CN116128139A - Cloud service profit optimization method and system based on customer sensitivity analysis - Google Patents

Cloud service profit optimization method and system based on customer sensitivity analysis Download PDF

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CN116128139A
CN116128139A CN202310107447.8A CN202310107447A CN116128139A CN 116128139 A CN116128139 A CN 116128139A CN 202310107447 A CN202310107447 A CN 202310107447A CN 116128139 A CN116128139 A CN 116128139A
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周俊龙
侯祥鹏
丛佩金
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Nanjing University of Science and Technology
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Abstract

The invention discloses a cloud service profit optimization method and system based on customer sensitivity analysis. The method comprises the following steps: firstly, a double-leasing multi-server system model is established, and a customer sensitivity factor is calculated according to the proposed customer sensitivity model; then calculating a predicted service request relative deadline and price function based on the sensitivity factors, and providing a profit calculation expression based on customer sensitivity analysis; and finally, deducing the characteristics of the optimal solution according to a mathematical analysis method, and designing a heuristic algorithm according to the characteristics to obtain an optimal service request scheduling scheme and corresponding maximum profit. The invention considers deadline constraint and customer sensitivity simultaneously when dispatching the service request, and increases the profit of the cloud service provider.

Description

Cloud service profit optimization method and system based on customer sensitivity analysis
Technical Field
The invention belongs to the technical field of profit optimization in a cloud service environment, and particularly relates to a cloud service profit optimization method and system based on customer sensitivity analysis.
Background
In the competitive cloud business market, how to maximize the profit of a cloud service provider while improving the service experience of customers is a key issue facing the cloud service provider. Therefore, in the links of cloud service pricing and cloud service request scheduling, timeliness of service request processing and good experience of customers are required to be ensured, and the cloud service provider obtains maximum profit. The profit of the cloud service provider is mainly obtained by subtracting the monetary cost of processing customer service requests from the revenue obtained by the customer purchasing the service, and the revenue can be increased and the cost can be reduced by reasonable cloud service pricing policies and service request scheduling schemes to maximize the profit. Efficient pricing strategies can increase revenue and reasonable service request scheduling schemes can reduce costs, so it is important to focus on pricing strategies and service request scheduling schemes in profit optimization efforts.
In terms of increasing revenue, xu and Li (Xu, hong, and bao hun Li., "Dynamic cloud pricing for revenue maximization," IEEE Transactions on Cloud Computing 1.2.2 (2013): 158-171.) propose a cloud service market demand driven dynamic cloud service pricing mechanism that can adaptively adjust cloud service prices based on instantaneous market supply and demand, enabling cloud service providers to dynamically control resource demand and ensure customer service experience. Zhu et al (Zhu, zhengfa, et al, "A game-based resource pricing and allocation mechanism forprofit maximization in cloud Computing," Soft Computing 24.6 (2020): 4191-4203.) propose a dynamic resource pricing strategy based on a deckelberg game to maximize cloud service revenue. Cao et al (J.Cao, K.Huang, K.Li, and A.Y. Zomaya, "Optimal multiserver configuration for profit maximization in cloud computing," IEEE Transactions on Parallel and Distributed Systems, vol.24, no.6, pp.1087-1096, jun.2013.) propose a dynamic pricing strategy to increase service revenue that yields optimal multi-server configuration through two speed models;
in terms of cost reduction, zhang et al (Zhang Q, fall Zhani M, boutaba R, l.hellerstein J. Dynamic diversity-aware resource provisioning in the cloud J. IEEE Transactions on Cloud Computing,2014,2 (1): 14-28.) propose a workload and resource heterogeneous aware capacity configuration framework that reduces overall energy consumption and scheduling delays by dynamic adjustment of workload classification and resource quantity. Beloglozov et al (Beloglozov A, abawajy J, buyya R.energy-aware resource allocation heuristics for efficient management of data centers for cloud computing [ J ]. Future Generation Computer Systems,2012,28 (5): 755-768) describe a resource supply and allocation algorithm that reduces energy consumption by reducing the number of resources and improves system energy efficiency.
However, in the existing pricing strategies, few people consider that the customer individuality, the customer category difference, the SLA and the like can embody a plurality of factors such as customer value, service experience and the like. Meanwhile, the conventional scheduling principle may not be applicable in the case that the quality of service metrics are not uniform, because inefficient service request scheduling and allocation generally causes customer churn, so that cloud service profit optimization cannot be achieved.
Disclosure of Invention
The invention aims to solve the problems in the prior art and provides a method for realizing cloud service profit maximization under the condition that deadline constraint and customer sensitivity are simultaneously considered during service request scheduling.
The technical solution for realizing the purpose of the invention is as follows: a cloud service profit optimization method based on customer sensitivity analysis, the method comprising the steps of:
step 1, establishing a cloud server system model with a long-term lease and short-term lease dual resource lease mode, and establishing a customer sensitive factor calculation model;
step 2, based on the customer sensitivity factor, providing a service request relative deadline prediction mechanism and a customer perceived cloud service pricing strategy, and then calculating income and cost of a cloud service provider and constructing a profit calculation expression;
and 3, deriving an optimal solution form of the service request scheduling problem by utilizing an analytic method based on the profit calculation expression and taking profit optimization as a target, and designing a heuristic service request scheduling algorithm according to the characteristics of the optimal solution to obtain an optimal scheduling scheme and corresponding maximum profit.
