CN115866054B - Non-periodic task scheduling method, device and medium based on bandwidth server - Google Patents
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Abstract
The embodiment of the invention discloses a non-periodic task scheduling method, a device and a medium based on a bandwidth server, wherein the method can comprise the following steps: according to the system state information of the non-periodic tasks in the single processor system, a hard constant bandwidth server feedback control algorithm is adopted to construct a system state dynamic equation; identifying unknown parameters in a dynamic equation of the system state by using a least square method according to control input of a controller, an actual value, a predicted value and a residual function of the system state to obtain an identification result of the unknown parameters; evaluating the identification result of the unknown parameters by using root mean square error and a judgment coefficient to obtain an optimal matching function of the average response delay of the non-periodic task and the difference between the non-periodic task utilization rate and the server bandwidth; and dynamically adjusting the bandwidth of the server according to the utilization rate of the non-periodic tasks by utilizing the optimal matching function so as to obtain the minimized average response delay of the non-periodic tasks.
Description
Technical Field
The embodiment of the invention relates to the field of non-periodic task scheduling of a star system, in particular to a non-periodic task scheduling method, device and medium based on a bandwidth server.
Background
When an external event occurs, the star system captures with non-periodic tasks in response to the resulting workload. The execution time and release interval of each non-periodic job vary greatly, and direct scheduling may miss deadlines for real-time tasks in the system. For this reason, the system response time of the non-periodic job is optimized at the same time without sacrificing the performance of the periodic real-time task, and further consideration of the scheduling policy is required.
Disclosure of Invention
Accordingly, embodiments of the present invention are expected to provide a method, an apparatus, and a medium for scheduling an aperiodic task based on a bandwidth server, which can reduce the average response delay of the aperiodic task while ensuring a lower periodic task miss rate.
The technical scheme of the embodiment of the invention is realized as follows:
In a first aspect, an embodiment of the present invention provides a method for scheduling an aperiodic task based on a bandwidth server, including:
According to the system state information of the non-periodic task operation process in the single processor system, a hard constant bandwidth server feedback control algorithm is adopted to construct a system state dynamic equation; the system state dynamic equation comprises unknown parameters of the average response delay of the non-periodic task and the relationship between the non-periodic task utilization rate and the difference multiple of the server bandwidth;
Identifying unknown parameters in a dynamic equation of the system state by using a least square method according to control input of a controller, an actual value, a predicted value and a residual function of the system state to obtain an identification result of the unknown parameters;
Evaluating the identification result of the unknown parameters by using root mean square error and a judgment coefficient to obtain an optimal matching function of the average response delay of the non-periodic task and the difference between the non-periodic task utilization rate and the server bandwidth;
and dynamically adjusting the server bandwidth according to the utilization rate of the non-periodic task by utilizing the optimal matching function of the average response delay of the non-periodic task and the difference between the non-periodic task utilization rate and the server bandwidth so as to obtain the minimized average response delay of the non-periodic task.
In a second aspect, an embodiment of the present invention provides an aperiodic task scheduling device based on a bandwidth server, where the device includes: a construction part, an identification part, an evaluation part and an adjustment part; wherein,
The construction part is configured to construct a system state dynamic equation by adopting a hard constant bandwidth server feedback control algorithm according to the system state information of the non-periodic task operation process in the single processor system; the system state dynamic equation comprises unknown parameters of the average response delay of the non-periodic task and the relationship between the non-periodic task utilization rate and the difference multiple of the server bandwidth;
the identification part is configured to identify the unknown parameters in the system state dynamic equation by using a least square method according to the control input of the controller, the actual value, the predicted value and the residual function of the system state so as to obtain an identification result of the unknown parameters;
the evaluation part is configured to evaluate the identification result of the unknown parameter by using root mean square error and a judgment coefficient so as to obtain an optimal matching function of the average response delay of the non-periodic task and the difference between the non-periodic task utilization and the server bandwidth;
The adjustment portion is configured to dynamically adjust the server bandwidth according to the utilization of the aperiodic task using a best matching function of the average response delay of the aperiodic task versus the difference between the aperiodic task utilization and the server bandwidth to obtain a minimized average response delay of the aperiodic task.
In a third aspect, embodiments of the present invention provide a computing device, the computing device comprising: a communication interface, a memory and a processor; the components are coupled together by a bus system; wherein,
The communication interface is used for receiving and transmitting signals in the process of receiving and transmitting information with other external network elements;
the memory is used for storing a computer program capable of running on the processor;
the processor is configured to execute the steps of the bandwidth server-based aperiodic task scheduling method according to the first aspect when the computer program is executed.
In a fourth aspect, an embodiment of the present invention provides a computer storage medium storing a program for bandwidth server-based aperiodic task scheduling, where the program for bandwidth server-based aperiodic task scheduling, when executed by at least one processor, implements the steps of the bandwidth server-based aperiodic task scheduling method according to the first aspect.
The embodiment of the invention provides a bandwidth server-based aperiodic task scheduling method, a bandwidth server-based aperiodic task scheduling device and a bandwidth server-based aperiodic task scheduling medium, and provides a scheduling method based on a feedback control algorithm of a hard bandwidth server on the basis of earliest time limit priority of an original scheduling algorithm, wherein a dynamic state space equation under the condition of overload of a single processor system is established, bandwidth parameters of the server are adjusted through a feedback control strategy, and parameter identification is performed by using a least square method. The algorithm can ensure the real-time performance of periodic tasks and reduce the response delay of non-periodic tasks under the condition of dynamic change of system load.
