WO2022178730A1

WO2022178730A1 - Microservice resource allocation method and apparatus, and computer-readable medium

Info

Publication number: WO2022178730A1
Application number: PCT/CN2021/077738
Authority: WO
Inventors: 韩捷; 高亮; 任文科
Original assignee: 西门子股份公司; 西门子（中国）有限公司
Priority date: 2021-02-24
Filing date: 2021-02-24
Publication date: 2022-09-01

Abstract

The embodiments of the present invention relate to microservice application technology, and particularly relate to a microservice resource allocation method and apparatus, and a computer-readable medium. In the microservice resource allocation method, by means of a plurality of resource adjustment processes, computing resources are allocated to each microservice in a target application program; in the current resource adjustment process, the number of computing resources allocated to each microservice, trace data, and spatial data are acquired, and accordingly, the sum of independent service computing times of the microservices is computed and obtained; the number of computing resources allocated to each microservice in the next resource adjustment process is predicted, and accordingly, the target application program is deployed; the independent service computing time of each microservice is obtained from a program output; and if the sum of predicted independent service computing times of the microservices after the next adjustment is greater than the sum of the current independent service computing times of the microservices, it is determined that the resource adjustment process ends, and the number of computing resources, which are currently allocated, is determined so as to conduct resource allocation.

Description

Microservice resource allocation method, apparatus and computer readable medium

technical field

Embodiments of the present invention relate to the technical field of microservice applications, and in particular, to a method, apparatus, and computer-readable medium for allocating resources for microservices.

Background technique

Software system design is the process of designing system elements, which can include architecture, components, and the interactions between components. In recent years, microservices are becoming the de facto standard way of software development in cloud computing and service-oriented computing, emphasizing self-management and lightweight as a means to improve software agility, scalability, and autonomy. Each microservice is implemented and operated as a small and independent system, providing access to its internal logic and data through a well-defined network interface. On the other hand, due to the increasing number of independent microservices in the system, it becomes extremely difficult to communicate, allocate and adjust resources between services. Currently, microservice resource allocation and adjustment greatly depends on the experience and understanding of system designers.

SUMMARY OF THE INVENTION

Embodiments of the present invention provide a microservice resource allocation method, apparatus, and computer-readable medium, which are used for microservice resource allocation and adjustment. Among them, computing resources are allocated to each microservice in a target application through multiple resource adjustment processes. If the predicted next adjustment process, the sum of the independent service computing time of each microservice is greater than the computing time of each first independent service If the sum is reached, it is determined that the resource adjustment process is over, and it is determined that resources are allocated according to the respective computing resource amounts allocated by the current adjustment process. This makes it possible to achieve the shortest overall running time of the target application under the condition that the total amount of computing resources allocated to each microservice is constant after several adjustments. Moreover, calculating the independent service computing time of each microservice according to the tracking data and spatial data of each microservice has the advantage of accurate calculation results. In addition, according to the amount of computing resources allocated to each microservice in the current adjustment process and the calculated independent service computing time, the amount of computing resources allocated to each microservice in the next resource adjustment process is predicted, so that the next adjustment can depend on the current The adjustment result is adjusted to the best computing resource allocation scheme as soon as possible. In addition, by deploying the target application according to the amount of computing resources allocated to each microservice in the next adjustment process, and obtaining the independent service computing time of each microservice after the next adjustment through the output of the target application, the calculation is accurate. The advantages.

In a first aspect, a microservice resource allocation method is provided. The method can be used to allocate computing resources to each microservice in a target application through a plurality of resource adjustment processes, wherein the total amount of computing resources allocated to each of the microservices is a preset constant, and the method includes: In the current resource adjustment process, the first computing resource amount allocated for each of the microservices is obtained; the tracking data and spatial data of each of the microservices are obtained; for each of the microservices, according to the tracking data of the microservice Calculate the computing time of the first independent service of the microservice with spatial data; according to the amount of each of the first computing resources and the computing time of each of the first independent services, predict the allocation for each of the microservices in the next resource adjustment process the second computing resource amount; allocate computing resources for each of the microservices according to the corresponding second computing resource amount; deploy the target application; obtain the output of the target application The second independent service computing time; if the sum of the respective second independent service computing times is greater than the sum of the respective first independent service computing times, it is determined that the resource adjustment process is over, and the amount of the first computing resources is determined according to each of the first independent services. Perform resource allocation; if the sum of the calculation time of each of the second independent services is not greater than the sum of the calculation time of each of the first independent services, it is determined to continue the next resource adjustment process.