Further, in the step 1, a cloud server system model with a long-term lease and short-term lease dual-resource lease mode is established, and a customer sensitivity factor calculation model is established, which specifically includes:
step 1-1, a cloud server system model with a long-term lease and short-term lease dual resource lease mode is established, and specifically comprises the following steps: regarding a cloud server in a cloud server system as an M/M/M+D queue model, wherein the speeds of M stations are { s }, respectively 1 ,s 2 ,...,s M Heterogeneous long-term rental server and D-station speed s short Wherein the long-term server operates for a long time in each time slot, and the short-term server operates only when a service request is allocated to the short-term server, so that it can be set that each service request allocated to the short-term server monopolizes one short-term server, and the lease is stopped after the task is finished;
step 1-2, establishing a customer sensitive factor model, and a customer sensitive factor sen i The calculation formula is as follows:
Figure BDA0004075618160000021
in the method, in the process of the invention,
Figure BDA0004075618160000031
is a vector parameter containing five elements, is obtained by fitting historical data of a transaction platform by a least square method and the like, and is a method for generating the transaction platform by adopting the method of the least square method and the like>
Figure BDA0004075618160000032
Scoring the personality of customer i, calculating according to ten large five-personality scale (Ten Item Personality Inventory, TIPI) questionnaires filled by the customer, and obtaining ∈10>
Figure BDA0004075618160000033
The calculation method is as follows:
Figure BDA0004075618160000034
wherein E is p 、A n 、C p 、N n 、O p 、E n 、A p 、C n 、N p O and O n The score for each item in the TIPI scale is shown separately.
Further, step 2 provides a service request relative deadline prediction mechanism and a customer-aware cloud service pricing strategy based on the customer sensitivity factor, and then calculates the income and cost of the cloud service provider, and constructs a profit calculation expression, which specifically includes:
step 2-1, predicting the service request relative deadline D based on the customer sensitivity factor i The calculation is as follows:
Figure BDA0004075618160000035
wherein D is sla The service request relative deadline specified in the SLA is represented, max (r) represents the upper limit of the service request workload in the scheduling time slot, and max(s) represents the running speed of the server with the highest speed in the cloud service platform;
step 2-2, customer perceived cloud service pricing, and a price function is calculated as:
Figure BDA0004075618160000036
wherein p is i With respect to service relative deadlines D i Is a piecewise function of p b Is a cloud service desired sales price specified by a cloud service provider, f (D i ) Is a deadline D relative to the service request i The related functions are calculated by the following modes:
f(D i )＝∣D i -D sla ∣*Δp (5)
wherein Δp represents the service relative cutoff time D i Relative deadline D to services specified in SLA SLA The magnitude of the cloud service price decrease (or increase) per unit time extension (or shortening);
step 2-3, calculating Cost of the cloud service provider, wherein the Cost of the cloud service provider comprises two parts: the server lease fees and the electricity fees generated by the operation of the server, wherein each part comprises fees respectively generated by a long-term lease server and a short-term lease server, and the Cost in one scheduling time slot is calculated in the following way:
Figure BDA0004075618160000041
wherein Ele represents the generated electricity fee, ren represents the generated rental fee, M represents the number of long-term servers, D represents the number of short-term servers, i.e., the number of service requests allocated to the short-term servers, |t|represents a scheduling slot length, n k Representing the number of service requests distributed to the long-term server k, C representing the unit price of electricity, C eff,k 、v k 、f k 、P sta,k 、s k And delta k Effective switch capacitance, supply voltage, clock frequency, static power consumption, speed and lease fees per unit time, ET, respectively, for long-term server k i,k Representing the time required for the service request i to run to completion on the long-term server k, expressed as
Figure BDA0004075618160000042
C eff,sho 、v sho 、f sho 、P sta,sho 、s sho And delta sho The effective switch capacitance, the power supply voltage, the clock frequency, the static power consumption, the speed and the lease fee of unit time of the short-term server are respectively represented; r is (r) i Representing the workload of service request i on long-term server, r j Representing the workload of service request j on the short-term server;
step 2-4, constructing a calculation expression of Profit:
Figure BDA0004075618160000043
wherein Rev is the income obtained by the cloud service provider for completing all service requests in one scheduling time slot, and n is the number of cloud service requests in one scheduling time slot.
Further, in the step 3, based on the profit calculation expression, with profit optimization as a goal, an optimal solution form of the service request scheduling problem is deduced by using an analytic method, and a heuristic service request scheduling algorithm is designed according to the characteristics of the optimal solution, so as to obtain an optimal scheduling scheme and a corresponding maximum profit, which specifically includes:
step 3-1, constructing profit maximization optimization problems based on the profit calculation expression;
step 3-2, deducing an optimal solution form of the service request scheduling problem according to the profit calculation expression by using an analytic method;
and 3-3, designing a heuristic service request scheduling algorithm according to the characteristics of the optimal solution, and solving the problem of optimizing the profit of the constrained cloud service, wherein the obtained optimal solution is the optimal service request scheduling scheme and the corresponding maximum profit.
