Provider policy (e.g., bandwidth rate limits, virtualization, CPU scheduling) can significantly impact application performance in cloud environments. This paper takes a first step towards understanding the impact of provider policy and tackling the complexity of selecting configurations that can best meet the cost and performance requirements of applications. We make three contributions. First, we conduct a measurement study spanning a 19 months period of a wide variety of applications on Amazon EC2 to understand issues involved in configuration selection. Our results show that provider policy can impact communication and computation performance in unpredictable ways. Moreover, seemingly sensible rules of thumb are inappropriate - e.g., VMs with latest hardware or larger VM sizes do not always provide the best performance. Second, we systematically characterize the overheads and resulting benefits of a range of testing strategies for configuration selection. A key focus of our characterization is understanding the overheads of a testing approach in the face of variability in performance across deployments and measurements. Finally, we present configuration pruning and short-listing techniques for minimizing testing overheads. Evaluations on a variety of compute, bandwidth and data intensive applications validate the effectiveness of these techniques in selecting good configurations with low overheads.

In this paper, we conduct a detailed study characterizing the performance of multi-tier web applications on commercial cloud platforms and evaluate the potential of techniques to improve the resilience of such applications to performance fluctuations in the cloud. In contrast to prior works that have studied the performance of individual cloud services or that of compute-intensive scientific applications (e.g., map-reduce based), our study focuses on multi-tier web applications. Our work is conducted in the context of four real-world web applications which we instrumented to collect the overall response time and the time spent in each application tier, for each transaction. Our results indicate that cloud applications undergo frequent periods of poor performance that typically (i) are short-lived lasting a few minutes; and (ii) may be attributed to a small subset of application components, though different subsets may be involved at different times. While geo-distributing applications can help mitigate performance variability, coarse-grained approaches that merely choose the best performing data-center (DC) provide only modest benefits. More significant benefits could accrue, however, if combination of cloud services located across multiple datacenters (DCs) are chosen to serve each request.

Deploying interactive applications in the cloud is a challenge due to the high variability in performance of cloud services. In this paper, we present Dealer — a system that helps geo-distributed, interactive and multi-tier applications meet their stringent requirements on response time despite such variability. Our approach is motivated by the fact that, at any time, only a small number of application components of large multi-tier applications experience poor performance. Dealer continually monitors the performance of individual components and communication latencies between them to build a global view of the application. In serving any given request, Dealer seeks to minimize user response times by picking the best combination of replicas (potentially located across different data centers). While Dealer requires modifications to application code, we show the changes required are modest. Our evaluations on two multi-tier applications using real cloud deployments indicate the 90%ile of response times could be reduced by more than a factor of 6 under natural cloud dynamics. Our results indicate the cost of inter-data-center traffic with Dealer is minor, and that Dealer can in fact be used to reduce the overall operational costs of applications by up to 15% by leveraging the difference in billing plans of cloud instances.

Deploying interactive applications in the cloud is a challenge due to the high variability in performance of cloud services. In this paper, we present Dealer-- a system that helps geo-distributed, interactive and multi-tier applications meet their stringent requirements on response time despite such variability. Our approach is motivated by the fact that, at any time, only a small number of application components of large multi-tier applications experience poor performance. Dealer abstracts application structure as a component graph, with nodes being application components and edges capturing inter-component communication patterns. Dealer continually monitors the performance of individual component replicas and communication latencies between replica pairs. In serving any given user request, Dealer seeks to minimize user response times by picking the best combination of replicas (potentially located across different data-centers). While Dealer does require modifications to application code, we show through integration with two multi-tier applications that the changes required are modest. Our evaluations on two multi-tier applications using real cloud deployments indicate the 90%ile of application response times could be reduced by a factor of 3 under natural cloud dynamics compared to conventional data-center redirection techniques which are agnostic of application structure.

The Internet has significantly evolved in the number and variety of applications. Network operators need mechanisms to constantly monitor and study these applications. Given modern applications routinely consist of several flows, potentially to many different destinations, existing measurement approaches such as Sampled NetFlow sample only a few flows per application session. To address this issue, in this paper, we introduce RelSamp architecture that implements the notion of related sampling where flows that are part of the same application session are given higher probability. In our evaluation using real traces, we show that RelSamp achieves 5-10x more flows per application session compared to Sampled NetFlow for the same effective number of sampled packets. We also show that behavioral and statistical classification approaches such as BLINC, SVM and C4.5 achieve up to 50% better classification accuracy compared to Sampled NetFlow, while not breaking existing management tasks such as volume estimation.

In this paper, we tackle challenges in migrating enterprise services into hybrid cloud-based deployments, where enterprise operations are partly hosted on-premise and partly in the cloud. Such hybrid architectures enable enterprises to benefit from cloud-based architectures, while honoring application performance requirements, and privacy restrictions on what services may be migrated to the cloud. We make several contributions. First, we highlight the complexity inherent in enterprise applications today in terms of their multi-tiered nature, large number of application components, and interdependencies. Second, we have developed a model to explore the benefits of a hybrid migration approach. Our model takes into account enterprise-specific constraints, cost savings, and increased transaction delays and wide-area communication costs that may result from the migration. Evaluations based on real enterprise applications and Azure-based cloud deployments show the benefits of a hybrid migration approach, and the importance of planning which components to migrate. Third, we shed insight on security policies associated with enterprise applications in data centers. We articulate the importance of ensuring assurable reconfiguration of security policies as enterprise applications are migrated to the cloud. We present algorithms to achieve this goal, and demonstrate their efficacy on realistic migration scenarios.