In a second aspect, an apparatus is provided, including modules for performing the steps in the method provided in the first aspect.

In a third aspect, an apparatus is provided, comprising: at least one memory configured to store computer-readable codes; at least one processor configured to invoke the computer-readable codes to perform steps in the method provided in the first aspect .

In a fourth aspect, a computer-readable medium, storing computer-readable instructions on the computer-readable medium, the computer-readable instructions, when executed by a processor, cause the processor to perform the method provided in the first aspect in each step.

For any of the above-mentioned aspects, optionally, when predicting the second computing resources allocated to each of the microservices in the next resource adjustment process, the amount of each of the second computing resources may be determined as having a longer first computing resource. The microservices that independently serve computing time reserve more computing resources. This adjustment can allocate computing resources more reasonably, so that microservices that require longer independent service computing time can obtain more computing resources.

For any of the above aspects, optionally, when predicting the second amount of computing resources allocated to each of the microservices in the next resource adjustment process, a learning rate is set, wherein, the larger the learning rate, the higher the amount of computing resources. The larger the adjustment amount. The speed of the resource adjustment process can be flexibly set by setting the learning rate.

For any of the above aspects, optionally, the target application is deployed in a k8s cluster, and when obtaining the first amount of computing resources allocated for each of the microservices, each of the microservices can be obtained through the k8s application programming interface API The computing resource amounts of the end points of the service are respectively taken as the respective first computing resource amounts.

Description of drawings

FIG. 1 is a schematic structural diagram of a microservice resource allocation apparatus according to an embodiment of the present invention.

FIG. 2 is a schematic diagram of interaction between modules in the apparatus for allocating resources for microservices provided by an embodiment of the present invention.

FIG. 3 is a flowchart of a method for allocating resources for microservices provided by an embodiment of the present invention.

List of reference numbers:

10: Microservice resource allocation device 101: Memory 102: Processor 103: Communication interface

20: Microservice resource allocation program 201: Configuration collector 202: Timer 203: Resource allocator

204: Deployment module 205: Data processor 30: k8s cluster 40: Control plane of service mesh

50: Tracking data and spatial data 300: Microservice resource allocation method S301～S309: Method steps

Detailed ways

The subject matter described herein will now be discussed with reference to example implementations. It should be understood that these embodiments are discussed only to enable those skilled in the art to better understand and implement the subject matter described herein, and not to limit the scope of protection, applicability, or examples set forth in the claims. Changes may be made in the function and arrangement of elements discussed without departing from the scope of the present disclosure. Various examples may omit, substitute, or add various procedures or components as desired. For example, the methods described may be performed in an order different from that described, and various steps may be added, omitted, or combined. Additionally, features described with respect to some examples may also be combined in other examples.

As used herein, the term "including" and variations thereof represent open-ended terms meaning "including but not limited to". The term "based on" means "based at least in part on". The terms "one embodiment" and "an embodiment" mean "at least one embodiment." The term "another embodiment" means "at least one other embodiment." The terms "first", "second", etc. may refer to different or the same objects. Other definitions, whether explicit or implicit, may be included below. The definition of a term is consistent throughout the specification unless the context clearly dictates otherwise.

To facilitate understanding of the contents of the embodiments of the present invention, some contents involved in the embodiments of the present invention are described below. It should be noted that these contents should not be regarded as limiting the protection scope of the present invention.

1. Kubernetes (k8s: production-level container orchestration)

The most popular open source system for automated deployment, tuning, and management of containerized applications. As an example of deployment tools and targets, k8s and k8s objects are used in the solutions provided by the embodiments of the present invention, but the embodiments of the present invention are not limited to using only k8s and its objects.

2. Computing resources reserved for microservices

The computing resources reserved for microservices include resources for things such as CPU, memory, external storage, processes, or other custom resources that can affect the computing power of the service. In current K8s or other deployments, computing resources can only be allocated and reserved according to the designer's understanding. By changing the resource request, the designer can change the computing power of the microservice.

