WO2017133192A1 - 一种业务控制方法以及业务控制装置 - Google Patents

一种业务控制方法以及业务控制装置 Download PDF

Info

Publication number
WO2017133192A1
WO2017133192A1 PCT/CN2016/091697 CN2016091697W WO2017133192A1 WO 2017133192 A1 WO2017133192 A1 WO 2017133192A1 CN 2016091697 W CN2016091697 W CN 2016091697W WO 2017133192 A1 WO2017133192 A1 WO 2017133192A1
Authority
WO
WIPO (PCT)
Prior art keywords
executed
service
service control
control device
services
Prior art date
Application number
PCT/CN2016/091697
Other languages
English (en)
French (fr)
Inventor
曾晓生
辛凯
董晓文
Original Assignee
华为技术有限公司
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by 华为技术有限公司 filed Critical 华为技术有限公司
Priority to EP16889007.7A priority Critical patent/EP3402142A4/en
Publication of WO2017133192A1 publication Critical patent/WO2017133192A1/zh
Priority to US16/054,605 priority patent/US10776176B2/en

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising
    • G06Q30/0201—Market modelling; Market analysis; Collecting market data
    • G06Q30/0206—Price or cost determination based on market factors
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L47/00—Traffic control in data switching networks
    • H04L47/10—Flow control; Congestion control
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F9/00—Arrangements for program control, e.g. control units
    • G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
    • G06F9/46—Multiprogramming arrangements
    • G06F9/50—Allocation of resources, e.g. of the central processing unit [CPU]
    • G06F9/5094—Allocation of resources, e.g. of the central processing unit [CPU] where the allocation takes into account power or heat criteria
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/06—Energy or water supply
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
    • H04L41/14—Network analysis or design
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L47/00—Traffic control in data switching networks
    • H04L47/10—Flow control; Congestion control
    • H04L47/24—Traffic characterised by specific attributes, e.g. priority or QoS
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L47/00—Traffic control in data switching networks
    • H04L47/50—Queue scheduling
    • H04L47/54—Loss aware scheduling
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2310/00—The network for supplying or distributing electric power characterised by its spatial reach or by the load
    • H02J2310/50—The network for supplying or distributing electric power characterised by its spatial reach or by the load for selectively controlling the operation of the loads
    • H02J2310/56—The network for supplying or distributing electric power characterised by its spatial reach or by the load for selectively controlling the operation of the loads characterised by the condition upon which the selective controlling is based
    • H02J2310/62—The condition being non-electrical, e.g. temperature
    • H02J2310/64—The condition being economic, e.g. tariff based load management
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02B—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO BUILDINGS, e.g. HOUSING, HOUSE APPLIANCES OR RELATED END-USER APPLICATIONS
    • Y02B70/00—Technologies for an efficient end-user side electric power management and consumption
    • Y02B70/30—Systems integrating technologies related to power network operation and communication or information technologies for improving the carbon footprint of the management of residential or tertiary loads, i.e. smart grids as climate change mitigation technology in the buildings sector, including also the last stages of power distribution and the control, monitoring or operating management systems at local level
    • Y02B70/3225—Demand response systems, e.g. load shedding, peak shaving
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
    • Y02D10/00—Energy efficient computing, e.g. low power processors, power management or thermal management
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S10/00—Systems supporting electrical power generation, transmission or distribution
    • Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S20/00—Management or operation of end-user stationary applications or the last stages of power distribution; Controlling, monitoring or operating thereof
    • Y04S20/20—End-user application control systems
    • Y04S20/222—Demand response systems, e.g. load shedding, peak shaving
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S50/00—Market activities related to the operation of systems integrating technologies related to power network operation or related to communication or information technologies
    • Y04S50/14—Marketing, i.e. market research and analysis, surveying, promotions, advertising, buyer profiling, customer management or rewards

