The Experts below are selected from a list of 3867 Experts worldwide ranked by ideXlab platform

Youyan Wang - One of the best experts on this subject based on the ideXlab platform.

  • workload optimization and energy consumption reduction strategy of private cloud in manufacturing industry
    International Conference on Software Engineering, 2020
    Co-Authors: Xiaoqin Wang, Youyan Wang
    Abstract:

    The private cloud of manufacturing industry whose business forms are rich and business chains long has numerous legacy systems. According to the analysis of the historical monitoring data of a private cloud system in manufacturing industry and interviews with people concerned, it is found in this paper that the private cloud in manufacturing industry could not only cope with the problems of limited capacity but also the issues of low resource utilization and excessive energy consumption in data centers caused by resource provisioning modes and characteristics of user psychology and behavior. Based on the creation of configuration management database (CMDB) and monitoring system, and combined with the features of private cloud resources provisioning, the paper tried to propose different strategies to optimize the private cloud for the platform administrator and resource users. Moreover, through optimization, sound economic and social values, such as workload improvement, energy consumption reduction, and longer expansion cycle for the private cloud platform, could be obtained.

Xiaoqin Wang - One of the best experts on this subject based on the ideXlab platform.

  • workload optimization and energy consumption reduction strategy of private cloud in manufacturing industry
    International Conference on Software Engineering, 2020
    Co-Authors: Xiaoqin Wang, Youyan Wang
    Abstract:

    The private cloud of manufacturing industry whose business forms are rich and business chains long has numerous legacy systems. According to the analysis of the historical monitoring data of a private cloud system in manufacturing industry and interviews with people concerned, it is found in this paper that the private cloud in manufacturing industry could not only cope with the problems of limited capacity but also the issues of low resource utilization and excessive energy consumption in data centers caused by resource provisioning modes and characteristics of user psychology and behavior. Based on the creation of configuration management database (CMDB) and monitoring system, and combined with the features of private cloud resources provisioning, the paper tried to propose different strategies to optimize the private cloud for the platform administrator and resource users. Moreover, through optimization, sound economic and social values, such as workload improvement, energy consumption reduction, and longer expansion cycle for the private cloud platform, could be obtained.

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

  • a big data on private cloud agile provisioning framework based on openstack
    International Conference on Cloud Computing, 2018
    Co-Authors: Ming Lu, Xu Zhou
    Abstract:

    On the bases of the OpenStack private cloud delivery big data platform, numerous entities yearn for attaining agile and standardized big data delivery platform, reclaiming the resources, managing the total cost of ownership (TCO) and adapting to multiple big data open source or commercial off-the-shelf (COTS) solutions. Nevertheless, as regards the big data platform running on cloud computing, the big data platform is disintegrated from the cloud computing system by virtual machines since neither being based on OpenStack private cloud nor on big data platform can achieve end-to-end resource delivery, together with ensuring that it is quite convenient for the long-term operations. Accordingly, establishing an across framework between private cloud and big data platform is quite essential. The big data on cloud agile provision framework could realize fast resource delivery based on predefined orchestration template of private cloud, operating system, big data platform, monitor, inspection system, etc. Through the deployment of this framework, it is capable of attaining the delivery of agile, low cost, standardized and high adaptability the big data on cloud, as well as the high-quality operation of the big data on cloud with the help of integration configuration management database (CMDB) with the automatic inspection system.

Ming Lu - One of the best experts on this subject based on the ideXlab platform.

  • a big data on private cloud agile provisioning framework based on openstack
    International Conference on Cloud Computing, 2018
    Co-Authors: Ming Lu, Xu Zhou
    Abstract:

    On the bases of the OpenStack private cloud delivery big data platform, numerous entities yearn for attaining agile and standardized big data delivery platform, reclaiming the resources, managing the total cost of ownership (TCO) and adapting to multiple big data open source or commercial off-the-shelf (COTS) solutions. Nevertheless, as regards the big data platform running on cloud computing, the big data platform is disintegrated from the cloud computing system by virtual machines since neither being based on OpenStack private cloud nor on big data platform can achieve end-to-end resource delivery, together with ensuring that it is quite convenient for the long-term operations. Accordingly, establishing an across framework between private cloud and big data platform is quite essential. The big data on cloud agile provision framework could realize fast resource delivery based on predefined orchestration template of private cloud, operating system, big data platform, monitor, inspection system, etc. Through the deployment of this framework, it is capable of attaining the delivery of agile, low cost, standardized and high adaptability the big data on cloud, as well as the high-quality operation of the big data on cloud with the help of integration configuration management database (CMDB) with the automatic inspection system.

Mukesh K Mohania - One of the best experts on this subject based on the ideXlab platform.

  • automating itsm incident management process
    International Conference on Autonomic Computing, 2008
    Co-Authors: Rajendra Gupta, K H Prasad, Mukesh K Mohania
    Abstract:

    Service desks are used by customers to report IT issues in enterprise systems. Most of these service requests are resolved by level-1 persons (service desk attendants) by providing information/quick-fix solutions to customers. For each service request, level- 1 personnel identify important keywords and see if the incoming request is similar to any historic incident. Otherwise, an incident ticket is created and, with other related information, forwarded to incident's subject matter expert (SME). Incident management process is used for managing the life cycle of all incidents. An organization spends lots of resources to keep its IT resources incident free and, therefore, timely resolution of incoming incident is required to attain that objective. Currently, the incident management process is largely manual, error prone and time consuming. In this paper, we use information integration techniques and machine learning to automate various processes in the incident management workflow. We give a method for correlating the incoming incident with configuration items (CIs) stored in configuration management database (CMDB). Such a correlation can be used for correctly routing the incident to SMEs, incident investigation and root cause analysis. In our technique, we discover relevant CIs by exploiting the structured and unstructured information available in the incident ticket. We present efficient algorithm which gives more than 70% improvement in accuracy of identifying the failing component by efficiently browsing relationships among CIs.