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A cloud data center application perceptible distributed multi-resource combined path optimal selection method

A cloud data center and combination path technology, applied in the field of distributed multi-resource combination paths, can solve problems such as CPU capacity limitation and high I/O throughput, and achieve the goals of accelerating search time, minimizing delay, and shortening total response time Effect

Active Publication Date: 2019-05-28
BEIJING UNIV OF TECH
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Problems solved by technology

But online video web services are data-intensive and require high I / O throughput, while some Map Reduce jobs (such as K-Means and Pagerank) are CPU-intensive and usually limited by CPU capacity

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  • A cloud data center application perceptible distributed multi-resource combined path optimal selection method
  • A cloud data center application perceptible distributed multi-resource combined path optimal selection method
  • A cloud data center application perceptible distributed multi-resource combined path optimal selection method

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Embodiment Construction

[0058] Such as figure 1 As shown, the present invention provides a cloud data center application-aware distributed multi-resource combination path optimal selection method, which includes the following steps:

[0059] The first step is to collect and process resource status information such as network and CPU in basic resources

[0060] From the request flow data warehouse RFDH, periodically obtain the flow forecast information of various request flows in each segment of resources; and obtain real-time network topology, each network link and server through the cloud data center application-aware cloud resource manager AACRM The status information of CPU, Memory, I / O and other resources in the cluster, and then store these resource status information in the HBase database in real time. First, feature engineering is used to process the collected resource status information (network bandwidth, delay, CPU, etc.). This step provides data for the model training constructed below. ...

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Abstract

The invention discloses a cloud data center application perceptible distributed multi-resource combined path optimal selection method. The method comprises the steps that a cloud data center application perceptible cloud resource manager is responsible for collecting resource state information of a network, a CPU and the like in basic resources; Forming an unsupervised deep hybrid architecture model by adopting a mode of combining reinforcement learning, a value network and a strategy network, and assessing model training and node moving positions of various request streams; A novel tree search algorithm is adopted, namely, a Parallel Monte Carlo Tree Search(PMCTS) algorithm is adopted, an appropriate resource path is searched for each type of request flow in an accelerated mode, and the value and the strategy network are combined, so that an optimal resource path selection result is given. By adopting the technical scheme provided by the invention, the minimum total response time delay of various intensive request streams of the cloud data center can be ensured.

Description

technical field [0001] The invention relates to an optimal selection method of a cloud data center application-aware distributed multi-resource combination path. Background technique [0002] In recent years, a cloud data center (Cloud Data Center, CDC) that provides application service hosting has become more and more popular among enterprises and users. Because the cloud architecture can be expanded quickly and efficiently, it can support more workloads than traditional data centers. At the same time, the pay-as-you-go mode and on-demand resource allocation capabilities can save time and cost for users to build IT infrastructure, and provide cloud data Central providers provide a more cost-effective method of running applications and deploying services. In view of the advantages provided by the cloud, more and more individuals and enterprises are gradually transferring their applications from traditional personal computers and internal data centers to the cloud, and this ...

Claims

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Application Information

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IPC IPC(8): H04L12/24H04L12/947H04L29/08G06F9/50G06N3/08
Inventor 毕敬刘恒张晓芬
Owner BEIJING UNIV OF TECH
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