Definitions

  • the present invention relates to the field of communications, and in particular, to a service control method and a service control apparatus.
  • the data center is a large electricity consumer.
  • the current data center power consumption level ranges from several megawatts to several hundred megawatts.
  • electricity bills account for a significant proportion of data center operating costs. Reducing data center electricity bills is very significant.
  • the time-of-use electricity price is generally determined by the government during the peak period of electricity use (for example, 8:00 to 22:00) and the electricity shortage period (for example, from 22:00 to 8:00), and then the electricity price for each period is determined, due to the peak period of electricity consumption and
  • the electricity trough period is based on long-term experience, so it is relatively fixed and will not be changed at will.
  • the data center adjusts the load based on the peak period of power consumption and the period of power consumption, while the peak period of power consumption and the period of power consumption are relatively fixed, and both last for a long time, but the number of services It changes in real time, and the load changes in real time. Therefore, the data center adjusts the load period longer, which affects the accuracy of load adjustment and is not conducive to controlling the cost of electricity.
  • the embodiment of the invention provides a service control method and a service control device, which are used for accurately adjusting the load and reducing the cost of the electricity.
  • the first aspect of the present invention provides a service control method, which can be applied to a time-sharing electricity price and a real-time electricity price, and the method includes:
  • the service control device acquires the current grid price and the number of services to be executed
  • the service control device determines the tariff threshold according to the quantity of services to be executed, and the number of services to be executed The electricity price threshold is positively correlated;
  • the service control device reduces the number of services to be executed by a first amount, and performs the service according to the number of services to be executed after the reduction;
  • the service control device increases the number of services to be executed by a second amount and performs the service according to the number of services to be executed after the increase.
  • the power price threshold in the embodiment of the present invention is determined by the quantity of services to be executed, the basis for adjusting the load is not only the current grid price, but also the number of services to be executed, and the business to be executed.
  • the quantity changes in real time, so the period of adjusting the load is short, so that the adjustment accuracy can be improved to reduce the cost of electricity.
  • the service control apparatus determines the power price threshold according to the quantity of services to be executed, including:
  • the service control device acquires a target relationship factor
  • the service control device uses the product of the target relationship factor and the number of services to be executed as the power price threshold.
  • the target relationship factor directly related to the quantity of the service to be executed is obtained, and the power price threshold corresponding to the quantity of the service to be executed can be accurately obtained.
  • the service control device acquires the relationship factors including:
  • the service control device acquires the first relationship parameter and the second relationship parameter, where the first relationship parameter is a relationship parameter between the power saving rate and the relationship factor, and the second relationship parameter is a relationship parameter between the quantity of the to-be-executed service and the relationship factor,
  • the electricity saving rate is positively correlated with the relationship factor
  • the quantity of the business to be executed is positively correlated with the relationship factor;
  • the service control device calculates, according to the first relationship parameter and the second relationship parameter, a relationship factor when the equilibrium value is maximum as the target relationship factor, and the balance value is positively correlated with the electricity rate saving rate, and the equilibrium value is inversely related to the quantity of the service to be executed.
  • the relationship factor can optimally adjust the data center to save electricity.
  • the service control device calculates the relationship factor when the equalization value is maximum according to the first relationship parameter and the second relationship parameter.
  • the target relationship factors include:
  • the service control device determines the target relationship factor by the following method
  • v is the relationship factor
  • V is the value range of the relationship factor
  • M(v) is the equilibrium value
  • C(v) is the electricity saving rate
  • Q(v) is the number of services to be executed
  • ⁇ is the first coefficient, which is used for The weight indicating the rate of saving electricity
  • ⁇ is the second coefficient, which is used to indicate the weight of the number of services to be executed.
  • determining, by the service control apparatus, the power price threshold according to the quantity of services to be executed includes:
  • the traffic control device calculates a tariff threshold based on the sample data set.
  • the service control apparatus calculates the power price threshold by using the regression analysis method according to the sample data set, including:
  • the service control device determines the tariff threshold by:
  • a and b are pending coefficients
  • i is the element symbol of the sample data set
  • (p th,i ,q i ) refers to the ith element pair in the sample data set.
  • a calculation method is also provided.
  • a least square method can be used to obtain a regression equation of the electricity price threshold and the number of services to be executed, and the algorithm can more accurately calculate the electricity price threshold. Relationship with the number of businesses to be executed.
  • the service control device calculates a first difference between the current grid electricity price and the electricity price threshold
  • the service control device calculates the first quantity according to the first difference
  • the service control device calculates a second difference between the current grid electricity price and the electricity price threshold
  • the service control device calculates the second amount based on the second difference.
  • the number of services to be executed that need to be increased or decreased can be accurately calculated according to the difference between the current grid price and the electricity price threshold, thereby achieving an optimal solution for the data center to save electricity.
  • the service control device reduces the number of services to be executed by a first amount, including:
  • the service control device moves the first number of delayable services in the upcoming service to the queue of the to-be-executed service;
  • the service control device allocates the first number of delayable services in the upcoming service to the first off-site data center, and the first off-site current grid power price obtained by the first off-site data center is lower than the current grid power price.
  • the real-time service cannot delay processing, only the delayable service is removed in the service to be executed, thereby avoiding unnecessary data transmission interference.
  • the service control device increases the number of services to be executed by a second amount including:
  • the service control device extracts a second quantity of services from the to-be-executed service and joins the queue of the service to be executed;
  • the service control device obtains a second quantity of services from the second off-site data center and joins the queue of the service to be executed, and the second off-site data center obtained by the second remote data center is higher than the current grid power price.
  • data centers in various places can coordinate and process services.
  • the data center of the current grid power price is higher than the electricity price threshold.
  • the data center can send the delayed service to the data center where the current grid price is lower than the electricity price threshold, so as to optimize and coordinate the data center processing services and save electricity.
  • a second aspect of the present invention provides a service control apparatus, the service control apparatus comprising means for performing the method of the first aspect and the various possible designs of the first aspect, the service control apparatus comprising:
  • Obtaining a module configured to obtain a current grid price and a quantity of services to be executed
  • a determining module configured to determine a power price threshold according to the quantity of services to be executed, where the quantity of the to-be-executed service is positively correlated with the power price threshold;
  • a comparison module for comparing the current grid price and the price threshold
  • An allocation module configured to reduce the number of services to be executed by a first quantity if the current grid price is higher than the power price threshold, and increase the number of services to be executed by a second quantity if the current grid price is lower than the power price threshold;
  • the determining module is specifically configured to obtain a target relationship factor; and the determining module is specifically configured to use a product of the target relationship factor and the number of services to be executed as a power threshold.
  • the determining module is specifically configured to obtain the first relationship parameter and the second relationship parameter, where the first relationship parameter is a relationship parameter between the power saving rate and the relationship factor, and the second relationship parameter is The relationship between the quantity of the business to be executed and the relationship factor, the electricity saving rate is positively correlated with the relationship factor, and the quantity of the business to be executed is positively correlated with the relationship factor;
  • the determining module is specifically configured to calculate, according to the first relationship parameter and the second relationship parameter, a relationship factor that maximizes the equilibrium value as the target relationship factor, and the equilibrium value is positively correlated with the power saving rate, and the equalization value is inversely related to the quantity of the service to be executed.
  • the determining module is specifically configured to determine the target relationship factor by
  • v is the relationship factor
  • V is the value range of the relationship factor
  • M(v) is the equilibrium value
  • C(v) is the electricity saving rate
  • Q(v) is the number of services to be executed
  • ⁇ is the first coefficient, which is used for The weight indicating the rate of saving electricity
  • ⁇ is the second coefficient, which is used to indicate the weight of the number of services to be executed.
  • the determining module is specifically configured to calculate the electricity price threshold based on the sample data set.
  • the determining module is specifically configured to determine the electricity price threshold by:
  • a and b are pending coefficients
  • i is the element symbol of the sample data set
  • (p th,i ,q i ) refers to the ith element pair in the sample data set.
  • the distribution module is specifically configured to calculate a first difference between the current grid electricity price and the electricity price threshold if the current grid electricity price is higher than the electricity price threshold;
  • the allocating module is specifically configured to calculate the first quantity according to the first difference
  • the allocation module is specifically configured to calculate a second difference between the current grid electricity price and the electricity price threshold if the current grid electricity price is lower than the electricity price threshold;
  • the allocation module is specifically configured to calculate the second quantity according to the second difference.
  • the allocation module is specifically configured to move the first number of delayable services in the service to be executed to the queue of the to-be-executed service;
  • the allocation module is specifically configured to allocate the first quantity of the deferrable service in the service to be executed to the first off-site data center, and the first off-site current grid power price obtained by the first off-site data center is lower than the current grid power price.
  • the allocation module is specifically used to perform services from the to-be-executed Extracting the second number of services into the queue of the upcoming business;
  • the allocating module is configured to obtain a second quantity of services from the second remote data center to join the queue of the upcoming service, and the second remote data center obtains the second remote current power price higher than the current grid power price.
  • a third aspect of the present invention provides a physical device of a service control device, including: an input device, an output device, a processor, and a memory;
  • the service control device is configured to perform the following steps by calling an operation instruction stored in the memory:
  • the electricity price threshold is determined according to the quantity of the service to be executed, and the quantity of the service to be executed is positively correlated with the power price threshold;
  • the number of services to be executed is reduced by the first amount, and the service is executed according to the number of services to be executed after the reduction;
  • the number of services to be executed is increased by a second amount, and the service is executed according to the number of services to be executed after the increase.
  • the service control device may determine the power price threshold according to the quantity of the service to be executed, and then compare the price between the power price threshold and the current grid power price, and reduce the number of services to be executed when the current grid power price is higher than the power price threshold. When the current grid power price is lower than the power price threshold, the number of services to be executed is increased. Since the power price threshold in the embodiment of the present invention is determined by the quantity of services to be executed, the basis for adjusting the load is not only the current grid price, but further Considering the number of services to be executed, the number of services to be executed changes in real time, so the cycle of adjusting the load is short, so that the adjustment accuracy can be improved to reduce the cost of electricity.
  • FIG. 1 is a structural diagram of a data center network in an embodiment of the present invention.
  • FIG. 2 is a topological view of a data center structure in an embodiment of the present invention.
  • FIG. 3 is a schematic diagram of an embodiment of a service control method according to an embodiment of the present invention.
  • FIG. 5 is a schematic diagram of an embodiment of a service control apparatus according to an embodiment of the present invention.
  • FIG. 6 is a schematic diagram of another embodiment of a service control apparatus according to an embodiment of the present invention.
  • the embodiment of the invention provides a service control method and a service control device, which are used for improving the adjustment precision and reducing the cost of electricity.
  • a data center is a global network of device-specific devices that deliver, accelerate, display, compute, and store data on the Internet infrastructure. As shown in Figure 1, when a computer, mobile phone, or wireless access point performs a service, it can upload data of these services to a data center through an Internet network or a GPRS (General Packet Radio Service) network. These data are then calculated, stored or fed back.
  • GPRS General Packet Radio Service
  • TC9.9 According to the American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE) Technical Committee 9.9 (referred to as TC9.9) statistical report, server energy consumption accounted for 46%, air conditioning refrigeration accounted for 31%, UPS accounted for 8%, lighting accounted for 4% and other accounted for 11 %, it can be seen that the energy consumption of the data center in processing the above data accounts for nearly one-half of the total energy consumption of the data center, that is, the energy consumption of the server is 46%. This part of the energy consumption is a key part of the data center energy consumption. Reducing this part of the energy consumption is decisive for reducing the data center electricity cost.
  • the data center electricity fee which is directed to the time-sharing electricity price, that is, the load is reduced during the high electricity price period, and the load is increased during the low electricity price period, and the adjustment period is generally one day, but in real life.
  • the load in one day changes in real time. Simply dividing the day into two segments to adjust the load does not effectively reduce the electricity cost of the data center.
  • the scheme refers to the electricity price that changes dynamically in real time with time. The change is frequent, the electricity price curve is a nonlinear curve, and the whole electricity price curve cannot be known in advance, but only the electricity price in a short time (such as 15 minutes).
  • An embodiment of the present invention provides a service control method and a service control apparatus.
  • the service control apparatus may determine a power price threshold according to the quantity of services to be executed, and then perform a price comparison between the power price threshold and the current grid power price, where the current grid power price is higher than the power price.
  • the threshold value is used to reduce the number of services to be executed.
  • the power price threshold in the embodiment of the present invention is determined by the number of services to be executed, the load is adjusted. Based on not only the current grid price, but also the number of services to be executed, the number of services to be executed changes in real time, so the period of adjusting the load is shorter, so that the adjustment accuracy can be improved to reduce the cost of electricity.
  • an embodiment of the service control method in the embodiment of the present invention includes:
  • the service control device acquires a current grid power price and a quantity of services to be executed
  • the electricity price acquisition unit can communicate with the power provider that sends the electricity price through the power provider outside the data center to obtain the current grid power price. It should be noted that the service control device can acquire every other cycle. Once the current grid price, the period can be 15 minutes.
  • the to-be-executed service quantity obtaining unit may communicate with the service allocation unit for distributing the service within the service control device, and obtain the quantity information of the current to-be-executed service, where the to-be-executed service may be the service requested to be executed at the current time, or may be the current time. Previously requested but delayed business.
  • the service processed by the data center may be divided into real Time business and delay business.
  • the real-time service is a non-delayed service with high time sensitivity. It requires timely response, and the response time is as short as possible.
  • services such as call and video call have high delay requirements and need to be processed immediately.
  • the delayable service is a batch processing service with low time sensitivity, and has a certain execution time window, as long as it can be completed within the time window, for example, the calculation service of user behavior analysis in the data center of the e-commerce platform company , is a batch processing business that can be completed offline.
  • the service control device determines a power price threshold according to the quantity of services to be executed.
  • the to-be-executed service changes with time.
  • the electricity price threshold generating unit shown in FIG. 2 calculates the electricity price threshold according to the quantity of the service to be executed
  • the electricity price threshold changes according to the quantity of the business to be executed. It can be understood that the power price threshold is positively correlated with the quantity of services to be executed, that is, when the number of services to be executed increases, the power price threshold increases; when the number of services to be executed decreases, the power price threshold decreases.
