WO2023030912A1 - Adaptation d'une capacité d'une ressource - Google Patents

Adaptation d'une capacité d'une ressource Download PDF

Info

Publication number
WO2023030912A1
WO2023030912A1 PCT/EP2022/073101 EP2022073101W WO2023030912A1 WO 2023030912 A1 WO2023030912 A1 WO 2023030912A1 EP 2022073101 W EP2022073101 W EP 2022073101W WO 2023030912 A1 WO2023030912 A1 WO 2023030912A1
Authority
WO
WIPO (PCT)
Prior art keywords
capacity
resource
forecast
plan
demand
Prior art date
Application number
PCT/EP2022/073101
Other languages
German (de)
English (en)
Inventor
Keno Buss
Roland Porsch
Original Assignee
Siemens Mobility GmbH
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 Siemens Mobility GmbH filed Critical Siemens Mobility GmbH
Priority to EP22768323.2A priority Critical patent/EP4374253A1/fr
Publication of WO2023030912A1 publication Critical patent/WO2023030912A1/fr

Links

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
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2209/00—Indexing scheme relating to G06F9/00
    • G06F2209/50—Indexing scheme relating to G06F9/50
    • G06F2209/5019—Workload prediction
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2209/00—Indexing scheme relating to G06F9/00
    • G06F2209/50—Indexing scheme relating to G06F9/50
    • G06F2209/506—Constraint