Drawings
Fig. 1 is a flowchart of an aperiodic task scheduling method based on a bandwidth server according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of bandwidth management with a single processor having multiple non-periodic tasks according to an embodiment of the present invention;
FIG. 3 is a graph of the results of RMSE and R2 for 10 sets of data with a single processor having only one non-periodic task and a release interval subject to normal distribution, provided by an embodiment of the invention;
FIG. 4 is a graph showing the best fit result of the release interval obeying the normal distribution and the comparison between the actual value and the predicted value of a certain state according to the embodiment of the present invention;
FIG. 5 is a graph of the results of RMSE and R2 for 10 sets of data with a single processor having only one non-periodic task and release intervals subject to an exponential distribution, according to an embodiment of the invention;
FIG. 6 is a graph showing the best fit result of the release interval obeying the exponential distribution and the comparison between the actual value and the predicted value of a certain state according to the embodiment of the present invention;
FIG. 7 is a graph showing the comparison of processor and task utilization for different scheduling strategies for a single processor with multiple non-periodic tasks according to an embodiment of the present invention;
FIG. 8 is a graph showing the comparison of the periodic task miss rates under different scheduling strategies for a single processor having multiple non-periodic tasks according to an embodiment of the present invention;
FIG. 9 is a graph showing the average response delay of non-periodic tasks under different scheduling strategies according to an embodiment of the present invention;
FIG. 10 is a graph comparing job throughput under different scheduling strategies provided by an embodiment of the present invention;
fig. 11 is a schematic diagram of an aperiodic task scheduling device based on a bandwidth server according to an embodiment of the present invention;
fig. 12 is a schematic hardware structure of a computing device according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.
When an external event occurs, the star system captures with non-periodic tasks in response to the resulting workload. Because the execution time and release interval of each non-periodic job vary greatly, direct scheduling may miss deadlines for real-time tasks in the system. For this reason, it is necessary to consider the task scheduling problem for a system in which there are mixed tasks, i.e., periodic tasks and non-periodic tasks in the system at the same time. Based on the above description, the embodiment of the invention is expected to provide an aperiodic task scheduling method based on a bandwidth server. Referring to fig. 1, the method may include:
S101: according to the system state information of the non-periodic task operation process in the single processor system, a hard constant bandwidth server feedback control algorithm is adopted to construct a system state dynamic equation; the system state dynamic equation comprises unknown parameters of the average response delay of the non-periodic task and the relationship between the non-periodic task utilization rate and the difference multiple of the server bandwidth;
S102: identifying unknown parameters in a dynamic equation of the system state by using a least square method according to control input of a controller, an actual value, a predicted value and a residual function of the system state to obtain an identification result of the unknown parameters;
s103: evaluating the identification result of the unknown parameters by using root mean square error and a judgment coefficient to obtain an optimal matching function of the average response delay of the non-periodic task and the difference between the non-periodic task utilization rate and the server bandwidth;
S104: and dynamically adjusting the server bandwidth according to the utilization rate of the non-periodic task by utilizing the optimal matching function of the average response delay of the non-periodic task and the difference between the non-periodic task utilization rate and the server bandwidth so as to obtain the minimized average response delay of the non-periodic task.
The periodic task is a periodic task when the release interval of the job is fixed; the non-periodic task is a task with non-fixed release time and minimum interval, and the task has weak timing constraint or no timing constraint, and is called the non-periodic task; the task utilization rate is the ratio of the task execution time to the total processor time; the residual function is a functional relationship of a difference between an actual value and a predicted value of the system state.
According to the description of the technical scheme, the embodiment of the invention provides a scheduling method based on a Hard-to-constant bandwidth server Feedback Control (H-CBS-FC) algorithm on the basis of earliest time limit priority (EARLIEST DEADLINE FIRST, EDF) of an original scheduling algorithm aiming at non-periodic task scheduling based on a given scheduler model, establishes a dynamic state space equation under the overload condition of a single processor system, adjusts server bandwidth parameters through a Feedback Control strategy and utilizes a least square method to carry out parameter identification. The algorithm can ensure the real-time performance of periodic tasks and reduce the response delay of non-periodic tasks under the condition of dynamic change of system load.
It should be noted that, the EDF algorithm is a fixed job priority scheduling algorithm, and allocates job priorities according to the absolute deadline of the tasks: the earlier the absolute deadline, the higher the priority.
For the technical solution shown in fig. 1, in some possible implementations, according to the system state information of the non-periodic task running process in the single processor system, a hard constant bandwidth server feedback control algorithm is adopted to construct a system state dynamic equation; the system state dynamic equation comprises unknown parameters of the average response delay of the non-periodic task and the relationship between the non-periodic task utilization and the difference multiple of the server bandwidth, and the unknown parameters comprise:
According to the relation between the response delay of the non-periodic task operation and the operation execution time, analyzing the relation between the bandwidth of the server and the average response delay of the non-periodic task to obtain the control input of the state feedback controller;
And constructing a system state dynamic equation by using the control input and selecting the system state information of the non-periodic tasks in a sampling interval.
For the above implementation, in some examples, the relationship between the server bandwidth and the average response delay of the aperiodic task is analyzed according to the relationship between the response delay of the aperiodic task job and the job execution time, so as to obtain the control input of the state feedback controller, specifically, because the hard bandwidth server (Hard Constant Bandwidth Server, H-CBS) algorithm generates a larger response delay when scheduling the aperiodic task with dynamically changing load. And the generation of the aperiodic task response delay is due to the inability of the server bandwidth of the H-CBS algorithm to match the aperiodic task utilization. Thus, there is a need to set an optimal matching function relationship for the average response delay of the aperiodic task to the aperiodic task utilization and the server bandwidth to achieve a minimized average response delay.