Further, the profit maximizing optimization problem constructed based on the profit calculation expression in the step 3-1 is as follows:
Figure BDA0004075618160000051
where i denotes the service request i, ET i Representing the execution time of service request i, D i Representing the relative deadline of the predicted service request i.
Further, in step 3-2, the optimal solution form of the service request scheduling problem is derived according to the profit calculation expression by using the parsing method:
Figure BDA0004075618160000052
since the workload of service requests allocated to short-term servers is equal to the total workload minus the workload of service requests allocated to long-term servers, i.e.:
Figure BDA0004075618160000053
wherein n is the total number of service requests, M is the number of long-term servers, D is the number of short-term servers, i.e. the number of service requests distributed to the short-term servers, n k The number of service requests distributed to the long-term server k; order the
Figure BDA0004075618160000054
Profit profits can therefore continue to be derived as:
Figure BDA0004075618160000061
after the configuration of the service request set, the long-term lease server and the short-term lease server is given in the scheduling time slot t, the first term and the third term of the Profit expression are constants, so that
Figure BDA0004075618160000062
The second term of the Profit expression, i.e
Figure BDA0004075618160000063
Reams the
Figure BDA0004075618160000064
Figure BDA0004075618160000065
Two vectors A M ＝[A 1 ,A 2 ,...,A M ]And B M ＝[B 1 ,B 2 ,...,B M ] T Can represent the part of the change in the Profit expression, A M Related to server configuration, B M In relation to the service request set, L may be calculated as:
Figure BDA0004075618160000066
for each long-term server, calculate its A k Value and according to A k Descending order of values due to B M The sum of the service requests is constant, and the dispatching result of the service requests meets B 1 ≤B 2 ≤...≤B M And (3) the situation is the optimal solution of the service request scheduling problem, and the Profit of the cloud service provider is maximum.
Further, in step 3-3, a heuristic service request scheduling algorithm is designed according to the characteristics of the optimal solution, so as to solve the problem of optimizing the profit of the cloud service, and the obtained optimal solution is the optimal service request scheduling scheme and the corresponding maximum profit, and specifically includes:
the algorithm inputs are: service request set
Figure BDA0004075618160000071
Each service request sr i Is a five-tuple (sen i ,r i ,p i ,D i ,W i ) Respectively represent service requests sr i Sensitivity factor, workload, price, relative deadline and maximum latency of the long-term server set +.>
Figure BDA0004075618160000072
Each long-term server MS k Is a power supply voltage and operation speed pair (v) k ,s k ) Short-term server operating speed s sho Response deadline D specified in SLA SLA And at bestLarge service latency W SLA Cloud service expected sales price p b Cloud service price variable deltap, scheduling time slot length |t|, long-term server k and short-term server static power consumption P sta,k 、P sta,sho Lease price delta for long term server k and short term server k 、δ sho Electricity price C;
obtaining an optimal solution of the problem, namely an optimal service request scheduling scheme, based on the algorithm input and the characteristics of the optimal solution in claim 6;
step 3-3-1, calculating A of each long-term server according to the formula (11), the formula (14) and the input parameters k Values and arranged in descending order of their values, server index from 1 to M; the service requests are arranged in ascending order according to the size of the workload, and the index is from 1 to n.
Step 3-3-2, starting with the order of arrival of the server indexes from small, ordering the service requests sr for each server k i Sequentially distributing the indexes to the current server k from small to large, and if the utilization rate of the current server is greater than 1, namely, the constraint of the formula (17) is not satisfied:
Figure BDA0004075618160000073
the allocation of service request to the current server k is stopped and the allocation of service request to the next indexed server k+1 is started, where n k Representing the number of service requests allocated to server k;
step 3-3-3, if all the service requests can be distributed and scheduled to the long-term server under the condition of meeting the constraint, ending the distribution; otherwise, the rest service requests are distributed to the short-term server, and the distribution is finished; at this time, the optimal service request scheduling scheme is obtained, and the corresponding maximum profit can be calculated according to the formula (12).
The invention provides a cloud service profit optimization system based on customer sensitivity analysis, which comprises:
the first module is used for establishing a cloud server system model with a long-term lease and short-term lease dual-resource lease mode and establishing a customer sensitive factor calculation model;
a second module for constructing a service request relative deadline prediction mechanism and a customer-perceived cloud service pricing policy based on the customer sensitivity factor, and then calculating the income and cost of the cloud service provider, and constructing a profit calculation expression;
and the third module is used for deriving an optimal solution form of the service request scheduling problem by utilizing an analytic method based on the profit calculation expression and taking profit optimization as a target, and designing a heuristic service request scheduling algorithm according to the characteristics of the optimal solution to obtain an optimal scheduling scheme and corresponding maximum profit.
Compared with the prior art, the invention has the remarkable advantages that: 1) Because the pricing strategy fully considers factors such as individuality, category difference and the like of the customers, the preference of the customers between the service price and the service quality is analyzed, and the purpose of improving the profits obtained by the cloud service provider can be achieved while the customer satisfaction degree is improved; 2) Based on the sensitivity factor, a prediction mechanism of the relative deadline of the service request is provided, and the experience of customers and the timeliness of the service request are ensured; 3) The invention provides a method for combining a mathematical analysis method and a heuristic algorithm, which can be used for obtaining a new quotation by deduction and analysis, and designs a heuristic service request scheduling algorithm according to the characteristics of the optimal solution.
