WO2013141018A1 - Dispositif pour supporter une conception de système optimale - Google Patents

Dispositif pour supporter une conception de système optimale Download PDF

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Publication number
WO2013141018A1
WO2013141018A1 PCT/JP2013/056014 JP2013056014W WO2013141018A1 WO 2013141018 A1 WO2013141018 A1 WO 2013141018A1 JP 2013056014 W JP2013056014 W JP 2013056014W WO 2013141018 A1 WO2013141018 A1 WO 2013141018A1
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Prior art keywords
parameter set
service level
system design
design support
optimal
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PCT/JP2013/056014
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English (en)
Japanese (ja)
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さやか 伊豆倉
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日本電気株式会社
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Publication of WO2013141018A1 publication Critical patent/WO2013141018A1/fr

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation

Definitions

  • the present invention relates to an optimum system design support apparatus, an optimum system design support method, and a program.
  • an SLA Service Level Agreement
  • SLO Service Level Objective
  • Evaluation indexes that indicate system performance include response time that represents the time from when a processing request is sent to the system until the processing is completed, the usage rate of each resource that makes up the system, and the transfer rate of the process.
  • the maximum throughput to be expressed is often used. As the SLO, it is often set that the response time and the resource usage rate are within an allowable range, and that the maximum throughput is a certain value or more.
  • Patent Document 1 uses queuing theory to estimate the response time from the number of requests arriving at the system, the specifications of the servers that make up the system, etc., and determine whether the value falls within a predetermined range. The technology is described.
  • Patent Document 2 a performance prediction method based on queuing theory is applied, and the response time is expressed as a function of a parameter set that affects the performance. Deriving a system configuration parameter set is described.
  • Patent Document 3 the amount of work (the load on the server) and the hardware configuration have been changed by measuring the operating status of the system and using the least squares method to calculate the work amount and response time. Techniques for predicting response times in cases are described.
  • Patent Document 4 as a method for calculating the processing capacity of an application, in addition to a calculation method based on an open queuing theory, interpolation of a plurality of sample data obtained by measuring response times and server usage rates is performed. The calculation method is described.
  • Patent Document 5 describes a technique for monitoring the system operating status, determining whether the service level is equal to or higher than a certain level from the CPU usage rate of the server, and increasing or decreasing the number of servers provided to the user. Yes.
  • response time and maximum throughput values in a system modeled by a queue.
  • values of response time and maximum throughput cannot be obtained as a closed expression.
  • the response time can be formulated as in Patent Documents 1 and 2 is limited to a few systems that perform only limited processing.
  • Patent Document 5 deals only with the CPU usage rate that allows simple proportional calculation as SLO and the upper and lower limits of the number of servers, and does not consider SLO for response time and maximum throughput.
  • an object of the present invention is to automatically derive an inexpensive system configuration that can guarantee to achieve SLO for performance values that cannot be formulated in various forms of systems.
  • An optimal system design support apparatus executes a simulation of the operation of the system based on system design information, calculates a service level prediction value in the system, and the service level prediction value is A service level determination unit that determines whether a preset service level target is achieved, and a parameter set generation that generates a parameter set that may achieve the service level target based on the result of the simulation Part.
  • An optimal system design support method includes a step of executing a simulation of the operation of the system based on system design information to calculate a predicted value of a service level in the system, and the predicted value of the service level Determining whether or not a preset service level target is achieved, and generating a parameter set that can achieve the service level target based on the result of the simulation. Is.
  • the program according to the present invention is a computer that executes a simulation of the operation of the system based on system design information, calculates a service level prediction value in the system, and the service level prediction value includes: A service level determination unit that determines whether a preset service level target is achieved, and a parameter set generation unit that generates a parameter set that can achieve the service level target based on the simulation result , To function as.
  • the block diagram showing the structure of the optimal system design assistance apparatus by Embodiment 1 of this invention The figure which shows the specific example of the setting parameter appended to the system model and the system model by Embodiment 1 of this invention.
  • FIG. 1 is a block diagram showing the configuration of an optimum system design support apparatus 10 according to the first embodiment of the present invention.
  • the optimum system design support apparatus 10 is realized by an information processing apparatus such as a computer that operates according to a program.
  • the optimum system design support apparatus 10 may be configured by a single computer or may be configured by a plurality of computers connected to each other via a communication line.
