WO2014073949A1 - Système et procédé de réservation de machine virtuelle pour applications de service sensibles aux retards - Google Patents

Système et procédé de réservation de machine virtuelle pour applications de service sensibles aux retards Download PDF

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Publication number
WO2014073949A1
WO2014073949A1 PCT/MY2013/000191 MY2013000191W WO2014073949A1 WO 2014073949 A1 WO2014073949 A1 WO 2014073949A1 MY 2013000191 W MY2013000191 W MY 2013000191W WO 2014073949 A1 WO2014073949 A1 WO 2014073949A1
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WIPO (PCT)
Prior art keywords
service
virtual machines
latency
virtual machine
module
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Application number
PCT/MY2013/000191
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English (en)
Inventor
Ping LIM BOON
Karuppiah ETTIKAN KANDASAMY
Kit CHONG POH
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Mimos Berhad
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Publication date
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Publication of WO2014073949A1 publication Critical patent/WO2014073949A1/fr

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    • 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
    • G06F9/5072—Grid computing
    • 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

  • the present invention relates to a system and method for virtual machine reservation for delay sensitive service applications.
  • the invention relates to systems and methods that leverage on service latency to predict requisite numbers of virtual machines requiring deployment to assure a specified service response time.
  • a request for bond price predictions may require intensive computation and the results may be required by a customer anywhere and at any time within seconds of the request.
  • the SaaS provider may not be able to predict the requirement of computer resources as the SaaS provided may not know when, where or how much computing resources are needed.
  • maximum numbers of virtual machines need to be deployed at dispersed locations which generally results in wasted resources. In particular, this may result in virtual machines standing idle when no service request is received.
  • United States Patent Publication No. 2011/0231899 describes a system that provides a cloud-computing service from a cloud-computing environment comprising a plurality of cloud-computing resources.
  • the system comprises a management module configured to manage a cloud-computing resource of the plurality of cloud-computing resources as a cloud-computing service.
  • the cloud-computing service performs a computer workload.
  • the system also comprises an adapter configured to connect to the cloud-computing resource to the system and translate a management instruction received from the management module into a proprietary cloud application program interface call for the cloud-computing resource.
  • a cloud service bus is provided that is configured to route the management instruction from the management module to the adapter and a consumption module is provided that is configured to allow a user to subscribe the cloud-computing service.
  • a planning module is provided that is configured to plan the cloud-computing service and a build module is provided that is configured to build the cloud-computing service from the cloud-computing resource and publish the cloud-computing service to the consumption module.
  • This publication exemplifies a system that involves reactive service provisioning in which the allocation of virtual machines is based on the computational workload assigned, and in which planning, management and spawning of virtual machines is based on service requests.
  • the scheduling policy described is not application aware, but focuses on virtual machine allocation based on hardware resource availability.
  • United States Patent Publication No. 2008/0304421 describes a prediction tree for estimating values of a network performance measure.
  • Leaf nodes of the prediction tree are associated with networked computing devices and interior nodes are not necessarily representative of physical network connections.
  • Values are assigned to edges in the prediction tree and the network performance measure relative to two computing devices represented by two nodes of the tree is estimated by aggregating the values assigned to the edges in the path in the prediction tree joining the two edges.
  • Mechanisms for adding nodes representing computing devices to the prediction tree, for identifying a closest node representing a computing device in the prediction tree, for identifying a cluster of devices represented by nodes of the tree, and for rebalancing the prediction tree are provided.
  • This publication exemplifies systems based on the well-known Euclidean Steiner Tree Model in combinatorial optimisation.
  • the input as exemplified in this publication relates to inter-nodal network performance measurements (i.e. network latency only).
  • Optimisation of the prediction tree does not refer to the mechanism behind network node selection.
  • the subject matter claimed herein is not limited to embodiments that solve any disadvantages or that operate only in environments such as those described above. Rather, this background is only provided to illustrate one exemplary technology area where some embodiments described herein may be practiced.
  • the present invention relates to a system and method for virtual machine reservation for delay sensitive service applications.
  • the invention relates to systems and methods that leverage on service latency to predict requisite numbers of virtual machines requiring deployment to assure a specified service response time.
  • the present invention provides a system for virtual machine reservation for delay sensitive service applications.
  • the system comprising at least one servicing module configured to manage cloud computing service requests; at least one scheduling module configured to provide and deploy virtual machines for cloud computing services to fulfil the requests; at least one prediction module configured to predict service latency of unmeasured virtual machine resources; and at least one measurement module configured to trigger measurement of service latency of virtual machines which emulate the cloud computing service, characterised in that the scheduling module is configured to provide and deploys the virtual machines to satisfy a pre-defined service response time based on predicted service latency of unmeasured virtual machine resources and measured service latency of virtual machines.