The invention is described in further detail below with reference to the accompanying drawings.
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FIG. 1 is a flow chart of the best scheduling scheme of computing service requests and corresponding maximum profits based on customer sensitivity analysis according to the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application will be further described in detail with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the present application.
It should be noted that, if there is a description of "first", "second", etc. in the embodiments of the present invention, the description of "first", "second", etc. is only for descriptive purposes, and is not to be construed as indicating or implying relative importance or implying that the number of technical features indicated is indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include at least one such feature. In addition, the technical solutions of the embodiments may be combined with each other, but it is necessary to base that the technical solutions can be realized by those skilled in the art, and when the technical solutions are contradictory or cannot be realized, the combination of the technical solutions should be considered to be absent and not within the scope of protection claimed in the present invention.
The cloud service profit optimization method based on customer sensitivity analysis provided by the invention is to consider deadline constraint and customer sensitivity simultaneously when service requests are scheduled so as to realize profit maximization. The customer sensitivity factor is calculated, based on the customer sensitivity factor, the predicted service request relative deadline and the customer perceived cloud service price function are calculated, and the deadline constraint and the customer sensitivity perceived cloud service profit are calculated. And designing a heuristic algorithm based on the optimal solution characteristics deduced by the mathematical analysis method to solve the problem of profit optimization.
In one embodiment, in conjunction with FIG. 1, the present invention provides a cloud service profit optimization method based on customer sensitivity analysis, the method comprising the steps of:
step 1, establishing a cloud server system model with a long-term lease and short-term lease dual resource lease mode, and establishing a customer sensitive factor calculation model;
step 2, based on the customer sensitivity factor, providing a service request relative deadline prediction mechanism and a customer perceived cloud service pricing strategy, and then calculating income and cost of a cloud service provider and constructing a profit calculation expression;
and 3, deriving an optimal solution form of the service request scheduling problem by utilizing an analytic method based on the profit calculation expression and taking profit optimization as a target, and designing a heuristic service request scheduling algorithm according to the characteristics of the optimal solution to obtain an optimal scheduling scheme and corresponding maximum profit.
Further, in one embodiment, in step 1, a cloud server system model with a long-term lease and short-term lease dual-resource lease mode is established, and a customer sensitivity factor calculation model is established, which specifically includes:
step 1-1, a cloud server system model with a long-term lease and short-term lease dual resource lease mode is established, and specifically comprises the following steps: regarding a cloud server in a cloud server system as an M/M/M+D queue model, wherein the speeds of M stations are { s }, respectively 1 ,s 2 ,...,s M Heterogeneous long-term rental server and D-station speed s short Wherein the long-term rental server operates for a long time in each time slot, and the short-term server operates only when a service request is allocated to the short-term rental server, so that it can be set that each service request allocated to the short-term rental server monopolizes one short-term rental server, and the rental is stopped after the task is finished;
step 1-2, establishing a customer sensitive factor model, and a customer sensitive factor sen i The calculation formula is as follows:
Figure BDA0004075618160000101
in the method, in the process of the invention,
Figure BDA0004075618160000102
is a vector parameter containing five elements, is obtained by fitting historical data of a transaction platform by a least square method and the like, and is a method for generating the transaction platform by adopting the method of the least square method and the like>
Figure BDA0004075618160000103
Scoring customer i personality according to customer fill-inTen large five personality scale (Ten Item Personality Inventory, TIPI) questionnaires calculated to give ∈,>
Figure BDA0004075618160000104
the calculation method is as follows:
Figure BDA0004075618160000105
wherein E is p 、A n 、C p 、N n 、O p 、E n 、A p 、C n 、N p O and O n The score for each item in the TIPI scale is shown separately.