Assuming that the average release interval of the non-periodic job is equal to the server period, taking into account the condition of the implicit deadline, obtaining a relation between the response delay delta of the non-periodic task job and the execution time of the job J:
Where c represents job execution time, d represents an absolute deadline for the job, Q S represents a server maximum budget, and T S represents a server period.
As can be seen from the above formula, when the server maximum budget is equal to or greater than the job execution time, the response delay is 0. The average response delay of the non-periodic tasks can be reduced by adjusting the maximum bandwidth of the server. Under the condition of a fixed server period T S, the variation amount Δu S＝ΔQS/TS of the server bandwidth is used as a control input of the state feedback controller:
u(k)＝ΔUS(k)
During the operation of the system, the server bandwidth should satisfy the following constraints:
wherein, For artificially set minimum server bandwidth,/>The average utilization of periodic real-time tasks within a sampling interval.
When a plurality of non-periodic tasks exist in a processor, aiming at the adjustment of the bandwidth of a server, the embodiment of the invention provides a method for compressing the bandwidth of the server, which is based on the proportional distribution of the utilization rate of the rest of the processor and compresses the control input of a controller so as to ensure the effect of bandwidth adjustment. The working principle is as follows: the H-CBS for each aperiodic task needs to establish a state feedback controller to regulate server bandwidth. The summation of the control inputs of multiple independent controllers may cause the total bandwidth of the server for the non-periodic tasks to exceed the upper system bandwidth limitThe dynamic regulation effect of the server bandwidth is reduced or the reverse effect is achieved. Therefore, it is necessary to reasonably compress all the server bandwidths adjusted by the feedback control to ensure the effectiveness of the bandwidth adjustment for the aperiodic task server. Referring to fig. 2, a schematic diagram of bandwidth management with a single processor having multiple non-periodic tasks is shown, after all controllers generate control inputs, the control inputs directly act on the servers from original to centralized management of the bandwidth of the servers by the bandwidth manager, and then new bandwidths are uniformly allocated to the servers.
The bandwidth manager first calculates the control input sum of all controllersTotal remaining utilization of processorAnd then by judging/>The bandwidth is adjusted with the size of U R (k):
(1) Proportional compression control input/> And calculates new server bandwidth/>
(2)A new server bandwidth U Si(k+1)＝USi(k)+ui (k) is calculated.
Wherein U Si (k) is the server bandwidth before adjustment,The average utilization of periodic real-time tasks within a sampling interval.
If any server bandwidthOrder/>The pseudo code of the server bandwidth compression algorithm is as follows:
For the above implementation, in some examples, the system state information of the non-periodic task within one sampling interval is selected by using the control input to construct a system state dynamic equation, and specifically, for calculating the system state information, the state quantity within one sampling interval may be selected, for example, the actual execution time and the task execution number of the non-periodic task job, and the release time and the completion time of the non-periodic job.
When the non-periodic task utilization is greater than the server bandwidth, i.e., the single processor is overloaded, the system may be described as a linear-stationary system, then the state space expression is:
y(k)＝Cx(k)
Because of variations in the average response delay of non-periodic tasks And/>Relatedness, assume/>The coefficient matrix can be expressed as:
To achieve arbitrary configuration of the closed loop poles, the following state feedback control law is selected:
The system state dynamic equation is:
wherein x 1 and x 2 represent system state quantity, and K represents variation of average response delay of non-periodic task Difference/>, between average utilization of non-periodic tasks and H-CBS bandwidth U S Multiple of (i.e./>)K 1 and K 2 are parameters used by the closed loop pole configuration and state feedback control law, i.e. feedback controller coefficients.
The system closed loop characteristic polynomial is:
f(λ)＝det[λI-(A-BK)]＝λ2+(K2-2)λ+1-K2+KK1
The above-mentioned Where N represents the number of executions of the aperiodic task τ, J i represents the job of the aperiodic task τ,/>D (J) represents an absolute deadline of the non-periodic task job, i=1, …, N;
representing the average utilization/>, of non-periodic tasks A difference from the H-CBS bandwidth U S; wherein,
Represents the average release interval of τ,/>The actual execution time of the non-periodic task job is represented, and N represents the execution times of the non-periodic task tau;
For the technical solution shown in fig. 1, in some possible implementations, the identifying, according to the control input of the controller, the actual value, the predicted value and the residual function of the system state, the unknown parameter in the dynamic equation of the system state by using the least square method, so as to obtain an identification result of the unknown parameter includes:
Identifying by using a least square method according to the control input of the controller, the system state predicted value, the actual value and the residual function so as to obtain an identification result of the unknown parameter;
And solving by using a system closed-loop characteristic polynomial according to the identification result of the unknown parameter and the expected closed-loop pole so as to obtain a feedback controller coefficient.
Specifically, the unknown parameter K in the system state dynamic equation is identified by using a least square method. Under the condition that the control input u (k) =0, the expression of the state x 1 is:
x1(k+1)＝x1(k)+Kx2(k)
assume the predicted value of state x 1 The following expression is satisfied:
the residual of state x 1 is defined as:
the values of a and b can be obtained by the least square method:
wherein,
It should be noted that the least square method is also called a least squares method, and is a mathematical optimization technique. It finds the best functional match for the data by minimizing the sum of squares of the errors. The least square method can be used for simply obtaining unknown data and enabling the square sum of errors between the obtained data and actual data to be minimum, the least square method can also be used for curve fitting, and other optimization problems can be expressed by the least square method through minimizing energy or maximizing entropy. The term "square" means square.
