CN110347477B - Service self-adaptive deployment method and device in cloud environment - Google Patents
Service self-adaptive deployment method and device in cloud environment Download PDFInfo
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- CN110347477B CN110347477B CN201910588189.3A CN201910588189A CN110347477B CN 110347477 B CN110347477 B CN 110347477B CN 201910588189 A CN201910588189 A CN 201910588189A CN 110347477 B CN110347477 B CN 110347477B
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Abstract
The application provides a service self-adaptive deployment method and device in a cloud environment, wherein the method comprises the following steps: acquiring the arrival rate of user requests of n multiplied by m time slices; calculating entropy and super-entropy of user request arrival rates of time slices with the same number in m preset periods, and calculating average values of entropy and super-entropy corresponding to n time slices with the same number; if the mean value of the entropies is smaller than a first preset threshold value and the mean value of the super entropies is smaller than a second preset threshold value, determining a time slice to which the next shortest virtual machine lease period of the current virtual machine lease period belongs, and calculating the arrival rate of the user request needing to be processed in the time slice; determining the number of the required virtual machines according to the calculated user request arrival rate required to be processed, the maximum processing time delay acceptable by a user and the user request processing rate of a single virtual machine; deploying the service on the determined number of virtual machines needed. The method can provide the service meeting the requirement on the basis of low cost.
Description
Technical Field
The invention relates to the technical field of service deployment in a cloud environment, in particular to a service self-adaptive deployment method and device in the cloud environment.
Background
In a cloud environment, a cloud resource provider provides computing resources by building a large-scale data center. Generally, these data are composed of thousands of physical machines, which are virtualized into virtual machines by virtualization technology and networked into large-scale virtual resource pools.
The cloud application service provider provides services to users by leasing virtual machines of the cloud resource provider and deploying applications on the virtual machines, and pays for resource usage.
In a cloud environment, dynamic deployment of services based on the number of user requests is an important issue. If the cloud application service provider rents excessive virtual machines to deploy services, resources are idle, the renting cost is wasted, if the number of the rented virtual machines is too small, the deployed services are insufficient, the service providing requirements are difficult to meet, and the user experience quality is reduced.
How to use the appropriate number of virtual machines to deploy services is a critical issue to be addressed.
Disclosure of Invention
In view of this, the present application provides a method and an apparatus for service adaptive deployment in a cloud environment, which can provide a service meeting requirements on the basis of low cost.
In order to solve the technical problem, the technical scheme of the application is realized as follows:
in one embodiment, a method for service adaptive deployment in a cloud environment is provided, and the method comprises the following steps:
acquiring the arrival rate of user requests of n multiplied by m time slices; wherein n is the number of time slices in a preset period, m is the number of the preset period, and the time slices in each preset period are numbered according to the time sequence;
calculating entropy and super-entropy of user request arrival rates of time slices with the same number in m preset periods, and calculating average values of entropy and super-entropy corresponding to n time slices with the same number;
if the mean value of the entropies is smaller than a first preset threshold value and the mean value of the super entropies is smaller than a second preset threshold value, determining a time slice to which the next shortest virtual machine lease period of the current virtual machine lease period belongs, and calculating the arrival rate of the user request needing to be processed in the time slice;
determining the number of the required virtual machines according to the calculated user request arrival rate required to be processed, the maximum processing time delay acceptable by a user and the user request processing rate of a single virtual machine;
deploying the service on the determined number of virtual machines needed.