Further, in one embodiment, based on the customer sensitivity factor, a service request relative deadline prediction mechanism and a customer-aware cloud service pricing policy are proposed in step 2, and then the income and cost of the cloud service provider are calculated, and a profit calculation expression is constructed, specifically including:
step 2-1, predicting the service request relative deadline D based on the customer sensitivity factor i The calculation is as follows:
Figure BDA0004075618160000106
wherein D is sla The service request relative deadline specified in the SLA is represented, max (r) represents the upper limit of the service request workload in the scheduling time slot, and max(s) represents the running speed of the server with the highest speed in the cloud service platform;
step 2-2, customer perceived cloud service pricing, and a price function is calculated as:
Figure BDA0004075618160000107
wherein p is i With respect to service relative deadlines D i Is a piecewise function of p b Is a cloud service specified by a cloud service providerDesired sales price, f (D i ) Is a deadline D relative to the service request i The related functions are calculated by the following modes:
f(D i )＝∣D i -D sla ∣*Δp (22)
wherein Δp represents the service relative cutoff time D i Relative deadline D to services specified in SLA SLA The magnitude of the cloud service price decrease (or increase) per unit time extension (or shortening);
step 2-3, calculating the Cost of the cloud service provider, comprising two parts: the server lease fees and the electricity fees generated by the operation of the server, wherein each part comprises fees respectively generated by a long-term lease server and a short-term lease server, and the Cost in one scheduling time slot is calculated in the following way:
Figure BDA0004075618160000111
wherein Ele represents the generated electricity fee, ren represents the generated rental fee, M represents the number of long-term servers, D represents the number of short-term servers, i.e., the number of service requests allocated to the short-term servers, |t|represents a scheduling slot length, n k Representing the number of service requests distributed to the long-term server k, C representing the unit price of electricity, C eff,k 、v k 、f k 、P sta,k 、s k And delta k Effective switch capacitance, supply voltage, clock frequency, static power consumption, speed and lease fees per unit time, ET, respectively, for long-term server k i,k Representing the time required for the service request i to run to completion on the long-term server k, expressed as
Figure BDA0004075618160000112
C eff,sho 、v sho 、f sho 、P sta,sho 、s sho And delta sho The effective switch capacitance, the power supply voltage, the clock frequency, the static power consumption, the speed and the lease fee of unit time of the short-term server are respectively represented; r is (r) i Representing the workload of service request i on long-term server, r j Representing the workload of service request j on the short-term server;
step 2-4, constructing a calculation expression of Profit:
Figure BDA0004075618160000113
wherein Rev is the income obtained by the cloud service provider for completing all service requests in one scheduling time slot, and n is the number of cloud service requests in one scheduling time slot.
Further, in one embodiment, in step 3, based on the profit calculation expression, with profit optimization as a target, an optimal solution form of the service request scheduling problem is deduced by using an analytic method, and a heuristic service request scheduling algorithm is designed according to the characteristics of the optimal solution, so as to obtain an optimal scheduling scheme and a corresponding maximum profit, which specifically includes:
step 3-1, constructing profit maximization optimization problems based on the profit calculation expression;
step 3-2, deducing an optimal solution form of the service request scheduling problem according to the profit calculation expression by using an analytic method;
and 3-3, designing a heuristic service request scheduling algorithm according to the characteristics of the optimal solution, and solving the problem of optimizing the profit of the constrained cloud service, wherein the obtained optimal solution is the optimal service request scheduling scheme and the corresponding maximum profit.
Further, in one embodiment, the profit maximizing optimization problem constructed based on the profit calculation expression in step 3-1 is:
Figure BDA0004075618160000121
where i denotes the service request i, ET i Representing the execution time of service request i, D i Representing the relative deadline of the predicted service request i.
Further, in one embodiment, the method of step 3-2 derives an optimal solution of the service request scheduling problem from the profit calculation expression:
Figure BDA0004075618160000122
since the workload of service requests allocated to short-term servers is equal to the total workload minus the workload of service requests allocated to long-term servers, i.e.:
Figure BDA0004075618160000123
wherein n is the total number of service requests, M is the number of long-term servers, D is the number of short-term servers, i.e. the number of service requests distributed to the short-term servers, n k The number of service requests distributed to the long-term server k; order the
Figure BDA0004075618160000131
Profit profits can therefore continue to be derived as:
Figure BDA0004075618160000132
after the configuration of the service request set, the long-term lease server and the short-term lease server is given in the scheduling time slot t, the first term and the third term of the Profile expression are constants, and L is the second term of the Profile expression, namely
Figure BDA0004075618160000133
Reams the
Figure BDA0004075618160000134
Figure BDA0004075618160000135
Two vectors A M ＝[A 1 ,A 2 ,...,A M ]And B M ＝[B 1 ,B 2 ,...,B M ] T Can represent the part of the change in the Profit expression, A M Related to server configuration, B M In relation to the service request set, L may be calculated as:
Figure BDA0004075618160000141
for each long-term server, calculate its A k Value and according to A k Descending order of values due to B M The sum of the service requests is constant, and the dispatching result of the service requests meets B 1 ≤B 2 ≤...≤B M And (3) the situation is the optimal solution of the service request scheduling problem, and the Profit of the cloud service provider is maximum.
Further, in one embodiment, in step 3-3, a heuristic service request scheduling algorithm is designed according to the characteristics of the optimal solution, so as to solve the problem of optimizing the profit of the cloud service, where the obtained optimal solution is the optimal service request scheduling scheme and the corresponding maximum profit, and specifically includes:
the algorithm inputs are: service request set
Figure BDA0004075618160000142
Each service request sr i Is a five-tuple (sen i ,r i ,p i ,D i ,W i ) Respectively represent service requests sr i Sensitivity factor, workload, price, relative deadline and maximum latency of the long-term server set +.>
Figure BDA0004075618160000143
Each of which isLong-term server MS k Is a power supply voltage and operation speed pair (v) k ,s k ) Short-term server operating speed s sho Response deadline D specified in SLA SLA And maximum service waiting time W SLA Cloud service expected sales price p b Cloud service price variable deltap, scheduling time slot length |t|, long-term server k and short-term server static power consumption P sta,k 、P sta,sho Lease price delta for long term server k and short term server k 、δ sho Electricity price C;
obtaining an optimal solution of the problem, namely an optimal service request scheduling scheme, based on the algorithm input and the characteristics of the optimal solution obtained in the step 3-2;
step 3-3-1, calculating A of each long-term server according to the formula (28), the formula (31) and the input parameters k Values and descending order according to the values, server index from 1 to M; the service requests are arranged in ascending order according to the size of the workload, and the indexes are from 1 to n.
