CN104123214A - Method and system for measuring and displaying task executing progress based on runtime data - Google Patents

Method and system for measuring and displaying task executing progress based on runtime data Download PDF

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CN104123214A
CN104123214A CN201310150738.1A CN201310150738A CN104123214A CN 104123214 A CN104123214 A CN 104123214A CN 201310150738 A CN201310150738 A CN 201310150738A CN 104123214 A CN104123214 A CN 104123214A
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subtask
runtime data
progress
task
time
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CN201310150738.1A
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CN104123214B (en
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冯照临
刘中胜
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阿里巴巴集团控股有限公司
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Abstract

The invention relates to a method and system for measuring and displaying task executing progress based on runtime data. The method comprises the steps that the executing progress of the current task and subtasks of the current task is measured based on the runtime data generated through execution of a task before the current task and subtasks of the task before the current task, and the real-time progress of execution of the subtasks of the current task and the positions at which the execution finishing progress of the subtasks of the current task arrives are controlled to be displayed. A large number of runtime data and self-learning prediction are utilized, the runtime data are constantly updated to correct the error of a progress bar of a measuring task and accurately execute the fitting of the progress and time, the system predicting precision is enhanced, and accurate measuring and displaying are achieved.

Description

Tasks carrying progress metrics based on runtime data and the method and system of displaying

Technical field

Patented claim of the present invention relates to the tasks carrying progress metrics of field of distributed type and the time series forecasting of displaying and optimization thereof, relate in particular to tasks carrying progress metrics and displaying based on runtime data, and the self study time series forecasting based on runtime data in tasks carrying progress metrics.

Background technology

Distributed system (distributed system) is the software systems that are based upon on network.Exactly because the characteristic of its software, so distributed system has cohesion and the transparency of height.Therefore, the difference between network and distributed system is more high layer software (particularly operating system), instead of hardware.

Cohesion refers to each database distribution node high degree of autonomy, has local data base management system (DBMS).

The transparency refers to that each database distribution node is transparent concerning user's application, does not see local or long-range.

In distributed data base system, the imperceptible data of user distribute, and user not must know whether relation is cut apart, has or not duplicate, data to be stored in which website and affairs are carried out etc. on which website.Distributed system comprises the computing environment of client, centralized server end composition.Along with the growth of whole business datum scale, be tending towards gradually a constant value for some runtime data of system, for example: the payment of Alipay water power coal.Wherein the portfolio of each mechanism is also in continuous increase, its total amount is also in continuous growth, each institution business amount shared proportion in total amount is also tending towards constant in fluctuation, this steady state value levels off to the market share of this mechanism, and system need to be by a large amount of historical datas under some application scenarios, can make prediction to this value of next time point, become time series forecasting.

Time series forecasting is a kind of statistical prediction methods (algorithm), the time series data obtaining according to systematic observation, set up mathematical model by curve and parameter estimation, analyze its trend over time, the quantitative forecasting technique that target of prediction is extrapolated.Time Series Forecasting Methods is commonly used in such as national economy macro-control, enterprise operation and management, market potential prediction, the aspects such as weather forecast.Conventional Time Series Forecasting Methods comprises: moving average, exponential smoothing, linear trend, secondary trend etc.But in distributed system, use this prediction algorithm, the accuracy of its prediction depends on the collection of measured data, and the collection of measured data is often very slow, and depends on the operation of client.

At present, traditional software systems normally used " time series forecasting " method is generally using historical data as entering to join the predicted value of predicting the next time point of certain index by conventional moving average, exponential smoothing, linear trend, secondary trend scheduling algorithm.But for constantly producing in the distributed system of runtime data in a large amount of clients, its predictablity rate need further raising.

Runtime data refers generally to system or the business datum that software produces in the time of operation, time as used in certain task run, task weights, and newly-increased record entry in certain data form bit time, etc.

Further, in the performance history of software and internet program, the problem that often can run into serial task implementation progress tolerance and show, the demonstration tool of conventionally using is " progress bar ".

Progress bar, computing machine is in the time of Processing tasks, and real-time, the speed with visual pattern Graphics Processing task, completed percentage, remain abortive size and may need the processing time, generally shows with rectangle strip.

The displaying progress of the progress bar that each client is seen depends on the weights of each subtask.

Task weights, are often referred to computer task (as task j 1, j 2j n) in serial process, individual task actual execution time T(j i) (1≤i≤n) account for the number percent of whole tasks carrying deadlines is called task weights W i=T(j i)/(T(j 1)+T(j 2)+... + T(j n)) * 100%.

These weights may be tending towards steady state value along with the development fluctuation of business, and in other words steady state value is representing the trend of weights fluctuation, if higher to the precision of its prediction, also could be higher to implementation progress displaying and the fitting precision of time.

At present, conventionally progress bar is the completed percentage (number/general assignment of having finished the work counts * 100%) of executing the task based on serial, the exhibition scheme of excess time is generally: excess time=residue task amount/instantaneous velocity, this mode can be used for the ratio that tolerance task completes, but there are the following problems:

If varying in size of these tasks, the time that completes each task is also different, and the implementation progress that user is concerned about is time-based, carries out for the serial of one group of big or small uneven task, and progress bar must be inaccurate to the displaying of its implementation progress.

This method cannot accurately provide the time that the required time of not executing the task and general assignment are carried out, and the installation process of Here it is a lot of softwares cannot provide the reason of accurate " excess time ".

The calculating of excess time is for depending on the not science of task that external environment condition (as network environment) is stronger; because the change of external environment condition can directly have influence on " instantaneous velocity ", often there will be the unimaginably queer phenomenon of similar " excess time is more and more longer ".

In the performance history of software and internet program; the problem that often can run into serial task implementation progress tolerance and show; conventionally the demonstration tool of using is " progress bar "; and stand in user's angle, and we also often can find, the progress that a lot of progress bars are shown often has larger error; as the progress bar of the installation process of some software; very fast has reached 99%, but last 1% use for a long time, user experiences non-constant.If can show and provide the true implementation progress of height precise and the matching of time the progress bar of some type, will experience user, the serial multi-task execution time estimates and monitoring has greatly improved.

Summary of the invention

For the technological deficiency of above prior art, the technical matters that patented claim of the present invention will solve is to provide a kind of tasks carrying progress metrics based on runtime data and the system and method for displaying, disperse to produce runtime data for distributed system client, the feature of server centered deal with data, adopt the method for the self study time series forecasting of the distributed system of more optimizing, the progress bar of some type is shown to the matching that provides the true implementation progress of height precise and event, thereby user is experienced, the serial multi-task execution time is estimated and monitoring has very large lifting, improve tasks carrying tolerance and real accuracy, and improve the precision of prediction of entire system.