In another embodiment, an apparatus for adaptively deploying services in a cloud environment is provided, where the apparatus includes:
an obtaining unit, configured to obtain user request arrival rates of n × m time slices; wherein n is the number of time slices in a preset period, m is the number of the preset period, and the time slices in each preset period are numbered according to the time sequence;
the first calculating unit is used for calculating the entropy and the super-entropy of the user request arrival rate of the time slices with the same number in m preset periods, and calculating the average value of the entropy and the super-entropy corresponding to the n time slices with the same number;
a first determining unit, configured to determine whether the average value of the entropies determined by the first calculating unit is smaller than a first preset threshold, and whether the average value of the super entropies is smaller than a second preset threshold;
the second calculating unit is used for determining a time slice to which the next shortest virtual machine lease period of the current virtual machine lease period belongs and calculating the arrival rate of the user request needing to be processed in the time slice if the first determining unit determines that the average value of the entropy is smaller than the first preset threshold value and the average value of the super entropy is smaller than the second preset threshold value;
a second determining unit, configured to determine the number of virtual machines according to the user request arrival rate to be processed, the maximum processing delay acceptable to the user, and the user request processing rate of a single virtual machine, which are calculated by the second calculating unit;
a deployment unit, configured to deploy services on the virtual machines of the required number of virtual machines determined by the second determination unit.
According to the technical scheme, in the embodiment, the arrival rate of the historical user requests is obtained, the time slices are divided into historical data, the average value of entropy and super entropy is calculated according to different time slices, whether the service request corresponding to the currently deployed service has periodicity is determined, if the periodicity exists, the number of virtual machines required by the next virtual machine leasing period is determined, and then the service is deployed on the virtual machines with the determined number of the required virtual machines. The scheme can carry out service deployment on the premise of the most reasonable number of the virtual machines, and further can provide services meeting requirements on the basis of low cost.
Drawings
The following drawings are only schematic illustrations and explanations of the present invention, and do not limit the scope of the present invention:
fig. 1 is a schematic diagram of a service adaptive deployment process in a cloud environment in an embodiment of the present application;
fig. 2 is a schematic structural diagram of an apparatus applied to the above-described technology in the embodiment of the present application.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
The technical solution of the present invention will be described in detail with specific examples. Several of the following embodiments may be combined with each other and some details of the same or similar concepts or processes may not be repeated in some embodiments.
The embodiment of the application provides a service self-adaptive deployment method in a cloud environment, which comprises the steps of obtaining the arrival rate of historical user requests, dividing time slices into historical data, calculating the average value of entropy and super entropy aiming at different time slices, further determining whether periodicity exists in service requests corresponding to currently deployed services, if the periodicity exists, determining the number of virtual machines required by the lease period of the next virtual machine, and further deploying the services on the virtual machines of the determined number of the required virtual machines. The scheme can carry out service deployment on the premise of the most reasonable number of the virtual machines, and further can provide services meeting requirements on the basis of low cost.
In the embodiment of the present application, the Device for implementing service adaptive deployment in a cloud environment may be a Device having simple computing capability, data obtaining capability, and information sending capability, and the Device may be a mobile Device, such as a mobile phone, a Tablet Personal Computer (Tablet Personal Computer), a Laptop Computer (Laptop Computer), a Personal Digital Assistant (PDA), or a Wearable Device (Wearable Device), or may also be a fixed Device, such as a PC.
In the embodiments of the present application, a service adaptive deployment process in a cloud environment is described in detail below with reference to the accompanying drawings.
Referring to fig. 1, fig. 1 is a schematic view of a service adaptive deployment process in a cloud environment in an embodiment of the present application. The method comprises the following specific steps:
m is an integer greater than 0, n is an integer greater than 1;
in the implementation of the step, data of m preset periods can be acquired, wherein the m preset periods can be continuous or discontinuous; the data of m preset periods is preferably time close to the current time, so that the acquired data has a reference meaning, and the number of the required virtual machines can be evaluated more accurately in the subsequent process.
The preset period here may be one day or one year, and this application does not limit this, and is set according to actual needs.
The n time slices are subdivided for each preset period, so that a total of m preset periods divides n × m time slices.
The time slices in each preset period are numbered according to the chronological order, for example, the n time slices divided in each preset period are numbered from 1 to n according to the chronological order, or the time slices in the same position are numbered in the same chronological order for each time slice in one preset period.
The n is a ratio of a preset period to the shortest virtual machine lease time, that is, the shortest virtual machine lease time is taken as the time slice length, and the lease time of each virtual machine is an integral multiple of the shortest virtual machine lease time.
