The Experts below are selected from a list of 108 Experts worldwide ranked by ideXlab platform

Jingang Zhou - One of the best experts on this subject based on the ideXlab platform.

  • energy aware Cloud application management in Private Cloud Data center
    Conference on Decision and Control, 2011
    Co-Authors: Guozhen Tan, Xia Zhang, Jingang Zhou
    Abstract:

    Cloud services decouple Cloud applications from IT infrastructure in Cloud environment. On demand resources provisioning pattern makes high efficient resource utility and application dynamic scaling possible. Hence Cloud Data center could provide infrastructure capabilities in a more energy efficient way. However, there exists a tradeoff between energy efficiency and application service level objectives. It is challenging to realize effective energy consumption and energy efficiency aware autonomic Cloud application service level agreement assurance mechanism. In this paper, we propose energy aware Cloud application management architecture for Private Cloud Data center. Furthermore we present a Cloud application and power management model. In order to metering server energy utility efficiency and Cloud applications' total energy consumption of running on a specific group of servers, we define related measurement metrics. The objective of our approach is to reduce Data center's total energy efficiency by controlling Cloud applications' overall energy consumption while ensuring Cloud applications' service level agreement. The experiment results show that the Cloud application energy consumption and energy efficiency is being improved effectively.

  • CDC - Energy aware Cloud application management in Private Cloud Data center
    2011 International Conference on Cloud and Service Computing, 2011
    Co-Authors: Guozhen Tan, Xia Zhang, Jingang Zhou
    Abstract:

    Cloud services decouple Cloud applications from IT infrastructure in Cloud environment. On demand resources provisioning pattern makes high efficient resource utility and application dynamic scaling possible. Hence Cloud Data center could provide infrastructure capabilities in a more energy efficient way. However, there exists a tradeoff between energy efficiency and application service level objectives. It is challenging to realize effective energy consumption and energy efficiency aware autonomic Cloud application service level agreement assurance mechanism. In this paper, we propose energy aware Cloud application management architecture for Private Cloud Data center. Furthermore we present a Cloud application and power management model. In order to metering server energy utility efficiency and Cloud applications' total energy consumption of running on a specific group of servers, we define related measurement metrics. The objective of our approach is to reduce Data center's total energy efficiency by controlling Cloud applications' overall energy consumption while ensuring Cloud applications' service level agreement. The experiment results show that the Cloud application energy consumption and energy efficiency is being improved effectively.

Thinn Thu Naing - One of the best experts on this subject based on the ideXlab platform.

  • the efficient Data storage management system on cluster based Private Cloud Data center
    International Conference on Cloud Computing, 2011
    Co-Authors: Cho Cho Khaing, Thinn Thu Naing
    Abstract:

    The widespread popularity of Cloud computing as a preferred platform for the deployment of web applications has resulted in an enormous number of applications moving to the Cloud, and the huge success of Cloud service providers. The Data center storage management plays a vital role in Cloud computing environments. Especially the PC cluster-based Data storage is necessary to manage Data on low cost storage servers in which storage space can be reduced. This system presents an efficient Data storage approach to work out many nodes in a cluster using Cloud-based Distributed File System (CDFS) compatible file system with variable chunk size to facilitate massive Data processing. This system introduces the implementation enhancement on MapReduce to improve the system throughput and the scalability to keep on working with the amount of existing physical storage capacity when the number of users and files increase. Then CDFS also reduces the storage space in the storage server using Huffman Compression.