The application provides a kind of tasks carrying progress metrics based on runtime data and the system of displaying, comprise: progress metrics and control exhibiting device, be configured to: the execution of the task based on before this subtask and each subtask wherein and the runtime data that generates, measure the implementation progress of this subtask and each subtask thereof, and control the real-time progress of each subtask execution of this subtask of displaying and the position that the complete progress in each subtask of this subtask of control displaying advances to.

The application provides a kind of tasks carrying progress metrics based on runtime data and the system of displaying, comprise: server end, collect each subtask in multiple client k subtasks and carry out the runtime data producing, during based on history run, in data and k subtask, the runtime data producing is carried out in each subtask, carry out time series forecasting, upgrade the runtime data for measuring k+1 subtask and each subtask implementation progress thereof, and the runtime data sending after upgrading arrives described multiple clients; Wherein k >=2 natural number.

The application provides a kind of tasks carrying progress metrics based on runtime data and the method for displaying, comprise: the execution of the task based on before this subtask and each subtask wherein and the runtime data that generates, measure the implementation progress of this subtask and each subtask thereof, and control the real-time progress of each subtask execution of this subtask of displaying and the position that the complete progress in each subtask of this subtask of control displaying advances to.

The application provides a kind of tasks carrying progress metrics based on runtime data and the method for displaying, comprise: collect each subtask in multiple client k subtasks and carry out the runtime data producing, during based on history run, in data and k subtask, the runtime data producing is carried out in each subtask, carry out time series forecasting, upgrade the runtime data for measuring k+1 subtask and each subtask implementation progress thereof, and the runtime data sending after upgrading arrives described multiple clients; Wherein k >=2 natural number.

The scheme of patented claim of the present invention, utilize client numerous and all can produce the feature of runtime data, by the self study process of system, the task weights in the runtime data in the historical data base based on constantly updating and then subtask when operation to a task are predicted, the error of the operation progress bar to task is revised, Task Progress bar is provided to the matching of accurate implementation progress and time, realize the effect that precision of prediction improves rapidly for whole system, thereby tasks carrying progress is measured accurately and shown.

Brief description of the drawings

In order to be illustrated more clearly in the technical scheme of the embodiment of the present application, below the accompanying drawing of required use during embodiment is described is briefly described, apparently, accompanying drawing in the following describes is only some embodiment of the application, for those of ordinary skill in the art, do not paying under the prerequisite of creative work, can also obtain according to these accompanying drawings other accompanying drawing.

The tasks carrying progress metrics of weights and the progress bar schematic diagram of methods of exhibiting when Fig. 1 is the operation of the application's embodiment.

The process flow diagram that when Fig. 2 is the operation of the application's embodiment, the tasks carrying progress metrics of weights and the task of methods of exhibiting are moved for the first time.

The tasks carrying progress metrics of weights and the process flow diagram of each time later first subtask in service for the second time of methods of exhibiting when Fig. 3 is the operation of the application's embodiment.

The tasks carrying progress metrics of weights and the flowchart of general assignment for the second time of methods of exhibiting when Fig. 4 is the operation of the application's embodiment.

Fig. 5 is the closed loop configuration figure that realizes self study of a kind of self study time series forecasting system and method based on client runtime data of the application's embodiment.

Fig. 6 is the server end structural representation of a kind of self study time series forecasting system and method based on client runtime data of the application's embodiment.

Fig. 7 is the overall work mechanism of a kind of self study time series forecasting system and method based on client runtime data of the application's embodiment.

Tasks carrying progress metrics and the display systems schematic diagram of weights when Fig. 8 is the operation of the application's embodiment.

Embodiment

Below in conjunction with the accompanying drawing in the embodiment of the present application, the technical scheme in the embodiment of the present application is clearly and completely described, obviously, described embodiment is only some embodiments of the present application, instead of whole embodiment.Based on the embodiment in the application, those of ordinary skill in the art are not making the every other embodiment obtaining under creative work prerequisite, all belong to the scope of the application's protection.

Below, patented claim embodiment of the present invention will be entered an item of expenditure in the accounts platform as example taking Alipay bill business, its be a typical distributed system as shown in Figure 7,8, the application's the tolerance of tasks carrying progress and the system and method for displaying described.Tasks carrying progress metrics based on runtime data and the system of displaying comprise server end and one or more client (at least one client), and realize corresponding method in this system.The tasks carrying such as download, installation progress metrics under other distributed systems and displaying are also similar.

Enter an item of expenditure in the accounts in platform in Alipay bill business, the serial of Alipay bill need to generate the demand file of mechanism of many families (multiple), and the time that the demand file that generates each mechanism needs is directly proportional to the data volume of database Zhong Gai mechanism, and when serial generates each mechanism file, background interface need to be shown file generated progress by progress bar, as shown in Figure 1 progress bar schematic diagram.In Fig. 1, general assignment comprises 6 subtasks, requires to show the completion status of each subtask in progress bar dialog box, and the excess time needing, and then estimates the progress bar of general assignment and total excess time.Estimation based on runtime data also can realize by the time series forecasting of self study.

Here, generate the demand file of multiple mechanisms in the client of distributed system, and the time that the demand file that generates each mechanism needs is directly proportional to the data volume of database Zhong Gai mechanism, along with the growth of whole business datum scale, the runtime data in system is tending towards a constant value gradually.And when server end serial generates each mechanism file, constantly adjust the weighted value of each subtask by self study sequence prediction working time, the subtask weighted value of client background interface based on continuous adjustment shown file generated progress by progress bar.Although the data of database Zhong Ge mechanism can constantly have newly-increased, delete, amendment, but because data scale is larger, between each organization data amount, relative scale is very constant, and along with the development dataset of business is more and more, this is more and more constant than regular meeting, and then the weights of task can be tending towards steady state value.

The realization of the application's method and system will be described with object lesson in detail below.

The application's the tasks carrying progress metrics based on runtime data and the method for displaying, the execution of the task based on before this subtask and each subtask wherein and the runtime data that generates, measure the implementation progress of this subtask and each subtask thereof, and control the real-time progress of each subtask execution of this subtask of displaying and the position (as progress metrics and control exhibiting device) that the complete progress in each subtask of this subtask of control displaying advances to; Complete after this subtask, according to the task before this subtask, this subtask, with and each subtask in the actual complete corresponding runtime data being generated in each subtask, predict to upgrade the runtime data of runtime data based on described renewal and measure the implementation progress of task next time and each subtask thereof and control displaying progress (as prediction unit).Its also relate to initialization (as apparatus for initializing), for the second time task run taking initialized runtime data as basis, and task run is afterwards taking runtime data before as basic self study forecasting process, thereby with this tolerance task and subtask implementation progress and control the displaying of progress bar.Corresponding tasks carrying progress metrics based on runtime data and the system of displaying, the distributed system building taking server end and multiple client is as example.Server end, collect each subtask in described multiple client k subtask and carry out the runtime data producing, during based on history run, in data and k subtask, the runtime data producing is carried out in each subtask, carry out time series forecasting, upgrade the runtime data for measuring k+1 subtask and each subtask implementation progress thereof, and the runtime data sending after upgrading arrives described multiple clients; Wherein k >=2 natural number.The concrete self study prediction of this system and progress metrics and displaying are controlled corresponding to said method.This all describes launching in example below.