Definitions

  • the invention relates to a method for adapting a capacity of a resource provided in a computer cloud, a system for carrying out the method, a computer program and a computer-readable medium.
  • device-independent resources such as data storage, computing power, runtime environments or software are provided in the form of services.
  • a user's scope of use of the services can be varied as required. This enables companies to purchase IT infrastructure, programming or runtime environments, software or functional content as needed as a service. If a requirement exceeds the capacity of the service that has already been purchased, additional capacities can be booked. So far, however, this has only happened after a bottleneck has been identified, for example when inquiries about further requirements are received. Eliminating the bottleneck is already delayed because it takes a certain amount of time to expand the capacity of the service itself. In addition, depending on the required service, initialization times can occur, which further delay the elimination of the bottleneck.
  • the object of the invention is to provide a needs-based capacity of a resource of a computer cloud in an efficient manner.
  • This object is achieved by a method having the features of claim 1. Furthermore, this object is achieved by a system according to the features of the independent system claim. Furthermore, the invention is based on the objects of specifying a computer program for carrying out the method and a computer-readable medium.
  • a future requirement for the capacity of the resource is forecast by means of a data processing device on the basis of a predetermined event plan. Furthermore, the capacity of the resource is adjusted depending on the forecast demand for capacity.
  • the event plan is to be understood as a plan in which a location, a time or a combination thereof is linked to an event.
  • the event plan can, for example, be a timetable, a duty roster, an operating plan and/or a maintenance plan.
  • the term "computer cloud” is also known in IT specialist circles under the term “cloud computing”.
  • Four service models are usually offered in connection with a computer cloud. Each service model has different resources.
  • the service models are an infrastructure service, a platform service, a software service and a function service.
  • the resource of the computer cloud should be understood to mean a resource from one of the four service models.
  • the resource is a virtualized access to a hardware resource, such as a processor or main memory.
  • the resource can be, for example, a runtime environment with changeable computing and/or storage capacities.
  • the runtime environment is to be understood as a runtime environment in the sense of computer science.
  • the runtime environment is configured to read, write, transport and/or manage data.
  • the aforementioned data processing device can be, for example, a computer, a microcontroller, a processor or another programmable hardware component known to those skilled in the art. It is also conceivable that the data processing device is a virtualized hardware resource of the computer cloud.
  • the prognosis can be determined, for example, using classic deterministic or numerical algorithms and/or using pattern recognition algorithms.
  • the pattern recognition algorithm can be implemented using artificial neural networks or using the support vector method.
  • An advantageous development provides that, in the event of a forecast increase in demand, an occurrence time of the first type of future demand is determined using the event plan and the capacity is increased before this occurrence time of the first type. This makes it possible to prevent capacity bottlenecks at low cost.
  • a further advantageous development provides that, in the event of a forecast reduction in demand, an occurrence time of a second type of future demand is determined using the event plan and the capacity is reduced after this occurrence time of the second type. In this way, excess capacity can be easily reduced. Furthermore, in this way an efficient use of the capacity of the resource is made possible.
  • an advantageous development provides that the entry time of the first type and/or the entry time of the second type is determined using the event plan and position information about at least one vehicle. This enables the capacity to be adjusted reliably and flexibly. Short-term deviations from the event plan, such as delays or advances, can easily be taken into account in the forecast demand.
  • an advantageous development provides that the future need for the capacity is forecast on the basis of the specified event plan and an existing need for the capacity.
  • the existing need for capacity mentioned is a need that exists, but can only be served at a later point in time.
  • a reason for this can be, for example, uninterrupted operation or poor accessibility of a system or at least part of the system, which requires the capacity of the resource. In this way, the accuracy of the forecast for the requirement can be increased.
  • a timetable, a duty roster, an operating plan and/or a maintenance plan of at least one vehicle is provided as an event plan.
  • the prognosis for the capacity requirements can thus easily be based on data that is already known. This enables a high level of reliability of the forecast demand for the capacity of the resource .
  • special features that are dependent on the season or on Sundays and public holidays can be taken into account in the simplest way in this way.
  • an advantageous development provides that the future need for capacity is forecast on the basis of the specified event plan and position information about at least one vehicle. A deviation of the forecast requirement from an actual requirement due to the vehicle being late or early can thus be prevented to the greatest possible extent.
  • the data transmission can be implemented, for example, by means of a runtime environment or by means of a program application that is executed on a virtualized infrastructure of the computer cloud. This has already proven itself in practice.
  • An advantageous embodiment variant provides that the future capacity requirement is forecast on the basis of the event plan and a known volume of data to be transmitted. Future capacity requirements can thus be forecast with a high degree of accuracy.
  • a further advantageous embodiment variant provides that the future capacity requirement is forecast from the event plan based on an available data transmission speed at the location of an event. Locations at which there is a high data transmission speed can be selected preferentially for data transmission. Based on knowledge of the amount of data to be transmitted, the duration of the data transmission can be estimated.
  • an advantageous development provides for the capacity to be automatically adapted to the forecast capacity requirement by means of the data processing device. In this way, the efficiency of the method can be increased.
  • the method according to the invention can be carried out by means of the system according to the invention.
  • the system for carrying out the method according to the invention has a storage device.
  • the memory device mentioned is a device to which data can be written, from which data can be read and from which data can be deleted.
  • the memory device can be a mechanical memory, a semiconductor memory, preferably a non-volatile semiconductor memory, a magnetic memory, an optical memory or a combination thereof.
  • the storage device can be a virtualized hardware resource of the computer cloud and/or a distributed storage environment.
  • the storage device is set up to store at least one predefined event plan.
  • the system has a data processing device which is set up to use the predefined event plan to forecast future capacity requirements for a resource provided in a computer cloud.
  • the computer cloud and the resource of the computer cloud are in particular the computer cloud and resource of the computer cloud already described in connection with the method.
  • the system according to the invention enables the method according to the invention to be carried out at low cost.
  • An advantageous development of the system provides that the data processing device is set up to adapt the capacity of the resource as a function of the forecast demand for the capacity of the resource. To this In this way, a system with a high level of efficiency can be created.