  • CCIS - The efficient Data storage management system on cluster-based Private Cloud Data center
    2011 IEEE International Conference on Cloud Computing and Intelligence Systems, 2011
    Co-Authors: Cho Cho Khaing, Thinn Thu Naing
    Abstract:

    The widespread popularity of Cloud computing as a preferred platform for the deployment of web applications has resulted in an enormous number of applications moving to the Cloud, and the huge success of Cloud service providers. The Data center storage management plays a vital role in Cloud computing environments. Especially the PC cluster-based Data storage is necessary to manage Data on low cost storage servers in which storage space can be reduced. This system presents an efficient Data storage approach to work out many nodes in a cluster using Cloud-based Distributed File System (CDFS) compatible file system with variable chunk size to facilitate massive Data processing. This system introduces the implementation enhancement on MapReduce to improve the system throughput and the scalability to keep on working with the amount of existing physical storage capacity when the number of users and files increase. Then CDFS also reduces the storage space in the storage server using Huffman Compression.

Marty Humphrey - One of the best experts on this subject based on the ideXlab platform.

  • icsi a Cloud garbage vm collector for addressing inactive vms with machine learning
    IEEE International Conference on Cloud Engineering, 2017
    Co-Authors: In Kee Kim, Sai Zeng, Christopher C Young, Jinho Hwang, Marty Humphrey
    Abstract:

    According to a recent study, 30% of VMs in Private Cloud Data centers are "comatose", in part because there is generally no strong incentive for their human owners to delete them at an appropriate time. These inactive VMs are still scheduled and executed on physical Cloud resources, taking valuable access away from productive VMs. In an extreme, Cloud infrastructure may deny legitimate requests for new VMs because capacity limits have been hit. It is not sufficient for Cloud infrastructure to identify such inactive VMs by monitoring resource utilization (e.g., CPU utilization) – e.g., management processes (e.g. virus-scan, software update) on inactive VMs often consume high CPU and memory resources, and active VMs with lightweight jobs (e.g. text editing) show almost zero resource utilization. To properly detect and address such inactive VMs, we present iCSI: a Cloud garbage VM collector to improve resource utilization and cost efficiency of enterprise Data centers. iCSI includes three main components, a lightweight Data collector, a VM identification model and a recommendation engine. The Data collector periodically gathers primitive information from VMs. The identification model infers the purpose of a VM from the Data collection and extracts the most relevant features associated with the purpose. The recommendation engine offers proper actions to end users i.e., suspending or resizing VMs. In this prototype phase, iCSI is deployed into multiple Data centers in IBM and manages more than 750 production VMs. iCSI achieves 20% better accuracy (90%) in identifying active/inactive VMs compared with state-of-the-art methods. With recommendations to end users, our estimation results show that iCSI can improve internal cost efficiency with 23% and resource utilization more than 45%.

  • IC2E - iCSI: A Cloud Garbage VM Collector for Addressing Inactive VMs with Machine Learning
    2017 IEEE International Conference on Cloud Engineering (IC2E), 2017
    Co-Authors: In Kee Kim, Sai Zeng, Christopher C Young, Jinho Hwang, Marty Humphrey
    Abstract:

    According to a recent study, 30% of VMs in Private Cloud Data centers are "comatose", in part because there is generally no strong incentive for their human owners to delete them at an appropriate time. These inactive VMs are still scheduled and executed on physical Cloud resources, taking valuable access away from productive VMs. In an extreme, Cloud infrastructure may deny legitimate requests for new VMs because capacity limits have been hit. It is not sufficient for Cloud infrastructure to identify such inactive VMs by monitoring resource utilization (e.g., CPU utilization) – e.g., management processes (e.g. virus-scan, software update) on inactive VMs often consume high CPU and memory resources, and active VMs with lightweight jobs (e.g. text editing) show almost zero resource utilization. To properly detect and address such inactive VMs, we present iCSI: a Cloud garbage VM collector to improve resource utilization and cost efficiency of enterprise Data centers. iCSI includes three main components, a lightweight Data collector, a VM identification model and a recommendation engine. The Data collector periodically gathers primitive information from VMs. The identification model infers the purpose of a VM from the Data collection and extracts the most relevant features associated with the purpose. The recommendation engine offers proper actions to end users i.e., suspending or resizing VMs. In this prototype phase, iCSI is deployed into multiple Data centers in IBM and manages more than 750 production VMs. iCSI achieves 20% better accuracy (90%) in identifying active/inactive VMs compared with state-of-the-art methods. With recommendations to end users, our estimation results show that iCSI can improve internal cost efficiency with 23% and resource utilization more than 45%.