  • the power price threshold is directly related to the number of services to be executed, and the specific relationship function may have various forms, including but not limited to the following functional relationships:
  • the target relationship factor V is an experimental parameter to be determined. According to experience, first assume a set of V values from small to large, under the same experimental conditions (using the same real-time electricity price curve and the same load curve in the same experimental period), conduct an experiment for each V, and then compare the results in percentages and electricity savings average number of services to be performed, to determine a more appropriate value V, to determine the threshold value P th price and the relationship of Q number of services to be performed. For example, as shown in FIG. 4, it is found through experiments that the change in the percentage of electricity savings (left) and the average value of the number of services to be executed (right) as V increases, it can be seen that with the increase of V, the electricity fee The percentage savings and the average number of businesses to be executed increase.
  • the average number of services to be executed is basically proportional to the V value. Relationship, and the percentage of electricity savings increases rapidly with the increase of V value and then increases slowly.
  • the V value at the inflection point can be selected.
  • the average value of the number of services to be executed corresponding to this V value is relatively small. Therefore, when it is necessary to balance the determination of the V value, the target relationship factor can be determined by the following method
  • v is the relationship factor
  • V is the value range of the relationship factor
  • M(v) is the equilibrium value
  • C(v) is the electricity saving rate
  • Q(v) is the number of services to be executed
  • ⁇ is the first coefficient. The weight used to indicate the rate of electricity savings
  • ⁇ is the second coefficient, which is used to indicate the weight of the number of services to be executed.
  • the regression analysis method can be a high-order equation, such as a one-time equation:
  • i is the element symbol of the sample data set
  • (p th,i ,q i ) refers to the ith element pair in the sample data set
  • the squared residual is solved by solving The smallest element pair can determine the undetermined coefficients a and b, and after obtaining the values of a and b, the regression analysis model of the electricity price threshold and the number of services to be executed is obtained.
  • the service control device compares whether the current grid power price is higher than the power price threshold, and if so, step 304 is performed, and if not, step 305 is performed;
  • the comparison unit in the service control device may compare with the current grid tariff obtained from the electricity price acquisition unit after obtaining the electricity price threshold calculated by the electricity price threshold generation unit, and the comparison manner may be Simple numerical comparisons can also be other forms of comparison. There is no limit here.
  • the service control device reduces the number of services to be executed
  • the first difference between the current grid power price and the electricity price threshold may be calculated, according to
  • the service allocation unit may select the first number of delayable services from the queue of the service to be executed, and move out to the queue of the service to be executed, and the manner of selecting the first number of delayable services may be The order of the queues of the services to be executed at the latest is selected in the order of the services to be executed, and the weights of the services are selected from the queues of the services to be executed in the order of the weights of the services.
  • the service allocation unit may select only the delayable service.
  • the service distribution unit can also synchronize with the remote data centers to upload the current grid price of the data center.
  • the service control device obtains the current grid power price lower than the current grid price, the The first number of deferrable services in the executed service are distributed to the first off-site data center.
  • the service execution unit in the service control device performs the service according to the number of services to be executed after the service allocation unit is reduced. It should be noted that the service execution unit may be multiple.
  • the service control device increases the number of services to be executed.
  • the second difference between the current grid power price and the electricity price threshold may be calculated.
  • the service allocation unit may select the second quantity from the queue of the service to be executed.
  • the service is added to the queue of the service to be executed, and the manner of selecting the second quantity of services may be sequentially selected according to the order in which the service is first added to the queue to be executed, or may be according to the weight of the service from large to small. It is selected from the services to be executed in sequence, and is not limited herein.
  • the service allocation unit may preferentially select real-time services.
  • the service allocation unit can also synchronize with the remote data centers to upload the current grid price of the data center at the same time, and the service control device can obtain the current grid price of the second off-site when the current grid price is higher than the current grid price.
  • the two remote data centers acquire a second number of services and join the queue of the upcoming service.
  • the service execution unit in the service control device performs the service according to the number of services to be executed after the service allocation unit is added. It should be noted that the service execution unit may be multiple.
  • the service execution unit may not perform any processing at this time, and keep the number of services to be executed unchanged.
  • the electricity price threshold is 0.6 yuan / kWh. At this time, the current grid electricity price is higher than the electricity price threshold.
  • the first difference between the current grid electricity price and the electricity price threshold can be calculated as 0.5;
  • the first difference 0.5 as P th can be inversely calculated to obtain the number Q of services to be executed, as follows:
  • the service control device when the current grid price is higher than the tariff threshold, the service control device reduces the number of services to be executed by 5,000.
  • the current grid price is higher than the electricity price threshold.
  • the number of pending services is 6000
  • the target relationship factor V is experimentally 0.0002.
  • the electricity price threshold is 1.2 yuan / kWh. At this time, the current grid electricity price is lower than the electricity price threshold.
  • the second difference between the current grid electricity price and the electricity price threshold can be calculated as 0.1;
  • the second difference value 0.1 as P th can be inversely calculated to obtain the number Q of services to be executed, as follows:
  • the service control device increases the number of services to be executed by 500.
  • the service control apparatus may determine the power price threshold according to the quantity of the service to be executed, and then perform a price comparison between the power price threshold and the current grid power price, and reduce the number of services to be executed when the current grid power price is higher than the power price threshold.
  • the number of services to be executed is increased. Since the power price threshold in this embodiment is determined by the quantity of services to be executed, the basis for adjusting the load is not only the current grid price, but further consideration. The number of services to be executed, and the number of services to be executed are changing in real time, so the period of adjusting the load is short, so that the adjustment accuracy can be improved to reduce the cost of electricity.
  • an embodiment of the service control device in the embodiment of the present invention includes:
  • An obtaining module 501 configured to acquire a current grid power price and a quantity of services to be executed
  • a determining module 502 configured to determine a power price threshold according to the quantity of services to be executed, where the quantity of the to-be-executed service is positively correlated with the power price threshold;
  • the comparing module 503 is configured to compare the current grid price and the price threshold
  • the distribution module 504 is configured to reduce the number of services to be executed by a first quantity if the current grid power price is higher than the power price threshold, and increase the number of services to be executed by a second quantity if the current grid power price is lower than the power price threshold;
  • the executing module 505 is configured to perform services according to the number of services to be executed after the reduction or increase.
  • the determining module 502 can further include:
  • the determining module 502 is specifically configured to acquire a target relationship factor
  • the determining module 502 is specifically configured to use the product of the target relationship factor and the number of services to be executed as Electricity price threshold.
  • the determining module 502 can further include:
  • the determining module 502 is specifically configured to obtain the first relationship parameter and the second relationship parameter, where the first relationship parameter is a relationship parameter between the power saving rate and the relationship factor, and the second relationship parameter is between the quantity of the to-be-executed service and the relationship factor.
  • the relationship parameter, the electricity saving rate is positively correlated with the relationship factor, and the quantity of the business to be executed is positively correlated with the relationship factor;
  • the determining module 502 is specifically configured to calculate, according to the first relationship parameter and the second relationship parameter, a relationship factor that maximizes the equalization value as the target relationship factor, and the equalization value is positively correlated with the electricity saving rate, and the equalization value is inversely related to the quantity of the service to be executed.
  • the determining module 502 can further include:
  • the determining module 502 is specifically configured to determine a target relationship factor by:
  • v is the relationship factor
  • V is the value range of the relationship factor
  • M(v) is the equilibrium value
  • C(v) is the electricity saving rate
  • Q(v) is the number of services to be executed
  • ⁇ is the first coefficient, which is used for The weight indicating the rate of saving electricity
  • ⁇ is the second coefficient, which is used to indicate the weight of the number of services to be executed.
  • the determining module 502 can further include:
  • the determining module 502 is specifically configured to calculate a power price threshold according to the sample data set.
  • the determining module 502 can further include:
  • the determining module 502 is specifically configured to determine the power price threshold by:
  • a and b are pending coefficients
  • i is the element symbol of the sample data set
  • (p th,i ,q i ) refers to the ith element pair in the sample data set.
  • the allocation module 504 can further include:
  • the distribution module 504 is specifically configured to calculate a first difference between the current grid electricity price and the electricity price threshold if the current grid electricity price is higher than the electricity price threshold;
  • the allocating module 504 is specifically configured to calculate a first quantity according to the first difference
  • the distribution module 504 is specifically configured to calculate a second difference between the current grid electricity price and the electricity price threshold if the current grid electricity price is lower than the electricity price threshold;
  • the allocating module 504 is specifically configured to calculate a second quantity according to the second difference.
  • the allocation module 504 can further include:
  • the allocating module 504 is specifically configured to move the first number of delayable services in the service to be executed into the queue of the to-be-executed service;
  • the allocating module 504 is specifically configured to allocate the first quantity of the deferrable service in the service to be executed to the first remote data center, where the first off-site current grid power price obtained by the first remote data center is lower than the current grid power price.
  • the allocation module 504 can further include:
  • the allocating module 504 is specifically configured to: extract a second quantity of services from the to-be-executed service into a queue of the service to be executed;
  • the allocating module 504 is specifically configured to obtain, from the second remote data center, a second quantity of services added to the queue of the upcoming service, and the second remote data center obtains the second remote current power price higher than the current grid power price.
  • the determining module 502 may determine the power price threshold according to the quantity of the to-be-executed service acquired by the obtaining module 501, and the comparing module 503 compares the power price threshold with the current grid power price obtained by the obtaining module 501, and the current grid price is high.
  • the allocation module 504 reduces the number of services to be executed.
  • the allocation module 504 increases the number of services to be executed, and the execution module 505 allocates the upcoming services according to the allocation module 504.
  • the basis for adjusting the load is not only the current grid price, but also the number of services to be executed, and the number of services to be executed is changing in real time. Therefore, the period of adjusting the load is short, so that the adjustment accuracy can be improved to reduce the electricity bill. cost.
  • the service control device in the embodiment of the present invention is described above from the perspective of a modular functional entity.
  • the service control device in the embodiment of the present invention is described from the perspective of hardware processing. Referring to FIG. 6, the service in the embodiment of the present invention is described.
  • Another embodiment of the control device includes:
  • the input device 601, the output device 602, the processor 603, and the memory 604 (wherein the number of processors 603 in the network device may be one or more, and one processor 603 is taken as an example in FIG. 6).
  • the input device 601, the output device 602, the processor 603, and the memory 604 may be connected by a bus or other means, wherein the bus connection is taken as an example in FIG.
  • the processor 603 is configured to perform the following steps by calling an operation instruction stored in the memory 604:
  • the electricity price threshold is determined according to the quantity of the service to be executed, and the quantity of the service to be executed is positively correlated with the power price threshold;
  • the number of services to be executed is reduced by the first amount, and the service is executed according to the number of services to be executed after the reduction;
  • the number of services to be executed is increased by a second amount, and the service is executed according to the number of services to be executed after the increase.
  • the processor 603 is further configured to perform the following steps:
  • the product of the target relationship factor and the number of services to be executed is used as the electricity price threshold.
  • the processor 603 is further configured to perform the following steps:
  • first relationship parameter is a relationship parameter between the power saving rate and the relationship factor
  • second relationship parameter is a relationship parameter between the quantity of the to-be-executed service and the relationship factor, and the power saving rate Positively related to the relationship factor, the number of services to be executed is positively correlated with the relationship factor
  • the relationship factor when the equilibrium value is maximum is calculated as the target relationship factor according to the first relationship parameter and the second relationship parameter, and the equilibrium value is positively correlated with the electricity rate saving rate, and the equilibrium value is inversely related to the quantity of the service to be executed.
  • the processor 603 is further configured to perform the following steps:
  • v is the relationship factor
  • V is the value range of the relationship factor
  • M(v) is the equilibrium value
  • C(v) is the electricity saving rate
  • Q(v) is the number of services to be executed
  • ⁇ is the first coefficient, which is used for The weight indicating the rate of saving electricity
  • ⁇ is the second coefficient, which is used to indicate the weight of the number of services to be executed.
  • the processor 603 is further configured to perform the following steps:
  • the electricity price threshold is calculated based on the sample data set.
  • the processor 603 is further configured to perform the following steps:
  • the electricity price threshold is determined as follows:
  • a and b are pending coefficients
  • i is the element symbol of the sample data set
  • (p th,i ,q i ) refers to the ith element pair in the sample data set.
  • the processor 603 is further configured to perform the following steps:
  • the second amount is calculated based on the second difference.
  • the processor 603 is further configured to perform the following steps:
  • the first number of delayable services in the upcoming business are allocated to the first off-site data center, and the first off-site current grid power price obtained by the first off-site data center is lower than the current grid power price.
  • the processor 603 is further configured to perform the following steps:
  • the second amount of services is obtained from the second remote data center and added to the queue of the upcoming service.
  • the second remote data center obtains the second remote current grid price higher than the current grid price.
  • the processor 603 may determine the power price threshold according to the quantity of the service to be executed, and then perform a price comparison between the power price threshold and the current grid power price, and reduce the number of services to be executed when the current grid power price is higher than the power price threshold.
  • the number of services to be executed is increased. Since the power price threshold in this embodiment is determined by the quantity of services to be executed, the basis for adjusting the load is not only the current grid price, but further consideration. The number of services to be executed, and the number of services to be executed are changing in real time, so the period of adjusting the load is short, so that the adjustment accuracy can be improved to reduce the cost of electricity.
  • the disclosed system, apparatus, and method may be implemented in other manners.
  • the device embodiments described above are merely illustrative.
  • the division of the unit is only a logical function division.
  • there may be another division manner for example, multiple units or components may be combined or Can be integrated into another system, or some features can be ignored or not executed.
  • the mutual coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection through some interface, device or unit, and may be in an electrical, mechanical or other form.
  • the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, may be located in one place, or may be distributed to multiple network units. You can choose some or all of them according to actual needs.
  • the unit is to achieve the purpose of the solution of the embodiment.
  • each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.
  • the above integrated unit can be implemented in the form of hardware or in the form of a software functional unit.
  • the integrated unit if implemented in the form of a software functional unit and sold or used as a standalone product, may be stored in a computer readable storage medium.
  • the technical solution of the present invention which is essential or contributes to the prior art, or all or part of the technical solution, may be embodied in the form of a software product stored in a storage medium.
  • a number of instructions are included to cause a computer device (which may be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods described in various embodiments of the present invention.
  • the foregoing storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk, and the like. .