  • the invention provides a computer-readable medium, which has instructions by means of which the system according to the invention is prompted to carry out the method according to the invention.
  • the computer-readable medium can be, for example, a CD-ROM, a DVD, a USB or flash memory or an intangible medium such as a data stream and/or a data carrier signal.
  • FIG. 1 illustrates an example of a method 100 for adjusting a capacity of a resource provided in a computer cloud 16 .
  • data transmission is implemented using the resource provided.
  • a runtime environment in terms of computer science is selected as a resource from the computer cloud 16 .
  • a computing power or a storage capacity can be adapted as the capacity of the runtime environment. It is also conceivable to provide several runtime environments whose capacity can be individually adapted to future capacity requirements.
  • the data transmission is implemented using a virtualized hardware resource, a software resource, a functional resource from the computer cloud 16 or a combination thereof.
  • a virtualized hardware resource e.g., a hardware resource
  • a software resource e.g., a software resource
  • a functional resource from the computer cloud 16 or a combination thereof.
  • the example of the method 100 is described below using the example of a runtime environment and its capacity as a resource of the computer cloud 16 .
  • the runtime environment mentioned is set up, for example, to transmit data from a sender to a receiver.
  • a future need for the capacity of the runtime environment is forecast 120 on the basis of a predefined event plan.
  • the capacity of the runtime environment is then adjusted 140, 160 depending on the forecast demand for the capacity of the runtime environment. If an increase in demand is forecast 120 , an occurrence time of the first type of future demand for the capacity of the runtime environment is determined 180 based on the event plan and the capacity before this occurrence time of the first type is increased 140 . If, on the other hand, a reduction in the demand for the capacity of the runtime environment is forecast 120 , an occurrence time of a second type of future demand is determined 200 on the basis of the event plan and the capacity is reduced 160 after this occurrence time of the second type.
  • the increase 140 or the reduction 160 of the capacity is effected by a corresponding requirement request to the computer cloud 16 .
  • FIG. 2 shows an exemplary embodiment of a system 10 for carrying out the example of the method 100 described in connection with FIG. 1 and illustrates the example of the method 100.
  • the system 10 has a data processing device 12 and a storage device 14 .
  • the data processing device 12 is set up to use the predefined event plan to forecast 120 the future need for the capacity of the runtime environment.
  • the storage device 14 is set up to store the predefined event plan.
  • the data processing device 12 is set up to adjust 140 , 160 the capacity of the runtime environment as a function of the forecast need for the capacity of the runtime environment. In this way, in the present exemplary embodiment, the capacity of the runtime environment is automatically adapted 140, 160 to the forecast demand.
  • the cited example of the method 100 is illustrated below using data transmission between a vehicle 18 and a central administration point 20 .
  • the central administration point 20 can be a server or an IT back office, for example.
  • the data processing device 12 and the storage device 14 are part of the central administration point 20 .
  • the central administration office 20, the data processing device 12 and the storage device 14 may be located separately from one another. For example, relocating the data processing device 12 and the storage device 14 to a virtualized hardware resource of the computer cloud 16 is conceivable.
  • Such predetermined locations can be characterized for example by a data transmission speed or a connection to a communication network.
  • the locations mentioned are train stations, sidings or depot locations.
  • the data transmission should only take place outside of operating or travel times of the vehicle 18 .
  • Said data to be transmitted can be, for example, passenger information data, such as video clips or timetable data, or vehicle-specific data, such as software updates or remote maintenance information.
  • the data mentioned can be recordings of process values from the vehicle or data for predictive maintenance activities on the vehicle. A certain data transmission speed and a sufficiently long stay of the vehicle 18 in the area in which a certain data transmission speed is provided is required for a transmission of the large amounts of data of such data.
  • a transmission of this data while the vehicle 18 is traveling is therefore usually ruled out. For this reason, such data locations are selected for transmission where a sufficiently high data transmission speed is available. It is often the case that a large number of vehicles drive to the locations considered suitable, such as stops at train stations, depots or sidings, within a short period of time. Furthermore, the time that vehicle 18 stays at the location suitable for data transmission can be relatively short. In order to prevent a bottleneck occurring in the capacity of the runtime environment required for data transmission, this capacity is adjusted 140, 160 to the forecast demand for the capacity of the runtime environment, as described below.
  • the future need for the capacity of the runtime environment is forecast 120 by the data processing device 12 on the basis of a predetermined event plan of the vehicle 18 .
  • this is the timetable which the vehicle 18 follows.
  • the event plan may be a maintenance plan, a roster, and/or an operation plan of the vehicle 18 .
  • the central administration point 20 knows the amount of data that is to be transmitted from the central administration point 20 to the vehicle 18 .
  • the central administration point 20 knows the amount of data that is to be transmitted from the vehicle 18 to the central administration point 20 .
  • the vehicle 18 transmits information about the amount of data to be transmitted to the central administration point 20 for this purpose.
  • the existing need for the amount of data to be transmitted between the vehicle 18 and the central administration point 20 is known from the operating plan, the service plan, the maintenance plan and/or the timetable of the vehicle 18 or is estimated.
  • the existing need for the capacity of the runtime environment is taken into account in the forecast 120 of future needs.
  • the data transmission speed at a location from the timetable of the vehicle 18 is taken into account in the prognosis 120 of the future need for the capacity of the runtime environment. This makes it possible to select suitable locations for data transmission.
  • a duration for the data transmission can be calculated. Based on the calculated duration of the data transmission and a length of stay of the vehicle 18 at one of the suitable locations from the timetable, which is known from the timetable, the future need for the capacity of the runtime environment is forecast 120 by means of the data processing device 12 .
  • the vehicle 18 transmits position information about its location to the central administration point 20 .
  • the position information can be used, for example, by means of the data processing device 12 to determine an arrival time of the vehicle 18 at the location suitable for the transmission of the data of the timetable are calculated. Deviations of the vehicle 18 from the times stipulated in the timetable are thus reliably detected and contribute to improving the accuracy of the prediction of the predicted need for the capacity of the transit time environment.
  • the capacity of the runtime environment is expanded 140 by means of the data processing device 12 before an entry time of the vehicle 18 at the location suitable for the data transmission from the event plan. After the required data is transmitted or after the vehicle 18 has left the data transmission location in the schedule, the capacity of the runtime environment is reduced 160 .
  • the system 10 was described purely by way of example using one vehicle 18 .
  • the system 10 can be expanded by any number of vehicles.
  • the method 100 is generally executable for any number of vehicles. It is also conceivable that instead of the central administration point 20 distributed administration points are provided.