For the solution shown in fig. 1, in some possible implementations, the evaluating the identification result of the unknown parameter using the root mean square error and the decision coefficient to obtain an optimal matching function of the average response delay of the aperiodic task and the difference between the aperiodic task utilization and the server bandwidth includes:
calculating root mean square error by using the actual value and the predicted value of the system state so as to judge the precision of measurement;
Calculating a judgment coefficient by using the regression square sum and the total dispersion square sum of the system state so as to judge the fitting goodness of the unknown parameter identification;
and judging the optimal value of the unknown parameter identification result of the system dynamic equation according to the root mean square error and the judgment coefficient obtained by calculation.
Specifically, the parameter identification result is evaluated using a root mean square error (Root Mean Square Error, RMSE) and a determination coefficient R 2. The root mean square error RMSE reflects the precision of the measurement, which is defined as:
The closer the decision coefficient R 2 is to 1, the higher the goodness of fit of the model. Typically, R 2 > 0.8 is considered an acceptable fit, and let var (x) be the variance of the parameter x, then it is defined as:
And judging the optimal value of the unknown parameter identification result according to the root mean square error and the calculation result of the judgment coefficient, and obtaining the optimal matching function of the average response delay of the non-periodic task and the difference between the non-periodic task utilization rate and the server bandwidth.
It should be noted that, the Root-Mean-Square Deviation RMSE is also called Root-Mean-Square Deviation (RMSD), which is a measure of the difference between commonly used measurement values, and RMSE is the Mean of the Square Root of the error between the predicted value and the actual value; the decision coefficient is also called a determinable coefficient or a decision coefficient, and refers to the ratio of the sum of squares of the regression to the sum of squares of the total dispersion in linear regression, wherein the value of the decision coefficient is equal to the square of the correlation coefficient, namely R 2, which is a measure of the goodness of fit of an estimated regression equation, and the closer the statistic is to 1, the higher the goodness of fit of a model is; the goodness of fit refers to the degree of fit of the regression line to the observed value.
For the solution shown in fig. 1, in some possible implementations, the best matching function of the average response delay of the non-periodic task and the difference between the non-periodic task utilization and the server bandwidth is used to dynamically adjust the server bandwidth according to the non-periodic task utilization, so as to obtain a minimized average response delay of the non-periodic task. Specifically, according to the optimal matching function, according to the dynamic change of the utilization rate load of the non-periodic task, the bandwidth of the server is dynamically adjusted in real time according to the method for proportionally compressing the bandwidth of the server, so that the effectiveness of the bandwidth of the non-periodic task server is ensured, the utilization rate of the non-periodic task is matched conveniently, and the minimized average response delay of the non-periodic task is obtained.
Based on the above technical solution, implementation manner and illustration, in order to verify the effect and superiority of feedback control H-CBS-FC algorithm based on the hard constant bandwidth server, the following two cases are respectively compared based on simulation experiments, and an analysis conclusion is obtained, where the two cases are respectively that one non-periodic task exists in a single processor and a plurality of non-periodic tasks exist in the single processor, specifically:
1. A set of mutually independent task sets tg= { τ 1,τ2 }, on a single processor.
Wherein τ 1 is a periodic task, the execution time c 1 =2ms, the period p 1 =5ms, the relative deadline is equal to the period, the task utilization rate U P＝0.4;τ2 is an aperiodic task, and under the normal running condition of the system, the execution time obeys normal distribution c 2 N (5, 5), and the relative deadline is equal to the release interval; H-CBS initial maximum budget Q S0 ms, initial period T S0 = 15ms, server minimum bandwidth
The sum of the periodic tasks and the H-CBS bandwidth is: u P+US0 = 0.733 < 1. The supersycle of the task set is h=15 ms, and the system sampling interval takes an integer multiple of the supersycle, i.e., T SW =60 ms. Assuming that τ 2 dynamically changes the execution time during system operation, c 2 satisfies:
Let the release interval of τ 2 obey the following two probability distribution cases, respectively: the release interval follows a normal distribution P 2 N (15, 15), the minimum release interval is 0.5 times the average release interval, namely 7.5ms; the release interval follows an exponential distribution P 2 E (1/15), with a minimum release interval of 0.5 times the average release interval, i.e. 7.5ms.
2. A set of mutually independent task sets tg= { τ 1,τ2,τ3,τ4,τ5,τ6 }, on a single processor.
Wherein { τ 1,τ2,τ3 } is a periodic task, { τ 4,τ5,τ6 } is an aperiodic task, under the condition of normal operation of the system, the relative deadline of the periodic task is equal to the period, the relative deadline of the aperiodic task is equal to the release interval, and the minimum bandwidth of the server is shown in the table 1-1 between the task and the serverThe sum of the periodic task and the H-CBS bandwidth is as follows: u P+US0 = 0.8 < 1. The supersycle of the task set is h=60 ms, and considering that each task can be executed for more than 1 time in one supersycle, the sampling interval of the system can be the supersycle of the task set, that is, the variation of the execution time of T SW＝60ms.τ5 in the running process of the system is the same as the variation of the execution time of τ 2. The release intervals of the non-periodic tasks are respectively subjected to normal distribution and exponential distribution, and the minimum release interval is 0.5 times of the average release interval.
TABLE 1-1 task parameters and server parameters
Simulation conclusion:
1. System parameter identification
Based on the system parameters in case 1, assuming that the execution time of τ 2 changes stepwise from the second job J 2,2 of τ 2, considering that in the worst case the system is overloaded, the execution time c 2 of J 2,2 is subject to N (12, 12), the total overload time is set to 1000ms, and the system operation is the same as the overload time. And respectively counting 10 groups of operation results aiming at the condition that the release interval of tau 2 obeys normal distribution and exponential distribution.