  • a supervised learning model for identifying inactive vms in Private Cloud Data centers
    International Middleware Conference, 2016
    Co-Authors: In Kee Kim, Sai Zeng, Christopher C Young, Jinho Hwang, Marty Humphrey
    Abstract:

    A Private Cloud has become an essential computing infrastructure for many enterprises. However, according to a recent study, 30% of VMs in Data centers are not being used for any productive work. These "inactive" (or "zombie") VMs can arise from faulty VM management code within the Cloud infrastructure but are usually the result of human neglect. Inactive VMs can hurt the performance of productive VMs, can distort internal cost management, and in the extreme can result in the Cloud infrastructure being unable to allocate resources for new VMs. Correctly assessing the productivity of a VM can be challenging: e.g., is a VM that has low CPU utilization being used to slowly edit source code or is it an inactive VM that happens to be performing routine maintenance (e.g., virus-scan and software updates)? To address this problem, we develop a supervised learning model that leverages primitive information (e.g., running process, login history, network connections) of VMs periodically collected by a lightweight Data collection framework. This model employs a linear support vector machine (SVM) approach that reflects single VM behavior as well as coordinated VM behaviors. We evaluated the identification accuracy of this model with a real-world Dataset within IBM of more than 750 VMs. Results show that our model has a 20% higher accuracy (90%) than state-of-the-art approaches. An accurate model is an important first step to enable Private Cloud infrastructures to achieve better resource management through such actions as suspending or dynamically downsizing inactive VMs.

  • Middleware Industry - A Supervised Learning Model for Identifying Inactive VMs in Private Cloud Data Centers
    Proceedings of the Industrial Track of the 17th International Middleware Conference, 2016
    Co-Authors: In Kee Kim, Sai Zeng, Christopher C Young, Jinho Hwang, Marty Humphrey
    Abstract:

    A Private Cloud has become an essential computing infrastructure for many enterprises. However, according to a recent study, 30% of VMs in Data centers are not being used for any productive work. These "inactive" (or "zombie") VMs can arise from faulty VM management code within the Cloud infrastructure but are usually the result of human neglect. Inactive VMs can hurt the performance of productive VMs, can distort internal cost management, and in the extreme can result in the Cloud infrastructure being unable to allocate resources for new VMs. Correctly assessing the productivity of a VM can be challenging: e.g., is a VM that has low CPU utilization being used to slowly edit source code or is it an inactive VM that happens to be performing routine maintenance (e.g., virus-scan and software updates)? To address this problem, we develop a supervised learning model that leverages primitive information (e.g., running process, login history, network connections) of VMs periodically collected by a lightweight Data collection framework. This model employs a linear support vector machine (SVM) approach that reflects single VM behavior as well as coordinated VM behaviors. We evaluated the identification accuracy of this model with a real-world Dataset within IBM of more than 750 VMs. Results show that our model has a 20% higher accuracy (90%) than state-of-the-art approaches. An accurate model is an important first step to enable Private Cloud infrastructures to achieve better resource management through such actions as suspending or dynamically downsizing inactive VMs.

Guozhen Tan - One of the best experts on this subject based on the ideXlab platform.