Landscapes

  • Engineering & Computer Science (AREA)
  • Business, Economics & Management (AREA)
  • Strategic Management (AREA)
  • Development Economics (AREA)
  • Finance (AREA)
  • Accounting & Taxation (AREA)
  • Theoretical Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Economics (AREA)
  • General Physics & Mathematics (AREA)
  • Entrepreneurship & Innovation (AREA)
  • Signal Processing (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Marketing (AREA)
  • General Business, Economics & Management (AREA)
  • Software Systems (AREA)
  • Health & Medical Sciences (AREA)
  • Data Mining & Analysis (AREA)
  • Game Theory and Decision Science (AREA)
  • Tourism & Hospitality (AREA)
  • General Engineering & Computer Science (AREA)
  • Primary Health Care (AREA)
  • Human Resources & Organizations (AREA)
  • General Health & Medical Sciences (AREA)
  • Water Supply & Treatment (AREA)
  • Public Health (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)
  • Power Engineering (AREA)
  • Supply And Distribution Of Alternating Current (AREA)

Abstract

本发明实施例公开了一种业务控制方法以及业务控制装置，用于精确调整负荷，降低电费成本。本发明实施例方法包括：业务控制装置获取当前电网电价以及待执行业务的数量；根据待执行业务的数量确定电价阈值，待执行业务的数量与电价阈值正相关；若当前电网电价高于电价阈值，则业务控制装置将即将执行的业务的数量减少第一数量，并按照减少后即将执行的业务的数量执行业务；若当前电网电价低于电价阈值，则业务控制装置将即将执行的业务的数量增加第二数量，并按照增加后即将执行的业务的数量执行业务。本发明实施例还提供一种业务控制装置。本发明实施例能够精确调整负荷，降低电费成本。