3. Lagrange Multiplier

It is a strategy for finding local maxima and minima of functions under equality constraints. The basic idea is to transform a constrained problem into an unconstrained problem. The variables used in the embodiment of the present invention include: n selection variables with one constraint:

1) In order to minimize the value of f(x ₁ , x ₂ , ... x _n ) when g(x ₁ , x ₂ , ... x _n )=0, there is a unique Lagrangian multiplier λ .

2) For λ, there is a function

solve

3)

Equivalent to solving n+1 equations with n+1 unknowns.

4) Using x ₁ , x ₂ , ..., finally obtain the minimum value of f(x ₁ , x ₂ , ... x _n ) that satisfies g(x ₁ , x ₂ , ... x _n )=0 .

4. Distributed tracing

Is the process of tracking and analyzing what happens to a request (transaction) across all the microservices it involves, which generate and collect raw data from each microservice. When distributed tracing starts, a trace ID (trace ID) is generated, and the trace ID will follow the request everywhere. A new duration (Span) is generated for each logical work block in the request, which contains the same tracking ID, the new duration ID, and the parent duration ID (the parent duration ID points to this new duration logically). parent duration). This parent duration ID creates a parent-child relationship between durations. In addition, the duration also includes the start timestamp and duration, so it can be placed on the timeline.

5. Service mesh

A service mesh is a framework for inter-service communication. With a service mesh, a given microservice does not have to communicate directly with other microservices. Instead, all service-to-service communication will happen on top of a software component called a side-car proxy.

6. Jaeger

is an open-source, end-to-end distributed tracing tool that can be used to monitor and troubleshoot transactions in complex distributed systems.

7. Independent service computing time

is the computation time of the microservice, typically, the execution time of the service - the serial sub-execution time.

The embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

In this embodiment of the present invention, the following presets and target definitions may be performed first:

1) The target application 100 may include n microservices 200: Si,i∈[1,n].

2) Reserve computing resources such as CPU and memory for each microservice 200, denoted as R _i ,i∈[1,n].

3) Definition

And the constant C is the computing resource reserved for all microservices 200 of the target application 100 .

4) For a pre-defined request, the Distributed Tracing library, service net and jaeger can track the request and obtain the "independent service computing time" of each microservice 200 (denoted as T _i , i ∈ [1, n]).

5) Definition

as the total response time for this request.

6) Based on the above presets, the goal is to find a resource combination R _i that meets the constraint condition R _s =C, so that the minimum total response time T _s can be obtained.

System Architecture and Data Workflow:

The microservice resource allocation device 10 provided by the embodiment of the present invention can work with the target application 100, and the target application 100 can be deployed in the k8s cluster where the service mesh and jaeger are installed, which helps the system designer to calculate the resources Allocate and adjust.

Among them, more computing resources can be reserved as much as possible for the microservice 200 with longer independent service computing time T _i , and finally a set of computing resources R _i corresponding to the minimum total response time T _s can be found.

For microservice S _i ,

If (T _i /T _s -R _i /R _s )>0, then increase the computing resources allocated for the microservice 200, and the number of microservices 200 whose computing resources are to be increased is denoted as N;

If (T _i /T _s -R _i /R _s )<0, the computing resources allocated to the microservice 200 are reduced, and the number of microservices 200 whose computing resources are to be reduced is denoted as M.

Definable multiplication factor

Specifically, the set objective is to find an optimal resource combination R _i that meets the constraint condition R _s =C in m consecutive resource adjustments.

The above-mentioned resource allocation apparatus 10 may be implemented as a network of computer processors to execute the microservice resource allocation method 300 in the embodiment of the present invention, or may also be a single computer, a single-chip microcomputer, or a processing chip as shown in FIG. A memory 101 comprising computer readable media such as random access memory (RAM). The apparatus 10 also includes at least one processor 102 coupled with at least one memory 101 . Computer-executable instructions are stored in at least one memory 101 and, when executed by at least one processor 102, can cause at least one processor 102 to perform the steps described herein.

At least one memory 101 shown in FIG. 1 may contain a microservice resource allocation program 20, so that at least one processor 102 executes the microservice resource allocation method 300 described in the embodiment of the present invention. As shown in FIG. 2, the microservice resource allocation program 20 may include:

The configuration collector 201 is configured to obtain the amount of computing resources allocated to each microservice 200 of the target application 100 in the rth resource adjustment process (or “current resource adjustment process”)

(For example: computing resources (ie, pods) at the endpoints of each microservice 200, where i∈[1,n] and r∈[1,m], and r represents the rth resource adjustment. The k8s application programming interface (API) obtains the data of the above computing resources.

