Abstract-Resource provisioning for N -tier web applications in Clouds is non-trivial due to at least two reasons. First, there is an inherent optimization conflict between cost of resources and Service Level Agreement (SLA) compliance. Second, the resource demands of the multiple tiers can be different from each other, and varying along with the time. Resources have to be allocated to multiple (virtual) containers to minimize the total amount of resources while meeting the end-to-end performance requirements for the application. In this paper we address these two challenges through the combination of the resource controllers on both application and container levels. On the application level, a decision maker (i.e., an adaptive feedback controller) determines the total budget of the resources that are required for the application to meet SLA requirements as the workload varies. On the container level, a second controller partitions the total resource budget among the components of the applications to optimize the application performance (i.e., to minimize the round trip time). We evaluated our method with three different workload models-open, closed, and semiopen-that were implemented in the RUBiS web application benchmark. Our evaluation indicates two major advantages of our method in comparison to previous approaches. First, fewer resources are provisioned to the applications to achieve the same performance. Second, our approach is robust enough to address various types of workloads with time-varying resource demand without reconfiguration.
Elastic n-tier applications have non-stationary workloads that require adaptive control of resources allocated to them. This presents not only an opportunity in pay-as-you-use clouds, but also a challenge to dynamically allocate virtual machines appropriately. Previous approaches based on control theory, queuing networks, and machine learning work well for some situations, but each model has its own limitations due to inaccuracies in performance prediction. In this paper we propose a multi-model controller, which integrates adaptation decisions from several models, choosing the best. The focus of our work is an empirical model, based on detailed measurement data from previous application runs. The main advantage of the empirical model is that it returns high quality performance predictions based on measured data. For new application scenarios, we use other models or heuristics as a starting point, and all performance data are continuously incorporated into the empirical model's knowledge base. Using a prototype implementation of the multi-model controller, a cloud testbed, and an ntier benchmark (RUBBoS), we evaluated and validated the advantages of the empirical model. For example, measured data show that it is more effective to add two nodes as a group, one for each tier, when two tiers approach saturation simultaneously.
Abstract-The increasing popularity of computing clouds continues to drive both industry and research to provide answers to a large variety of new and challenging questions. We aim to answer some of these questions by evaluating performance and scalability when an n-tier application is migrated from a traditional datacenter environment to an IaaS cloud. We used a representative n-tier macro-benchmark (RUBBoS) and compared its performance and scalability in three different testbeds: Amazon EC2, Open Cirrus (an open scientific research cloud), and Emulab (academic research testbed). Interestingly, we found that the best-performing configuration in Emulab can become the worst-performing configuration in EC2. Subsequently, we identified the bottleneck components, high context switch overhead and network driver processing overhead, to be at the system level. These overhead problems were confirmed at a finer granularity through micro-benchmark experiments that measure component performance directly. We describe concrete alternative approaches as practical solutions for resolving these problems.
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