Autoscaling is a hallmark of cloud computing as it allows flexible just-in-time allocation and release of computational resources in response to dynamic and often unpredictable workloads. This is especially important for web applications whose workload is time dependent and prone to flash crowds. Most of them follow the 3-tier architectural pattern, and are divided into presentation, application domain and data layers. In this work we focus on the application layer. Reactive autoscaling policies of the type "Instantiate a new Virtual Machine (VM) when the average server CPU utilisation reaches X " have been used successfully since the dawn of cloud computing. But which VM type is the most suitable for the specific application at the moment remains an open question. In this work, we propose an approach for dynamic VM type selection. It uses a combination of online machine learning techniques, works in real time and adapts to changes in the users' workload patterns, application changes as well as middleware upgrades and reconfigurations. We have developed a prototype, which we tested with the CloudStone benchmark deployed on AWS EC2. Results show that our method quickly adapts to workload changes and reduces the total cost compared to the industry standard approach.
propose a system for autoscaling web applications in clouds @cite_24 . They monitor the performance of different VM types to infer their capacities. Our approach to this is different, as we inspect the available to each VM CPU capacity and measure the amount of stolen'' CPU instructions by the hypervisor from within the VM itself. This allows us to normalise the VMs' resource capacities to a common scale, which we use to compare them and for further analysis. Furthermore, their approach relies on a workload predictor, while ours is usable even in the case of purely reactive autoscaling.
@cite_24
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Elastic resource provisioning is a key feature of cloud computing, allowing users to scale up or down resource allocation for their applications at run-time. To date, most practical approaches to managing elasticity are based on allocation de-allocation of the virtual machine (VM) instances to the application. This VM-level elasticity typically incurs both considerable overhead and extra costs, especially for applications with rapidly fluctuating demands. In this paper, we propose a lightweight approach to enable cost-effective elasticity for cloud applications. Our approach operates fine-grained scaling at the resource level itself (CPUs, memory, I O, etc) in addition to VM-level scaling. We also present the design and implementation of an intelligent platform for light-weight resource management of cloud applications. We describe our algorithms for light-weight scaling and VM-level scaling and show their interaction. We then use an industry standard benchmark to evaluate the effectiveness of our approach and compare its performance against traditional approaches.