  • energy aware Cloud application management in Private Cloud Data center
    Conference on Decision and Control, 2011
    Co-Authors: Guozhen Tan, Xia Zhang, Jingang Zhou
    Abstract:

    Cloud services decouple Cloud applications from IT infrastructure in Cloud environment. On demand resources provisioning pattern makes high efficient resource utility and application dynamic scaling possible. Hence Cloud Data center could provide infrastructure capabilities in a more energy efficient way. However, there exists a tradeoff between energy efficiency and application service level objectives. It is challenging to realize effective energy consumption and energy efficiency aware autonomic Cloud application service level agreement assurance mechanism. In this paper, we propose energy aware Cloud application management architecture for Private Cloud Data center. Furthermore we present a Cloud application and power management model. In order to metering server energy utility efficiency and Cloud applications' total energy consumption of running on a specific group of servers, we define related measurement metrics. The objective of our approach is to reduce Data center's total energy efficiency by controlling Cloud applications' overall energy consumption while ensuring Cloud applications' service level agreement. The experiment results show that the Cloud application energy consumption and energy efficiency is being improved effectively.

  • CDC - Energy aware Cloud application management in Private Cloud Data center
    2011 International Conference on Cloud and Service Computing, 2011
    Co-Authors: Guozhen Tan, Xia Zhang, Jingang Zhou
    Abstract:

    Cloud services decouple Cloud applications from IT infrastructure in Cloud environment. On demand resources provisioning pattern makes high efficient resource utility and application dynamic scaling possible. Hence Cloud Data center could provide infrastructure capabilities in a more energy efficient way. However, there exists a tradeoff between energy efficiency and application service level objectives. It is challenging to realize effective energy consumption and energy efficiency aware autonomic Cloud application service level agreement assurance mechanism. In this paper, we propose energy aware Cloud application management architecture for Private Cloud Data center. Furthermore we present a Cloud application and power management model. In order to metering server energy utility efficiency and Cloud applications' total energy consumption of running on a specific group of servers, we define related measurement metrics. The objective of our approach is to reduce Data center's total energy efficiency by controlling Cloud applications' overall energy consumption while ensuring Cloud applications' service level agreement. The experiment results show that the Cloud application energy consumption and energy efficiency is being improved effectively.

Cho Cho Khaing - One of the best experts on this subject based on the ideXlab platform.

  • the efficient Data storage management system on cluster based Private Cloud Data center
    International Conference on Cloud Computing, 2011
    Co-Authors: Cho Cho Khaing, Thinn Thu Naing
    Abstract:

    The widespread popularity of Cloud computing as a preferred platform for the deployment of web applications has resulted in an enormous number of applications moving to the Cloud, and the huge success of Cloud service providers. The Data center storage management plays a vital role in Cloud computing environments. Especially the PC cluster-based Data storage is necessary to manage Data on low cost storage servers in which storage space can be reduced. This system presents an efficient Data storage approach to work out many nodes in a cluster using Cloud-based Distributed File System (CDFS) compatible file system with variable chunk size to facilitate massive Data processing. This system introduces the implementation enhancement on MapReduce to improve the system throughput and the scalability to keep on working with the amount of existing physical storage capacity when the number of users and files increase. Then CDFS also reduces the storage space in the storage server using Huffman Compression.

  • CCIS - The efficient Data storage management system on cluster-based Private Cloud Data center
    2011 IEEE International Conference on Cloud Computing and Intelligence Systems, 2011
    Co-Authors: Cho Cho Khaing, Thinn Thu Naing
    Abstract:

    The widespread popularity of Cloud computing as a preferred platform for the deployment of web applications has resulted in an enormous number of applications moving to the Cloud, and the huge success of Cloud service providers. The Data center storage management plays a vital role in Cloud computing environments. Especially the PC cluster-based Data storage is necessary to manage Data on low cost storage servers in which storage space can be reduced. This system presents an efficient Data storage approach to work out many nodes in a cluster using Cloud-based Distributed File System (CDFS) compatible file system with variable chunk size to facilitate massive Data processing. This system introduces the implementation enhancement on MapReduce to improve the system throughput and the scalability to keep on working with the amount of existing physical storage capacity when the number of users and files increase. Then CDFS also reduces the storage space in the storage server using Huffman Compression.