  • the optimal system design support apparatus 10 includes a simulator 11, a simulation result storage unit 12, a service level determination unit 13, an optimal possibility determination unit 14, a parameter set generation unit 15, and an optimal parameter set output unit 16. It has.
  • the simulator 11, the service level determination unit 13, the optimal possibility determination unit 14, the parameter set generation unit 15, and the optimal parameter set output unit 16 are realized by the CPU executing a predetermined program stored in a ROM or the like. Corresponds to the function module.
  • the simulation result storage unit 12 is implemented by an external storage device.
  • FIG. 2 shows a specific example of the system model and the setting parameters appended to the system model.
  • the simulation result storage unit 12 is realized by a storage device such as a magnetic disk device or an optical disk device, and stores the predicted performance value of the system calculated by the simulator 11.
  • the service level determination unit 13 determines whether the predicted response time of the system included in the simulation result has achieved SLO (service level target).
  • the optimal possibility determination unit 14 compares the price of a system composed of a certain parameter set and another parameter set for the parameter set determined to achieve the SLO in the service level determination unit 13, and the parameter set with a lower price. Is determined to be optimal.
  • the price of the system is represented by a total value of individual prices set in advance for each server, components such as the mounted CPU, and network devices such as routers.
  • the optimal possibility determination unit 14 determines that the parameter set determined to achieve the SLO after the sufficient number of trials designated in advance is optimal.
  • the parameter set generation unit 15 refers to the value of the parameter set determined to achieve the SLO in the service level determination unit 13 and the simulation result, and newly sets a parameter set that achieves the SLO and has a lower cost. Generate.
  • the parameter set generation unit 15 specifies a resource that is estimated to be least loaded from the simulation result, and reduces the resource in the parameter set to obtain a new parameter set.
  • the parameter set generation unit 15 refers to the value of the parameter set determined by the service level determination unit 14 as not to achieve SLO, and sets any one of the settings from the values of various setting parameters included in the parameter set. Generate a parameter set with large parameters.
  • the parameter set generation unit 15 generates a parameter set in which the number of either one or both servers is increased by one or more.
  • the parameter set generation unit 15 refers to the price of the system with the parameter set determined to be optimal by the optimal possibility determination unit 14, and generates a parameter set whose price is lower than that.
  • the optimum parameter set output unit 16 refers to the value of the parameter set determined not to achieve the SLO in the service level determination unit 13 and the simulation result, and newly sets a parameter set that is estimated to be highly likely to achieve the SLO. To generate. Specifically, from the simulation result, a resource that is estimated to be the most loaded is identified, and the resource is increased in the parameter set to obtain a new parameter set.
  • the optimum parameter set output unit 16 outputs the parameter set that is finally determined to be optimum as the optimum parameter set. Specifically, the optimum parameter set is presented to the user by writing out the file in an arbitrary format or displaying the file on a display device such as a display.
  • step A1 the optimum system design support apparatus 10 sets the number of trials (numTrial), and an integer value i representing the number of trials (hereinafter referred to as trial count; i ⁇ numTrial) is set as an initial value.
  • numTrial an integer value that is considered to be sufficient to cover the entire parameter set space is set based on the scale of the system described in the system model and the number of types of setting parameters. Generally, when there are many types of large-scale systems and setting parameters, it is necessary to set a large value. In addition, a large value that is impossible for the price of the system is set as the initial value of the initial value (costMin) of the lowest price of the system that is a reference for determining the optimum possibility of each parameter set.
  • a parameter set (paramSet) is randomly generated (step A2), and a predicted value of performance in the system model of the parameter set is calculated by the simulator 11 (step A3). Further, the service level determination unit 13 determines whether or not the predicted performance value has achieved SLO (step A4).
  • the parameter set generation unit 15 When the SLO has not been achieved (No), the parameter set generation unit 15 newly generates a parameter set that is considered highly likely to achieve the SLO. Specifically, the simulation result is referred to, a resource that is estimated to be most loaded is specified, and a parameter set (numMaxSvr) with the increased resource is newly generated (step A5). Steps A3 to A5 are repeated until a parameter set that achieves SLO is found.
  • Step A6 when the SLO has been achieved in Step A4 (Yes), or when the process of Steps A3, A4, and A5 is repeated to reach a parameter set that achieves the SLO, the value of the parameter set is set as paramSetTmp. Save (step A6).
  • the parameter set generation unit 15 newly generates a parameter set having a lower price configuration.
  • the simulation result is referred to, a resource that is estimated to be least loaded is specified, and a parameter set (numMinSvr) in which the resource is reduced is newly generated (step A7). This is repeated as long as the predicted performance value achieves SLO (steps A8 and A9).
  • step A10 the price (cost) when the system is configured with the parameter set before reducing the resource by one is calculated (step A10).