The timer 202 is configured to obtain the tracking data and spatial data 50 of each microservice 200 of the target application 100 during the rth resource adjustment process, and calculate the independent service computing time respectively

i∈[1,n],r∈[1,m]. The above-mentioned tracking data and spatial data 50 can be obtained through the distributed tracking library API.

The resource allocator 203 is configured to calculate the amount of computing resources allocated to each microservice 200 in the r+1th resource adjustment process (or referred to as the "next adjustment process")

The deployment module 204 is configured to be based on the amount of computing resources allocated to each microservice 200 obtained after m times of resource adjustment

The target application 100 is deployed in the k8s cluster 30 .

Among them, the following algorithm can be used for resource adjustment:

The learning rate η can be defined,

i∈[1,n], where,

is the computing resource allocated to each microservice 200 before resource adjustment is performed.

Invoke the data processor 205 to calculate the independent service computing time of each microservice 200 before resource adjustment is performed

i∈[1,n].

definition

Repeat the following steps until (θ _i -θ _i-1 >0):

Invoke the data processor 205 to calculate the independent service computing time of each microservice 200 in the rth resource adjustment

i∈[1,n];

Calculate θ ^r ;

Define the amount of computing resource adjustment for the _microservice Si

where γ is the aforementioned multiplication factor, which can be calculated from the values of R _i and T _i in the r-1th resource adjustment;

pass

i∈[1,n] to update

use

to update the resource allocation of each microservice 200 of the target application 100;

Invoke the deployment module 204 to redeploy the target application 100 to the k8s cluster 30;

The independent service computing time Ti of each microservice 200 is obtained from the output of the target application 100 .

The above modules can also be regarded as various functional modules implemented by hardware, which are used to implement various functions involved in the microservice resource allocation apparatus 10 when executing the microservice resource allocation method, such as pre-processing of each process involved in the method. The control logic is fired into a chip such as a Field-Programmable Gate Array (FPGA) or a Complex Programmable Logic Device (CPLD), and these chips or devices perform the functions of the above modules, The specific implementation method can be determined according to the engineering practice.

In addition, the microservice resource allocation apparatus 10 may further include a communication interface 103 for communication between the microservice resource allocation apparatus 10 and other devices.

It should be mentioned that embodiments of the present invention may include apparatuses having architectures different from those shown in FIG. 1 . The above architecture is only exemplary, and is used to explain the microservice resource allocation method 300 provided by the embodiment of the present invention.

The at least one processor 102 may include a microprocessor, an application specific integrated circuit (ASIC), a digital signal processor (DSP), a central processing unit (CPU), a graphics processing unit (GPU), a state machine, and the like. Examples of computer readable media include, but are not limited to, floppy disks, CD-ROMs, magnetic disks, memory chips, ROM, RAM, ASICs, configured processors, all-optical media, all magnetic tapes or other magnetic media, or from which a computer processor can Any other medium from which to read instructions. In addition, various other forms of computer-readable media can transmit or carry instructions to a computer, including routers, private or public networks, or other wired and wireless transmission devices or channels. Instructions can include code in any computer programming language, including C, C++, C, Visual Basic, java, and JavaScript.

The following describes a microservice resource allocation method 300 provided by an embodiment of the present invention with reference to FIG. 3 . The method can be executed by the aforementioned microservice resource allocation apparatus 10, and is used to allocate computing resources to each microservice 200 in a target application 100 through a plurality of resource adjustment processes, wherein the total amount of computing resources allocated to each microservice 200 is is a preset constant, the method may include: during the current resource adjustment process,

-S301: Obtain the first computing resource amount allocated for each microservice 200;

-S302: Obtain the tracking data and spatial data of each microservice 200;

-S303: For each microservice 200, calculate the computing time of the first independent service of the microservice 200 according to the tracking data and spatial data of the microservice 200;

- S304: Predict the amount of second computing resources allocated to each microservice 200 in the next resource adjustment process according to the amount of each first computing resource and the computing time of each first independent service;

-S305: Allocate computing resources for each microservice 200 according to the corresponding second computing resource amount;

-S306: deploy target application 100;

-S307: Obtain the second independent service computing time of each microservice 200 from the output of the target application 100;

-S308: if the sum of each second independent service calculation time is greater than the sum of each first independent service calculation time, then determine that the resource adjustment process ends, and determine that resource allocation is performed according to each first calculation resource amount;

-S309: If the sum of the calculation times of the second independent services is not greater than the sum of the calculation times of the first independent services, it is determined to continue the next resource adjustment process.