Referring to fig. 3 and 5, the root mean square error and the determination coefficient of 10 sets of data with normal distribution and exponential distribution are respectively released. It can be seen that the decision coefficients are all greater than 0.8, so a group with the smallest root mean square error is selected as the final result of the system identification. As shown in fig. 4, in the case where the release interval of τ 2 follows a normal distribution, rmse=4.573, r 2 =0.999, a=0.987, b= 84.369; as shown in fig. 6, in the case where the release interval of τ 2 follows an exponential distribution, rmse=11.592, r 2 =0.996, a=1.010, and b= 42.590.
2. Algorithm verification
In order to verify the superiority of the algorithm, by comparing the effects of three different scheduling strategies, namely, the EDF scheduling without H-CBS (which may be abbreviated as EDF), the EDF scheduling based on H-CBS (which may be abbreviated as H-CBS), and the EDF scheduling based on H-CBS feedback control (which may be abbreviated as H-CBS-FC), on the utilization rate of the system processor, the periodic task miss rate, the average response delay of the non-periodic tasks, and the job throughput under the experimental conditions that the single processor has only one non-periodic task and the single processor has a plurality of non-periodic tasks, the embodiment of the invention only shows the comparison results in the case that the single processor has only one non-periodic task, the comparison results are basically similar to the comparison results in the case that the single processor has a plurality of non-periodic tasks, and the specific comparison results in the case that the single processor has a plurality of non-periodic tasks are as follows:
(1) Processor and task utilization
Referring to FIG. 7, a graph of processor and task utilization under different scheduling strategies is shown, where processor utilization varies synchronously with load when scheduling using an EDF algorithm without H-CBS; when the EDF algorithm based on the H-CBS is used for scheduling, the utilization rate of the processor is basically kept constant; when the EDF algorithm based on the H-CBS-FC is used for scheduling, the utilization rate of the processor is increased along with the increase of the load, and the processor is reduced after the load is reduced for a period of higher utilization rate.
(2) Periodic task miss rate
Referring to FIG. 8, a graph of periodic task miss rates versus different scheduling strategies is shown. The EDF algorithm without H-CBS is used for scheduling, so that the highest error rate is achieved, and the maximum values are 0.565 and 0.680 respectively; scheduling using an H-CBS based EDF algorithm with little periodic task missing; the EDF algorithm based on the H-CBS-FC is used for scheduling, so that the scheduling has lower periodic task missing rate, and the maximum values are respectively 0.078 and 0.042.
(3) Aperiodic task average response delay
Referring to fig. 9, a graph of average response delay versus non-periodic tasks under different scheduling strategies is shown. When the EDF algorithm without H-CBS is used for scheduling, three non-periodic tasks have similar response delays, and the maximum values are 2311ms, 2236ms and 2232ms and 1308ms, 1311ms and 1308ms respectively; when scheduling is performed using the H-CBS based EDF algorithm, when the release interval follows a normal distribution, the average response delay of the task τ 5 rises rapidly with increasing load, and the average response delays of the tasks τ 4 and τ 6 rise gradually and slowly with time. When the release interval obeys the exponential distribution, the average response delay of the task tau 5 rises firstly and then gradually falls down along with the increase of the load, the maximum value is 10780ms, and the average response delay of the tasks tau 4 and tau 6 fluctuates at a lower level; when using the H-CBS-FC based EDF algorithm for scheduling, there is bandwidth compression, and when the system load increases, the server bandwidth of all non-periodic tasks may be reallocated, resulting in that the average response delays of tasks τ 4 and τ 6, in addition to task τ 5, also increase with the increase of the system load, with maximum values of 5167ms, 7295ms, 2796ms and 4551ms, 3194ms, 724ms, respectively, but with the decrease of the load, the average response delays of three non-periodic tasks gradually decrease to 0.
(4) Job throughput
Referring to fig. 10, a graph of job throughput versus different scheduling strategies is shown. The job throughput of the three scheduling strategies is similar, and when the load is increased, the job throughput is gradually reduced; when the load is reduced, the throughput is increased and then reduced when the EDF algorithm without H-CBS and the EDF algorithm based on H-CBS-FC are used for scheduling, and finally the throughput is kept stable, wherein the fluctuation amplitude of the throughput of the EDF algorithm without H-CBS is smaller than that of the EDF algorithm based on H-CBS-FC.
Therefore, aiming at the condition that a plurality of non-periodic tasks exist in a processor, the EDF scheduling strategy based on the H-CBS-FC can reduce response delay of all non-periodic tasks while guaranteeing low periodic task miss rate and job throughput.
Based on the same inventive concept as the previous technical solution, referring to fig. 11, there is shown an aperiodic task scheduling device 1100 based on a bandwidth server, where the device 1100 includes: a constructing section 1101, a recognizing section 1102, an evaluating section 1103, and an adjusting section 1104; wherein,
The constructing part 1101 is configured to construct a system state dynamic equation according to system state information of an aperiodic task running process in the single processor system by adopting a hard constant bandwidth server feedback control algorithm; the system state dynamic equation comprises unknown parameters of the average response delay of the non-periodic task and the relationship between the non-periodic task utilization rate and the difference multiple of the server bandwidth;
The identifying part 1102 is configured to identify an unknown parameter in the dynamic equation of the system state by using a least square method according to a control input of a controller, an actual value, a predicted value and a residual function of the system state, so as to obtain an identification result of the unknown parameter;
The evaluation portion 1103 is configured to evaluate the identification result of the unknown parameter using the root mean square error and the determination coefficient to obtain an optimal matching function of the average response delay of the aperiodic task and the difference between the aperiodic task utilization and the server bandwidth;
The adjustment portion 1104 is configured to dynamically adjust the server bandwidth according to the utilization of the aperiodic task using a best matching function of the average response delay of the aperiodic task versus the difference between the aperiodic task utilization and the server bandwidth to obtain a minimized average response delay of the aperiodic task.