  • the said scheduling module further comprises a scheduler configured to deploy virtual machines for service emulation, to reserve virtual machines according to a given policy defined by policy making module, and to shutdown virtual machines for resource optimisation.
  • the servicing module further comprises a service request handler configured to input a service configuration of a service type and/or a range of tolerable service response times for the service request; a planning module configured to identify a set of cloud computing services to be deployed on virtual machines for service latency computation; and a policy making module configured to receive the predicted service latency and the measured service latency, estimate total service response time and define policy to optimise virtual machine resources to be reserved.
  • the planning module further comprises a task categorisation module configured to classify tasks required to satisfy the service request; and a task provisioning module configured to identify at least one virtual machine available and required to satisfy the service by forming a service performance zone based on the pre-defined service response time.
  • the policy making module further comprises at least one appointed host a plurality of virtual machines to be reserved at the appointed host; a plurality of CPU resources to be reserved at the appointed host; and a plurality of memory resources to be reserved at the appointed host.
  • the prediction module further comprises an estimation module configured to select at least one virtual machine for service latency measurement and receive obtained service latency measurement(s); and a tree construction module configured to construct at least one prediction tree and predict service latency of unmeasured virtual machine resources.
  • the measurement module comprises a controller module configured to request service emulation on virtual machines, trigger service latency measurement on selected virtual machines, receive measured service latency and feedback to the prediction module; and a repository handler module configured to retrieve historical service latency data for selected virtual machines and feedback to the prediction module.
  • the invention provides a method for virtual machine reservation for delay sensitive service applications comprising receiving a service request from a client network (410);providing cloud computing resources requested (420); instantiating virtual machines service (430); emulating the service request and collecting machine service latency (440); predicting service latency of unmeasured virtual machines (450);forming at least one servicing performance zone based on the service latency of virtual machines (460); and determining virtual machine resources to be reserved (470), characterised in that the virtual machines are reserved to satisfy a pre-defined service response time based on predicted service latency of unmeasured virtual machine resources and measured service latency of virtual machines further comprises steps of.
  • providing cloud computing resources comprises identifying service type and/or required range of service response time (421); determining a set of cloud computing services to be deployed on at least one virtual machine for service latency computation (422); receiving predicted service latency and measured service latency information (423); estimating total service response time (424); and providing a virtual machine resource to be reserved (425).
  • emulating the service request comprises triggering a set of cloud computing services on at least one selected virtual machine (441); measuring service latency from the selected virtual machine(s) (442); and feedback of the service latency (443).
  • predicting service latency of unmeasured virtual machines comprises selecting at least two virtual machines (451); receiving at least one service latency measurement (452); constructing at least one prediction tree (453); and predicting service latency of unmeasured virtual machines (454).
  • forming at least one servicing performance zone comprises retrieving service latency information (461); identifying a range of service response time (462); identifying response intervals for servicing performance zone(s) (463); and forming the performance service zone(s) on the prediction tree based on the range of service response time (464).
  • calculating the virtual resource required to fulfil the pre-defined service response time comprises determining at least one appointed host (476); determining the number of virtual machines to be reserved at the appointed host (477); determining the number of CPU resources to be reserved at the appointed host (478); and determining the number of memory resources to be reserved at the appointed host (479).
  • FIG. 1.0 illustrates the system of an embodiment of the invention.
  • FIG. 2.0 illustrates the servicing module of the system of Figure 1.0 in more detail.
  • FIG. 3.0 illustrates the protection module, measurement module and scheduling module of the system of Figure 1.0 in more detail.
  • FIG. 4.0 illustrates a flow diagram of the method of an embodiment of the invention.
  • FIG. 5.0 illustrates step 2 of the flow diagram of Figure 4 in more detail.
  • FIG. 6.0 illustrates step 4 of the flow diagram of Figure 4 in more detail.
  • FIG. 7.0 illustrates step 5 of the flow diagram of Figure 4 in more detail.
  • FIG. 8.0 illustrates step 6 of the flow diagram of Figure 4 in more detail.
  • FIG. 9.0 illustrates step 7 of the flow diagram of Figure 4 in more detail.
  • FIG. 10.0 illustrates step 7.3 of the flow diagram of Figure 9 in more detail.
  • FIG. 11.0 illustrates diagrammatically instantiation of the virtual machine service.
  • FIG. 12 illustrates diagrammatically emulation and execution of the service request and prediction of service latency.
  • Figure 13 illustrates diagrammatically the formation of servicing performance zones.
  • Figure 14 illustrates diagrammatically the determination and optimisation of resource reservation.
  • the present invention provides a system and method for virtual machine reservation for delay sensitive service applications.
  • the invention relates to systems and methods that leverage on service latency to predict requisite numbers of virtual machines requiring deployment to assure a specified service response time.
  • the system (100) includes a servicing module (110) that is adapted to receive and manage a service request (120) from a client network.