Description

一种业务控制方法以及业务控制装置
本申请要求于2016年2月5日提交中国专利局、申请号为201610081988.8、发明名称为“一种业务控制方法以及业务控制装置”的中国专利申请的优先权，其全部内容通过引用结合在本申请中。
技术领域
本发明涉及通信领域，尤其涉及一种业务控制方法以及业务控制装置。
背景技术
数据中心是用电大户，目前的数据中心用电功率等级从几兆瓦到几百兆瓦，随着IT产业的发展，其用电功率将不断增大。因此，电费在数据中心运营成本中占据相当大的比例。减少数据中心电费具有非常大的意义。
现有技术中有一种控制数据中心电费的方法，其针对分时电价，在每个周期(一般为一天)的高电价期(即用电高峰期)，减少负荷，在低电价期(即用电低谷期)，增大负荷，从而减少数据中心的电费。
分时电价一般由政府划定用电高峰期(例如8点至22点)和用电低谷期(例如22点至次日8点)，然后再确定各期的电价，由于用电高峰期和用电低谷期是根据长期的经验统计得出，所以相对比较固定，不会随意更改。
现有技术中的方案中，数据中心调整负荷的依据是用电高峰期和用电低谷期，而用电高峰期和用电低谷期相对比较固定，且都持续较长时间，但业务的数量是实时变化的，负荷也是实时变化的，所以数据中心调整负荷的周期较长，从而影响了负荷调整的精度，不利于控制电费成本。
发明内容
本发明实施例提供了一种业务控制方法以及业务控制装置，用于精确调整负荷，降低电费成本。
有鉴于此，本发明第一方面提供一种业务控制方法，该方法即可应用于分时电价，又可应用于实时电价，该方法包括：
业务控制装置获取当前电网电价以及待执行业务的数量；
业务控制装置根据待执行业务的数量确定电价阈值，待执行业务的数量与 电价阈值正相关；
若当前电网电价高于电价阈值，则业务控制装置将即将执行的业务的数量减少第一数量，并按照减少后即将执行的业务的数量执行业务；
若当前电网电价低于电价阈值，则业务控制装置将即将执行的业务的数量增加第二数量，并按照增加后即将执行的业务的数量执行业务。
本发明实施例中，由于本发明实施例中的电价阈值由待执行业务的数量确定，所以调整负荷的依据不仅仅是当前电网电价，还进一步考虑到了待执行业务的数量，而待执行业务的数量实时都在发生变化，因此调整负荷的周期较短，从而能够提高调整精度，以降低电费成本。
结合本发明第一方面，在一个可能的设计中，业务控制装置根据待执行业务的数量确定电价阈值包括：
业务控制装置获取目标关系因子；
业务控制装置将目标关系因子与待执行业务的数量的乘积作为电价阈值。
本发明实施例中，获取与待执行业务的数量存在直接关联的目标关系因子，可以准确得到与待执行业务的数量相对应的电价阈值。
可以理解的是，在一个可能的设计中，业务控制装置获取关系因子包括：
业务控制装置获取第一关系参数以及第二关系参数，第一关系参数为电费节约率与关系因子之间的关系参数，第二关系参数为待执行业务的数量与关系因子之间的关系参数，电费节约率与关系因子正相关，待执行业务的数量与关系因子正相关；
业务控制装置根据第一关系参数以及第二关系参数计算使得均衡值最大时的关系因子作为目标关系因子，均衡值与电费节约率正相关，均衡值与待执行业务的数量反相关。
本发明实施例中，对于数据中心而言，电费节省百分比越大越好，但待执行业务数量的平均值越小越好，此处选取与电费节约率和待执行业务的数量均衡值最大时的关系因子作为目标关系因子，可以最优化调节数据中心节约电费。
结合本发明第一方面，可以理解的是，在一个可能的设计中，业务控制装置根据第一关系参数以及第二关系参数计算使得均衡值最大时的关系因子作 为目标关系因子包括：
业务控制装置通过如下方式确定目标关系因子
Figure PCTCN2016091697-appb-000001
Figure PCTCN2016091697-appb-000002
Figure PCTCN2016091697-appb-000003
v为关系因子，V为关系因子的取值范围，M(v)为均衡值，C(v)为电费节约率，Q(v)为待执行业务的数量，α为第一系数，用于表示电费节约率的权重，β为第二系数，用于表示待执行业务的数量的权重。
结合本发明第一方面，可选的是，在本发明实施例中，业务控制装置根据待执行业务的数量确定电价阈值包括：
业务控制装置通过离线试验获取电价阈值与待执行业务的数量的样本数据集φ＝(Pth，Q)，样本数据集中电价阈值Pth为因变量pth，待执行业务的数量Q为自变量q；
业务控制装置根据样本数据集，计算电价阈值。
结合本发明第一方面，可选的是，在本发明实施例中，业务控制装置根据样本数据集，通过回归分析法计算电价阈值包括：
业务控制装置通过如下方式确定电价阈值：
Figure PCTCN2016091697-appb-000004
Figure PCTCN2016091697-appb-000005
即
Figure PCTCN2016091697-appb-000006
Figure PCTCN2016091697-appb-000007
为pth的预测值，a和b为待定系数，
Figure PCTCN2016091697-appb-000008
为残差平方和，i为样品数据集的元素符号，(pth,i,qi)指的是样品数据集中第i个元素对。
本发明实施例中，还提供了一种计算方法，通过提取一定数量的样本数据，利用最小二乘法可以解得电价阈值与待执行业务数量的回归方程，此算法能够更准确的计算得到电价阈值与待执行业务数量的关系。
结合本发明第一方面，在一个可能的设计中，若当前电网电价高于电价阈值，业务控制装置计算当前电网电价与电价阈值之间的第一差值；
业务控制装置根据第一差值计算第一数量；
若当前电网电价低于电价阈值，业务控制装置计算当前电网电价与电价阈值之间的第二差值；
业务控制装置根据第二差值计算第二数量。
本发明实施例中，可以依据当前电网电价与电价阈值的差值，准确计算出需要增加或减少的即将执行的业务的数量，从而达到最优化的解决数据中心节省电费的问题。
需要说明的是，在一个可能的设计中，业务控制装置将第一差值作为
Figure PCTCN2016091697-appb-000009
Figure PCTCN2016091697-appb-000010
代入到方程Pth＝V*Q或
Figure PCTCN2016091697-appb-000011
中，反向计算出Q(q)作为第一数量的值，业务控制装置将第二差值作为
Figure PCTCN2016091697-appb-000012
代入到方程Pth＝V*Q或
Figure PCTCN2016091697-appb-000013
中，反向计算出Q(q)作为第二数量的值。
需要说明的是，在一个可能的设计中，业务控制装置将即将执行的业务的数量减少第一数量包括：
业务控制装置将即将执行的业务中第一数量的可延迟业务移至待执行业务的队列中；
或，
业务控制装置将即将执行的业务中第一数量的可延迟业务分配至第一异地数据中心，第一异地数据中心获得的第一异地当前电网电价低于当前电网电价。
本发明实施例中，由于实时业务不可以延迟处理，所以在即将执行的业务中只将可延迟业务移出，避免造成不必要的数据传输干扰。
需要说明的是，在又一个可能的设计中，业务控制装置将即将执行的业务的数量增加第二数量包括：
业务控制装置从待执行业务中抽取第二数量的业务加入到即将执行的业务的队列中；
或，
业务控制装置从第二异地数据中心获取第二数量的业务加入到即将执行的业务的队列中，第二异地数据中心获得的第二异地当前电网电价高于当前电网电价。
本发明实施例中，在大数据时代，各地的数据中心可以互相协调处理业务， 当前电网电价高于电价阈值的地区的数据中心可以将可延迟业务发送至当前电网电价低于电价阈值地区的数据中心进行处理，以此最优化协调各地数据中心处理业务，达到节约电费的目的。
本发明第二方面提供了一种业务控制装置，该业务控制装置包括用于执行上述第一方面以及第一方面的各种可能的设计中所述的方法的模块，该业务控制装置可以包括：
获取模块，用于获取当前电网电价以及待执行业务的数量；
确定模块，用于根据待执行业务的数量确定电价阈值，待执行业务的数量与电价阈值正相关；
比较模块，用于比较当前电网电价和电价阈值的大小；
分配模块，用于若当前电网电价高于电价阈值，则将即将执行的业务的数量减少第一数量，若当前电网电价低于电价阈值，则将即将执行的业务的数量增加第二数量；
执行模块，用于按照减少或增加后即将执行的业务的数量执行业务。
结合本发明第二方面，在一个可能的设计中，确定模块具体用于获取目标关系因子；确定模块具体用于将目标关系因子与待执行业务的数量的乘积作为电价阈值。
可以理解的是，在一个可能的设计中，确定模块具体用于获取第一关系参数以及第二关系参数，第一关系参数为电费节约率与关系因子之间的关系参数，第二关系参数为待执行业务的数量与关系因子之间的关系参数，电费节约率与关系因子正相关，待执行业务的数量与关系因子正相关；
确定模块具体用于根据第一关系参数以及第二关系参数计算使得均衡值最大时的关系因子作为目标关系因子，均衡值与电费节约率正相关，均衡值与待执行业务的数量反相关。
结合本发明的第二方面，可以理解的是，在一个可能的设计中，确定模块具体用于通过如下方式确定目标关系因子
Figure PCTCN2016091697-appb-000014
Figure PCTCN2016091697-appb-000015
Figure PCTCN2016091697-appb-000016
v为关系因子，V为关系因子的取值范围，M(v)为均衡值，C(v)为电费节约率，Q(v)为待执行业务的数量，α为第一系数，用于表示电费节约率的权重，β为第二系数，用于表示待执行业务的数量的权重。
结合本发明第二方面，可选的是，
确定模块还可以用于通过离线试验获取电价阈值与待执行业务的数量的样本数据集φ＝(Pth，Q)，样本数据集中电价阈值Pth为因变量pth，待执行业务的数量Q为自变量q；
确定模块具体用于根据样本数据集，计算电价阈值。
结合本发明第二方面，可选的是，
确定模块具体用于通过如下方式确定电价阈值：
Figure PCTCN2016091697-appb-000017
Figure PCTCN2016091697-appb-000018
即
Figure PCTCN2016091697-appb-000019
Figure PCTCN2016091697-appb-000020
为pth的预测值，a和b为待定系数，
Figure PCTCN2016091697-appb-000021
为残差平方和，i为样品数据集的元素符号，(pth,i,qi)指的是样品数据集中第i个元素对。
结合本发明第二方面，在一个可能的设计中，分配模块具体用于若当前电网电价高于电价阈值，计算当前电网电价与电价阈值之间的第一差值；
分配模块具体用于根据第一差值计算第一数量；
分配模块具体用于若当前电网电价低于电价阈值，计算当前电网电价与电价阈值之间的第二差值；
分配模块具体用于根据第二差值计算第二数量。
需要说明的是，在一个可能的设计中，分配模块具体用于将即将执行的业务中第一数量的可延迟业务移至待执行业务的队列中；
或，
分配模块具体用于将即将执行的业务中第一数量的可延迟业务分配至第一异地数据中心，第一异地数据中心获得的第一异地当前电网电价低于当前电网电价。
需要说明的是，在又一个可能的设计中，分配模块具体用于从待执行业务 中抽取第二数量的业务加入到即将执行的业务的队列中；
或，
分配模块具体用于从第二异地数据中心获取第二数量的业务加入到即将执行的业务的队列中，第二异地数据中心获得的第二异地当前电网电价高于当前电网电价。
本发明第三方面提供了一种业务控制装置的实体装置，其特征在于，包括：输入装置、输出装置、处理器和存储器；
通过调用存储器存储的操作指令，业务控制装置，用于执行如下步骤：
获取当前电网电价以及待执行业务的数量；
根据待执行业务的数量确定电价阈值，待执行业务的数量与电价阈值正相关；
若当前电网电价高于电价阈值，则将即将执行的业务的数量减少第一数量，并按照减少后即将执行的业务的数量执行业务；
若当前电网电价低于电价阈值，则将即将执行的业务的数量增加第二数量，并按照增加后即将执行的业务的数量执行业务。
从以上技术方案可以看出，本发明实施例具有以下优点：
本发明实施例中，业务控制装置可以根据待执行业务的数量确定电价阈值，再根据电价阈值与当前电网电价之间进行比价，在当前电网电价高于电价阈值时，减少即将执行的业务的数量，在当前电网电价低于电价阈值时，增加即将执行的业务的数量，由于本发明实施例中的电价阈值由待执行业务的数量确定，所以调整负荷的依据不仅仅是当前电网电价，还进一步考虑到了待执行业务的数量，而待执行业务的数量实时都在发生变化，因此调整负荷的周期较短，从而能够提高调整精度，以降低电费成本。
附图说明
图1为本发明实施例中数据中心网络架构图；
图2为本发明实施例中数据中心结构拓扑图；
图3为本发明实施例中业务控制方法一个实施例示意图；
图4为本发明实施例中实验参数V的数据分析图；
图5为本发明实施例中业务控制装置一个实施例示意图；
图6为本发明实施例中业务控制装置另一实施例示意图。
具体实施方式
本发明实施例提供了一种业务控制方法以及业务控制装置，用于提高调整精度，降低电费成本。
为了使本技术领域的人员更好地理解本发明方案，下面将结合本发明实施例中的附图，对本发明实施例中的技术方案进行清楚、完整地描述，显然，所描述的实施例仅仅是本发明一部分的实施例，而不是全部的实施例。基于本发明中的实施例，本领域普通技术人员在没有做出创造性劳动前提下所获得的所有其他实施例，都应当属于本发明保护的范围。
本发明的说明书和权利要求书及上述附图中的术语“第一”、“第二”、“第三”、“第四”等(如果存在)是用于区别类似的对象，而不必用于描述特定的顺序或先后次序。应该理解这样使用的数据在适当情况下可以互换，以便这里描述的实施例能够以除了在这里图示或描述的内容以外的顺序实施。此外，术语“包括”和“具有”以及他们的任何变形，意图在于覆盖不排他的包含，例如，包含了一系列步骤或单元的过程、方法、系统、产品或设备不必限于清楚地列出的那些步骤或单元，而是可包括没有清楚地列出的或对于这些过程、方法、产品或设备固有的其它步骤或单元。
数据中心是全球协作的特定设备网络，用来在Internet网络基础设施上传递、加速、展示、计算、存储数据信息。如图1所示，电脑、手机或者无线接入点在进行业务的时候，能够通过Internet网络或者GPRS(通用分组无线服务技术General Packet Radio Service)网络将这些业务的数据上传至数据中心，数据中心再对这些数据进行计算、储存或者反馈。据美国采暖制冷与空调工程师学会(ASHRAE)技术委员会9.9(简称TC9.9)统计报告显示，服务器能耗占46％、空调制冷占31％、UPS占8％、照明占4％以及其他占11％，可以看出数据中心在处理上述数据时的能耗占数据中心总能耗将近二分之一，即服务器部分能耗46％。此部分能耗是数据中心能耗的关键部分，减少该部分能耗对于降低数据中心电费成本具有决定性作用。
现有技术中有一种控制数据中心电费的方法，其针对分时电价，即在高电价期减少负荷，在低电价期增大负荷，其调整周期一般为一天，但在现实生活 中，数据中心一天中的负荷是实时变化的，单纯的将一天划分为两个分段进行调整负荷并不能有效降低数据中心的电费成本。
而且，随着电网的不断发展，目前已出现了一种实时电价方案，而且将来可能会成为电价的主流方案。该方案是指随时间实时动态变化的电价，变化频繁，电价曲线是非线性的曲线，而且整条电价曲线不能提前知道，而只能知道未来较短时间(比如15分钟)内的电价。
由于实时电价方案中并没有固定的高电价期和低电价期，所以按照现有技术的方案就难以准确的调整负荷，从而影响了数据中心电费的控制精度。
本发明实施例提供了一种业务控制方法以及业务控制装置，业务控制装置可以根据待执行业务的数量确定电价阈值，再根据电价阈值与当前电网电价之间进行比价，在当前电网电价高于电价阈值时，减少即将执行的业务的数量，在当前电网电价低于电价阈值时，增加即将执行的业务的数量，由于本发明实施例中的电价阈值由待执行业务的数量确定，所以调整负荷的依据不仅仅是当前电网电价，还进一步考虑到了待执行业务的数量，而待执行业务的数量实时都在发生变化，因此调整负荷的周期较短，从而能够提高调整精度，以降低电费成本。
为便于理解，下面对本发明实施例中的业务控制方法进行详细描述，可以理解的是，本发明实施例既可以应用于分时电价场景，又可以应用有实时电价场景，本发明技术方案还适用于其他任何在实行实时电价政策且有可延迟业务的计算机集群。请结合图2所示的数据中心结构拓扑图参阅图3，本发明实施例中业务控制方法一个实施例包括：
301、业务控制装置获取当前电网电价以及待执行业务的数量；
本实施例中，如图2所示，电价获取单元可以通过互联网与数据中心之外的电力提供方发送电价的装置通信，获得当前电网电价，需要说明的是业务控制装置可以每隔一个周期获取一次当前电网电价，该周期可以为15分钟一次。
待执行业务数量获取单元可以与业务控制装置内部用于分配业务的业务分配单元通信，获取当前待执行业务的数量信息，该待执行业务可以是当前时刻请求执行的业务，也可以是在当前时刻以前请求的但被延迟的业务。
此处需要说明的是，在本发明实施例中，数据中心处理的业务可以为分实 时业务和可延迟业务。实时业务是一种对时间敏感度高的不可延迟业务，要求及时响应，响应时间越短越好，比如通话、视屏通话等业务，延迟要求较高，需要立即处理。可延迟业务则是一种对时间敏感度低的批处理业务，有一定的执行时间窗口，只要在该时间窗口内完成都可以，比如电子商务平台公司的数据中心中做用户行为分析的计算业务，是一种批处理业务，可以离线完成。
302、业务控制装置根据待执行业务的数量确定电价阈值；
本实施例中，待执行业务随时间的变化而变化，图2所示的电价阈值生成单元在依据待执行业务的数量计算电价阈值的时候，电价阈值是随着待执行业务的数量变化而变化的，可以理解的是，电价阈值与待执行业务的数量成正相关关系，即当待执行业务的数量增加时，该电价阈值增加；当待执行业务的数量减小时，该电价阈值减小。
其中，在本发明实施例中只规定电价阈值与待执行业务数量成正相关关系，其具体的关系函数可以有多种不同的形式，包括但不限于以下函数关系式：
函数关系式一：
假设电价阈值Pth与待执行业务的数量Q的关系为：
Pth＝V*Q。
其中，目标关系因子V为待确定的实验参数。按照经验先假定一组从小到大变化的V值，在相同的实验条件下(在相同的实验周期内采用相同的实时电价曲线和相同的负载曲线)，对每一个V进行一次实验，然后比较实验结果中电费节省百分比和待执行业务数量平均值，确定一个比较合适的V值，从而确定电价阈值Pth与待执行业务数量Q的关系式。例如图4所示，通过实验得到随着V的增大，电费节省百分比的变化(左)和待执行业务数量平均值(右)的变化曲线，可以看出，随着V的增大，电费节省百分比和待执行业务数量平均值都增大。