In some examples, the build portion 1101 is configured to:
According to the relation between the response delay of the non-periodic task operation and the operation execution time, analyzing the relation between the bandwidth of the server and the average response delay of the non-periodic task to obtain the control input of the state feedback controller;
And constructing a system state dynamic equation by using the control input and selecting the system state information of the non-periodic tasks in a sampling interval.
In some examples, the recognition portion 1102 is configured to:
Identifying by using a least square method according to the control input of the controller, the system state predicted value, the actual value and the residual function so as to obtain an identification result of the unknown parameter;
And solving by using a system closed-loop characteristic polynomial according to the identification result of the unknown parameter and the expected closed-loop pole so as to obtain a feedback controller coefficient.
In some examples, the evaluation portion 1103 is configured to:
calculating root mean square error by using the actual value and the predicted value of the system state so as to judge the precision of measurement;
Calculating a judgment coefficient by using the regression square sum and the total dispersion square sum of the system state so as to judge the fitting goodness of the unknown parameter identification;
and judging the optimal value of the unknown parameter identification result of the system dynamic equation according to the root mean square error and the judgment coefficient obtained by calculation.
It will be appreciated that in this embodiment, a "part" may be a part of a circuit, a part of a processor, a part of a program or software, etc., and of course may be a unit, or a module may be non-modular.
In addition, each component in the present embodiment may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional modules.
The integrated units, if implemented in the form of software functional modules, may be stored in a computer-readable storage medium, if not sold or used as separate products, and based on such understanding, the technical solution of the present embodiment may be embodied essentially or partly in the form of a software product, which is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, a server, or a network device, etc.) or processor to perform all or part of the steps of the method described in the present embodiment. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read Only Memory (ROM), a random access Memory (Random Access Memory, RAM), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
Accordingly, the present embodiment provides a computer storage medium storing a program for bandwidth server-based non-periodic task scheduling, where the program for bandwidth server-based non-periodic task scheduling, when executed by at least one processor, implements the steps of the bandwidth server-based non-periodic task scheduling method in the above technical solution.
According to the above-mentioned non-periodic task scheduling device 1100 based on a bandwidth server and a computer storage medium, referring to fig. 12, a specific hardware structure of a computing device 1200 capable of implementing the non-periodic task scheduling device 1100 based on a bandwidth server according to an embodiment of the present invention is shown, where the computing device 1200 may be a wireless device, a mobile or cellular phone (including a so-called smart phone), a Personal Digital Assistant (PDA), a video game console (including a video display, a mobile video game device, a mobile video conference unit), a laptop computer, a desktop computer, a television set-top box, a tablet computing device, an electronic book reader, a fixed or mobile media player, etc. The computing device 1200 includes: a communication interface 1201, a memory 1202 and a processor 1203; the various components are coupled together by a bus system 1204. It is appreciated that the bus system 1204 is used to facilitate connected communications between these components. The bus system 1204 includes a power bus, a control bus, and a status signal bus in addition to the data bus. But for clarity of illustration, the various buses are labeled as bus system 1204 in fig. 12. Wherein,
The communication interface 1201 is configured to receive and send signals during the process of receiving and sending information with other external network elements;
The memory 1202 for storing a computer program capable of running on the processor 1203;
The processor 1203 is configured to execute the steps of the bandwidth server-based aperiodic task scheduling method in the foregoing technical solution when running the computer program.
It is to be appreciated that the memory 1202 in embodiments of the present invention can be either volatile memory or nonvolatile memory, or can include both volatile and nonvolatile memory. The nonvolatile Memory may be a Read-Only Memory (ROM), a Programmable ROM (PROM), an Erasable PROM (EPROM), an Electrically Erasable EPROM (EEPROM), or a flash Memory. The volatile memory may be random access memory (Random Access Memory, RAM) which acts as external cache memory. By way of example, and not limitation, many forms of RAM are available, such as static random access memory (STATIC RAM, SRAM), dynamic random access memory (DYNAMIC RAM, DRAM), synchronous Dynamic Random Access Memory (SDRAM), double data rate Synchronous dynamic random access memory (Double DATA RATE SDRAM, DDRSDRAM), enhanced Synchronous dynamic random access memory (ENHANCED SDRAM, ESDRAM), synchronous link dynamic random access memory (SYNCHLINK DRAM, SLDRAM), and Direct memory bus random access memory (DRRAM). The memory 1202 of the systems and methods described herein is intended to comprise, without being limited to, these and any other suitable types of memory.
While the processor 1203 may be an integrated circuit chip with signal processing capabilities. In implementation, the steps of the method described above may be performed by integrated logic circuitry in hardware or instructions in software in the processor 1203. The Processor 1203 described above may be a general purpose Processor, a digital signal Processor (DIGITAL SIGNAL Processor, DSP), an Application SPECIFIC INTEGRATED Circuit (ASIC), a field programmable gate array (Field Programmable GATE ARRAY, FPGA) or other programmable logic device, a discrete gate or transistor logic device, a discrete hardware component. The disclosed methods, steps, and logic blocks in the embodiments of the present invention may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of the method disclosed in connection with the embodiments of the present invention may be embodied directly in the execution of a hardware decoding processor, or in the execution of a combination of hardware and software modules in a decoding processor. The software modules may be located in a random access memory, flash memory, read only memory, programmable read only memory, or electrically erasable programmable memory, registers, etc. as well known in the art. The storage medium is located in the memory 1202, and the processor 1203 reads the information in the memory 1202 and performs the steps of the above method in combination with its hardware.