  • a scheduling module (130) is in communication with the servicing module (110) and facilitates the provision and deployment of virtual machines in order to fulfil the service requests (120).
  • a prediction module (140) is provided and is configured to predict service latency of unmeasured virtual machine resources and communicate estimated service delay to the servicing module (110).
  • a measurement module (150) is also provided. This is configured to trigger measurement of service latency of virtual machines which emulate the cloud computing service.
  • the servicing module (110) includes a service request handler (112) configured to input a service configuration for the service request.
  • the service configuration may comprise a service type and/or a range of tolerable service response times.
  • the servicing module includes a planning module (114) which is configured to identify a set of cloud computing services to be deployed on virtual machines for service latency computation.
  • a policy making module (116) is also includes and is configured to receive the predicted service latency and measured service latency. The policy making module (116) also estimates total service response time and defines policy to optimise virtual machine resources to be reserved.
  • the planning module (1 14) includes a task categorisation module (118) that is configured to classify tasks required to satisfy the service request that has been made by the network client. It also includes a task provisioning module (1 19) that is configured to identify at least one virtual machine available and required to satisfy the service by forming a service performance zone based on a pre-defined service response time. This will be discussed in more detail below.
  • the scheduling module (130) comprises a scheduler (132) that is configured to deploy virtual machines for service emulation.
  • the scheduler (132) is adapted to reserve virtual machines according to a given policy defined by the policy making module (1 16), and to shutdown virtual machines for resource optimisation.
  • the prediction module (140) generally includes an estimation module (142) and a tree construction module (144).
  • the estimation module (142) is configured to select at least one virtual machine for service latency measurement and receive obtained service latency measurement(s), while the tree construction module (144) is configured to construct at least one prediction tree and predict service latency of unmeasured virtual machine resources.
  • the measurement module (150) includes a controller module (152) and a repository handler module (154).
  • the controller module ( 52) is configured to request service emulation on virtual machines, trigger service latency measurement on selected virtual machines, receive measured service latency and feedback to the prediction module (140), while the repository handler module (154) is configured to retrieve historical service latency data for selected virtual machines and feedback to the prediction module (140).
  • the invention includes the steps of receiving a service request from a client network (410), providing cloud computing resources requested (420), instantiating virtual machines service (430), emulating the service request and collecting machine service latency (440), predicting service latency of unmeasured virtual machines (450), forming at least one servicing performance zone based on the service latency of virtual machines (460) and determining virtual machine resources to be reserved (470).
  • the step of providing cloud computing resources (420) includes the steps of identifying service type and/or required range of service response time (421), determining a set of cloud computing services to be deployed on at least one virtual machine for service latency computation (422), receiving predicted service latency and measured service latency information (423), estimating total service response time (424) and providing a virtual machine resource to be reserved.
  • the step of emulating the service request (440) includes triggering a set of cloud computing services on at least one selected virtual machine (441), measuring service latency from the selected virtual machine(s) (442), and feedback of the service latency (443).
  • the step of predicting service latency of unmeasured virtual machines (450) includes selecting at least two virtual machines (451), receiving at least one service latency measurement (452), constructing at least one prediction tree (453) and predicting service latency of unmeasured virtual machines (454).
  • the step of forming at least one servicing performance zone (460) includes retrieving service latency information (461), identifying a range of service response time (462), identifying response intervals for servicing performance zone(s) (463) and forming the performance service zone(s) on the prediction tree based on the range of service response time (464).
  • the step of determining virtual machine resources to be reserved includes identifying the number of virtual machines available in each servicing performance zone (471), determining service response times for each servicing performance zone (472), calculating the virtual resource required to fulfil the pre-defined service response time (473) and, if the pre-defined service response time is not fulfilled by the service response time within the servicing performance zone, deploying additional virtual machines (474), or, if the service response time within the servicing performance zone outperforms the pre-defined service response time, shutting down virtual machine(s) (475).
  • Calculating the virtual resource required to fulfil the pre-defined service response time includes determining at least one appointed host (476), determining the number of virtual machines to be reserved at the appointed host (477), determining the number of CPU resources to be reserved at the appointed host (478); and determining the number of memory resources to be reserved at the appointed host.
  • Figure 11 illustrates diagrammatically instantiation of the virtual machine service 430. As noted, this step involves identification of an application server, request for virtual machines and subsequent instantiation of the virtual machines. Emulation and execution of the service request 440 and prediction of service latency (450) are diagrammatically illustrated in Figure 12, while Figure 13 provides a diagrammatic representation of the formation of servicing performance zones (460). Finally, Figure 14 illustrates determination and optimisation of resource reservation (470).