但在实际应用中，对于数据中心而言，电费节省百分比越大越好，但待执行业务数量平均值越小越好，如图4中所示，待执行业务数量平均值基本与V值成正比例关系，而电费节省百分比随V值的增大先迅速增大后缓慢增大，可以选择拐点处的V值，这个V值对应的待执行业务数量平均值比较小。因此在需要两者权衡确定V值时，可以通过以下方式确定目标关系因子
Figure PCTCN2016091697-appb-000022
Figure PCTCN2016091697-appb-000023
Figure PCTCN2016091697-appb-000024
上述式中v为关系因子，V为关系因子的取值范围，M(v)为均衡值，C(v)为电费节约率，Q(v)为待执行业务的数量，α为第一系数，用于表示电费节约率的权重，β为第二系数，用于表示待执行业务的数量的权重。
函数关系式二：
此处可以采用数据挖掘分析方法，如聚类分析、回归分析、Bayes分析、方差分析等；或者采用机器学习的方法，如自适应学习系统、神经网络、支持向量机、传统优化方法等，来获得电价阈值与待执行业务数量的关系。比如用回归分析法，通过离线实验获取电价阈值与待执行业务数量的样本数据集φ＝(Pth，Q)，视电价阈值Pth为因变量pth，待执行业务的数量Q为自变量q，建立pth和q的回归分析预测方程。其中，回归分析预测方程可以是高次方程，例如一次方程：
Figure PCTCN2016091697-appb-000025
其中
Figure PCTCN2016091697-appb-000026
是pth的预测值，a和b为待定系数。
对于样本数据集
Figure PCTCN2016091697-appb-000027
内的所有元素，通过求解下式，可以得到a和b的值：
Figure PCTCN2016091697-appb-000028
即
Figure PCTCN2016091697-appb-000029
其中，
Figure PCTCN2016091697-appb-000030
为残差平方和，i为样品数据集的元素符号，(pth,i,qi)指的是样品数据集中第i个元素对，通过求解使得残差平方和
Figure PCTCN2016091697-appb-000031
最小的元素对可以确定待定系数a和b，求得a和b的值后，即得到电价阈值与待执行业务数量的回归分析模型。
303、业务控制装置比较当前电网电价是否高于电价阈值，若是，则执行步骤304，若否，则执行步骤305；
本实施例中，如图2所示，业务控制装置内的比较单元可以在获得电价阈值生成单元计算得到的电价阈值后，与从电价获取单元获得的当前电网电价进行比较，该比较方式可以是简单的数值大小的比较，还可以是其他形式的比较， 此处不做限定。
304、业务控制装置减少即将执行的业务的数量；
本实施例中，如图2所示，业务控制装置中的业务分配单元在比较得到当前电网电价要高于电价阈值之后，可以计算得到当前电网电价与电价阈值之间的第一差值，依据该第一差值可以计算出需要减少的即将执行的业务的第一数量，该计算方式可以是将第一差值作为
Figure PCTCN2016091697-appb-000032
代入到方程Pth＝V*Q或
Figure PCTCN2016091697-appb-000033
中，反向计算出Q(q)作为第一数量的值，该计算方式还可以是其他方式的计算方式，具体此处不做限定。
本实施例中，业务分配单元可以从即将执行的业务的队列中挑选出第一数量的可延迟业务，移出至待执行业务的队列中，该挑选第一数量的可延迟业务的方式可以为按照最晚加入即将执行的业务的队列中的顺序依次挑选而出，还可以是按照业务的权重大小由小到大依次从即将执行的业务的队列中挑选出，具体此处不做限定。
需要说明的是，在本实施例中，业务分配单元可以只挑选可延迟业务。
需要说明的是，业务分配单元还可以与各异地数据中心联网同步，相互上传数据中心所在地的当前电网电价，业务控制装置在获得低于当前电网电价的第一异地当前电网电价时，可以将即将执行的业务中的第一数量的可延迟业务分配至第一异地数据中心。
本实施例中，如图2所示，业务控制装置中的业务执行单元按照业务分配单元减少后的即将执行的业务的数量执行业务，此处需要说明的是，业务执行单元可以为多个。
305、业务控制装置增加即将执行的业务的数量。
本实施例中，如图2所示，业务控制装置中的业务分配单元在比较得到当前电网电价要低于电价阈值之后，可以计算得到当前电网电价与电价阈值之间的第二差值，依据该第二差值可以计算出需要增加的即将执行的业务的第二数量，该计算方式可以是将第二差值作为
Figure PCTCN2016091697-appb-000034
代入到方程Pth＝V*Q或
Figure PCTCN2016091697-appb-000035
中，反向计算出Q(q)作为第二数量的值，该计算方式还可以是其他方式的计算方式，具体此处不做限定。
本实施例中，业务分配单元可以从待执行业务的队列中挑选出第二数量的 业务，加入到即将执行的业务的队列中，该挑选第二数量的业务的方式可以为按照最早加入待执行业务的队列中的顺序依次挑选出，还可以是按照业务的权重大小由大到小依次从待执行的业务中挑选出，具体此处不做限定。
需要说明的是，本实施例中，业务分配单元可以优先挑选出实时业务。
需要说明的是，业务分配单元还可以与各异地数据中心联网同步，相互上传数据中心所在地的当前电网电价，业务控制装置在获得高于当前电网电价的第二异地当前电网电价时，可以从第二异地数据中心获取第二数量的业务加入到即将执行的业务的队列中。
本实施例中，如图2所示，业务控制装置中的业务执行单元按照业务分配单元增加后的即将执行的业务的数量执行业务，此处需要说明的是，业务执行单元可以为多个。
本实施例中，若出现当前电网电价等于电价阈值情况，此时业务执行单元可以不作任何处理，保持即将执行的业务的数量不变。
为便于理解，在本发明实施例一个具体应用场景中，假设当前电网电价为1.1元/千瓦时，待执行业务数量为6000，目标关系因子V通过实验取值为0.0001，此时通过函数关系式一可解得：
Pth＝V*Q＝0.0001×6000＝0.6
电价阈值为0.6元/千瓦时，此时当前电网电价是要高于电价阈值的，当前电网电价与电价阈值之间的第一差值可以计算得到为0.5；
根据上述方程式，将第一差值0.5作为Pth可以反向计算得到即将执行的业务的数量Q，如下式：
0.5＝0.0001×Q
Q＝5000
据以上所述，在当前电网电价高于电价阈值的时候，业务控制装置将即将执行的业务的数量减少5000。
以上是当前电网电价高于电价阈值的情况，在不高于的情况下，例如：假设当前电网电价为1.1元/千瓦时，待执行业务数量为6000，目标关系因子V通过实验取值为0.0002，此时通过函数关系式一可解得：
Pth＝V*Q＝0.0002×6000＝1.2
电价阈值为1.2元/千瓦时，此时当前电网电价是要低于电价阈值的，当前电网电价与电价阈值之间的第二差值可以计算得到为0.1；
根据上述方程式，将第二差值0.1作为Pth可以反向计算得到即将执行的业务的数量Q，如下式：
0.1＝0.0002×Q
Q＝500
可以看出，在当前电网电价低于电价阈值的时候，业务控制装置将即将执行的业务的数量增加500。
本实施例中，业务控制装置可以根据待执行业务的数量确定电价阈值，再根据电价阈值与当前电网电价之间进行比价，在当前电网电价高于电价阈值时，减少即将执行的业务的数量，在当前电网电价低于电价阈值时，增加即将执行的业务的数量，由于本实施例中的电价阈值由待执行业务的数量确定，所以调整负荷的依据不仅仅是当前电网电价，还进一步考虑到了待执行业务的数量，而待执行业务的数量实时都在发生变化，因此调整负荷的周期较短，从而能够提高调整精度，以降低电费成本。
上面对本发明实施例中的业务控制方法进行了描述，下面对本发明实施例中的业务控制装置进行描述，请参阅图5，本发明实施例中业务控制装置一个实施例包括：
获取模块501，用于获取当前电网电价以及待执行业务的数量；
确定模块502，用于根据待执行业务的数量确定电价阈值，待执行业务的数量与电价阈值正相关；
比较模块503，用于比较当前电网电价和电价阈值的大小；
分配模块504，用于若当前电网电价高于电价阈值，则将即将执行的业务的数量减少第一数量，若当前电网电价低于电价阈值，则将即将执行的业务的数量增加第二数量；
执行模块505，用于按照减少或增加后即将执行的业务的数量执行业务。
确定模块502可以进一步包括：
确定模块502具体用于获取目标关系因子；
确定模块502具体用于将目标关系因子与待执行业务的数量的乘积作为 电价阈值。
确定模块502可以进一步包括：
确定模块502具体用于获取第一关系参数以及第二关系参数，第一关系参数为电费节约率与关系因子之间的关系参数，第二关系参数为待执行业务的数量与关系因子之间的关系参数，电费节约率与关系因子正相关，待执行业务的数量与关系因子正相关；
确定模块502具体用于根据第一关系参数以及第二关系参数计算使得均衡值最大时的关系因子作为目标关系因子，均衡值与电费节约率正相关，均衡值与待执行业务的数量反相关。
确定模块502可以进一步包括：
确定模块502具体用于通过如下方式确定目标关系因子
Figure PCTCN2016091697-appb-000036
Figure PCTCN2016091697-appb-000037
Figure PCTCN2016091697-appb-000038
v为关系因子，V为关系因子的取值范围，M(v)为均衡值，C(v)为电费节约率，Q(v)为待执行业务的数量，α为第一系数，用于表示电费节约率的权重，β为第二系数，用于表示待执行业务的数量的权重。
可选的，
确定模块502还可以进一步包括：
确定模块502具体用于通过离线试验获取电价阈值与待执行业务的数量的样本数据集φ＝(Pth，Q)，样本数据集中电价阈值Pth为因变量pth，待执行业务的数量Q为自变量q；
确定模块502具体用于根据样本数据集，计算电价阈值。
确定模块502还可以进一步包括：
确定模块502具体用于通过如下方式确定电价阈值：
Figure PCTCN2016091697-appb-000039
Figure PCTCN2016091697-appb-000040
即
Figure PCTCN2016091697-appb-000041
Figure PCTCN2016091697-appb-000042
为pth的预测值，a和b为待定系数，
Figure PCTCN2016091697-appb-000043
为残差平方和，i为样品数据集的元素符号，(pth,i,qi)指的是样品数据集中第i个元素对。
分配模块504可以进一步包括：
分配模块504具体用于若当前电网电价高于电价阈值，计算当前电网电价与电价阈值之间的第一差值；
分配模块504具体用于根据第一差值计算第一数量；
分配模块504具体用于若当前电网电价低于电价阈值，计算当前电网电价与电价阈值之间的第二差值；
分配模块504具体用于根据第二差值计算第二数量。
分配模块504可以进一步包括：
分配模块504具体用于将即将执行的业务中第一数量的可延迟业务移至待执行业务的队列中；
或，
分配模块504具体用于将即将执行的业务中第一数量的可延迟业务分配至第一异地数据中心，第一异地数据中心获得的第一异地当前电网电价低于当前电网电价。
分配模块504可以进一步包括：
分配模块504具体用于从待执行业务中抽取第二数量的业务加入到即将执行的业务的队列中；
或，
分配模块504具体用于从第二异地数据中心获取第二数量的业务加入到即将执行的业务的队列中，第二异地数据中心获得的第二异地当前电网电价高于当前电网电价。
本实施例中，确定模块502可以根据获取模块501获取的待执行业务的数量确定电价阈值，比较模块503再根据电价阈值与获取模块501获取的当前电网电价之间进行比价，在当前电网电价高于电价阈值时，分配模块504减少即将执行的业务的数量，在当前电网电价低于电价阈值时，分配模块504增加即将执行的业务的数量，执行模块505按照分配模块504分配的即将执行的业务的数量执行业务，由于本实施例中的电价阈值由待执行业务的数量确定，所以 调整负荷的依据不仅仅是当前电网电价，还进一步考虑到了待执行业务的数量，而待执行业务的数量实时都在发生变化，因此调整负荷的周期较短，从而能够提高调整精度，以降低电费成本。
上面从模块化功能实体的角度对本发明实施例中的业务控制装置进行描述，下面从硬件处理的角度对本发明实施例中的业务控制装置进行描述，请参阅图6，本发明实施例中的业务控制装置另一实施例包括：
输入装置601、输出装置602、处理器603和存储器604(其中网络设备中的处理器603的数量可以一个或多个，图6中以一个处理器603为例)。在本发明的一些实施例中，输入装置601、输出装置602、处理器603和存储器604可通过总线或其它方式连接，其中，图6中以通过总线连接为例。
其中，
通过调用存储器604存储的操作指令，处理器603，用于执行如下步骤：
获取当前电网电价以及待执行业务的数量；
根据待执行业务的数量确定电价阈值，待执行业务的数量与电价阈值正相关；
若当前电网电价高于电价阈值，则将即将执行的业务的数量减少第一数量，并按照减少后即将执行的业务的数量执行业务；
若当前电网电价低于电价阈值，则将即将执行的业务的数量增加第二数量，并按照增加后即将执行的业务的数量执行业务。
在本发明的一些实施例中，处理器603还用于执行以下步骤：
获取目标关系因子；
将目标关系因子与待执行业务的数量的乘积作为电价阈值。
在本发明的一些实施例中，处理器603还用于执行以下步骤：
获取第一关系参数以及第二关系参数，第一关系参数为电费节约率与关系因子之间的关系参数，第二关系参数为待执行业务的数量与关系因子之间的关系参数，电费节约率与关系因子正相关，待执行业务的数量与关系因子正相关；
根据第一关系参数以及第二关系参数计算使得均衡值最大时的关系因子作为目标关系因子，均衡值与电费节约率正相关，均衡值与待执行业务的数量反相关。
在本发明的一些实施例中，处理器603还用于执行以下步骤：
通过如下方式确定目标关系因子
Figure PCTCN2016091697-appb-000044
Figure PCTCN2016091697-appb-000045
Figure PCTCN2016091697-appb-000046
v为关系因子，V为关系因子的取值范围，M(v)为均衡值，C(v)为电费节约率，Q(v)为待执行业务的数量，α为第一系数，用于表示电费节约率的权重，β为第二系数，用于表示待执行业务的数量的权重。
在本发明的一些实施例中，处理器603还用于执行以下步骤：
通过离线试验获取电价阈值与待执行业务的数量的样本数据集φ＝(Pth，Q)，样本数据集中电价阈值Pth为因变量pth，待执行业务的数量Q为自变量q；
根据样本数据集，计算电价阈值。
在本发明的一些实施例中，处理器603还用于执行以下步骤：
通过如下方式确定电价阈值：
Figure PCTCN2016091697-appb-000047
Figure PCTCN2016091697-appb-000048
即
Figure PCTCN2016091697-appb-000049
Figure PCTCN2016091697-appb-000050
为pth的预测值，a和b为待定系数，
Figure PCTCN2016091697-appb-000051
为残差平方和，i为样品数据集的元素符号，(pth,i,qi)指的是样品数据集中第i个元素对。
在本发明的一些实施例中，处理器603还用于执行以下步骤：
若当前电网电价高于电价阈值，计算当前电网电价与电价阈值之间的第一差值；
根据第一差值计算第一数量；
若当前电网电价低于电价阈值，计算当前电网电价与电价阈值之间的第二差值；
根据第二差值计算第二数量。
在本发明的一些实施例中，处理器603还用于执行以下步骤：
将即将执行的业务中第一数量的可延迟业务移至待执行业务的队列中；
或，
将即将执行的业务中第一数量的可延迟业务分配至第一异地数据中心，第一异地数据中心获得的第一异地当前电网电价低于当前电网电价。
在本发明的一些实施例中，处理器603还用于执行以下步骤：
从待执行业务中抽取第二数量的业务加入到即将执行的业务的队列中；
或，
从第二异地数据中心获取第二数量的业务加入到即将执行的业务的队列中，第二异地数据中心获得的第二异地当前电网电价高于当前电网电价。
本实施例中，处理器603可以根据待执行业务的数量确定电价阈值，再根据电价阈值与当前电网电价之间进行比价，在当前电网电价高于电价阈值时，减少即将执行的业务的数量，在当前电网电价低于电价阈值时，增加即将执行的业务的数量，由于本实施例中的电价阈值由待执行业务的数量确定，所以调整负荷的依据不仅仅是当前电网电价，还进一步考虑到了待执行业务的数量，而待执行业务的数量实时都在发生变化，因此调整负荷的周期较短，从而能够提高调整精度，以降低电费成本。
所属领域的技术人员可以清楚地了解到，为描述的方便和简洁，上述描述的系统，装置和单元的具体工作过程，可以参考前述方法实施例中的对应过程，在此不再赘述。
在本申请所提供的几个实施例中，应该理解到，所揭露的系统，装置和方法，可以通过其它的方式实现。例如，以上所描述的装置实施例仅仅是示意性的，例如，所述单元的划分，仅仅为一种逻辑功能划分，实际实现时可以有另外的划分方式，例如多个单元或组件可以结合或者可以集成到另一个系统，或一些特征可以忽略，或不执行。另一点，所显示或讨论的相互之间的耦合或直接耦合或通信连接可以是通过一些接口，装置或单元的间接耦合或通信连接，可以是电性，机械或其它的形式。
所述作为分离部件说明的单元可以是或者也可以不是物理上分开的，作为单元显示的部件可以是或者也可以不是物理单元，即可以位于一个地方，或者也可以分布到多个网络单元上。可以根据实际的需要选择其中的部分或者全部 单元来实现本实施例方案的目的。
另外，在本发明各个实施例中的各功能单元可以集成在一个处理单元中，也可以是各个单元单独物理存在，也可以两个或两个以上单元集成在一个单元中。上述集成的单元既可以采用硬件的形式实现，也可以采用软件功能单元的形式实现。
所述集成的单元如果以软件功能单元的形式实现并作为独立的产品销售或使用时，可以存储在一个计算机可读取存储介质中。基于这样的理解，本发明的技术方案本质上或者说对现有技术做出贡献的部分或者该技术方案的全部或部分可以以软件产品的形式体现出来，该计算机软件产品存储在一个存储介质中，包括若干指令用以使得一台计算机设备(可以是个人计算机，服务器，或者网络设备等)执行本发明各个实施例所述方法的全部或部分步骤。而前述的存储介质包括：U盘、移动硬盘、只读存储器(ROM，Read-Only Memory)、随机存取存储器(RAM，Random Access Memory)、磁碟或者光盘等各种可以存储程序代码的介质。
以上所述，以上实施例仅用以说明本发明的技术方案，而非对其限制；尽管参照前述实施例对本发明进行了详细的说明，本领域的普通技术人员应当理解：其依然可以对前述各实施例所记载的技术方案进行修改，或者对其中部分技术特征进行等同替换；而这些修改或者替换，并不使相应技术方案的本质脱离本发明各实施例技术方案的精神和范围。

