allows it to be always enabled in the production environment. iReplayer enables a range of possibilities, and this paper presents three examples: two automatic tools for detecting buffer overflows and use-after-free bugs, and one interactive debugging tool that is integrated with GDB. 

The paradigm shift of deploying applications to the cloud has introduced both opportunities and challenges. Although clouds use elasticity to scale resource usage at runtime to help meet an application’s performance requirements, developers are still challenged by unpredictable performance, little control of execution environment, and differences among cloud service providers, all while being charged for their cloud usages. Application performance stability is particularly affected by multi-tenancy in which the hardware is shared among varying applications and virtual machines. Developers porting their applications need to meet performance requirements, but testing on the cloud under the effects of performance uncertainty is difficult and expensive, due to high cloud usage costs. This paper presents a first approach to testing an application with typical inputs for how its performance will be affected by performance uncertainty, without incurring undue costs of bruteforce testing in the cloud. We specify cloud uncertainty testing criteria, design a test-based strategy to characterize the blackbox cloud’s performance distributions using these testing criteria, and support execution of tests to characterize the resource usage and cloud baseline performance of the application to be deployed. Importantly, we developed a smart test oracle that estimates the application’s performance with certain confidence levels using the above characterization test results and determines whether it will meet its performance requirements. We evaluated our testing approach on both the Chameleon cloud and Amazon web services; results indicate that this testing strategy shows promise as a cost-effective approach to test for performance effects of cloud uncertainty when porting an application to the cloud.