In tasks carrying progress metrics based on runtime data and the method and system of displaying, agreement mark used is as follows:

The pending task that is provided with serial comprise n subtask (1≤i≤n) the k time is in service, and its implementation progress shows by progress bar, and agreement variable is as follows:

The actual execution time of i subtask: T k(j i);

The prediction execution time of i subtask: ET k(j i);

The T.T. T that k subtask is actual complete k is total;

K subtask is estimated complete T.T. ET k is total=T k(j 1)/W (k-1) 1;

In k subtask, the task weights of the subtask in general assignment k of i subtask are estimated: W ki=T k-1(j i)/(T k-1(j 1)+T k-1(j 2)+... + T k-1(j n)) * 100%(k>=2).Here, due to this subtask also off-duty complete, this subtask the unknown working time, T.T. is also unknown, thereby can be by last time T k-1as estimated value.This is a kind of better simply method of estimation, in the present embodiment, also a kind of optimization algorithm based on runtime data will be provided, for example, below by specifically described preferred self study Time Series Forecasting Methods: " the self study time series forecasting based on runtime data in distributed system " algorithm.

The progress bar total length setting in advance: L;

I the length (i.e. this subtask shared length on progress bar) that the complete progress bar of sub-tasks carrying advances: L in k subtask ki;

The length that the current display line of progress bar enters: L realtime

I used time of subtask: UT in k subtask k(j i)

K subtask general assignment has been used time (monitor k subtask reality and carried out the time how long): UT k is total

K subtask is estimated excess time: ET k is surplus=ET k is total-UT k is total

The application's tasks carrying progress metrics and displaying, mainly describe from three phases:

1) task run (initial phase can claim) for the first time

Software is in development and testing process, and certain process through debugging has just been carried out " operation for the first time " of task in this process, as shown in Figure 2 (flow process that task is moved for the first time in test environment or true environment).Due to task do not carry out completely out-of-date, the time data relevant without any each subtask, now can only use classic method as previously described to show that progress (conventionally, this initial procedure does not show user, not accurately also not serious), each subtask schedule bar 1/n that advances, importantly, now needs to write down the actual execution time T that carry out each subtask 1(j i), and calculate its weights W in T.T. 1i.As the simplest a kind of account form W 1i=T 1(j i)/T 1 is total* 100%.

In Fig. 2, in running software or debug process, after general assignment starts to carry out, as step S201, n subtask starts to carry out.In step S207, judge whether that whole subtasks (n) are complete, if "No" (N), to the step S203 progress bar 1/n*100% that advances, and performs step S205 and records this subtask execution time T 1(j i), return step S201 and continue next subtask to carry out.Until that step S207 is judged as whole subtasks is complete, be judged as "Yes" (Y), enter step S209 progress bar and advance to 100%.In step S211, calculate each subtask weights W li=T 1(j i)/T 1 is total, and store each subtask weights at step S213.

Wherein, in step S205, system starts to record the execution time T of each subtask 1(j i), in this process, progress bar shows (out of true is also not serious, because for once) according to usual way, the weight (weights) that default each subtask accounts for general assignment is with regard to the task weights for 1/n(subtask).So after a subtask completes, Task Progress bar is by this initialized a kind of mode of just giving an example of 1/n*100%(of advancing, other progress mode also can adopt), obtain after the execution time of each subtask, calculate the task weights of i subtask of initialization (task for the first time):

W li=T 1(j i)/T 1 is total.

For example: the task of initialization for the first time (debug phase) has 3 subtasks.The 1st sub-tasks carrying, judges not complete (3 altogether), and the progress bar 1/3*100% that advances, records the 1st the actual execution in subtask T 1(j 1)=20s, then carries out the 2nd, the 3rd subtask successively, the progress bar 1/3*100% that in succession advances, and will record actual execution time T 1(j 2)=35s, T 1(j 3)=45s, T 1 is total=100s.Judgement executes 3 sub-Task Progress bars and advances to 1(100%), calculate the task weights of each subtask: W 11=T 1(j 1)/100=0.2, W 12=0.35, W 13=0.45, and storage.(s refers to unit: second).

Like this, in the time carrying out the subtask of a subtask, produce and have running time T k(j i), generate task weights W based on this ki, just can be used for the time series forecasting of each subtask of task next time, show its progress bar tolerance, utilize runtime data tasks carrying is measured and shown.

2) first subtask of later moving for the second time

Finish the work after the initialization of weights calculates in task run process for the first time, based on this, for the second time and later moved task can record and predict.Wherein, prediction mode preferably can adopt the self study Time Series Forecasting Methods of describing to carry out the prediction more accurately of lower subtask weights (runtime data) below.

For example: as shown in Figure 3, the execution time T of first subtask of later each subtask operation for the second time k(j 1) (k>=2) be to weigh the bases of other subtask execution time, estimates ET working time k(j 1) be the real time T of first subtask of moving in the k-1 time (last) task k-1(j 1), as estimated value, show this first subtask implementation progress, and then estimate excess time, point out as shown in Figure 1 subtask to estimate T excess time k-1(j 1)-UT k(j 1).After completing, record this T k(j 1), and the progress bar W that advances (k-1) 1* L.Importantly, calculate thus and predict T.T. and each subtask execution time of recording all the other tasks, can draw:

This subtask is estimated complete T.T.: ET k is total=T k(j 1)/W (k-1) 1

I subtask (prediction execution time of 2≤i≤n): ET k(j i)=W (k-1) i* ET k is total

I subtask shared length on progress bar is estimated: L ki=W (k-1) i* L

Calculate after result output result of calculation.These results are for example used for: according to execution ET excess time of execution time prompting this subtask of user of this prediction k(j i)-UT k(j i); After this will be complete in the shared length L of progress bar with this subtask kiand actual this subtask carries out with the time etc., the pace when controlling progress bar and showing is as shown L more accurately realtime, to realize the displaying of more accurate progress bar and the matching of tasks carrying progress.Here prediction mode can preferably adopt the self study Forecasting Methodology of describing below.

Complete behind this subtask, and then the progress bar W that advances (k-1) 1* 100%.

Detailed process is carried out in later first subtask for the second time of Fig. 3:

Step S301, first subtask is complete and record execution time T k(j 1).