Step 3-3-2, starting with the order of server indexes from small to large, for each server k, ordering the service requests sr i Sequentially distributing the indexes to the current server k from small to large, and if the utilization rate of the current server is greater than 1, namely, the constraint of the formula (34) is not satisfied:
Figure BDA0004075618160000151
the allocation of service request to the current server k is stopped and the allocation of service request to the next indexed server k+1 is started, where n k Representing the number of service requests allocated to server k;
step 3-3-3, if all the service requests can be distributed and scheduled to the long-term server under the condition of meeting the constraint, ending the distribution; otherwise, the rest service requests are distributed to the short-term server, and the distribution is finished; at this time, the optimal service request scheduling scheme is obtained, and the corresponding maximum profit can be calculated according to the formula (29).
In one embodiment, a cloud service profit optimization system based on customer sensitivity analysis is provided, the system comprising:
the first module is used for establishing a cloud server system model with a long-term lease and short-term lease dual-resource lease mode and establishing a customer sensitive factor calculation model;
a second module for providing a service request relative deadline prediction mechanism and a customer-perceived cloud service pricing strategy based on customer sensitivity factors, and then calculating the income and cost of the cloud service provider and constructing a profit calculation expression;
and the third module is used for deriving an optimal solution form of the service request scheduling problem by utilizing an analytic method based on the profit calculation expression and taking profit optimization as a target, and designing a heuristic service request scheduling algorithm according to the characteristics of the optimal solution to obtain an optimal scheduling scheme and corresponding maximum profit.
For specific definition of the cloud service profit optimization system based on the customer sensitivity analysis, reference may be made to the definition of the cloud service profit optimization method based on the customer sensitivity analysis hereinabove, and the detailed description thereof will be omitted. The various modules in the cloud service profit optimization system based on customer sensitivity analysis described above may be implemented in whole or in part by software, hardware, and combinations thereof. The above modules may be embedded in hardware or may be independent of a processor in the computer device, or may be stored in software in a memory in the computer device, so that the processor may call and execute operations corresponding to the above modules.
In one embodiment, a computer device is provided comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, the processor implementing the steps of when executing the computer program:
step 1, establishing a cloud server system model with a long-term lease and short-term lease dual resource lease mode, and establishing a customer sensitive factor calculation model;
step 2, based on the customer sensitivity factors, constructing a service request relative deadline prediction mechanism and a customer-perceived cloud service pricing strategy, and then calculating income and cost of a cloud service provider, and constructing a profit calculation expression;
and 3, deriving an optimal solution form of the service request scheduling problem by utilizing an analytic method based on the profit calculation expression and taking profit optimization as a target, and designing a heuristic service request scheduling algorithm according to the characteristics of the optimal solution to obtain an optimal scheduling scheme and corresponding maximum profit.
The specific definition of each step can be referred to as the definition of the cloud service profit optimization method based on customer sensitivity analysis hereinabove, and will not be described in detail herein.
In one embodiment, a computer readable storage medium is provided having a computer program stored thereon, which when executed by a processor, performs the steps of:
step 1, establishing a cloud server system model with a long-term lease and short-term lease dual resource lease mode, and establishing a customer sensitive factor calculation model;
step 2, based on the customer sensitivity factors, constructing a service request relative deadline prediction mechanism and a customer-perceived cloud service pricing strategy, and then calculating income and cost of a cloud service provider, and constructing a profit calculation expression;
and 3, deriving an optimal solution form of the service request scheduling problem by utilizing an analytic method based on the profit calculation expression and taking profit optimization as a target, and designing a heuristic service request scheduling algorithm according to the characteristics of the optimal solution to obtain an optimal scheduling scheme and corresponding maximum profit.
The specific definition of each step can be referred to as the definition of the cloud service profit optimization method based on customer sensitivity analysis hereinabove, and will not be described in detail herein.
The invention considers deadline constraint and customer sensitivity simultaneously when dispatching the service request, and increases the profit of the cloud service provider.
The foregoing has outlined and described the basic principles, features, and advantages of the present invention. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, and that the above embodiments and descriptions are merely illustrative of the principles of the present invention, and various changes and modifications may be made without departing from the spirit and scope of the invention, which is defined in the appended claims.

Claims (10)

1. A cloud service profit optimization method based on customer sensitivity analysis, the method comprising the steps of:
step 1, establishing a cloud server system model with a long-term lease and short-term lease dual resource lease mode, and establishing a customer sensitive factor calculation model;
step 2, based on the customer sensitivity factors, constructing a service request relative deadline prediction mechanism and a customer-perceived cloud service pricing strategy, and then calculating income and cost of a cloud service provider, and constructing a profit calculation expression;
and 3, deriving an optimal solution form of the service request scheduling problem by utilizing an analytic method based on the profit calculation expression and taking profit optimization as a target, and designing a heuristic service request scheduling algorithm according to the characteristics of the optimal solution to obtain an optimal scheduling scheme and corresponding maximum profit.