  • step A11 the optimal possibility determination unit 14 compares the values of costMin and cost. If cost is greater than costMin, it is determined that the optimum parameter set has not been updated in the current trial, the trial count i is incremented by 1 in step A12, and the processing from step A2 is repeated.
  • paramSet is assigned to the optimal parameter set (optParam), and the cost value is assigned to costMin (step A13). ).
  • step A14 When i reaches numTrial, optParam is output and the processing is terminated (step 15).
  • step A12 the value of i is increased by 1 (step A12), and the processing from step A2 is repeated.
  • the paramSet is generated in step A3
  • the past simulation result is referred to, and the value of one or more setting parameters among the setting parameters included in the parameter set that does not achieve SLO.
  • the parameter set is generated so as to have a configuration with a lower price than CostMin.
  • the performance improvement rate for each setting parameter is estimated using the simulation result.
  • a parameter set that achieves SLO can be efficiently extracted.
  • the simulation result by excluding parameter set candidates that cannot be the minimum system configuration from the search range, it is possible to efficiently derive a parameter set that achieves SLO and has the minimum price. it can.
  • FIG. FIG. 4 is a block diagram showing the configuration of the optimum system design support apparatus 40 according to Embodiment 2 of the present invention.
  • the optimum system design support apparatus 40 according to the second embodiment includes a search range limiting unit 41 in addition to the configuration of the optimum system design support apparatus 10 according to the first embodiment.
  • the search range limiting unit 41 checks the upper limit and lower limit of each setting parameter set in the system model, and obtains a parameter set that is an intermediate value thereof. Then, a simulation is performed using the parameter set, and the upper limit or lower limit of the setting parameter is updated with reference to the simulation result.
  • the search range limiting unit 41 further limits the range where the optimal parameter set exists. Specifically, when the predicted performance value in the intermediate parameter set has achieved SLO, the resource with the least load is identified from the simulation result, and the upper limit of the setting parameter corresponding to the resource is set to the parameter. Replace with set value.
  • the resource that is most heavily loaded that is, the bottleneck of processing
  • the lower limit of the setting parameter corresponding to the resource is set as the parameter. Replace with set value. The above processing is repeated until the parameter range is sufficiently narrowed down.
  • an upper limit (paramMax) and a lower limit (paramMin) of the parameter set are set.
  • These are one-dimensional arrays in which the number of elements is equal to the number of setting parameters.
  • values preset in the system model can be used or arbitrarily set.
  • the number of elements in the parameter set is 2.
  • the array representing each parameter set is expressed in the order ⁇ number of Web / AP servers, number of DB servers ⁇ .
  • step B2 a simulation is performed at the intermediate point (parammid) of the initial value set in step B1 (step B2).
  • step B3 it is determined whether or not the predicted performance value in parammid has achieved SLO (step B3).
  • SLO is achieved
  • the simulation result is referred to, and the upper limit value (paramMax [svrMin]) of the setting parameter corresponding to the resource (svrMin) that is considered to be least loaded is an intermediate value during the simulation.
  • step B6 the processing from step A1.
  • the parameter set generated in step A3 is limited to that included in the range narrowed down as described above.
  • the range in which the boundary surface between the region where the predicted performance value achieves SLO and the region where the predicted performance value does not achieve is limited, and the simulation is performed using the parameter set included in the vicinity of the boundary surface. Start. As a result, parameter set candidates can be searched in the vicinity of the boundary surface, so that an optimum parameter set can be derived more efficiently with a small number of simulations.
  • the optimum system design support device of the present invention is not limited to the configuration of the above embodiment, and various modifications and changes from the configuration of the above embodiment are also included in the scope of the present invention. It is.
  • the present invention has been described by taking the response time as an example of SLO, the present invention can be applied by replacing with other evaluation indexes such as maximum throughput.
  • a simulator for executing an operation simulation of the system and calculating a predicted value of the service level in the system, and a service level in which the predicted value of the service level is set in advance Optimal comprising: a service level determination unit that determines whether or not the target has been achieved; and a parameter set generation unit that generates a parameter set that may achieve the service level target based on the result of the simulation System design support device.
  • An optimal system design support apparatus comprising: a search range limiting unit that limits a range of a certain parameter set, wherein the parameter set generation unit generates only a parameter set included in the range.
  • a simulator that performs a simulation of the operation of the system and calculates a predicted value of a service level in the system;
  • a service level determination unit for determining whether the predicted value of the service level has achieved a preset service level target;
  • the program for functioning as a parameter set production
  • the present invention is suitable for automatically deriving an inexpensive system configuration that can guarantee to achieve SLO for performance values that cannot be formulated in various types of systems.