Step S303, owing to having obtained the weights W of each subtask after the tasks carrying for the first time of " 1) for the first time task run " li, and first subtask is actual complete and the execution time T of record k(j 1), can calculate (prediction or estimation) result:

(1) this subtask is estimated complete T.T. ET k is total;

(2) i the subtask of this subtask (the prediction execution time ET of 2≤i≤n) k(j i);

(3) i subtask shared length L on progress bar of this subtask ki.

Step S305 exports the result calculating.

The step S307 progress bar W that advances (k-1) 1* 100%, enter next task.

Hold above-mentioned 1) in example: 3 of task subtasks for the second time, its 1st sub-tasks carrying, estimate working time ET 2(j 1)=T 1(j 1)=20s, the progress bar 0.2L that advances after completing.And record execution time T 2(j 1)=25s.Can calculate the T.T. of the complete needs of estimation of task: ET for the second time 2 is total=T 2(j 1)/W l1=25/0.2=125s.Then provide the prediction ET of the execution time of 2nd~3 subtasks below 2(j 2)=W l2* ET 2 is total=0.35*125=43.75s, ET 2(j 3)=56.25s.Calculate 1st~3 subtasks in the shared length L of progress bar 21=W l1* L=0.2L, L 22=W l2* L=0.35L, L 23=0.45L, the progress of each subtask of estimating.Preferably can adopt self study Forecasting Methodology described below herein.

Here, when progress display, can also control finer and smoothlyer, for example: 20s is carried out in the expectation of the 1st subtask, suppose that L is 100mm, the 1st subtask estimated to account for progress bar length 20mm, controls the advance mode of lattice of its each second or shorter time, progress display can be finer and smoother, promotes user and experience impression.The control of other subtask progresses also can adopt this every a very short time, the mode just taking a step forward.

3) other subtasks of later moving for the second time

Fig. 4 has described general assignment flowchart for the second time.For the second time, other subtasks of operation refer in the implementation of general assignment for the second time, other subtasks beyond its first subtask.The step according to identical is carried out in the execution of each general assignment afterwards.Complete the operation of general assignment for the first time obtain initialized task weight and by after at every turn behind first subtask of task run, this subtask T.T. based on predicting, an i subtask (prediction execution time and i the subtask shared length on progress bar of 2≤i≤n); After this i in service sub-tasks carrying is complete, progress bar advances to L realtime=(L k1+ L k2+ ... + L ki); And the real-time exhibition position of progress bar is L in i sub-tasks carrying process realtime=(L k1+ L k2+ ... + L ki-1)+(UT k(j i)/ET k(j i)) * L ki.By the tolerance of runtime data (the subtask weights of subtask working time, last time etc.) antithetical phrase tasks carrying progress, control the speed that progress bar is shown, make this L realtimeit is more accurate to show.And adopt self study Forecasting Methodology described below to predict and will more contribute to the accuracy of its tolerance runtime data.

Complete behind whole subtasks of the k time all operations, progress bar has shown, and has recalculated these each subtask weights and store each subtask weights according to this operation.Each subtask is afterwards carried out the weights based on self study is recalculated to a sub-task weight, is upgraded one after another with the weight of this subtask, more and more accurate, more and more tends to be steady, and system also tends towards stability.

Step S401 general assignment is carried out and is started.The general assignment here not 1) task initialization task in other words for the first time.

Step S403, first subtask is carried out and is handled accordingly: the flow process that carry out first subtask is as 2) for the second time after first subtask of operation, referring to the example of Fig. 3, the progress that after completing, the progress bar of output result of calculation, estimation advances is W (k-1) 1* 100%.

Step S411 judges whether that whole subtasks (n-1) are individual complete, and (N) enters step S405, step S407 if not.

In step S405, in i sub-tasks carrying process, progress bar advances to L in real time realtime=(L k1+ L k2+ ... + L ki-1)+(UT k(j i)/ET k(j i)) * L ki.Enter step S407, i sub-tasks carrying advances to L completely again realtime=(L k1+ L k2+ ... + L ki).Based on runtime data as task weights, the actual monitoring of subtask carry out use time, the execution time of estimation, estimate shared length when complete, measuring tasks carrying progress and controlling the pace of progress bar, to realize the displaying of more accurate progress bar and the matching of tasks carrying progress.Here, because the prediction based on data with existing of advancing in real time of progress bar is carried out, to estimate more accurately, if and progress bar can be adjusted to relevant position and carries out next subtask execution (as utilized step S405, S407) again after i sub-tasks carrying completes, so again can be more accurate, subtask tolerance is below also more convenient, thereby can play role of correcting.

At step S409, record this subtask execution time T k(j i).

Until that step S411 is judged as whole subtasks of this time operation is complete, if be judged as (Y), enter step S413 progress bar and advance to 100%.

At step S415, calculate this each subtask weights W k,i, store each subtask weights at step S417, then enter step S419 weights and recalculate.Preferably, adopt the self study Time Series Forecasting Methods of runtime data in the distributed system of hereinafter mentioning, based on the runtime data of new collection, the weight that task will be used is next time carried out to recalculating of weights.

Wherein, in step S409, each subtask execution time T is recorded in the subtask of each client after all completing k(j i), just obtained new data T because each subtask is complete k(j i), calculate the weights of new subtask.Preferably, be exactly the runtime data that this client this time produces in the operational process of (the k time) general assignment based on these execution time, weights, can be used to enrich historical data base, and then raising precision of prediction, and other clients also can produce runtime data in using this software, also further reach the effect of enriching rapidly historical data base and then improving precision of prediction; And all predicted values are as ET k(j i), ET k is total, an i subtask shared length L on progress bar ki, can recalculate (mode with above describe similar) further re-computation weights (step S411) at this.Self study Time Series Forecasting Methods based on runtime data in a kind of improved distributed system is provided here, as preferred prediction mode, will be explained hereinafter.

Hold above-mentioned 1), 2) in example: task run for the second time, its the 1st sub-tasks carrying also completes the 2nd) article operation, judgement do not execute all tasks, enter the execution of the 2nd subtask, calculate in the 2nd sub-tasks carrying process the position L that progress bar advances in real time realtime=L 21+ (UT 2(j 2)/ET 2(j 2)) * L 22if, monitor the time UT that has carried out the 2nd subtask here 2(j 2)=10s, is now controlling progress bar pace, shows and arrives L realtime=0.2L+(10/43.75) * 0.35L=0.28L; The 2nd sub-tasks carrying is complete advances to:

L realtime=L 21+ L 22=0.2L+0.35L=0.55L, and the 2nd the actual time T that executes use in subtask of record 2(j 2)=45s.