If the shortest virtual machine leasing time is in the unit of hour, converting the preset period into hour (24 hours); if the shortest virtual machine lease time is 3 hours and the preset period is 1 day, 8 time slices can be divided in 1 day, and in specific implementation, if the ratio of the preset period to the shortest virtual machine lease time is not an integer, the time slices can be rounded upwards, so that the time length of the last time slice is less than the time length of one time slice and is taken as one time slice.
The obtained user request arrival rates of n × m time slices can be recorded in a matrix manner, where a matrix a is expressed as follows:
element a in the matrixijThe user request arrival rate of the jth time slice in the ith preset period is shown, i is an integer which is greater than or equal to 1 and less than or equal to m, and j is an integer which is greater than or equal to 1 and less than or equal to n.
If the obtained user request achievement rates are stored in a matrix mode, the expectation, entropy and super-entropy of each time slice can be calculated conveniently.
102, calculating entropy and super-entropy of user request arrival rates of time slices with the same number in m preset periods, and calculating average values of entropy and super-entropy corresponding to n time slices with the same number.
In specific implementation, three attribute values of the cloud model in the cloud environment can be calculated based on the obtained arrival rate of the user request: ex, entropy En and super entropy He are expected. The three attribute values can be calculated by the following formula:
wherein, ExjAs expected for the jth time slice, EnjEntropy of the jth time slice, HejThe entropy is the super entropy of the jth time slice, i.e. the expectation, entropy and super entropy of the time slice numbered j in each cycle.
The mean values of entropy and hyper-entropy are respectively expressed as:
and
the entropy and the mean value of the super-entropy of the n time slices can be calculated by the following formula:
and 103, if the average value of the entropy is smaller than a first preset threshold value and the average value of the super entropy is smaller than a second preset threshold value, determining a time slice to which the next shortest virtual machine lease period of the current virtual machine lease period belongs, and calculating the arrival rate of the user request needing to be processed in the time slice.
If the average value of the entropies is not smaller than the first preset threshold value and/or the average value of the super entropies is not smaller than the second preset threshold value, it is determined that the user request arrival rate in the cloud environment does not have periodicity, and the method can be implemented according to the prior art, for example, data of a currently used virtual machine are not adjusted.
If the average value of the entropies is smaller than the first preset threshold value and the average value of the super entropies is smaller than the second preset threshold value, the user request arrival rate in the cloud environment is determined to have certain periodicity, and the service deployment scheme provided by the application can be executed.
Determining a time slice to which the next shortest virtual machine lease period of the current virtual machine lease period belongs, including:
it can be seen from the above that n time slices are divided in each preset period, and the time slice to which the virtual machine lease period belongs is determined according to the time of the next shortest virtual machine lease period, assuming that the time slice to which the virtual machine lease period belongs is determined to be the time slice j.
Calculating the arrival rate of the user request needing to be processed in the time slice j, comprising the following steps:
the sum of the expectations, entropy and super entropy determined for the user request arrival rate for that time slice is taken as the user request arrival rate that needs to be processed at that time slice j.
And step 104, determining the number of the required virtual machines according to the calculated user request arrival rate required to be processed, the maximum processing time delay acceptable by the user and the user request processing rate of the single virtual machine.
The required number y of virtual machines can be determined by:
where y is the arrival rate of user requests to be processed, t△The maximum processing time delay acceptable for the user can be set according to actual needs, and mu is a value configured for the system, wherein mu is the user request processing rate of a single virtual machine.
Step 105, deploying services on the determined number of virtual machines needed.
In the embodiment of the application, if y is larger than x, the lease of y-x virtual machines in y is cancelled;
if y is equal to x, keeping the y virtual machines rented currently;
if y is smaller than x, renting x-y virtual machines; wherein y is the determined number of virtual machines needed, and x is the number of virtual machines currently leased.
That is to say, the virtual machines with more leases need to be leased, and when the number of the leased virtual machines is not enough, the virtual machines need to be leased again, so as to implement service deployment by using the determined required number of the virtual machines.
