Alexei Karve

Dynamic Scaling of Web Applications in a Virtualized Cloud Computing Environment

Scalability is critical to the success of many enterprises currently involved in doing business on the web and in providing information that may vary drastically from one time to another. Maintaining sufficient resources just to meet peak requirements can be costly. Cloud computing provides a powerful computing model that allows users to access resources on-demand. In this paper, we will describe a novel architecture for the dynamic scaling of web applications based on thresholds in a virtualized Cloud Computing environment. We will illustrate our scaling approach with a front-end load-balancer for routing and balancing user requests to web applications deployed on web servers installed in virtual machine instances. A dynamic scaling algorithm for automated provisioning of virtual machine resources based on threshold number of active sessions will be introduced. The on-demand capability of the Cloud to rapidly provision and dynamically allocate resources to users will be discussed. Our work has demonstrated the compelling benefits of the Cloud which is capable of handling sudden load surges, delivering IT resources on-demands to users, and maintaining higher resource utilization, thus reducing infrastructure and management costs.

Dynamic placement for clustered web applications

We introduce and evaluate a middleware clustering technology capable of allocating resources to web applications through dynamic application instance placement. We define application instance placement as the problem of placing application instances on a given set of server machines to adjust the amount of resources available to applications in response to varying resource demands of application clusters. The objective is to maximize the amount of demand that may be satisfied using a configured placement. To limit the disturbance to the system caused by starting and stopping application instances, the placement algorithm attempts to minimize the number of placement changes. It also strives to keep resource utilization balanced across all server machines. Two types of resources are managed, one load-dependent and one load-independent. When putting the chosen placement in effect our controller schedules placement changes in a manner that limits the disruption to the system.

Solution-based deployment of complex application services on a Cloud

Managing and containing runaway IT costs for solution deployment is one of the top priorities for enterprises. Cloud Computing, with its on-demand provisioning capability on shared resources, has emerged as a new paradigm for reducing IT costs. In this paper, we describe a solution-based provisioning mechanism to automate the deployment of complex application services on a Cloud infrastructure. We introduce the concept of Composite Appliance and show how it can be used to deploy a complete solution and to simplify management tasks. We illustrate the advantages of our approach with a prototype solution consisting of two-tier application services that are deployed and configured automatically on virtual machine instances without manual intervention.

Scalability and Performance of Web Applications in a Compute Cloud

Scalability and performance are key factors to the success of many enterprises involved in doing business on the web. Maintaining sufficient web resources just to meet performance during peak demands can be costly. Compute Cloud provides a powerful environment to allow dynamic scaling of web applications without the needs for user intervention. In this paper, we present a case study on the scalability and performance of web applications in a Cloud. We describe a novel dynamic scaling architecture with a front-end load-balancer for routing user requests to web applications deployed on virtual machine instances with the goal of maximizing resource utilization in instances while minimizing total number of instances. A scaling algorithm for automated provisioning of virtual resources based on threshold number of active user sessions will be introduced. The on-demand capability of the Cloud to rapidly provision and dynamically allocate resources to users will be discussed. Our work has demonstrated the compelling benefits of a Cloud which is capable of sustaining performance upon sudden load surges, delivering satisfactory IT resources on-demands to users, and maintaining high resource utilization, thus reducing infrastructure and management costs.

Leveraging local image redundancy for efficient virtual machine provisioning

Virtualized data centers became ubiquitous and led to invention of new delivery model called Cloud computing. Cloud service providers pursue high degree of automation for the management processes in order to make their services responsive and inexpensive. One of such critical processes is virtual machine provisioning. This article proposes and evaluates a provisioning model that leverages virtual machine image similarity to reduce the data volume transferred from the storage server to the hypervisor on which the virtual machine is being instantiated. We also present an analytical model of such a provisioning process for a single hypervisor shedding light on the impact of degree of image similarity, system utilization, and hypervisor capacity on the performance of the system. The model is validated using discrete event simulator. The algorithm has been implemented on a testbed system and also extensive simulations of virtualized server clusters were conducted. The proposed provisioning scheme can offer significant (up to 80%) reduction in data transfer between storage server and hypervisors thus significantly reducing provisioning time while also decreasing cost.

Simplifying solution deployment on a Cloud through composite appliances

Containing runaway IT costs is one of the top priorities for enterprises. Cloud Computing, with its on-demand provisioning capability on shared resources, has emerged as a new paradigm for managing IT costs. In this paper, we describe a framework to simplify deployment of complex solutions on a Cloud infrastructure. We discuss the concept of a composite appliance and show how it can be used to reduce management costs. We illustrate the benefits of our approach with a complex three-tiered solution that can be deployed and configured on a set of virtual machines instances without any manual intervention.

Modeling the Impact of Workload on Cloud Resource Scaling

Cloud computing offers the flexibility to dynamically size the infrastructure in response to changes in workload demand. While both horizontal and vertical scaling of infrastructure is supported by major cloud providers, these scaling options differ significantly in terms of their cost, provisioning time, and their impact on workload performance. Importantly, the efficacy of horizontal and vertical scaling critically depends on the workload characteristics, such as the workloads parallelizability and its core scalability. In todays cloud systems, the scaling decision is left to the users, requiring them to fully understand the tradeoffs associated with the different scaling options. In this paper, we present our solution for optimizing the resource scaling of cloud deployments via implementation in OpenStack. The key component of our solution is the modelling engine that characterizes the workload and then quantitatively evaluates different scaling options for that workload. Our modelling engine leverages Amdahls Law to model service time scaling in scaleup environments and queueing-theoretic concepts to model performance scaling in scale-out environments. We further employ Kalman filtering to account for inaccuracies in the model-based methodology, and to dynamically track changes in the workload and cloud environment.

VMAR: Optimizing I/O Performance and Resource Utilization in the Cloud

A key enabler for standardized cloud services is the encapsulation of software and data into VM images. With the rapid evolution of the cloud ecosystem, the number of VM images is growing at high speed. These images, each containing gigabytes or tens of gigabytes of data, create heavy disk and network I/O workloads in cloud data centers. Because these images contain identical or similar OS, middleware, and applications, there are plenty of data blocks with duplicate content among the VM images. However, current deduplication techniques cannot efficiently capitalize on this content similarity due to their warmup delay, resource overhead and algorithmic complexity.

Evaluation of Redundancy Driven Provisioning for Hypervisors with Locally Attached Storage

Virtualized data centers became ubiquitous and led to invention of new delivery model called cloud computing. Cloud service providers pursue high degree of automation for the management processes in order to make their services responsive and inexpensive. One of such critical processes is virtual machine provisioning. This article reports on our on-going research to evaluate the efficiency of the provisioning process that leverages virtual machine similarity to reduce the amount of data that has to be transferred from the storage server to the hyper visor on which the virtual machine is being instantiated. The simulation experiments point to conclusion that the redundancy based provisioning can provide very substantial reduction in terms of amount of data that needs to be copied from the storage server.

A Cloud Provisioning System for Deploying Complex Application Services

Cloud Computing, with its on-demand provisioning capability on shared resources, has emerged as a new paradigm for reducing IT costs. In this paper, we present the architecture of a provisioning system that simplifies the deployment of complex application services on a Cloud infrastructure. We will introduce the concept of Composite Appliance and explain how it can be implemented and utilized to simplify management tasks and to reduce costs. We illustrate the extensibility and advantages of our design with a prototype solution consisting of a 3-tier application services that are deployed and configured automatically without manual intervention on a set of virtual machines instances in a Cloud.