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  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
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Abstract

L'invention comporte un simulateur pour simuler le fonctionnement d'un système sur la base d'informations de conception de système et calculer une valeur prédictive d'un niveau de service pour le système ; une unité de décision de niveau de service pour décider si la valeur prédictive de niveau de service a ou non atteint un objectif de niveau de service préconfiguré ; et un générateur d'ensemble de paramètres pour générer, sur la base du résultat de simulation, un ensemble de paramètres apte à atteindre l'objectif de niveau de service.
PCT/JP2013/056014 2012-03-21 2013-03-05 Dispositif pour supporter une conception de système optimale WO2013141018A1 (fr)

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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2018025603A1 (fr) * 2016-08-05 2018-02-08 株式会社日立製作所 Dispositif d'assistance au développement d'un système et procédé d'assistance au développement d'un système
CN113449899A (zh) * 2020-03-25 2021-09-28 株式会社日立制作所 服务器负荷预测系统及服务器负荷预测方法

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JP2002268922A (ja) * 2001-03-09 2002-09-20 Ntt Data Corp Ｗｗｗサイトの性能監視装置
JP2005099973A (ja) * 2003-09-24 2005-04-14 Hitachi Ltd 運用管理システム
JP2006092053A (ja) * 2004-09-22 2006-04-06 Nec Corp システム使用率管理装置及びそれに用いるシステム使用率管理方法並びにそのプログラム
JP2007207117A (ja) * 2006-02-03 2007-08-16 Ns Solutions Corp 性能監視装置、性能監視方法及びプログラム
JP2007257163A (ja) * 2006-03-22 2007-10-04 Hitachi Ltd 分散型プログラム実行環境における稼動品質管理方法
JP2008108262A (ja) * 2006-10-26 2008-05-08 Hewlett-Packard Development Co Lp コンピュータネットワークの改良
JP2010218049A (ja) * 2009-03-13 2010-09-30 Ns Solutions Corp 情報処理装置、情報処理方法及びプログラム

Patent Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2002268922A (ja) * 2001-03-09 2002-09-20 Ntt Data Corp Ｗｗｗサイトの性能監視装置
JP2005099973A (ja) * 2003-09-24 2005-04-14 Hitachi Ltd 運用管理システム
JP2006092053A (ja) * 2004-09-22 2006-04-06 Nec Corp システム使用率管理装置及びそれに用いるシステム使用率管理方法並びにそのプログラム
JP2007207117A (ja) * 2006-02-03 2007-08-16 Ns Solutions Corp 性能監視装置、性能監視方法及びプログラム
JP2007257163A (ja) * 2006-03-22 2007-10-04 Hitachi Ltd 分散型プログラム実行環境における稼動品質管理方法
JP2008108262A (ja) * 2006-10-26 2008-05-08 Hewlett-Packard Development Co Lp コンピュータネットワークの改良
JP2010218049A (ja) * 2009-03-13 2010-09-30 Ns Solutions Corp 情報処理装置、情報処理方法及びプログラム

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2018025603A1 (fr) * 2016-08-05 2018-02-08 株式会社日立製作所 Dispositif d'assistance au développement d'un système et procédé d'assistance au développement d'un système
JP2018022424A (ja) * 2016-08-05 2018-02-08 株式会社日立製作所 システム開発支援装置およびシステム開発支援方法
CN113449899A (zh) * 2020-03-25 2021-09-28 株式会社日立制作所 服务器负荷预测系统及服务器负荷预测方法

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