WO2017010922A1 - Attribution de ressources informatiques en nuage - Google Patents

Attribution de ressources informatiques en nuage Download PDF

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Publication number
WO2017010922A1
WO2017010922A1 PCT/SE2015/050824 SE2015050824W WO2017010922A1 WO 2017010922 A1 WO2017010922 A1 WO 2017010922A1 SE 2015050824 W SE2015050824 W SE 2015050824W WO 2017010922 A1 WO2017010922 A1 WO 2017010922A1
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WO
WIPO (PCT)
Prior art keywords
cloud computing
resource utilization
hosts
weight
predicted
Prior art date
Application number
PCT/SE2015/050824
Other languages
English (en)
Inventor
Nicolas Seyvet
Ignacio Manuel MULAS VIELA
Tony Larsson
Original Assignee
Telefonaktiebolaget Lm Ericsson (Publ)
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Telefonaktiebolaget Lm Ericsson (Publ) filed Critical Telefonaktiebolaget Lm Ericsson (Publ)
Priority to PCT/SE2015/050824 priority Critical patent/WO2017010922A1/fr
Publication of WO2017010922A1 publication Critical patent/WO2017010922A1/fr

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Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F9/00—Arrangements for program control, e.g. control units
    • G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
    • G06F9/46—Multiprogramming arrangements
    • G06F9/50—Allocation of resources, e.g. of the central processing unit [CPU]
    • G06F9/5005—Allocation of resources, e.g. of the central processing unit [CPU] to service a request
    • G06F9/5027—Allocation of resources, e.g. of the central processing unit [CPU] to service a request the resource being a machine, e.g. CPUs, Servers, Terminals
    • G06F9/505—Allocation of resources, e.g. of the central processing unit [CPU] to service a request the resource being a machine, e.g. CPUs, Servers, Terminals considering the load
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F9/00—Arrangements for program control, e.g. control units
    • G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
    • G06F9/46—Multiprogramming arrangements
    • G06F9/50—Allocation of resources, e.g. of the central processing unit [CPU]
    • G06F9/5061—Partitioning or combining of resources
    • G06F9/5077—Logical partitioning of resources; Management or configuration of virtualized resources

Definitions

  • Cloud computing components usually make use of different drivers, such as hypervisors and virtual switches, installed on hosts.
  • a hypervisor or virtual machine monitor (VMM) is a piece of computer software, firmware or hardware that creates and runs Virtual Machines (VMs).
  • a virtual switch is a logical switching fabric built into a VM infrastructure so that the Virtual Machines (VMs) can be networked wherever you need them.
  • VMs Virtual Machines
  • Cloud computing can run in bare-metal in some occasions in order to avoid virtualization overheads and achieve better performance using other kinds of isolation systems like containers. Virtualization can also be used in other contexts than cloud computing.
  • the weight for each of the plurality of hosts maybe calculated by adding a predicted resource utilization to a current resource utilization, wherein the predicted resource utilization has a reduced significance compared to the current resource utilization.
  • the weight may also comprise a plurality of predicted resource utilizations for a plurality of time instances, and a prediction further into the future has a reduced significance compared to a prediction nearer into the future.
  • the significance of each predicted resource utilization of the plurality of predicted resource utilizations may be set in dependence on accuracy of the predictions.
  • the significance of each predicted resource utilization of the plurality of predicted resource utilizations may be dynamically set by machine learning by building a model and extracting each predicted resource utilization of the plurality of predicted resource
  • the instructions may further cause the scheduler server to: cause prediction of a resource utilization for each of a plurality of a first type of cloud computing recourses; cause aggregation of the predicted resource utilizations of the first type for each of the plurality of hosts controlling the plurality of cloud computing resources; cause determination of a first weight for each of the plurality of hosts, in dependence on current resource utilization of the first type and predicted resource utilization of the first type; cause prediction of a resource utilization for each of a plurality of a second type of cloud computing recourses; cause aggregation of the predicted resource utilizations of the second type for each of the plurality of hosts controlling the plurality of cloud computing resources; cause determination of a second weight for each of the plurality of hosts, in dependence on current resource utilization of the second type and predicted resource utilization of the second type; and cause determination of a total weight for each of the plurality of hosts, in
  • Fig. 2 is a schematic diagram illustrating an environment
  • a scheduler needs to be aware at any time what the situation is for the cloud computing resources to be able to efficiently decide where to allocate new virtual resources.
  • the scheduler follows a weight mechanism in order to decide where to allocate newly incoming requests. It creates a weight for each of the hosts connected to the cluster and takes into account the currently consumed resources by the virtual resources allocated on it. In this way these resources are ordered in a prioritized list of hosts. The scheduler can then allocate the requested resources and comply with the available ones on a given host.
  • the prediction scores for all users are then sorted from high to low. This is illustrated in Fig. 7, which contains 18 users (each bar in the histogram) and their prediction score. Based on this sorting a number of user classes can be defined. This may for instance be "high”, “med” and “low”. The number of classes and the range for each class is configurable.
  • a high predictability class may be used define a stable user.
  • a low predictability class may be used to define a very dynamic user.
  • a med predictability class maybe used to define users having a predictability being between stable users and very dynamic users. Details for implementation of classification of predictability of users can be found in patent application PCT/EP2014/ 079143.
  • the weights a and bn are dynamically set based on how good/accurately the prediction model captures the resource utilization. Intuitively this means that the predicted resource utilization Yn will get a higher weight bn when the predictions are working well. This can for instance be when we have stable and long-running services that have a certain regularity in its resource utilization data pattern.
  • the machine learning takes into account how stable a host is so it can give more or less importance to the future predicted samples. However, if the host is unstable, the machine learning algorithm will give very little importance to the future samples as they might not be reliable.
  • the difference between the two processes above is that in the first one, the weights are static (for example the a and bn coefficients may be a negative exponential function), while in the second one, the coefficients are dynamic and depend on the accuracy of the predictions done before.
  • the two nodes may e.g. be a centralized scheduler node and a distributed prediction node.
  • This module can e.g. be implemented by the processor 60 of Fig. 8, when running the computer program.