Landscapes

  • Engineering & Computer Science (AREA)
  • Software Systems (AREA)
  • Theoretical Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Engineering & Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)

Abstract

L'invention concerne un procédé (100) d'adaptation d'une capacité d'une ressource fournie dans un nuage informatique (16). Un plan d'événement prédéfini est pris comme base pour prévoir (120) une future demande en matière de capacité de la ressource au moyen d'un dispositif de traitement de données (12). La capacité de la ressource est ensuite adaptée (140, 160) sur la base de la demande prévue en matière de capacité de la ressource.
PCT/EP2022/073101 2021-09-01 2022-08-18 Adaptation d'une capacité d'une ressource WO2023030912A1 (fr)

Priority Applications (1)

Application Number Priority Date Filing Date Title
EP22768323.2A EP4374253A1 (fr) 2021-09-01 2022-08-18 Adaptation d'une capacité d'une ressource

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
DE102021209633.6A DE102021209633A1 (de) 2021-09-01 2021-09-01 Anpassung einer Kapazität einer Ressource
DE102021209633.6 2021-09-01

Publications (1)

Publication Number Publication Date
WO2023030912A1 true WO2023030912A1 (fr) 2023-03-09

Family

ID=83271473

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/EP2022/073101 WO2023030912A1 (fr) 2021-09-01 2022-08-18 Adaptation d'une capacité d'une ressource

Country Status (3)

Country Link
EP (1) EP4374253A1 (fr)
DE (1) DE102021209633A1 (fr)
WO (1) WO2023030912A1 (fr)

Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20180209789A1 (en) * 2017-01-23 2018-07-26 International Business Machines Corporation System and method of acquiring road data
US20190158606A1 (en) * 2018-12-28 2019-05-23 Francesc Guim Bernat QUALITY OF SERVICE (QoS) MANAGEMENT IN EDGE COMPUTING ENVIRONMENTS

Family Cites Families (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
DE102012221355A1 (de) 2012-11-22 2014-05-22 Siemens Aktiengesellschaft Verfahren zum Bereitstellen von Ressourcen in einer Cloud und Vorrichtung
DE202016101711U1 (de) 2016-03-31 2017-07-03 Dextradata Gmbh Kapazitätsplanungswerkzeug, insbesondere einer Informationstechnologie-Infrastruktur
DE102019211347B4 (de) 2019-07-30 2023-08-17 Volkswagen Aktiengesellschaft Datenübertragung zwischen einem Kraftfahrzeug und einem Mobilfunknetz