Claims (20)

  1. 一种业务控制方法，其特征在于，包括：
    业务控制装置获取当前电网电价以及待执行业务的数量；
    所述业务控制装置根据所述待执行业务的数量确定电价阈值，所述待执行业务的数量与所述电价阈值正相关；
    若所述当前电网电价高于所述电价阈值，则所述业务控制装置将即将执行的业务的数量减少第一数量，并按照减少后即将执行的业务的数量执行业务。
  2. 根据权利要求1所述的业务控制方法，其特征在于，所述业务控制装置根据所述待执行业务的数量确定电价阈值包括：
    所述业务控制装置获取目标关系因子；
    所述业务控制装置将所述目标关系因子与所述待执行业务的数量的乘积作为所述电价阈值。
  3. 根据权利要求2所述的业务控制方法，其特征在于，所述业务控制装置获取关系因子包括：
    所述业务控制装置获取第一关系参数以及第二关系参数，所述第一关系参数为电费节约率与所述关系因子之间的关系参数，所述第二关系参数为所述待执行业务的数量与所述关系因子之间的关系参数，所述电费节约率与所述关系因子正相关，所述待执行业务的数量与所述关系因子正相关；
    所述业务控制装置根据所述第一关系参数以及第二关系参数计算使得均衡值最大时的关系因子作为所述目标关系因子，所述均衡值与所述电费节约率正相关，所述均衡值与所述待执行业务的数量反相关。
  4. 根据权利要求3所述的业务控制方法，其特征在于，所述业务控制装置根据所述第一关系参数以及第二关系参数计算使得均衡值最大时的关系因子作为所述目标关系因子包括：
    所述业务控制装置通过如下方式确定所述目标关系因子
    Figure PCTCN2016091697-appb-100001
    Figure PCTCN2016091697-appb-100002
    Figure PCTCN2016091697-appb-100003
    所述v为关系因子，所述V为关系因子的取值范围，所述M(v)为所述 均衡值，所述C(v)为所述电费节约率，所述Q(v)为所述待执行业务的数量，所述α为第一系数，用于表示所述电费节约率的权重，所述β为第二系数，用于表示所述待执行业务的数量的权重。
  5. 根据权利要求1所述的业务控制方法，其特征在于，所述业务控制装置根据所述待执行业务的数量确定电价阈值包括：
    所述业务控制装置通过预先试验获取所述电价阈值与所述待执行业务的数量的样本数据集φ＝(Pth，Q)，所述样本数据集中所述电价阈值Pth为因变量pth，待执行业务的数量Q为自变量q；
    所述业务控制装置根据所述样本数据集，计算所述电价阈值。
  6. 根据权利要求5所述的业务控制方法，其特征在于，所述业务控制装置根据所述样本数据集，计算所述电价阈值包括：
    所述业务控制装置通过如下方式确定所述电价阈值：
    Figure PCTCN2016091697-appb-100004
    Figure PCTCN2016091697-appb-100005
    即
    Figure PCTCN2016091697-appb-100006
    所述
    Figure PCTCN2016091697-appb-100007
    为所述pth的预测值，所述a和所述b为待定系数，所述
    Figure PCTCN2016091697-appb-100008
    为残差平方和，所述i为所述样品数据集的元素符号，所述(pth,i,qi)指的是所述样品数据集中第i个元素对。
  7. 根据权利要求1至6中任一项所述的业务控制方法，其特征在于，所述业务控制装置根据所述待执行业务的数量确定电价阈值之后，所述方法还包括：
    若所述当前电网电价低于所述电价阈值，则所述业务控制装置将即将执行的业务的数量增加第二数量，并按照增加后即将执行的业务的数量执行业务。
  8. 根据权利要求7所述的业务控制方法，其特征在于，所述方法还包括：
    若所述当前电网电价高于所述电价阈值，所述业务控制装置计算所述当前电网电价与所述电价阈值之间的第一差值；
    所述业务控制装置根据所述第一差值计算所述第一数量；
    若所述当前电网电价低于所述电价阈值，所述业务控制装置计算所述当前 电网电价与所述电价阈值之间的第二差值；
    所述业务控制装置根据所述第二差值计算所述第二数量。
  9. 根据权利要求7或8所述的业务控制方法，其特征在于，所述业务控制装置将即将执行的业务的数量增加第二数量包括：
    所述业务控制装置从所述待执行业务中抽取所述第二数量的业务加入到所述即将执行的业务的队列中；
    或，
    所述业务控制装置从第二异地数据中心获取所述第二数量的业务加入到所述即将执行的业务的队列中，所述第二异地数据中心获得的第二异地当前电网电价高于所述当前电网电价。
  10. 根据权利要求1至9中任一项所述的业务控制方法，其特征在于，所述业务控制装置将即将执行的业务的数量减少第一数量包括：
    所述业务控制装置将所述即将执行的业务中所述第一数量的可延迟业务移至所述待执行业务的队列中；
    或，
    所述业务控制装置将所述即将执行的业务中所述第一数量的可延迟业务分配至第一异地数据中心，所述第一异地数据中心获得的第一异地当前电网电价低于所述当前电网电价。
  11. 一种业务控制装置，其特征在于，包括：
    获取模块，用于获取当前电网电价以及待执行业务的数量；
    确定模块，用于根据所述待执行业务的数量确定电价阈值，所述待执行业务的数量与所述电价阈值正相关；
    比较模块，用于比较所述当前电网电价和所述电价阈值的大小；
    分配模块，用于若所述当前电网电价高于所述电价阈值，则将即将执行的业务的数量减少第一数量；
    执行模块，用于按照减少或增加后即将执行的业务的数量执行业务。
  12. 根据权利要求11所述的业务控制装置，其特征在于，
    所述确定模块，用于获取目标关系因子；还用于将所述目标关系因子与所 述待执行业务的数量的乘积作为所述电价阈值。
  13. 根据权利要求12所述的业务控制装置，其特征在于，
    所述确定模块，用于获取第一关系参数以及第二关系参数，所述第一关系参数为电费节约率与所述关系因子之间的关系参数，所述第二关系参数为所述待执行业务的数量与所述关系因子之间的关系参数，所述电费节约率与所述关系因子正相关，所述待执行业务的数量与所述关系因子正相关；
    所述确定模块，还用于根据所述第一关系参数以及第二关系参数计算使得均衡值最大时的关系因子作为所述目标关系因子，所述均衡值与所述电费节约率正相关，所述均衡值与所述待执行业务的数量反相关。
  14. 根据权利要求13所述的业务控制装置，其特征在于，
    所述确定模块，用于通过如下方式确定所述目标关系因子
    Figure PCTCN2016091697-appb-100009
    Figure PCTCN2016091697-appb-100010
    Figure PCTCN2016091697-appb-100011
    所述v为关系因子，所述V为关系因子的取值范围，所述M(v)为所述均衡值，所述C(v)为所述电费节约率，所述Q(v)为所述待执行业务的数量，所述α为第一系数，用于表示所述电费节约率的权重，所述β为第二系数，用于表示所述待执行业务的数量的权重。
  15. 根据权利要求11所述的业务控制装置，其特征在于，
    所述确定模块，用于通过离线试验获取所述电价阈值与所述待执行业务的数量的样本数据集φ＝(Pth，Q)，所述样本数据集中所述电价阈值Pth为因变量pth，待执行业务的数量Q为自变量q；
    所述确定模块，还用于根据所述样本数据集，计算所述电价阈值。
  16. 根据权利要求15所述的业务控制装置，其特征在于，
    所述确定模块，用于通过如下方式确定所述电价阈值：
    Figure PCTCN2016091697-appb-100012
    Figure PCTCN2016091697-appb-100013
    即
    Figure PCTCN2016091697-appb-100014
    所述
    Figure PCTCN2016091697-appb-100015
    为所述pth的预测值，所述a和所述b为待定系数，所述
    Figure PCTCN2016091697-appb-100016
    为残差平方和，所述i为所述样品数据集的元素符号，所述(pth,i,qi)指的是所述样品数据集中第i个元素对。
  17. 根据权利要求11至16中任一项所述的业务控制装置，其特征在于，
    所述分配模块，还用于若所述当前电网电价低于所述电价阈值，则将即将执行的业务的数量增加第二数量。
  18. 根据权利要求17所述的业务控制装置，其特征在于，所述分配模块具体用于若所述当前电网电价高于所述电价阈值，计算所述当前电网电价与所述电价阈值之间的第一差值；
    所述分配模块，还用于根据所述第一差值计算所述第一数量；
    所述分配模块，还用于若所述当前电网电价低于所述电价阈值，计算所述当前电网电价与所述电价阈值之间的第二差值；
    所述分配模块，还用于根据所述第二差值计算所述第二数量。
  19. 根据权利要求17或18所述的业务控制装置，其特征在于，若所述当前电网电价低于所述电价阈值，所述分配模块具体用于从所述待执行业务中抽取所述第二数量的业务加入到所述即将执行的业务的队列中；
    或，
    所述分配模块具体用于从第二异地数据中心获取所述第二数量的业务加入到所述即将执行的业务的队列中，所述第二异地数据中心获得的第二异地当前电网电价高于所述当前电网电价。
  20. 根据权利要求11至19中任一项所述的业务控制装置，其特征在于，若所述当前电网电价高于所述电价阈值，所述分配模块具体用于将所述即将执行的业务中所述第一数量的可延迟业务移至所述待执行业务的队列中；
    或，
    所述分配模块具体用于将所述即将执行的业务中所述第一数量的可延迟业务分配至第一异地数据中心，所述第一异地数据中心获得的第一异地当前电网电价低于所述当前电网电价。
PCT/CN2016/091697 2016-02-05 2016-07-26 一种业务控制方法以及业务控制装置 WO2017133192A1 (zh)