It is to be understood that the embodiments described herein may be implemented in hardware, software, firmware, middleware, microcode, or a combination thereof. For a hardware implementation, the Processing units may be implemented within one or more Application SPECIFIC INTEGRATED Circuits (ASICs), digital signal processors (DIGITAL SIGNAL Processing, DSPs), digital signal Processing devices (DSP DEVICE, DSPD), programmable logic devices (Programmable Logic Device, PLDs), field-Programmable gate arrays (Field-Programmable GATE ARRAY, FPGA), general purpose processors, controllers, micro-controllers, microprocessors, other electronic units for performing the functions described herein, or a combination thereof.
For a software implementation, the techniques described herein may be implemented with modules (e.g., procedures, functions, and so on) that perform the functions described herein. The software codes may be stored in a memory and executed by a processor. The memory may be implemented within the processor or external to the processor.
It should be understood that the exemplary technical solutions of the bandwidth server-based aperiodic task scheduling device 1100 and the computing device 1200 described above are the same as the technical solutions of the bandwidth server-based aperiodic task scheduling method described above, and therefore, for details that are not described in detail in the technical solutions of the bandwidth server-based aperiodic task scheduling device 1100 and the computing device 1200 described above, reference may be made to the description of the technical solutions of the bandwidth server-based aperiodic task scheduling method described above. The embodiments of the present invention will not be described in detail.
It should be noted that: the technical schemes described in the embodiments of the present invention may be arbitrarily combined without any collision.
The foregoing is merely illustrative of the present invention, and the present invention is not limited thereto, and any person skilled in the art will readily recognize that variations or substitutions are within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (8)
1. A method for scheduling non-periodic tasks based on a bandwidth server, the method comprising:
According to the system state information of the non-periodic task operation process in the single processor system, a hard constant bandwidth server feedback control algorithm is adopted to construct a system state dynamic equation; the feedback control algorithm of the hard constant bandwidth server is to establish a dynamic state space equation under the overload condition of a single processor system, namely a system state dynamic equation, adjust bandwidth parameters of the server through a feedback control strategy and perform parameter identification by utilizing a least square method, wherein the system state dynamic equation comprises an unknown parameter K, the unknown parameter K represents a multiple relation between a change delta of an average response delay of an aperiodic task and a difference delta U between an average utilization rate of the aperiodic task and the bandwidth U S of the server, namely delta = K delta U, and the average utilization rate of the task is the ratio of the average execution time of the task to the total processor time;
Identifying unknown parameters in a dynamic equation of the system state by using a least square method according to control input of a controller, an actual value, a predicted value and a residual function of the system state to obtain an identification result of the unknown parameters;
The identification result of the unknown parameters is evaluated by using the root mean square error and the judgment coefficient, and the optimal value of the identification result of the unknown parameters is judged according to the calculation result of the root mean square error and the judgment coefficient, so that an optimal matching function of the variation of the average response delay of the non-periodic task and the difference between the average utilization rate of the non-periodic task and the server bandwidth is obtained;
Dynamically adjusting the bandwidth of the server according to the average utilization rate of the non-periodic tasks by utilizing the optimal matching function so as to obtain the minimized average response delay of the non-periodic tasks;
The identifying the unknown parameters in the dynamic equation of the system state by using a least square method according to the control input of the controller, the actual value, the predicted value and the residual function of the system state to obtain the identification result of the unknown parameters comprises the following steps:
Identifying by using a least square method according to the control input of the controller, the system state predicted value, the actual value and the residual function so as to obtain an identification result of the unknown parameter;
And solving by using a system closed-loop characteristic polynomial according to the identification result of the unknown parameter and the expected closed-loop pole so as to obtain a feedback controller coefficient.
2. The method of claim 1, wherein constructing a system state dynamic equation using a hard constant bandwidth server feedback control algorithm based on system state information of non-periodic task execution in the single processor system comprises:
According to the relation between the response delay of the non-periodic task operation and the operation execution time, analyzing the relation between the bandwidth of the server and the average response delay of the non-periodic task to obtain the control input of the state feedback controller;
And constructing a system state dynamic equation by using the control input and selecting the system state information of the non-periodic tasks in a sampling interval.
3. The method according to claim 1, wherein the estimating the identification result of the unknown parameter using the root mean square error and the determination coefficient, and determining the optimal value of the identification result of the unknown parameter according to the calculation result of the root mean square error and the determination coefficient to obtain the optimal matching function of the variation of the average response delay of the non-periodic task and the difference between the average utilization of the non-periodic task and the server bandwidth, comprises:
calculating root mean square error by using the actual value and the predicted value of the system state so as to judge the precision of measurement;
Calculating a judgment coefficient by using the regression square sum and the total dispersion square sum of the system state so as to judge the fitting goodness of the unknown parameter identification;
And judging the optimal value of the unknown parameter identification result of the system state dynamic equation according to the root mean square error and the judgment coefficient obtained by calculation.