Abstract

L'invention concerne un procédé pour attribuer des ressources informatiques en nuage (22a, 22b) dans un environnement informatique en nuage (21). Le procédé est réalisé par un serveur de planificateur (20) et comprend les étapes consistant : à entraîner la détermination (42) d'un poids pour chacun d'une pluralité d'hôtes (23a, 23b, 23c), en fonction d'une utilisation de ressource courante et d'une utilisation de ressource prédite ; et à entraîner l'attribution (43) d'une ressource informatique en nuage (22a, 22b) en fonction du poids déterminé. L'invention concerne également un serveur de planificateur, un programme d'ordinateur et un produit programme d'ordinateur.
PCT/SE2015/050824 2015-07-14 2015-07-14 Attribution de ressources informatiques en nuage WO2017010922A1 (fr)

Priority Applications (1)

Application Number Priority Date Filing Date Title
PCT/SE2015/050824 WO2017010922A1 (fr) 2015-07-14 2015-07-14 Attribution de ressources informatiques en nuage

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
PCT/SE2015/050824 WO2017010922A1 (fr) 2015-07-14 2015-07-14 Attribution de ressources informatiques en nuage

Publications (1)

Publication Number Publication Date
WO2017010922A1 true WO2017010922A1 (fr) 2017-01-19

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Family Applications (1)

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WO (1) WO2017010922A1 (fr)

Cited By (15)