In the embodiment of the application, the rule of the user request is analyzed based on the historical data of the user request, and the most suitable number of virtual machines is determined based on the rule of the user request so as to perform self-adaptive deployment of the service. Therefore, the technical scheme provided by the application can provide the service meeting the requirement on the basis of low cost.
Based on the same inventive concept, the embodiment of the application provides a service self-adaptive deployment device in a cloud environment. Referring to fig. 2, fig. 2 is a schematic structural diagram of an apparatus applied to the above technology in the embodiment of the present application. The device includes:
an obtaining unit 201, configured to obtain user request arrival rates of n × m time slices; wherein n is the number of time slices in a preset period, m is the number of the preset period, and the time slices in each preset period are numbered according to the time sequence;
the first calculating unit 202 is configured to calculate entropy and super-entropy of user request arrival rates of time slices with the same number in m preset periods, which are acquired by the acquiring unit 201, and calculate an average value of the entropy and the super-entropy corresponding to n time slices with the same number;
a first determining unit 203, configured to determine whether the average value of the entropies determined by the first calculating unit 202 is smaller than a first preset threshold, and whether the average value of the super entropies is smaller than a second preset threshold;
a second calculating unit 204, configured to determine, if the first determining unit 203 determines that the average value of the entropy is smaller than a first preset threshold and the average value of the super entropy is smaller than a second preset threshold, a time slice to which a next shortest virtual machine lease period of the current virtual machine lease period belongs, and calculate an arrival rate of the user request to be processed at the time slice;
a second determining unit 205, configured to determine the number of virtual machines needed according to the user request arrival rate to be processed, the maximum processing delay acceptable to the user, and the user request processing rate of a single virtual machine, which are calculated by the second calculating unit 204;
a deployment unit 206, configured to deploy the service on the virtual machines of the required number of virtual machines determined by the second determination unit 205.
Preferably, the first and second electrodes are formed of a metal,
the deployment unit 206 is further configured to cancel lease of y-x virtual machines in y if it is determined that y is greater than x; if y is determined to be equal to x, keeping the y virtual machines rented currently; if y is determined to be smaller than x, renting x-y virtual machines; wherein y is the determined number of virtual machines needed, and x is the number of virtual machines currently leased.
Preferably, the first and second electrodes are formed of a metal,
n is the ratio of the preset period to the shortest virtual machine leasing time.
Preferably, the first and second electrodes are formed of a metal,
the second calculating unit 204 is specifically configured to, when calculating the user request arrival rate that needs to be processed in the time slice, use the sum of the expectation, the entropy, and the super-entropy determined for the user request arrival rate in the time slice as the user request arrival rate that needs to be processed in the time slice.
Preferably, the first and second electrodes are formed of a metal,
the second determining unit 205 is specifically configured to determine the number of virtual machines needed according to the user request arrival rate to be processed, the maximum processing delay acceptable by the user, and the user request processing rate of a single virtual machine, and includes:
where y is the arrival rate of user requests to be processed, t△Mu is the user request processing rate of a single virtual machine for the maximum processing latency acceptable to the user.
The units of the above embodiments may be integrated into one body, or may be separately deployed; may be combined into one unit or further divided into a plurality of sub-units.
In summary, in the embodiment of the present application, rules of user requests are analyzed based on historical data of the user requests, and the most suitable number of virtual machines is determined based on the rules of the user requests to perform adaptive deployment of services. Therefore, the technical scheme provided by the application can provide the service meeting the requirement on the basis of low cost.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like made within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (10)
1. A service adaptive deployment method in a cloud environment is characterized by comprising the following steps:
acquiring the arrival rate of user requests of n multiplied by m time slices; wherein n is the number of time slices in a preset period, m is the number of the preset period, and the time slices in each preset period are numbered according to the time sequence;
calculating entropy and super-entropy of user request arrival rates of time slices with the same number in m preset periods, and calculating average values of entropy and super-entropy corresponding to n time slices with the same number;
if the mean value of the entropies is smaller than a first preset threshold value and the mean value of the super entropies is smaller than a second preset threshold value, determining a time slice to which the next shortest virtual machine lease period of the current virtual machine lease period belongs, and calculating the arrival rate of the user request needing to be processed in the time slice;
determining the number of the required virtual machines according to the calculated user request arrival rate required to be processed, the maximum processing time delay acceptable by a user and the user request processing rate of a single virtual machine;
deploying the service on the determined number of virtual machines needed.