Abstract

L'invention concerne un système (100) qui comprend un module de service (110) conçu pour recevoir et gérer une demande de service (120) d'un réseau client. Un module de programmation (130) est en communication avec le module de fourniture de service (110) et facilite la fourniture et le déploiement de machines virtuelles afin de satisfaire aux demandes de service (120). Un module de prédiction (140) est prévu et configuré pour prédire une latence de service de ressources non mesurées de machine virtuelle et pour communiquer un retard de service estimé au module de fourniture de service (110). La présente invention a également trait à un module de mesure (150) qui est configuré pour mesurer la latence de service de machines virtuelles émulant le service informatique de nuage. Le module de programmation (130) est configuré pour assurer et déployer lesdites machines virtuelles afin de satisfaire à un temps de réponse de service prédéfini sur la base de la latence de service prédite de ressources non mesurées de machines virtuelles et de la latence mesurée de service de machines virtuelles.
PCT/MY2013/000191 2012-11-12 2013-11-11 Système et procédé de réservation de machine virtuelle pour applications de service sensibles aux retards WO2014073949A1 (fr)

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Cited By (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105045667A (zh) * 2015-07-13 2015-11-11 中国科学院计算技术研究所 一种用于虚拟机vCPU调度的资源池管理方法
CN109002342A (zh) * 2017-06-07 2018-12-14 中国科学院信息工程研究所 一种基于OpenStack的计算资源定向调度方法及系统
US10270711B2 (en) 2017-03-16 2019-04-23 Red Hat, Inc. Efficient cloud service capacity scaling
CN111782355A (zh) * 2020-06-03 2020-10-16 上海交通大学 一种基于混合负载的云计算任务调度方法及系统
US11720425B1 (en) 2021-05-20 2023-08-08 Amazon Technologies, Inc. Multi-tenant radio-based application pipeline processing system
WO2023192776A1 (fr) * 2022-03-31 2023-10-05 Amazon Technologies, Inc. Orchestration en nuage de fonctions de réseau
US11800404B1 (en) 2021-05-20 2023-10-24 Amazon Technologies, Inc. Multi-tenant radio-based application pipeline processing server
US11916999B1 (en) 2021-06-30 2024-02-27 Amazon Technologies, Inc. Network traffic management at radio-based application pipeline processing servers