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20180209789A1 (en) * 2017-01-23 2018-07-26 International Business Machines Corporation System and method of acquiring road data
US20190158606A1 (en) * 2018-12-28 2019-05-23 Francesc Guim Bernat QUALITY OF SERVICE (QoS) MANAGEMENT IN EDGE COMPUTING ENVIRONMENTS

Also Published As

Publication number Publication date
EP4374253A1 (fr) 2024-05-29
DE102021209633A1 (de) 2023-03-02

Similar Documents

Publication Publication Date Title
DE102015109660A1 (de) Verfahren und System für On-Demand-Transportdienste
EP1026649B1 (fr) Procédé et dispositif de préparation d'information de circulation
DE202013012418U1 (de) Reiseplanung im öffentlichen Personenverkehr
DE102015208287A1 (de) Gemeinschaftsfahrzeugsysteme und -verfahren
WO2019072524A1 (fr) Procédé pour cartographier un tronçon de route
DE102020112315A1 (de) Verfügbarkeitsvorhersagesysteme und -verfahren für parkplätze
DE102014225122A1 (de) Verfahren und System zur Bereitstellung von Informationen zur Verfügbarkeit von Ladestationen
DE102022105459A1 (de) Systeme und verfahren zum zuweisen von fahrtrouten zu fahrzeugen mit alternativer energie
DE102020007059A1 (de) Verfahren zur gezielten Beeinflussung einer Lichtsignalanlage durch ein bevorrechtigtes Fahrzeug, insbesondere durch Fahrzeuge des öffentlichen Personennahverkehrs
WO2023030912A1 (fr) Adaptation d'une capacité d'une ressource
WO2018036759A1 (fr) Prévision de parcours de train
DE102019208038A1 (de) Verfahren zum Ausgeben eines Steuersignals an ein Fahrzeug eines Fahrzeugverbunds
DE112021003100T5 (de) Verfahren zum Verwalten der Verteilung eines zum Ankunftspunkt fahrenden Fahrzeug, dazu verwendeter Verwaltungsserver, und Aufzeichnungsmedium, auf dem Programm zum Ausführen des Verfahrens aufgezeichnet ist
DE102018206526A1 (de) Verfahren zum Planen einer automatisierten Fahrzeug-Kolonne
DE102008046777A1 (de) Verfahren und Vorrichtung zur Ermittlung von fahrleistungsbezogenen Daten eines oder mehrerer Fahrzeuge
DE102021209632A1 (de) Anpassung einer Kapazität einer Ressource
DE102016213610A1 (de) Elektronische Fahrzeugsteuereinrichtung und Überschreibsystem
DE102022208766A1 (de) Übertragung von Dateien mittels Zwischenspeichern
DE102018220734A1 (de) Verfahren zur Verteilung von Netzwerkressourcen
WO2024041940A1 (fr) Transmission de blocs de données utilisant un processus de stockage intermédiaire
EP3838709A1 (fr) Procédé de transfert de télégrammes d'un poste de commande central à un véhicule et poste de commande central
DE102016218113A1 (de) Bedarfsorientierter Einsatz von Transportfahrzeugen im öffentlichen Personenverkehr
DE102023103220A1 (de) Verfahren zur Planung eines effizienten Routenplans für Lieferungen sowie wenigstens teilautonomer Kraftwagen
DE102020003822A1 (de) Verfahren und Vorrichtung zur Optimierung einer Kapazitätsauslastung von Transportfahrzeugen
DE102018212960A1 (de) System zum Auswählen eines Kraftfahrzeugs aus einer Datenbank, in der mehrere Kraftfahrzeuge mit ihrer jeweiligen Fahrzeugposition gespeichert sind

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 22768323

Country of ref document: EP

Kind code of ref document: A1

WWE Wipo information: entry into national phase

Ref document number: 2022768323

Country of ref document: EP

ENP Entry into the national phase

Ref document number: 2022768323

Country of ref document: EP

Effective date: 20240223

NENP Non-entry into the national phase

Ref country code: DE