2. The cloud service profit optimization method based on customer sensitivity analysis according to claim 1, wherein the establishing a cloud server system model with a long-term lease and short-term lease dual resource lease mode in step 1, establishing a customer sensitivity factor calculation model, specifically comprises:
step 1-1, a cloud server system model with a long-term lease and short-term lease dual resource lease mode is established, and specifically comprises the following steps: regarding a cloud server in a cloud server system as an M/M/M+D queue model, wherein the speeds of M stations are { s }, respectively 1 ,s 2 ,...,s M Heterogeneous long-term rental server and D-station speed s short Wherein the long-term rental server operates for a long period of time in each time slot, the short-term rental server operates only when a service request is assigned to the short-term rental serverA line, thus setting to exclusive one short-term rental server per service request allocated to the short-term rental server, and stopping rental after the task ends;
step 1-2, establishing a customer sensitive factor model, and a customer sensitive factor sen i The calculation formula is as follows:
Figure FDA0004075618150000011
in the method, in the process of the invention,
Figure FDA0004075618150000012
is a vector parameter containing five elements, is obtained by fitting historical data of a transaction platform by a least square method and the like, and is a method for generating the transaction platform by adopting the method of the least square method and the like>
Figure FDA0004075618150000013
Score customer i personality->
Figure FDA0004075618150000014
The calculation method is as follows:
Figure FDA0004075618150000015
wherein E is p 、A n 、C p 、N n 、O p 、E n 、A p 、C n 、N p O and O n The score for each item in the TIPI scale is shown separately.
3. The cloud service profit optimization method based on customer sensitivity analysis of claim 2, wherein the constructing a service request relative deadline prediction mechanism and a customer-aware cloud service pricing policy based on customer sensitivity factors in step 2, then calculating the income and cost of cloud service providers, and constructing profit calculation expressions, specifically comprises:
step 2-1, predicting the service request relative deadline D based on the customer sensitivity factor i The calculation formula is as follows:
Figure FDA0004075618150000021
wherein D is sla The service request relative deadline specified in the service level agreement SLA is represented, max (r) represents the upper limit of the service request workload in the scheduling time slot, and max(s) represents the running speed of the server with the highest speed in the cloud service platform;
step 2-2, customer perceived cloud service pricing, and a price function is calculated as:
Figure FDA0004075618150000022
wherein p is i With respect to service relative deadlines D i Is a piecewise function of p b Is a cloud service desired sales price specified by a cloud service provider, f (D i ) Is a deadline D relative to the service request i The related functions are calculated by the following modes:
f(D i )＝∣D i -D sla ∣*Δp (5)
wherein Δp represents the service relative cutoff time D i Relative deadline D to services specified in SLA SLA The price of the cloud service is reduced or increased by a certain amount when the unit time is prolonged or shortened;
step 2-3, calculating the Cost of the cloud service provider, comprising two parts: the server lease fees and the electricity fees generated by the operation of the server, wherein each part comprises fees respectively generated by a long-term lease server and a short-term lease server, and the Cost in one scheduling time slot is calculated in the following way:
Figure FDA0004075618150000031
wherein Ele represents the generated electricity fee, ren represents the generated rental fee, M represents the number of long-term rental servers, D represents the number of short-term rental servers, i.e., the number of service requests allocated to the short-term rental servers, |t|represents a scheduling time slot length, n k Indicating the number of service requests allocated to the long-term rental server k,
Figure FDA0004075618150000032
represents the unit electricity price, C eff,k 、v k 、f k 、P sta,k 、s k And delta k Effective switch capacitance, supply voltage, clock frequency, static power consumption, speed and lease fees per unit time, ET, respectively, for long-term lease server k i,k Representing the time required for the service request i to run on the long-term rental server k, expressed as +.>
Figure FDA0004075618150000033
C eff,sho 、v sho 、f sho 、P sta,sho 、s sho And delta sho Respectively representing the effective switch capacitance, the power supply voltage, the clock frequency, the static power consumption, the speed and the lease fee of unit time of the short-term lease server; r is (r) i Indicating the workload of service request i on long-term rental server, r j Representing the workload of service request j on the short-term rental server;
step 2-4, constructing a calculation expression of Profit:
Figure FDA0004075618150000034
wherein Rev is the income obtained by the cloud service provider for completing all service requests in one scheduling time slot, and n is the number of cloud service requests in one scheduling time slot.
4. The cloud service profit optimization method based on customer sensitivity analysis according to claim 3, wherein the profit calculation expression based on step 3 aims at profit optimization, derives an optimal solution form of service request scheduling problem by using an analytic method, designs a heuristic service request scheduling algorithm according to the characteristics of the optimal solution, and obtains an optimal scheduling scheme and a corresponding maximum profit, and specifically comprises:
step 3-1, constructing profit maximization optimization problems based on the profit calculation expression;
step 3-2, deducing an optimal solution form of the service request scheduling problem according to the profit calculation expression by using an analytic method;
and 3-3, designing a heuristic service request scheduling algorithm according to the characteristics of the optimal solution, and obtaining the optimal solution, namely the optimal service request scheduling scheme and the corresponding maximum profit.