Optionally, in step S304, when predicting the second computing resources allocated to each user in the next resource adjustment process, the amount of each second computing resource may be determined, so as to predict the microservice 200 with a longer first independent service computing time. Reserve more computing resources.

Optionally, in step S304, a learning rate may be set when predicting the second amount of computing resources allocated to each user in the next resource adjustment process, wherein the larger the learning rate, the larger the adjustment amount of computing resources.

Optionally, the target application 100 is deployed in the k8s cluster 30. When obtaining the first amount of computing resources allocated for each microservice 200 in step S301, the computing resources of the endpoints of each microservice 200 can be obtained through the k8s application programming interface API. The quantities are respectively used as the associated quantities of the respective first computing resources.

Optionally, the k8s cluster 30 is installed with a service grid and jaeger, and the target application 100 is used to process a request message. In step S302, when the tracking data and spatial data of each microservice 200 are collected and obtained, the service grid and jaeger can be used to pair with each other. The request message is tracked in a distributed manner to obtain tracking data and spatial data; wherein, the computing time of the first independent service and the computing time of the second independent service are the computing time for each microservice 200 to process the request message.

In addition, embodiments of the present invention further provide a computer-readable medium, where computer-readable instructions are stored on the computer-readable medium, and when the computer-readable instructions are executed by the processor, the processor executes the aforementioned microservice resources allocation method. Examples of computer-readable media include floppy disks, hard disks, magneto-optical disks, optical disks (eg, CD-ROM, CD-R, CD-RW, DVD-ROM, DVD-RAM, DVD-RW, DVD+RW), magnetic tape, non- Volatile memory cards and ROMs. Optionally, the computer readable instructions may be downloaded from a server computer or cloud over a communications network.

It should be noted that not all steps and modules in the above-mentioned processes and system structure diagrams are necessary, and some steps or modules may be omitted according to actual needs. The execution order of each step is not fixed and can be adjusted as required. The system structure described in the above embodiments may be a physical structure or a logical structure, that is, some modules may be implemented by the same physical entity, or some modules may be implemented by multiple physical entities, or may be implemented by multiple physical entities. Some components in separate devices are implemented together.

Claims

A microservice resource allocation method (300), characterized in that it is used for allocating computing resources to each microservice (200) in a target application (100) through a plurality of resource adjustment processes, wherein for each of the microservices (200) The total amount of computing resources allocated is a preset constant, and the method includes: during the current resource adjustment process,

- obtaining (S301) the first amount of computing resources allocated for each of the microservices (200);

- obtaining (S302) tracking data and spatial data of each of the microservices (200);

- For each of the microservices (200), calculate (S303) according to the tracking data and spatial data of the microservice (200) to obtain the first independent service computing time of the microservice (200);

- predicting (S304) the amount of second computing resources allocated to each of the micro-services (200) in the next resource adjustment process according to the amount of each of the first computing resources and the computing time of each of the first independent services;

- Allocate (S305) computing resources for each of the microservices (200) according to the corresponding second computing resource amount;

- deploying (S306) the target application (100);

- Obtaining (S307) the second independent service computing time of each microservice (200) from the output of the target application (100);

- If the sum of the computing time of each of the second independent services is greater than the sum of the computing time of each of the first independent services, determine (S308) that the resource adjustment process ends, and determine to perform resource allocation according to the amount of each of the first computing resources ;

- If the sum of the calculation times of each of the second independent services is not greater than the sum of the calculation times of the first independent services, it is determined to continue (S309) to perform the next resource adjustment process.
The method according to claim 1, characterized in that, predicting (S304) the second computing resources allocated to each of the microservices (200) in the next resource adjustment process, comprising:

- Determining each of the second computing resource amounts to reserve more computing resources for the microservice (200) with a longer first independent service computing time.
The method according to claim 1, wherein the predicting (S304) the second amount of computing resources allocated to each of the microservices (200) in the next resource adjustment process comprises:

- Setting a learning rate, wherein the greater the learning rate, the greater the adjustment of computing resources.
The method according to claim 1, wherein the target application (100) is deployed in a k8s cluster (30), and the acquiring (S301) the first computing resource amount allocated for each of the microservices (200), including :

- Obtaining the computing resource amounts of the endpoints of each of the microservices (200) through the k8s application programming interface API as each of the first computing resource amounts.
The method according to claim 4, characterized in that, the k8s cluster (30) is installed with a service mesh and jaeger, the target application (100) is used to process a request message, and the acquisition (S302) each The tracking data and spatial data of the microservice (200) include:

- Distributed tracing of the request message using the service mesh and the jaeger to obtain the tracing data and the spatial data;

Wherein, the computing time of the first independent service and the computing time of the second independent service are the computing time of each of the microservices (200) for processing the request message.
A microservice resource allocation device (10), characterized in that it is used for allocating computing resources to each microservice (200) in a target application (100) through a plurality of resource adjustment processes, wherein each of the microservices (200) The total amount of computing resources allocated is a preset constant, and the device includes a configuration collector (201), a timer (202), a data processor (205), a resource allocator (203), and a deployment module (204), during the current resource adjustment process,

- said configuration collector (201), configured to obtain a first amount of computing resources allocated for each of said microservices (200);

- said timer (202), configured to obtain tracking data and spatial data of each of said microservices (200);

- said data processor (205), configured to:

- for each of the microservices (200), calculating the first independent service computing time of the microservice (200) according to the tracking data and spatial data of the microservice (200);

- predicting the second amount of computing resources allocated to each of the micro-services (200) in the next resource adjustment process according to the amount of each of the first computing resources and the computing time of each of the first independent services;

- the resource allocator (203) is configured to allocate computing resources for each of the microservices (200) according to the corresponding second computing resource amount;

- the deployment module (204) configured to deploy the target application (100);

- said data processor (205), further configured to:

- obtaining the second independent service computation time of the respective microservices (200) from the output of the target application (100);

- if the sum of the calculation times of each of the second independent services is greater than the sum of the calculation times of each of the first independent services, determining that the resource adjustment process ends, and determining that resource allocation is performed according to each of the first computing resource amounts;

- If the sum of the calculation times of each of the second independent services is not greater than the sum of the calculation times of the first independent services, it is determined to continue the next resource adjustment process.
The apparatus according to claim 6, wherein the data processor (205) is specifically configured when predicting the second amount of computing resources allocated to each of the microservices (200) in the next resource adjustment process for:

- Determining each of the second computing resource amounts to reserve more computing resources for the microservice (200) with a longer first independent service computing time.
The apparatus according to claim 6, wherein the data processor (205) is specifically configured when predicting the second amount of computing resources allocated to each of the microservices (200) in the next resource adjustment process for:

- Setting a learning rate, wherein the greater the learning rate, the greater the adjustment of computing resources.
The apparatus according to claim 6, wherein the target application (100) is deployed in a k8s cluster (30), and the configuration collector (201) acquires the first computing allocated for each of the microservices (200). The amount of resources is specifically configured as:

- Obtaining the computing resource amounts of the endpoints of each of the microservices (200) through the k8s application programming interface API as each of the first computing resource amounts.
The apparatus according to claim 9, characterized in that the k8s cluster (30) is installed with a service mesh and jaeger, the target application (100) is used to process a request message, and the timer (202) When acquiring the tracking data and spatial data of each of the microservices (200), it is specifically configured as:

- Distributed tracing of the request message using the service mesh and the jaeger to obtain the tracing data and the spatial data;

Wherein, the computing time of the first independent service and the computing time of the second independent service are the computing time for each of the microservices (200) to process the request message.
A microservice resource allocation device (10) for allocating computing resources to each microservice (200) in a target application (100) through a plurality of resource adjustment processes, characterized in that it comprises:

at least one memory (101) configured to store computer readable code;

At least one processor (102) configured to invoke the computer-readable code to perform the method of any of claims 1-5.
A computer-readable medium, characterized in that, computer-readable instructions are stored on the computer-readable medium, and when the computer-readable instructions are executed by a processor, the processor can cause the processor to perform any one of claims 1 to 5. one of the methods described.