Judge whole subtasks, continued the 3rd sub-tasks carrying, calculated its real-time progressive position L realtime=0.55L+10/56.25*0.45L=0.63L, it executes and advances to L realtime=L, and the time T of the actual execution in the 3rd subtask of record use 2(j 3)=40s.It is actual that to complete T.T. be T 2 is total=25+45+40=110s.

Judged whole subtasks, progress bar advances to last 100%.And the calculating task weights W of each subtask for the second time 21=25/110=0.227, W 22=45/110=0.409, W 23=40/110=0.364, and storage.And can be according to W 11=0.2, W 12=0.35, W 13=0.45 and W 21=0.227, W 22=0.409, W 23=0.364 recalculates each subtask weights, for example, calculate by time series forecasting mode, or describe the improved a kind of self study time series forecasting of the application after more optimally adopting.Here simply, for example mean value mode is predicted, obtains new for the task weights of each subtask of task: W for the third time 21=0.214, W 22=0.380, W 23=0.407.

Like this, these task weights are all according in current tasks carrying, (re-computation) that the runtime data (execution time, task weights etc.) of subtask upgrades, to can predict more accurately tasks carrying progress and show the tasks carrying progress of measuring in the time of upper once tasks carrying.

Hold above-mentioned example:

If tasks carrying for the third time, the 1st sub-tasks carrying, estimates ET working time 3(j 1)=T 2(j 1)=25s, and record execution time T 3(j 1)=20s.

Calculate the T.T. ET of task for the second time 3 is total=T 3(j 1)/W 21=20/0.214=93s;

Then provide the prediction ET of the execution time of 2nd~3 subtasks below 3(j 2)=W 22* T 3 is total=0.380*93=35.34s, ET 3(j 3)=37.85s;

Calculate 1st~3 subtasks of prediction in the shared length L of progress bar 31=W 21* L=0.214L, L 32=W 22* L=0.380L, L 33=0.407L;

Output result of calculation and for complete the 1st subtask of tasks carrying for the third time, the progress bar 1/3*100% that advances.

Judgement does not execute all tasks, enters the execution of the 2nd subtask, calculates in the 2nd sub-tasks carrying process the position L that progress bar advances in real time realtime=0.214L+(10/35.34) * 0.380L=0.322L;

The 2nd the complete L that advances to of sub-tasks carrying realtime=0.214L+0.380L=0.594L, and the 2nd the actual time T that executes use in subtask of record 3(j 2)=35s.

Judge whole subtasks, continued the 3rd sub-tasks carrying, calculated its real-time progressive position L realtime=0.594L+10/37.85*0.407L=0.702L, it executes and advances to L realtime=L, and the time T of the actual execution in the 3rd subtask of record use 3(j 3)=40s.

Judged whole subtasks, progress bar advances to last 100%.And the calculating task weights W of each subtask for the third time 31=20/(20+35+40)=0.211, W 32=35/95=0.368, W 33=40/95=0.421, and storage.And can be according to W 11=0.2, W 12=0.35, W 13=0.45 and W 21=0.227, W 22=0.409, W 23=0.364 and W 31=0.211, W 32=0.368, W 33=0.421 recalculates each subtask weights, and for example average mode calculates for the weights of the 4th subtask and is: W 31=0.213, W 22=0.376, W 23=0.412.

So, runtime data based on storage (as the historic task weights of storage) and new runtime data (as completing the task weights that calculated according to the execution time subtask) repeatedly, recalculate the weights of going out on missions, at below the 4th time, the 5th time ... in the progress metrics prediction and displaying of task, to more be tending towards reasonable, accurate and stable, be the value that weights more and more tend towards stability, more accurate, it is also just more accurate to show in order to estimation tasks implementation progress and control progress bar.

Here, subtask progress matching has Recursivity, if some subtask can become more fine-grained secondary subtask by Further Division, still can use so this scheme on these secondary subtasks, by that analogy, until can not use.The so more accurately precision of matching subtasks at different levels.

By specifically describing the improved a kind of more excellent time series forecasting algorithm of the application, based on self-learning method, carry out weights re-computation below.

The self study time series forecasting of distributed runtime data: below in conjunction with a system that comprises server end and multiple client (as the Alipay bill business platform of entering an item of expenditure in the accounts), above-described tasks carrying progress metrics and real method are specifically described.Be in operation and improve constantly weights Wk by self study process, the levels of precision of i.The closed loop of Fig. 5 has been described the self study process of whole distributed system, and the solution that the application provides just possesses the self-learning capability based on runtime data.In the time that each tasks carrying produces service data, recalculate, to reach the increase along with number of run, progress bar progress shows the more accurate object of fitting degree with true progress.

First, step S501 system is moved with up-to-date weights directs client; Step S503, along with the operation of the subtask of client constantly produces runtime data, as task weights W ki; Then, step S505, server end is by the client runtime data the receiving historical data base of enriching constantly, as the historical data storage module 603 in Fig. 6; Step S507, the higher weights of database computational accuracy that system utilization is new, realize self study.

Like this, utilize distributed system multi-client, the feature of server centered, adopts distributed data collection (as the client 1,3,4 in Fig. 7), centralized data processing and prediction (as server end).Processing procedure is transparent for single client (as the client 8 in Fig. 7), and single client has improved rapidly precision of prediction without computing in the situation that in unaware.

The runtime data of client, if the prediction of task weights is to complete at server end, refers to Fig. 6.Comprise runtime data receiver module 601 in the structure of server end 600, historical data storage module 603, time series forecasting module 605 and the sending module 607 that predicts the outcome.

First, the runtime data of server end 600 is accepted module 601 and is received client 1, 3, 4 runtime datas, then deposited in history data store module 603, and notify time series forecasting module 605, time series forecasting module 605 finds that there is new historical data and enters in historical data storage module 603, the data that start to call in historical data storage module 603 are carried out task weights prediction (will introducing in detail the prediction of server end 600 task weights below), finally by result (new task weights), by predicting the outcome, sending module 607 sends to client 8, then the task weights based on upgrading carry out tasks carrying progress on progress bar, measure and show.It should be noted that the time that historical data storage module 603 need to receive according to runtime data, safeguard that its time series carries out unified time-sequencing, use in order to time series forecasting module 605.

The overall work mechanism of the application's the self study time series forecasting based on client runtime data has been described in Fig. 7.The system of its operation comprises server end 600 and the multiple clients (as client 1~8) associated with it.The execution of task is according to time point t 0~t 6(time shaft as shown in Figure 7) gradually backward, the existing subtask taking one of them client 8, as example, illustrates the application's embodiment.Stand in the angle of client 8:

At t 0in the moment, client 8 is executed the task for the k time, and progress bar is measured according to old task weights and shown (client is measured and shows according to old weights as shown in [0]);

At t 1moment is (as time: t 1shown in), the weights of each subtask of client 8 are sent to server end 600, and record the actual execution time T of this moment i subtask k(j i) and calculate corresponding weights W kiand store in historical data storage module 603.

