The Experts below are selected from a list of 18 Experts worldwide ranked by ideXlab platform

Savitri A M - One of the best experts on this subject based on the ideXlab platform.

  • tps a novel approach to increase thenumber of guest virtual machines byreducing Physical Memory Usage
    International Journal of Innovative Research in Computer and Communication Engineering, 2014
    Co-Authors: Anusha C S, Savitri A M
    Abstract:

    Improving Memory utilization is important for improving the efficiency of a cloud datacenter by increasing the number of usable VMs. Memory over commitment is a common technique for this purpose. Transparent Page Sharing (TPS) is a technique to improve the utilization by sharing identical Memory pages to reduce the total Memory consumption. For a cloud datacenter, we might expect TPS will reduce Memory Usage because VMs often execute the same OS and middleware and thus they may have many identical pages. However, TPS is less effective for Java - based middleware because the Java VM finds it difficult to manage the layouts of internal data structures that depend on the execution of Java programs. This paper presents detailed breakdowns of the Memory Usage of KVM guest VMs executing a Web application server. Then we propose increasing the amount of page sharing by VM.

Anusha C S - One of the best experts on this subject based on the ideXlab platform.

  • tps a novel approach to increase thenumber of guest virtual machines byreducing Physical Memory Usage
    International Journal of Innovative Research in Computer and Communication Engineering, 2014
    Co-Authors: Anusha C S, Savitri A M
    Abstract:

    Improving Memory utilization is important for improving the efficiency of a cloud datacenter by increasing the number of usable VMs. Memory over commitment is a common technique for this purpose. Transparent Page Sharing (TPS) is a technique to improve the utilization by sharing identical Memory pages to reduce the total Memory consumption. For a cloud datacenter, we might expect TPS will reduce Memory Usage because VMs often execute the same OS and middleware and thus they may have many identical pages. However, TPS is less effective for Java - based middleware because the Java VM finds it difficult to manage the layouts of internal data structures that depend on the execution of Java programs. This paper presents detailed breakdowns of the Memory Usage of KVM guest VMs executing a Web application server. Then we propose increasing the amount of page sharing by VM.

Wei Song - One of the best experts on this subject based on the ideXlab platform.

  • A Novel QoS Prediction Approach for Cloud Service Based on Bayesian Networks Model
    2016 IEEE International Conference on Mobile Services (MS), 2016
    Co-Authors: Pengcheng Zhang, Wenrui Li, Hareton Leung, Wei Song
    Abstract:

    Considered as the next generation computing model, cloud computing plays an important role in scientific and commercial computing and draws wide attention from both academia and industry. In the dynamic, complex and changeable cloud computing environment, Quality Of Service (QoS) is an important basis for the selection of different cloud services. Therefore, the prediction of cloud services QoS can help users to choose the most suitable service at hand. The software and hardware and resources of three-layer structure for cloud computing will impact on cloud services QoS, but existing QoS prediction approaches are not consider the three-layer structure on the influence of thecloud service QoS. The CPU Usage, Physical Memory Usage andthe number of processes of infrastructure layer have definitely influenced QoS. In order to address this limitation, in the paper, a Bayesian network model of QoS prediction for cloud servicesis proposed. Firstly, an initial and basic Bayesian network modelis established by collecting data from the infrastructure layer, the platform layer and the application layer. Then the Bayesian network is trained and updated to obtain the cloud service QoS prediction model. Finally, a set of experiments based on collected data from the real cloud service environment has been conducted to validate the proposed approach. Experimental results show that the prediction approach is effective and accurate.

Pradeep Padala - One of the best experts on this subject based on the ideXlab platform.

  • Crowdsourced Resource-Sizing of Virtual Appliances
    2016
    Co-Authors: Pinar Yanardag Delul, Rean Griffith, Anne Holler, K. Shankari, Xiaoyun Zhu, Ravi Soundararajan, Adarsh Jagadeeshwaran, Pradeep Padala
    Abstract:

    Using a population of VMware Virtual Center Virtual Ap-pliances (VCVA) and their respective workloads we de-scribe techniques for constructing a model of their resource consumption and performance, specifically Memory require-ments, and average operation-latency by mining logs of ap-plication (VCVA) performance. We use our model to provide sizing recommendations for the virtual appliance and iden-tify features that can be used to provide rough estimates of expected Memory consumption. We show results of bet-ter than ∼70 % prediction accuracy (recall) for predicting Physical Memory Usage and better than ∼80 % prediction accuracy (recall) for predicting the average latency of work-load operations. We describe modeling techniques from sta-tistical machine learning that are amenable to representing complex, non-linear systems. Further, via the choice of tech-niques, we present an approach for reasoning about the lim-itations of our model, i.e., identifying when (and why) our model is expected to perform well and poorly.

Pengcheng Zhang - One of the best experts on this subject based on the ideXlab platform.

  • A Novel QoS Prediction Approach for Cloud Service Based on Bayesian Networks Model
    2016 IEEE International Conference on Mobile Services (MS), 2016
    Co-Authors: Pengcheng Zhang, Wenrui Li, Hareton Leung, Wei Song
    Abstract:

    Considered as the next generation computing model, cloud computing plays an important role in scientific and commercial computing and draws wide attention from both academia and industry. In the dynamic, complex and changeable cloud computing environment, Quality Of Service (QoS) is an important basis for the selection of different cloud services. Therefore, the prediction of cloud services QoS can help users to choose the most suitable service at hand. The software and hardware and resources of three-layer structure for cloud computing will impact on cloud services QoS, but existing QoS prediction approaches are not consider the three-layer structure on the influence of thecloud service QoS. The CPU Usage, Physical Memory Usage andthe number of processes of infrastructure layer have definitely influenced QoS. In order to address this limitation, in the paper, a Bayesian network model of QoS prediction for cloud servicesis proposed. Firstly, an initial and basic Bayesian network modelis established by collecting data from the infrastructure layer, the platform layer and the application layer. Then the Bayesian network is trained and updated to obtain the cloud service QoS prediction model. Finally, a set of experiments based on collected data from the real cloud service environment has been conducted to validate the proposed approach. Experimental results show that the prediction approach is effective and accurate.