4. An aperiodic task scheduling device based on a bandwidth server, the device comprising: a construction part, an identification part, an evaluation part and an adjustment part; wherein,
The construction part is configured to construct a system state dynamic equation by adopting a hard constant bandwidth server feedback control algorithm according to the system state information of the non-periodic task operation process in the single processor system; the feedback control algorithm of the hard constant bandwidth server is to establish a dynamic state space equation under the overload condition of a single processor system, namely a system state dynamic equation, adjust bandwidth parameters of the server through a feedback control strategy and perform parameter identification by utilizing a least square method, wherein the system state dynamic equation comprises an unknown parameter K, the unknown parameter K represents a multiple relation between a change delta of an average response delay of an aperiodic task and a difference delta U between an average utilization rate of the aperiodic task and the bandwidth U S of the server, namely delta = K delta U, and the average utilization rate of the task is the ratio of the average execution time of the task to the total processor time;
the identification part is configured to identify the unknown parameters in the system state dynamic equation by using a least square method according to the control input of the controller, the actual value, the predicted value and the residual function of the system state so as to obtain an identification result of the unknown parameters;
the evaluation part is configured to evaluate the identification result of the unknown parameter by using the root mean square error and the judgment coefficient, and judge the optimal value of the identification result of the unknown parameter according to the calculation result of the root mean square error and the judgment coefficient so as to obtain an optimal matching function of the change of the average response delay of the non-periodic task and the difference between the average utilization rate of the non-periodic task and the bandwidth of the server;
the adjusting part is configured to dynamically adjust the bandwidth of the server according to the average utilization rate of the non-periodic tasks by utilizing the optimal matching function so as to obtain the minimized average response delay of the non-periodic tasks;
Wherein the identification portion is further configured to:
Identifying by using a least square method according to the control input of the controller, the system state predicted value, the actual value and the residual function so as to obtain an identification result of the unknown parameter;
And solving by using a system closed-loop characteristic polynomial according to the identification result of the unknown parameter and the expected closed-loop pole so as to obtain a feedback controller coefficient.
5. The apparatus of claim 4, wherein the building portion is configured to:
According to the relation between the response delay of the non-periodic task operation and the operation execution time, analyzing the relation between the bandwidth of the server and the average response delay of the non-periodic task to obtain the control input of the state feedback controller;
And constructing a system state dynamic equation by using the control input and selecting the system state information of the non-periodic tasks in a sampling interval.
6. The apparatus of claim 4, wherein the evaluation portion is configured to:
calculating root mean square error by using the actual value and the predicted value of the system state so as to judge the precision of measurement;
Calculating a judgment coefficient by using the regression square sum and the total dispersion square sum of the system state so as to judge the fitting goodness of the unknown parameter identification;
And judging the optimal value of the unknown parameter identification result of the system state dynamic equation according to the root mean square error and the judgment coefficient obtained by calculation.
7. A computing device, the computing device comprising: a communication interface, a processor, a memory; the components are coupled together by a bus system; wherein,
The communication interface is used for receiving and transmitting signals in the process of receiving and transmitting information with other external network elements;
the memory is used for storing a computer program capable of running on the processor;
the processor, when executing the computer program, is configured to perform the steps of the bandwidth server-based aperiodic task scheduling method according to any one of claims 1 to 3.
8. A computer storage medium storing a program for bandwidth server based non-periodic task scheduling, which when executed by at least one processor implements the steps of the bandwidth server based non-periodic task scheduling method according to any of claims 1 to 3.
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103049314A (en) * | 2012-12-17 | 2013-04-17 | 南昌航空大学 | Real-time scheduling method for open system |
| CN105630126A (en) * | 2014-11-05 | 2016-06-01 | 中国科学院沈阳计算技术研究所有限公司 | Low-power scheduling method for mixed task based on constant bandwidth server |
| CN107181617A (en) * | 2010-12-09 | 2017-09-19 | 台湾积体电路制造股份有限公司 | Packet management system and method in communication network |
| CN108304257A (en) * | 2018-02-09 | 2018-07-20 | 中国船舶重工集团公司第七六研究所 | Hard real time hybrid tasks scheduling method based on Delay Service device |
| CN115066935A (en) * | 2020-02-06 | 2022-09-16 | 联想（新加坡）私人有限公司 | Power control using at least one power control parameter |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US11457458B2 (en) * | 2019-01-31 | 2022-09-27 | Apple Inc. | Dynamic bandwidth adaptation with network scheduling |
-
2022
- 2022-09-26 CN CN202211177286.1A patent/CN115866054B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107181617A (en) * | 2010-12-09 | 2017-09-19 | 台湾积体电路制造股份有限公司 | Packet management system and method in communication network |
| CN103049314A (en) * | 2012-12-17 | 2013-04-17 | 南昌航空大学 | Real-time scheduling method for open system |
| CN105630126A (en) * | 2014-11-05 | 2016-06-01 | 中国科学院沈阳计算技术研究所有限公司 | Low-power scheduling method for mixed task based on constant bandwidth server |
| CN108304257A (en) * | 2018-02-09 | 2018-07-20 | 中国船舶重工集团公司第七六研究所 | Hard real time hybrid tasks scheduling method based on Delay Service device |
| CN115066935A (en) * | 2020-02-06 | 2022-09-16 | 联想（新加坡）私人有限公司 | Power control using at least one power control parameter |
Non-Patent Citations (4)
| Title |
|---|
| Constant Bandwidth Servers with Constrained Deadlines;Casini, Daniel;ACM;20171004;全文 * |
| Execution allowance based fixed priority scheduling for probaliistic real-time systems;jiankang Ren;Elsevier;20190302;全文 * |
| Hard Constant Bandwidth Server:Comprehensive formulation and critical scenarios;Alessandro Biondi;IEEE;20140807;全文 * |
| Resource reservation for real-time self-suspending tasks: theory and practice;Alessandro Biondi;ACM;20151104;全文 * |
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