5. The cloud service profit optimization method based on customer sensitivity analysis according to claim 4, wherein constructing profit maximization optimization problem based on the profit calculation expression in step 3-1 is:
Figure FDA0004075618150000041
where i denotes the service request i, ET i Representing the execution time of service request i, D i Representing the relative deadline of the predicted service request i.
6. The cloud service profit optimization method based on customer sensitivity analysis according to claim 5, wherein the deriving an optimal solution form of the service request scheduling problem from the profit calculation expression using the parsing in step 3-2 is:
Figure FDA0004075618150000042
the workload of service requests allocated to the short-term rental server is equal to the total workload minus the workload of service requests allocated to the long-term rental server, i.e.:
Figure FDA0004075618150000043
wherein n is the total number of service requests, M is the number of long-term lease servers, D is the number of short-term lease servers, i.e. the number of service requests distributed to the short-term lease servers, n k The number of service requests distributed to the long-term leasing server k; order the
Figure FDA0004075618150000044
Profit profits continue to be derived as:
Figure FDA0004075618150000051
after the configuration of the service request set, the long-term lease server and the short-term lease server is given in the scheduling time slot t, the first term and the third term of the Profit expression are constants, so that
Figure FDA0004075618150000052
The second term of the Profit expression, i.e
Figure FDA0004075618150000053
Reams the
Figure FDA0004075618150000054
Figure FDA0004075618150000055
Two vectors A M ＝[A 1 ,A 2 ,...,A M ]And B M ＝[B 1 ,B 2 ,...,B M ] T Part representing a change in a Profit expression, A M In connection with server configuration, B M Related to the set of service requests, then
Figure FDA0004075618150000056
The calculation formula of (2) is as follows:
Figure FDA0004075618150000057
for each long-term rental server, calculate its A k Value and according to A k The values are arranged in descending order; b (B) M The sum of the service requests is constant, and the dispatching result of the service requests meets B 1 ≤B 2 ≤...≤B M And (3) the situation is the optimal solution of the service request scheduling problem, and the Profit of the cloud service provider is maximum.
7. The cloud service profit optimization method based on customer sensitivity analysis according to claim 6, wherein the designing a heuristic service request scheduling algorithm according to the characteristics of the optimal solution in step 3-3, to obtain the optimal solution, namely the optimal service request scheduling scheme and the corresponding maximum profit, specifically comprises:
the algorithm inputs include: service request set
Figure FDA0004075618150000061
Each service request sr i Is a five-tuple (sen i ,r i ,p i ,D i ,W i ) Respectively represent service requests sr i Sensitivity factor, workload, price, relative deadline and maximum wait time of the long-term rental server set +.>
Figure FDA0004075618150000062
Each lease long-term server MS k Is a power supply voltage and operation speed pair (v) k ,s k ) Short term rental server operating speed s sho Response deadline D specified in SLA SLA And maximum service waiting time W SLA Cloud service expected sales price p b Cloud service price variable delta P, scheduling time slot length |t|, static power consumption P of long-term lease server k and short-term lease server sta,k 、P sta,sho Rental price delta of long-term rental server k and short-term rental server k 、δ sho Electric price->
Figure FDA0004075618150000063
Obtaining an optimal solution of the problem, namely an optimal service request scheduling scheme, based on the algorithm input and the characteristics of the optimal solution obtained in the step 3-2;
step 3-3-1, calculating A for each long-term rental server based on the parameters input by the formulas (11), (14) and the algorithm k Values and descending order according to the values, server index from 1 to M; the service requests are arranged in ascending order according to the size of the workload, and the index is from 1 to n;
step 3-3-2, for each server k, ordering the service requests sr in order of server index from small to large i Sequentially distributing the indexes to the current server k from small to large, and if the utilization rate of the current server is greater than 1, namely, the constraint of the formula (17) is not satisfied:
Figure FDA0004075618150000064
the allocation of service request to the current server k is stopped and the allocation of service request to the next indexed server k+1 is started, where n k Representing the number of service requests allocated to server k;
step 3-3-3, if all the service requests can be distributed and scheduled to the long-term lease server under the condition of meeting the constraint of the formula (17), ending the distribution; otherwise, the remaining service requests are distributed to the short-term lease server, and the distribution is finished; at this time, the optimal service request scheduling scheme is obtained, and the corresponding maximum profit can be calculated according to the formula (12).
8. A cloud service profit optimization system based on customer sensitivity analysis implementing the method of any one of claims 1 to 7, characterized in that the system comprises:
the first module is used for establishing a cloud server system model with a long-term lease and short-term lease dual-resource lease mode and establishing a customer sensitive factor calculation model;
a second module for constructing a service request relative deadline prediction mechanism and a customer-perceived cloud service pricing policy based on the customer sensitivity factor, and then calculating the income and cost of the cloud service provider, and constructing a profit calculation expression;
and the third module is used for deriving an optimal solution form of the service request scheduling problem by utilizing an analytic method based on the profit calculation expression and taking profit optimization as a target, and designing a heuristic service request scheduling algorithm according to the characteristics of the optimal solution to obtain an optimal scheduling scheme and corresponding maximum profit.
9. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the steps of the method according to any one of claims 1 to 7 when the computer program is executed by the processor.
10. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method according to any one of claims 1 to 7.
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