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In PaaS, the issue of resource provisioning becomes more challenging because a great number of applications share and compete for resources simultaneously. PaaS platform should be able to maximize the resources utilization, while satisfying the performance requirements of all applications. However on-line request workload can be fluctuated during the run time of applications and static resource allocation may cause either under provisioning or over provisioning problem. In this paper, we propose an adaptive resource provisioning approach to dynamically allocate resources for applications according to the workload variation. In case of performance violation, our approach can make an efficient plan to keep application performance within a valid range.
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Infrastructure-as-a-service (IaaS) clouds enable customers to allocate computing resources in a flexible manner to satisfy their needs, and pay only for the allocated resources. One of the challenges for IaaS customers is the correct provisioning of their resources. Many users end up under provisioning, hurting application performance, or over provisioning, paying for resources that are not really necessary. Memory is an essential resource for any computing system, and is frequently a performance-limiting factor in cloud environments. Our work uses monitoring to enable a cloud customer to determine if the memory allocated to his virtual machines is correctly provisioned, under provisioned, or over provisioned. Experimental results with the Xen platform demonstrate the effectiveness of the proposed approach.
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Cloud computing is being viewed as the technology of today and the future. Through this paradigm, the customers gain access to shared computing resources located in remote data centers that are hosted by cloud providers (CP). This technology allows for provisioning of various resources such as virtual machines (VM), physical machines, processors, memory, network, storage and software as per the needs of customers. Application providers (AP), who are customers of the CP, deploy applications on the cloud infrastructure and then these applications are used by the end-users. To meet the fluctuating application workload demands, dynamic provisioning is essential and this article provides a detailed literature survey of dynamic provisioning within cloud systems with focus on application performance. The well-known types of provisioning and the associated problems are clearly and pictorially explained and the provisioning terminology is clarified. A very detailed and general cloud provisioning classification is presented, which views provisioning from different perspectives, aiding in understanding the process inside-out. Cloud dynamic provisioning is explained by considering resources, stakeholders, techniques, technologies, algorithms, problems, goals and more.
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Provisioning ESs include all physical products and materials obtained from ecosystems, such as timber, food and woodfuel. Because they represent tangible and visible outputs, the identification and quantification of provisioning ESs is generally easier than for other ESs. Tools are available to collect information on the products people collect, the time spent in their collection, and the sources from which they are obtained. For example, FAO et al. (2016) developed guidance and modules for socio-economic surveys in forestry.
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Cloud Computing is a recent buzzword in IT industry. Cloud computing is used for sharing of data ,information and resources. Machine learning used for prediction. Resource Provisioning provide resources to the end user. It has problems like auto scaling and resource utilization .A perfect Resource provisioning aims at avoid under provisioning and over provisioning of resources. Machine learning technique predict which request needs resources in which amount from the past data and Cloud computing checks the resource utilization. This paper aims to accurate resource provisioning and also scaling decision is accurate and it uses SVM algorithm for the better prediction of resources.
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Role-based provisioning has been adopted as a standard component in leading Identity Management products due to its low administration cost. However, the cost of adjusting existing roles to entitlements from newly deployed applications is usually very high. In this paper, a learning-based approach to automate the provisioning process is proposed and its effectiveness is verified by real provisioning data. Specific learning issues related to provisioning are identified and relevant solutions are presented.
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In PaaS, the issue of resource provisioning becomes more challenging because a great number of applications share and compete for resources simultaneously. PaaS platform should be able to maximize the resources utilization, while satisfying the performance requirements of all applications. However on-line request workload can be fluctuated during the run time of applications and static resource allocation may cause either under provisioning or over provisioning problem. In this paper, we propose an adaptive resource provisioning approach to dynamically allocate resources for applications according to the workload variation. In case of performance violation, our approach can make an efficient plan to keep application performance within a valid range.
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It can be difficult to provision assets in a cloud environment. Both inadequate and excessive provisioning are bad for the system's overall performance. Researchers are increasingly turning to proactive provisioning techniques that foresee resource needs in advance and set the system up to accommodate such real-time demands. Although proactive provisioning methods are more complicated than reactive provisioning strategies, they offer a generally faster reaction time since decisions about resource provisioning are made in advance of the actual requirement for those resources. The adoption of an analytical framework that anticipates resource requirements is necessary for these proactive provisioning strategies to function well. The provisioning of resources for computational operations in cloud computing is a significant concern. Unsurprisingly, previous research has identified a number of ways to deliver Cloud resources effectively. However, a forecast of the impending computational jobs' future resource consumption is necessary to implement a comprehensive resource provisioning model is required. However, the subject of cloud prediction the use of resources is still in its early stages
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The nationally-recognized Susquehanna
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singing that will take you to new
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