Priority Applications (2)

Application Number Priority Date Filing Date Title
EP16889007.7A EP3402142A4 (en) 2016-02-05 2016-07-26 SERVICE CONTROL METHOD AND DEVICE
US16/054,605 US10776176B2 (en) 2016-02-05 2018-08-03 Service control method and service control apparatus

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
CN201610081988.8A CN107046505B (zh) 2016-02-05 2016-02-05 一种业务控制方法以及业务控制装置
CN201610081988.8 2016-02-05

Related Child Applications (1)

Application Number Title Priority Date Filing Date
US16/054,605 Continuation US10776176B2 (en) 2016-02-05 2018-08-03 Service control method and service control apparatus

Publications (1)

Publication Number Publication Date
WO2017133192A1 true WO2017133192A1 (zh) 2017-08-10

Family

ID=59499310

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/CN2016/091697 WO2017133192A1 (zh) 2016-02-05 2016-07-26 一种业务控制方法以及业务控制装置

Country Status (4)

Country Link
US (1) US10776176B2 (zh)
EP (1) EP3402142A4 (zh)
CN (1) CN107046505B (zh)
WO (1) WO2017133192A1 (zh)

Families Citing this family (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US10956992B2 (en) 2016-09-15 2021-03-23 Racepoint Energy, LLC System and methods for creating dynamic nano grids and for aggregating electric power consumers to participate in energy markets
CN108171887B (zh) * 2017-12-20 2020-10-20 新华三技术有限公司 一种电量计费的方法及装置
CN110895728B (zh) * 2019-11-28 2023-06-13 海南电网有限责任公司 面向网格化配电网的线路路径自动规划方法
US11424641B1 (en) 2021-06-09 2022-08-23 Savant Systems, Inc. Flexible load management system
US11621580B2 (en) 2021-08-31 2023-04-04 Savant Systems, Inc. Microgrid switchover using zero-cross detection
US11941228B2 (en) 2021-08-31 2024-03-26 Savant Systems, Inc. Guidance set-up for companion module graphical display

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20110178959A1 (en) * 2010-01-19 2011-07-21 Kabushiki Kaisha Toshiba Charge and discharge control device
CN104065098A (zh) * 2013-03-20 2014-09-24 西门子公司 能耗系统中的能源调配方法及装置
CN104239966A (zh) * 2014-08-28 2014-12-24 广东电网公司电力科学研究院 一种主动配电网基于用电成本差异化的运营方法
CN104268681A (zh) * 2014-09-18 2015-01-07 东南大学 基于负荷分类的分时电价下家居设备优化运行方法
US9201486B2 (en) * 2012-02-23 2015-12-01 Cisco Technology, Inc. Large scale dynamic power budget adjustments for optimizing power utilization in a data center

Family Cites Families (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20080040296A1 (en) * 2006-08-10 2008-02-14 V2 Green Inc. Electric Resource Power Meter in a Power Aggregation System for Distributed Electric Resources
US20110029461A1 (en) * 2009-07-31 2011-02-03 Invensys Systems Inc. Dynamic Electrical Power Pricing Communication Architecture
US20110040417A1 (en) * 2009-08-13 2011-02-17 Andrew Wolfe Task Scheduling Based on Financial Impact
US9250962B2 (en) * 2011-01-10 2016-02-02 International Business Machines Corporation Optimizing energy use in a data center by workload scheduling and management
US9003216B2 (en) * 2011-10-03 2015-04-07 Microsoft Technology Licensing, Llc Power regulation of power grid via datacenter
CN103490956A (zh) * 2013-09-22 2014-01-01 杭州华为数字技术有限公司 基于业务量预测的自适应节能控制方法及设备、系统
CN104217111B (zh) 2014-09-02 2017-07-11 华中科技大学 一种数据中心的能源调度方法
US10210568B2 (en) * 2014-09-26 2019-02-19 Battelle Memorial Institute Coordination of thermostatically controlled loads with unknown parameters
CN104680339B (zh) 2015-03-26 2017-11-07 中国地质大学（武汉） 一种基于实时电价的家用电器调度方法
CN104915725B (zh) * 2015-05-06 2018-04-20 浙江大学 计及实时电价的微网用户群电能优化互济交易方法

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20110178959A1 (en) * 2010-01-19 2011-07-21 Kabushiki Kaisha Toshiba Charge and discharge control device
US9201486B2 (en) * 2012-02-23 2015-12-01 Cisco Technology, Inc. Large scale dynamic power budget adjustments for optimizing power utilization in a data center
CN104065098A (zh) * 2013-03-20 2014-09-24 西门子公司 能耗系统中的能源调配方法及装置
CN104239966A (zh) * 2014-08-28 2014-12-24 广东电网公司电力科学研究院 一种主动配电网基于用电成本差异化的运营方法
CN104268681A (zh) * 2014-09-18 2015-01-07 东南大学 基于负荷分类的分时电价下家居设备优化运行方法

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
See also references of EP3402142A4 *

Also Published As

Publication number Publication date
EP3402142A1 (en) 2018-11-14
US20180342870A1 (en) 2018-11-29
CN107046505B (zh) 2020-01-03
EP3402142A4 (en) 2019-01-09
CN107046505A (zh) 2017-08-15
US10776176B2 (en) 2020-09-15

Similar Documents

Publication Publication Date Title
WO2017133192A1 (zh) 一种业务控制方法以及业务控制装置
Duggan et al. A multitime‐steps‐ahead prediction approach for scheduling live migration in cloud data centers
Biswas et al. An approach towards development of new linear regression prediction model for reduced energy consumption and SLA violation in the domain of green cloud computing
JP2017126975A (ja) データセンターのネットワークトラフィックスケジューリング方法及び装置
Nir et al. Economic and energy considerations for resource augmentation in mobile cloud computing
CN111949409B (zh) 一种电力无线异构网中计算任务卸载方法及系统
KR102109418B1 (ko) 에너지 수요 모델 생성 방법 및 시스템
CN104636187A (zh) 基于负载预测的numa架构中虚拟机调度方法
Li et al. Performance evaluation and optimization of a task offloading strategy on the mobile edge computing with edge heterogeneity
CN103957251A (zh) 一种实现服务器负载均衡的方法及系统
Li et al. Load prediction-based automatic scaling cloud computing
Niu et al. JouleMR: Towards cost-effective and green-aware data processing frameworks
Feng et al. Optimizing computation offloading strategy in mobile edge computing based on swarm intelligence algorithms
CN109756372B (zh) 一种电信计费系统的弹性伸缩方法及装置
Liu et al. Computation offloading strategy for multi user mobile data streaming applications
Tian et al. Efficient algorithms for VM placement in cloud data centers
Fan et al. Knative autoscaler optimize based on double exponential smoothing
CN113434270B (zh) 数据资源调度方法、装置、电子设备及存储介质
Zhou et al. Enable the proactively load-balanced control plane for SDN via intelligent switch-to-controller selection strategy
Zhu et al. Load balancing algorithm for web server based on weighted minimal connections
CN109586971B (zh) 一种基于线性关系的负载资源需求量评估方法
Sartzetakis et al. Resource Allocation for Distributed Machine Learning at the Edge-Cloud Continuum
Čaušević et al. Flexibility prediction in Smart Grids: Making a case for Federated Learning
Pletzer et al. Distributed task allocation for visual sensor networks: a market-based approach
Shankar et al. Optimization of Computation and Communication Driven Resource Allocation in Mobile Cloud

Legal Events

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

Ref document number: 16889007

Country of ref document: EP

Kind code of ref document: A1

NENP Non-entry into the national phase

Ref country code: DE

WWE Wipo information: entry into national phase

Ref document number: 2016889007

Country of ref document: EP

ENP Entry into the national phase

Ref document number: 2016889007

Country of ref document: EP

Effective date: 20180809