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CN107783822A (zh) * 2017-11-10 2018-03-09 郑州云海信息技术有限公司 一种资源管理方法及装置
AU2018200013A1 (en) * 2017-05-05 2018-11-22 Servicenow, Inc. Shared machine learning
CN110502344A (zh) * 2019-08-26 2019-11-26 联想(北京)有限公司 一种数据调整方法及装置
WO2020019017A1 (fr) * 2018-07-24 2020-01-30 Joseph Matthew Appareil, système et procédé de détection de contraintes sans agents dans le nuage avec ia
CN111143050A (zh) * 2018-11-02 2020-05-12 中移（杭州）信息技术有限公司 一种容器集群调度的方法和设备
CN111404974A (zh) * 2019-01-02 2020-07-10 中国移动通信有限公司研究院 一种云计算效能评估方法、装置及评估设备
CN112667398A (zh) * 2020-12-28 2021-04-16 北京奇艺世纪科技有限公司 资源调度方法、装置、电子设备及存储介质
US11126541B2 (en) 2018-05-24 2021-09-21 Red Hat, Inc. Managing resources used during a development pipeline
US11150931B2 (en) 2018-10-30 2021-10-19 Hewlett Packard Enterprise Development Lp Virtual workload migrations
CN114629959A (zh) * 2022-03-22 2022-06-14 北方工业大学 一种云环境中上下文感知的IoT服务调度策略生成方法
US11489731B2 (en) 2016-09-30 2022-11-01 Salesforce.Com, Inc. Techniques and architectures for efficient allocation of under-utilized resources
CN115412467A (zh) * 2022-09-01 2022-11-29 山东正中信息技术股份有限公司 一种电子政务云中租户云资源利用率评估方法及系统
US11609796B2 (en) 2017-12-14 2023-03-21 Google Llc Dynamic capacity optimization for shared computing resources segmented into reservation zones
US11620571B2 (en) 2017-05-05 2023-04-04 Servicenow, Inc. Machine learning with distributed training
CN117608809A (zh) * 2024-01-18 2024-02-27 中国电子科技集团公司第十五研究所 基于梯度提升决策树的多任务计划进度预测系统

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US6763372B1 (en) * 2000-07-06 2004-07-13 Nishant V. Dani Load balancing of chat servers based on gradients
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Cited By (21)

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US11902102B2 (en) 2016-09-30 2024-02-13 Salesforce, Inc. Techniques and architectures for efficient allocation of under-utilized resources
US11489731B2 (en) 2016-09-30 2022-11-01 Salesforce.Com, Inc. Techniques and architectures for efficient allocation of under-utilized resources
AU2018200013A1 (en) * 2017-05-05 2018-11-22 Servicenow, Inc. Shared machine learning
US10445661B2 (en) 2017-05-05 2019-10-15 Servicenow, Inc. Shared machine learning
US11620571B2 (en) 2017-05-05 2023-04-04 Servicenow, Inc. Machine learning with distributed training
CN107783822A (zh) * 2017-11-10 2018-03-09 郑州云海信息技术有限公司 一种资源管理方法及装置
US11609796B2 (en) 2017-12-14 2023-03-21 Google Llc Dynamic capacity optimization for shared computing resources segmented into reservation zones
US11126541B2 (en) 2018-05-24 2021-09-21 Red Hat, Inc. Managing resources used during a development pipeline
WO2020019017A1 (fr) * 2018-07-24 2020-01-30 Joseph Matthew Appareil, système et procédé de détection de contraintes sans agents dans le nuage avec ia
US11150931B2 (en) 2018-10-30 2021-10-19 Hewlett Packard Enterprise Development Lp Virtual workload migrations
CN111143050A (zh) * 2018-11-02 2020-05-12 中移（杭州）信息技术有限公司 一种容器集群调度的方法和设备
CN111143050B (zh) * 2018-11-02 2023-09-19 中移（杭州）信息技术有限公司 一种容器集群调度的方法和设备
CN111404974A (zh) * 2019-01-02 2020-07-10 中国移动通信有限公司研究院 一种云计算效能评估方法、装置及评估设备
CN110502344A (zh) * 2019-08-26 2019-11-26 联想(北京)有限公司 一种数据调整方法及装置
CN112667398B (zh) * 2020-12-28 2023-09-01 北京奇艺世纪科技有限公司 资源调度方法、装置、电子设备及存储介质
CN112667398A (zh) * 2020-12-28 2021-04-16 北京奇艺世纪科技有限公司 资源调度方法、装置、电子设备及存储介质
CN114629959A (zh) * 2022-03-22 2022-06-14 北方工业大学 一种云环境中上下文感知的IoT服务调度策略生成方法
CN114629959B (zh) * 2022-03-22 2023-11-17 北方工业大学 一种云环境中上下文感知的IoT服务调度策略方法
CN115412467A (zh) * 2022-09-01 2022-11-29 山东正中信息技术股份有限公司 一种电子政务云中租户云资源利用率评估方法及系统
CN115412467B (zh) * 2022-09-01 2023-11-07 山东正中信息技术股份有限公司 一种电子政务云中租户云资源利用率评估方法及系统
CN117608809A (zh) * 2024-01-18 2024-02-27 中国电子科技集团公司第十五研究所 基于梯度提升决策树的多任务计划进度预测系统

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