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP1508855A2 (fr) * 2003-08-20 2005-02-23 Katana Technology, Inc. Méthode et appareil pour fournir des services de calcul virtuels
US20070226449A1 (en) * 2006-03-22 2007-09-27 Nec Corporation Virtual computer system, and physical resource reconfiguration method and program thereof
US20080304421A1 (en) 2007-06-07 2008-12-11 Microsoft Corporation Internet Latencies Through Prediction Trees
US20110231899A1 (en) 2009-06-19 2011-09-22 ServiceMesh Corporation System and method for a cloud computing abstraction layer
US20110307889A1 (en) * 2010-06-11 2011-12-15 Hitachi, Ltd. Virtual machine system, networking device and monitoring method of virtual machine system
WO2012125144A1 (fr) * 2011-03-11 2012-09-20 Joyent, Inc. Systèmes et procédés de dimensionnement de ressources dans environnement en nuage

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP1508855A2 (fr) * 2003-08-20 2005-02-23 Katana Technology, Inc. Méthode et appareil pour fournir des services de calcul virtuels
US20070226449A1 (en) * 2006-03-22 2007-09-27 Nec Corporation Virtual computer system, and physical resource reconfiguration method and program thereof
US20080304421A1 (en) 2007-06-07 2008-12-11 Microsoft Corporation Internet Latencies Through Prediction Trees
US20110231899A1 (en) 2009-06-19 2011-09-22 ServiceMesh Corporation System and method for a cloud computing abstraction layer
US20110307889A1 (en) * 2010-06-11 2011-12-15 Hitachi, Ltd. Virtual machine system, networking device and monitoring method of virtual machine system
WO2012125144A1 (fr) * 2011-03-11 2012-09-20 Joyent, Inc. Systèmes et procédés de dimensionnement de ressources dans environnement en nuage

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
APOSTOL, BALUTA, GORGOI, CRISTEA: "Efficient manager for virtualized resource provisioning in cloud systems", INTELLIGENT COMPUTER COMMUNICATION AND PROCESSING (ICCP), 25 August 2011 (2011-08-25), Bucharest, pages 511 - 517, XP032063552 *
GARG, SRINIVASA, GOPALAIYENGAR, BUYYA: "SLA-based resource provisioning for heterogeneous workloads in a virtualized cloud datacenter", ALGORITHMS AND ARCHITECTURES FOR PARALLEL PROCESSING, 1 January 2011 (2011-01-01), Melbourne, XP019168277 *

Cited By (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105045667A (zh) * 2015-07-13 2015-11-11 中国科学院计算技术研究所 一种用于虚拟机vCPU调度的资源池管理方法
CN105045667B (zh) * 2015-07-13 2018-11-30 中国科学院计算技术研究所 一种用于虚拟机vCPU调度的资源池管理方法
US10270711B2 (en) 2017-03-16 2019-04-23 Red Hat, Inc. Efficient cloud service capacity scaling
CN109002342A (zh) * 2017-06-07 2018-12-14 中国科学院信息工程研究所 一种基于OpenStack的计算资源定向调度方法及系统
CN109002342B (zh) * 2017-06-07 2022-09-23 中国科学院信息工程研究所 一种基于OpenStack的计算资源定向调度方法及系统
CN111782355A (zh) * 2020-06-03 2020-10-16 上海交通大学 一种基于混合负载的云计算任务调度方法及系统
US11720425B1 (en) 2021-05-20 2023-08-08 Amazon Technologies, Inc. Multi-tenant radio-based application pipeline processing system
US11800404B1 (en) 2021-05-20 2023-10-24 Amazon Technologies, Inc. Multi-tenant radio-based application pipeline processing server
US11916999B1 (en) 2021-06-30 2024-02-27 Amazon Technologies, Inc. Network traffic management at radio-based application pipeline processing servers
WO2023192776A1 (fr) * 2022-03-31 2023-10-05 Amazon Technologies, Inc. Orchestration en nuage de fonctions de réseau

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