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. Virtual machines (VMs) have recently emerged as the basis for allocating resources in enterprise settings and hosting centers. One benefit of VMs in these environments is the ability to multiplex several operating systems on hardware based on dynamically changing system characteristics. However, such multiplexing must often be done while observing per-VM performance guarantees or service level agreements. Thus, one important requirement in this environment is effective performance isolation among VMs. In this paper, we address performance isolation across virtual machines in Xen [1]. For instance, while Xen can allocate fixed shares of CPU among competing VMs, it does not currently account for work done on behalf of individual VMs in device drivers. Thus, the behavior of one VM can negatively impact resources available to other VMs even if appropriate per-VM resource limits are in place. In this paper, we present the design and evaluation of a set of primitives implemented in Xen to address this issue. First, XenMon accurately measures per-VM resource consumption, including work done on behalf of a particular VM in Xen's driver domains. Next, our SEDF-DC scheduler accounts for aggregate VM resource consumption in allocating CPU. Finally, ShareGuard limits the total amount of resources consumed in privileged and driver domains based on administrator-specified limits. Our performance evaluation indicates that our mechanisms effectively enforce performance isolation for a variety of workloads and configurations. Cloud computing makes extensive use of virtual machines because they permit workloads to be isolated from one another and for the resource usage to be somewhat controlled. In this paper, we explore the performance of traditional virtual machine (VM) deployments, and contrast them with the use of Linux containers. We use KVM as a representative hypervisor and Docker as a container manager. Our results show that containers result in equal or better performance than VMs in almost all cases. Both VMs and containers require tuning to support I Ointensive applications. We also discuss the implications of our performance results for future cloud architectures. The primary motivation for enterprises to adopt virtualization technologies is to create a more agile and dynamic IT infrastructure -- with server consolidation, high resource utilization, the ability to quickly add and adjust capacity on demand -- while lowering total cost of ownership and responding more effectively to changing business conditions. However, effective management of virtualized IT environments introduces new and unique requirements, such as dynamically resizing and migrating virtual machines (VMs) in response to changing application demands. Such capacity management methods should work in conjunction with the underlying resource management mechanisms. In general, resource multiplexing and scheduling among virtual machines is poorly understood. CPU scheduling for virtual machines, for instance, has largely been borrowed from the process scheduling research in operating systems. However, it is not clear whether a straight-forward port of process schedulers to VM schedulers would perform just as well. We use the open source Xen virtual machine monitor to perform a comparative evaluation of three different CPU schedulers for virtual machines. We analyze the impact of the choice of scheduler and its parameters on application performance, and discuss challenges in estimating the application resource requirements in virtualized environments.