At t 2~t 4moment is (as time: t 2, t 3, t 4shown in), client 1,3,4, has also moved identical task, and corresponding each subtask weights are sent to server end 600;

At this moment, server end 600 has collected more runtime data (as [1] client sends as shown in real-time running data as runtime data receiver module 601, send in real time runtime data to server end, and predict (as [2] calculate each task weights in real time) according to Time Series Forecasting Methods, and send to client 8(as time: t new forecast power 5shown in);

At t 6moment is (as time: t 6shown in), client 8 is executed the task for the k+1 time, and progress bar is measured and shows according to new weights, and new weights estimation formulas is W k+1i=T k(j i)/(T k(j 1)+T k(j i)+... + T k(j n)) * 100%(k>=2).By the repeatedly execution of task of each client, server end 600 will be it is predicted the weights of the subtask that obtains each client, i.e. time series forecasting process in distributed system according to service hours.The runtime data that server end 600 obtains as shown in table 1 is below (with the task weights W of subtask kifor example):

Table 1:

? W k,1 Wk,2 W k,3 W k,4 …… The 1st operation 0.234 0.102 0.028 0.428 ? The 2nd operation 0.239 0.100 0.021 0.421 ? The 3rd operation 0.134 0.095 0.031 0.431 ? The 4th operation 0.230 0.097 0.029 0.429 ? The 5th operation 0.232 0.096 0.028 0.427 ? The 6th operation 0.231 0.098 0.028 0.428 ? …… ? ? ? ? ?

W in table 1 k,irepresent i subtask weights in the time of the operation in the k time when operation, these runtime datas are bases of prediction, in the time collecting k secondary data in historical data storage, the object of time series forecasting is exactly to utilize these data predictions to go out the value of k+1 subtask weights Wi.As W in table 1 2by one group of weights of the 1st time to the 6th time, predict the weights of the 7th time.Time series forecasting module can adopt conventional Time Series Forecasting Methods to predict, these methods comprise moving average, exponential smoothing, linear trend, secondary trend etc.

Obtaining each subtask after the weights of the k time operation are estimated, similarly can show according to formula L the present position of progress bar by the method for going forward one by one realtime=(L k1+ L k2+ ... + L ki-1)+(UT k(j i)/ET k(j i)) * L ki.(as client 8 will be measured and show according to new weights).

So, client 8 term of execution of the k time and k+1 subtask, not any other behavior, but it is more accurately in the weights predicted value obtaining for the k+1 time because new weights be based on upgrade and more weights.Like this, client (as client 8,1,3,4 etc.) in executing the task, can constantly obtain the runtime data self producing, these data have been enriched database again, for prediction provides more Data support.And time series forecasting is the accuracy that therefore can increase prediction and then guidance system operation based on historical data base.

Conventionally, this class distributed system has a large amount of clients, and these clients constantly produce runtime data simultaneously, if by these data applications, can expand rapidly historical data base.And for single client, each tasks carrying loops data prediction, the precision of prediction between its adjacent 2 subtasks execution can promote rapidly, in table 1.

To this, improve tasks carrying progress metrics and the ways of presentation based on the runtime data that obtain, subtask matching Recursivity, so that the displaying of the implementation progress of matching task accurately and progress, predict in high accuracy each task execution time and excess time, whole tasks carrying T.T.s.It is particularly useful for the more stable group task of each task scale relative scale.

Improved self study time series forecasting system and method in patented claim of the present invention, disperses to produce runtime data for distributed system client, and the feature of server centered deal with data, improves classic method, and principal feature is as follows:

1, some clients are in executing the task, (these data have been enriched historical data base again can constantly to obtain the runtime data self producing, for prediction provides more Data support), and time series forecasting is based on historical data base, therefore can increase the accuracy of prediction, and then the operation of guidance system.

2, distributed system has a large amount of clients conventionally, and these clients may, also constantly producing runtime data, also can expand rapidly historical data base if these data are put into historical data storage module simultaneously.

3. the self study process that used and progress bar tolerance and display packing, without knowing task T.T. and each subtask time, only need be operation or test period obtain the weights of each subtask for the first time, by the matching of pin-point accuracy, for multi-level task, can be further subdivided into next stage subsystem to subtask, by subtask recurrence matching, task weights can constantly be corrected by self study, constantly to increase prediction accuracy.

Its beneficial effect bringing as: for single client, each tasks carrying loops data prediction, precision of prediction between its adjacent 2 subtasks execution can promote rapidly, processing procedure is transparent for single client, and single client has improved rapidly precision of prediction without computing in the situation that in unaware.

Corresponding the application's the tasks carrying progress metrics based on runtime data and the method for displaying, also have the tasks carrying progress metrics based on runtime data of its application and the system of displaying, comprise server end and multiple client wherein, server end or also comprise in the internal memory of server end: runtime data receiver module, receives the described runtime data from described multiple clients; Historical data storage module, storage data and receive the new described runtime data that described client produces when history run; Time series forecasting module, data when the described runtime data receiving according to described runtime data receiver module and the history run of historical data storage module storage, by described self study Time Series Forecasting Methods, predict next Runtime, weights while calculating described new operation; The sending module that predicts the outcome, sends in described multiple client by predicting the outcome of time series forecasting module.

Each embodiment in this instructions is general, and the mode of going forward one by one that adopts is described, and what each embodiment stressed is and the difference of other embodiment, between each embodiment identical similar part mutually referring to.

The application can describe in the general context of computer executable instructions, for example program module or unit.Usually, program module or unit can comprise and carry out particular task or realize routine, program, object, assembly, data structure of particular abstract data type etc.In general, program module or unit can be realized by software, hardware or both combinations.Also can in distributed computing environment, put into practice the application, in these distributed computing environment, be executed the task by the teleprocessing equipment being connected by communication network.In distributed computing environment, program module or unit can be arranged in the local and remote computer-readable storage medium including memory device.

In a typical configuration, computing equipment comprises one or more processors (CPU), input/output interface, network interface and internal memory.

Internal memory may comprise the volatile memory in computer-readable medium, and the forms such as random access memory (RAM) and/or Nonvolatile memory, as ROM (read-only memory) (ROM) or flash memory (flash RAM).Internal memory is the example of computer-readable medium.