2. The method of claim 1, further comprising:
if y is determined to be larger than x, cancelling lease of y-x virtual machines in y;
if y is determined to be equal to x, keeping the y virtual machines rented currently;
if y is determined to be smaller than x, renting x-y virtual machines; wherein y is the determined number of virtual machines needed, and x is the number of virtual machines currently leased.
3. The method of claim 1,
n is the ratio of the preset period to the shortest virtual machine leasing time.
4. The method of claim 1, wherein calculating the arrival rate of the user requests to be processed in the time slice comprises:
the sum of the expectations, entropy and super entropy determined for the user request arrival rate for that time slice is taken as the user request arrival rate that needs to be processed at that time slice.
5. The method according to any one of claims 1 to 4, wherein determining the number of virtual machines required according to the user request arrival rate required to be processed, the maximum processing delay acceptable to the user, and the user request processing rate of a single virtual machine comprises:
wherein n isjFor user request arrival rates to be handled, tΔMu is the user request processing rate of a single virtual machine for the maximum processing latency acceptable to the user.
6. An apparatus for service adaptive deployment in a cloud environment, the apparatus comprising:
an obtaining unit, configured to obtain user request arrival rates of n × m time slices; wherein n is the number of time slices in a preset period, m is the number of the preset period, and the time slices in each preset period are numbered according to the time sequence;
the first calculating unit is used for calculating the entropy and the super-entropy of the user request arrival rate of the time slices with the same number in m preset periods, and calculating the average value of the entropy and the super-entropy corresponding to the n time slices with the same number;
a first determining unit, configured to determine whether the average value of the entropies determined by the first calculating unit is smaller than a first preset threshold, and whether the average value of the super entropies is smaller than a second preset threshold;
the second calculating unit is used for determining a time slice to which the next shortest virtual machine lease period of the current virtual machine lease period belongs and calculating the arrival rate of the user request needing to be processed in the time slice if the first determining unit determines that the average value of the entropy is smaller than the first preset threshold value and the average value of the super entropy is smaller than the second preset threshold value;
a second determining unit, configured to determine the number of virtual machines according to the user request arrival rate to be processed, the maximum processing delay acceptable to the user, and the user request processing rate of a single virtual machine, which are calculated by the second calculating unit;
a deployment unit, configured to deploy services on the virtual machines of the required number of virtual machines determined by the second determination unit.
7. The apparatus of claim 6,
the deployment unit is further used for cancelling lease of y-x virtual machines in y if y is determined to be larger than x; if y is determined to be equal to x, keeping the y virtual machines rented currently; if y is determined to be smaller than x, renting x-y virtual machines; wherein y is the determined number of virtual machines needed, and x is the number of virtual machines currently leased.
8. The apparatus of claim 6,
n is the ratio of the preset period to the shortest virtual machine leasing time.
9. The apparatus of claim 6,
the second calculating unit is specifically configured to, when the user request arrival rate required to be processed in the time slice is calculated, use the sum of the expectation, the entropy, and the super-entropy determined for the user request arrival rate of the time slice as the user request arrival rate required to be processed in the time slice.
10. The apparatus according to any one of claims 6 to 9,
the second determining unit is specifically configured to determine the number of virtual machines required according to the user request arrival rate required to be processed, the maximum processing delay acceptable by the user, and the user request processing rate of a single virtual machine, and includes:
wherein n isjFor user request arrival rates to be handled, tΔMu is the user request processing rate of a single virtual machine for the maximum processing latency acceptable to the user.
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