Computer-readable medium comprises that permanent and impermanency, removable and non-removable media can realize information storage by any method or technology.Information can be module or other data of computer-readable instruction, data structure, program.The example of the storage medium of computing machine comprises, but be not limited to phase transition internal memory (PRAM), static RAM (SRAM), dynamic RAM (DRAM), the random access memory (RAM) of other types, ROM (read-only memory) (ROM), Electrically Erasable Read Only Memory (EEPROM), fast flash memory bank or other memory techniques, read-only optical disc ROM (read-only memory) (CD-ROM), digital versatile disc (DVD) or other optical memory, magnetic magnetic tape cassette, the storage of tape magnetic rigid disk or other magnetic storage apparatus or any other non-transmission medium, can be used for the information that storage can be accessed by computing equipment.According to defining herein, computer-readable medium does not comprise non-temporary computer readable media (transitory media), as data-signal and the carrier wave of modulation.

Finally, also it should be noted that, term " comprises ", " comprising " or its any other variant are intended to contain comprising of nonexcludability, thereby the process, method, commodity or the equipment that make to comprise a series of key elements not only comprise those key elements, but also comprise other key elements of clearly not listing, or be also included as the intrinsic key element of this process, method, commodity or equipment.The in the situation that of more restrictions not, the key element being limited by statement " comprising ... ", and be not precluded within process, method, commodity or the equipment that comprises described key element and also have other identical element.

Applied principle and the embodiment of specific case to the application herein and set forth, the explanation of above embodiment is just for helping to understand the application's method and main thought thereof; , for one of ordinary skill in the art, according to the application's thought, all will change in specific embodiments and applications, in sum, this description should not be construed as the restriction to the application meanwhile.

Claims (25)

1. the tasks carrying progress metrics based on runtime data and a method for displaying, is characterized in that, comprising:
The execution of the task based on before this subtask and each subtask wherein and the runtime data that generates, measure the implementation progress of this subtask and each subtask thereof, and control the real-time progress of each subtask execution of this subtask of displaying and the position that the complete progress in each subtask of this subtask of control displaying advances to.
2. the method for claim 1, is characterized in that, comprising:
Task is initialization task for the first time;
The subtask of carrying out based on serial in initialization task completes the ratio that accounts for total subtask number of counting, the implementation progress of tolerance initialization task and each subtask thereof;
According to the initialization task of tolerance and the implementation progress of each subtask thereof, show progress in the complete ratio that accounts for total subtask number in each subtask;
Based on the execution of each subtask in initialization task, generate corresponding runtime data.
3. method as claimed in claim 2, it is characterized in that, the execution of the task based on before this subtask and each subtask wherein and the runtime data that generates, measure the implementation progress of this subtask and each subtask thereof, and control the real-time progress of each subtask execution of this subtask of displaying and the position that the complete progress in each subtask of this subtask of control displaying advances to, comprising:
Carry out the corresponding runtime data generating based on first subtask in initialization task, measure the implementation progress of first subtask in task for the second time the implementation progress of showing described first subtask;
First subtask complete in task, records the execution time for the second time;
The corresponding runtime data that execution based on recording each subtask in execution time and initialization task generates, generates: tasks carrying T.T., i sub-task execution time, the shared progress length in an i subtask in task for the second time for the second time;
The result that output generates;
Control shows that the progress after first subtask in task for the second time complete advances to 1/n;
Wherein, 2≤i≤n, n represents subtask number, n is natural number.
4. the method for claim 1, it is characterized in that, the execution of the task based on before this subtask and each subtask wherein and the runtime data that generates, measure the implementation progress of this subtask and each subtask thereof, and control the real-time progress of each subtask execution of this subtask of displaying and the position that the complete progress in each subtask of this subtask of control displaying advances to, comprising:
The corresponding runtime data that the execution of first subtask of the each subtask in the task based on before this subtask generates is predicted upgraded runtime data, measures the implementation progress of first subtask in this subtask the implementation progress of showing described first subtask;
In this subtask, first subtask is complete, records the execution time;
The corresponding runtime data that the execution of each subtask based on recording the each subtask in execution time and the task based on before this subtask generates is predicted upgraded runtime data, generates: i sub-task execution time, the shared progress length in an i subtask in this tasks carrying T.T., this subtask;
Output generates result;
Control the progress of showing after first subtask in this subtask complete and advance to 1/n;
Wherein, 2≤i≤n, n represents subtask number, n is natural number.
5. method as claimed in claim 3, it is characterized in that, the execution of the task based on before this subtask and each subtask wherein and the runtime data that generates, measure the implementation progress of this subtask and each subtask thereof, and control the real-time progress of each subtask execution of this subtask of displaying and the position that the complete progress in each subtask of this subtask of control displaying advances to, comprising:
Generate result according to described output, control the change in location of showing that in this subtask, the real-time progress in i sub-tasks carrying process advances to;
The position that progress advances to after i in this subtask sub-tasks carrying completes, and i the sub-tasks carrying of this subtask of control displaying completes;
In minute book subtask, i sub-tasks carrying completes the execution time used.
6. method as claimed in claim 4, it is characterized in that, the execution of the task based on before this subtask and each subtask wherein and the runtime data that generates, measure the implementation progress of this subtask and each subtask thereof, and control the real-time progress of each subtask execution of this subtask of displaying and the position that the complete progress in each subtask of this subtask of control displaying advances to, comprising:
Generate result according to described output, control the change in location of showing that in this subtask, the real-time progress in i sub-tasks carrying process advances to;
The position that progress advances to after i in this subtask sub-tasks carrying completes, and i the sub-tasks carrying of this subtask of control displaying completes;
In minute book subtask, i sub-tasks carrying completes the execution time used.
7. the method as described in one of claim 1-6, is characterized in that, comprising:
Complete after this subtask, according to the task before this subtask, this subtask, with and each subtask in the actual complete corresponding runtime data being generated in each subtask, predict to upgrade the runtime data of runtime data based on described renewal and measure the implementation progress of task next time and each subtask thereof and control displaying progress.
8. method as claimed in claim 7, it is characterized in that, according to the task before this subtask, this subtask, with and each subtask in the actual complete corresponding runtime data being generated in each subtask, predict to upgrade runtime data, runtime data based on described renewal is measured the implementation progress of task next time and each subtask thereof and is controlled displaying progress, comprising:
In this subtask, all subtasks are complete, control and show that this Task Progress advances to final position;
According to complete each execution time used of each subtask in this subtask of record, generate corresponding runtime data, with with this before task and the actual complete corresponding runtime data being generated in each subtask wherein, predict to upgrade runtime data, and runtime data based on described renewal is measured the implementation progress of task next time and each subtask thereof.
9. method as claimed in claim 7 or 8, it is characterized in that, according to the task before this subtask, this subtask, with and each subtask in the actual complete corresponding runtime data being generated in each subtask, predict to upgrade runtime data, comprising:
Based on the corresponding runtime data generating, carry out time series forecasting, to upgrade runtime data.
10. method as claimed in claim 9, is characterized in that, described time series forecasting is self study time series forecasting; Described self study time series forecasting comprises: the data when history run of the corresponding runtime data based on generating and the runtime data rich store that constantly generates when executing the task, thus predict that the required runtime data of new tasks carrying upgrades the runtime data of the implementation progress of the task next time of measuring and each subtask thereof.
11. the method for claim 1, is characterized in that, the task weights that described runtime data is each subtask.
12. the method for claim 1, is characterized in that, the task before described subtask is: the repeatedly task before all subtasks before this subtask or the last task of this subtask or this subtask.
13. 1 kinds of tasks carrying progress metrics based on runtime data and the system of displaying, is characterized in that, comprising:
Progress metrics and control exhibiting device, be configured to: the execution of the task based on before this subtask and each subtask wherein and the runtime data that generates, measure the implementation progress of this subtask and each subtask thereof, and control the real-time progress of each subtask execution of this subtask of displaying and the position that the complete progress in each subtask of this subtask of control displaying advances to.
14. systems as claimed in claim 13, is characterized in that, progress metrics and control exhibiting device comprise:
The corresponding runtime data that the execution of first subtask of the each subtask in the task based on before this subtask generates is predicted upgraded runtime data, measures the implementation progress of first subtask in this subtask the implementation progress of showing described first subtask;
In this subtask, first subtask is complete, records the execution time;
The corresponding runtime data that the execution of each subtask based on recording the each subtask in execution time and the task based on before this subtask generates is predicted upgraded runtime data, generates: i sub-task execution time, the shared progress length in an i subtask in this tasks carrying T.T., this subtask;
Output generates result;
Control the progress of showing after first subtask in this subtask complete and advance to 1/n;
Wherein, 2≤i≤n, n represents subtask number, n is natural number.
15. systems as claimed in claim 14, is characterized in that, progress metrics and control exhibiting device comprise:
Generate result according to described output, control the change in location of showing that in this subtask, the real-time progress in i sub-tasks carrying process advances to;
The position that progress advances to after i in this subtask sub-tasks carrying completes, and i the sub-tasks carrying of this subtask of control displaying completes;
In minute book subtask, i sub-tasks carrying completes the execution time used.
16. systems as described in one of claim 13-15, is characterized in that, comprising:
Prediction unit, be configured to: complete after this subtask, according to the task before this subtask, this subtask, with and each subtask in the actual complete corresponding runtime data being generated in each subtask, predict to upgrade the runtime data of runtime data based on described renewal and measure the implementation progress of task next time and each subtask thereof and control displaying progress.
17. systems as claimed in claim 16, is characterized in that, prediction unit comprises:
In this subtask, all subtasks are complete, control and show that this Task Progress advances to final position; According to complete each execution time used of each subtask in this subtask of record, generate corresponding runtime data, with with this before task and the actual complete corresponding runtime data being generated in each subtask wherein, predict to upgrade runtime data, and runtime data based on described renewal is measured the implementation progress of task next time and each subtask thereof;
Wherein, based on the corresponding runtime data generating, carry out time series forecasting, to upgrade runtime data; Described time series forecasting is self study time series forecasting; Described self study time series forecasting comprises: the data when history run of the corresponding runtime data based on generating and the runtime data rich store that constantly generates when executing the task, thus predict that the required runtime data of new tasks carrying upgrades the runtime data of the implementation progress of the task next time of measuring and each subtask thereof.
18. 1 kinds of tasks carrying progress metrics based on runtime data and the system of displaying, is characterized in that, comprising:
Server end, collect each subtask in multiple client k subtasks and carry out the runtime data producing, during based on history run, in data and k subtask, the runtime data producing is carried out in each subtask, carry out time series forecasting, upgrade the runtime data for measuring k+1 subtask and each subtask implementation progress thereof, and the runtime data sending after upgrading arrives described multiple clients; Wherein k >=2 natural number.
19. systems as claimed in claim 18, is characterized in that, comprising:
Any in described multiple client is in the time carrying out k+1 subtask, utilize the runtime data after described renewal, measure k+1 subtask and each subtask implementation progress thereof, and control show the real-time progress that carries out each subtask of k+1 subtask and complete after progress.
20. systems as claimed in claim 19, is characterized in that, comprising:
Described server end, carries out described time series forecasting, and it is the self study time series forecasting based on runtime data;
Described self study time series forecasting, the data when history run of the runtime data rich store constantly producing during by described multiple client executing task, the described runtime data of data and collection when described server end utilizes described history run, predict the new required runtime data of tasks carrying, and send runtime data after upgrading to described multiple clients, to utilize runtime data after described renewal to measure new tasks carrying progress and to control the displaying of its implementation progress.
21. systems as claimed in claim 20, is characterized in that, described server end also comprises:
Runtime data receiver module, carries out for collecting the each subtask of described multiple client k subtask the runtime data producing;
Historical data storage module, data when storage history run, and receive the described runtime data from the collection of runtime data receiver module;
Time series forecasting module, during based on history run, in data and k subtask, the runtime data producing is carried out in each subtask, carries out time series forecasting, upgrades the runtime data for measuring k+1 subtask and each subtask implementation progress thereof;
The sending module that predicts the outcome, sends runtime data after upgrading to described multiple clients.
22. 1 kinds of tasks carrying progress metrics based on runtime data and the method for displaying, is characterized in that, comprising:
Collect each subtask in multiple client k subtasks and carry out the runtime data producing, during based on history run, in data and k subtask, the runtime data producing is carried out in each subtask, carry out time series forecasting, upgrade the runtime data for measuring k+1 subtask and each subtask implementation progress thereof, and the runtime data sending after upgrading arrives described multiple clients; Wherein k >=2 natural number.
23. methods as claimed in claim 22, is characterized in that, comprising:
Any in described multiple client is in the time carrying out k+1 subtask, utilize the runtime data after described renewal, measure k+1 subtask and each subtask implementation progress thereof, and control show the real-time progress that carries out each subtask of k+1 subtask and complete after progress.
24. methods as claimed in claim 23, is characterized in that, comprising:
Described time series forecasting, it is the self study time series forecasting based on runtime data;
Described self study time series forecasting, the data when history run of the runtime data rich store constantly producing during by described multiple client executing task, the described runtime data of data and collection while utilizing described history run, predict the new required runtime data of tasks carrying, and send runtime data after upgrading to described multiple clients, to utilize runtime data after described renewal to measure new tasks carrying progress and to control the displaying of its implementation progress.
25. methods as claimed in claim 24, is characterized in that, collect each subtask in described multiple client k subtask and carry out the runtime data producing;
Data when storage history run, and the described runtime data of collecting;
When history run based on described storage, in data and k subtask, the runtime data producing is carried out in each subtask, carries out time series forecasting, upgrades the runtime data for measuring k+1 subtask and each subtask implementation progress thereof;
The runtime data sending after upgrading arrives described multiple clients.
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