CN111352728A

CN111352728A - Self-adaptive scheduling method of data service cluster

Info

Publication number: CN111352728A
Application number: CN201910803526.6A
Authority: CN
Inventors: 黄罡; 董瀚; 景翔; 蔡华谦; 姜海鸥
Original assignee: Peking University Information Technology Institute (tianjin Binhai)
Current assignee: Peking University Information Technology Institute (tianjin Binhai)
Priority date: 2019-08-28
Filing date: 2019-08-28
Publication date: 2020-06-30
Anticipated expiration: 2039-08-28
Also published as: CN111352728B

Abstract

The invention relates to the field of task scheduling, in particular to a self-adaptive scheduling method of a data service cluster. The method comprises the following steps: sending a calling request, analyzing the request and reading a requested interface; screening candidate equipment meeting the conditions; selecting a candidate device with the lowest load; executing the request on the candidate equipment, if the set time is exceeded or the execution fails, recording the execution failure, judging the failure condition, and executing the next instruction; if the execution is successful, the execution is recorded to be successful, the success condition is judged, and the next instruction is executed. The invention selects the equipment with the minimum load to realize the average flow distribution without accurately monitoring the instantaneous request flow reaching the equipment; and automatically adjusting according to the success or failure of interface calling to realize automatic adaptation to the unknown interface.

Description

Self-adaptive scheduling method of data service cluster

Technical Field

The invention relates to the field of data scheduling, in particular to a self-adaptive scheduling method of a data service cluster.

Background

The current artificial intelligence can not leave data, the collection of big data becomes an obvious bottleneck, a large number of data barriers appear, and the big data research faces the dilemma of unavailable data due to a large number of 'information islands'. The problem of "information islanding" is more serious for smart devices due to the inherent closeness of mobile applications.

One idea for solving the problem of information island of the intelligent device is to develop a novel software definition theory based on the classical software definition theory, that is, a controllable component of the intelligent device is exposed through an Application Programming Interface (API) to realize the on-demand management and on-demand service of the intelligent device.

Unlike the classical data service cluster, the novel data service cluster has the following characteristics: the service capacities of different interfaces are greatly different; the service capabilities of the device are greatly affected by the requested traffic.

Disclosure of Invention

The embodiment of the invention provides a self-adaptive scheduling method of a data service cluster, which is used for maintaining a receiving window for each device, controlling the quantity of requests processed by the devices at the same time, and simultaneously feeding back and adjusting the size of the receiving window according to the request processing result to realize the self-adaptation of flow control.

According to a first aspect of the embodiments of the present invention, a method for adaptive scheduling of a data service cluster includes:

sending out calling requests, and arranging the calling requests in a request queue according to a first-in first-out sequence;

reading a request of a queue head, analyzing the request, and reading an interface of the request;

screening candidate equipment meeting the conditions;

if no candidate equipment exists, adding the call request to the tail of the queue; if the candidate equipment exists, selecting the candidate equipment with the lowest load;

executing the request on the candidate equipment, if the execution request exceeds the set time or the execution request fails, recording the execution failure, and executing the next instruction;

if the execution is successful, the execution is recorded to be successful, and the next instruction is executed.

The method includes the steps of executing a request on a candidate device, recording a failure condition after the execution fails if the execution request exceeds a set time or the execution request fails, and executing a next instruction, wherein the failure condition is specifically:

judging whether F is satisfied_j′，i′≥λV_j′，i′Is established, F_j′，i′Maintaining a failed count, V, at device j' for calling interface i_j′，i′For the rate at which requests for invoking interface i 'arrive at device j', λ is a real number between 0 and 1, which may be 0.5, and if true, makes the order

W_j′，i′The maximum number of simultaneous calls that can be made to the interface i 'on the recording device j', α being a real number between 0 and 1, S_j′，i′＝0，F_j′，i′＝0，S_j′，i′Performing a successful count at device j 'for the call interface i'; then returning to execute the next instruction; f_j′，i′≥λV_j′，i′If not, returning to execute the next instruction.

If the execution is successful, after the execution is successfully recorded, a success condition is determined, and a next instruction is executed, wherein the determination of the success condition and the execution of the next instruction specifically include:

judging whether S is satisfied_j′，i′≥μV_j′，i′Is true, mu is a rational number between 0 and 1, S_j′，i′To invoke the interface i 'to perform a successful count at device j', V_j′，i′The rate at which requests for call interface i 'reach device j'; when it is established, let W_j′，i′＝0，S_j′，i′＝0，F _j′，i′0, wherein F_j′，i′Maintaining a failed count at the device j 'for the calling interface i', and then returning to execute the next instruction; s_j′，i′≥μV_j′，i′If not, returning to execute the next instruction.

A method for adaptive scheduling of a data service cluster is provided, wherein values of parameters α, λ and μ are optimized, and specifically:

selecting a group of parameters α, lambda and mu, wherein the value ranges of α, lambda and mu are all 0 to 1;

at a velocity v_i＝v₀Sending a call request v to a calling interface i of the cluster gateway₀Measuring a rate w' at which the request is completed for setting the rate; repeating the operation for multiple times to obtain the mean value of w

Rate at which interface i cluster completes requests

Selecting different v_iIn the same way, different v can be measured_iRate G of completion request of corresponding interface i cluster_Q，i(v_i)；

Compute interface i scheduling efficiency function H_Q，i(v_i)；

Changing to request the interface i from the cluster gateway until all the interfaces go through, and repeatedly calculating to obtain a scheduling efficiency function H_Q，i(v_i) (ii) a Measuring comprehensive scheduling efficiency function H of m interfaces on cluster Q_Q(v)；

Setting step length, changing α, lambda and mu, repeatedly measuring to obtain comprehensive scheduling efficiency function H_Q(v) Selecting a comprehensive scheduling efficiency function H_Q(v) α ', λ ' and μ ' corresponding to the maximum value.

The scheduling efficiency function H_Q，i(v_i) The calculation method comprises the following steps:

wherein F ″)_i(x) rate F of completion of single device interface i request under flow control condition_i(ii) an approximate representation of an ideal value of (),

wherein v is_i ^*Is to follow v_iIncrease assay F_i(v_i) Increasing to an overload threshold; f_i(v_i ^*) At a velocity v_i ^*The rate at which a single device requests completion, | D_iAnd | is the number of devices of the interface i.

V is_i ^*And F_i(v_i) The measuring method comprises the following steps:

controlling an external variable;

at a velocity v_i＝v₀Sending a request for calling an interface i to the equipment, and measuring the rate w' of the completion of the request; repeating the operation for multiple times to obtain the mean value of w

Then

Change v_iMeasuring F_i(v_i) At v_iTaking the results for other values to give F_i(v_i) And v_i ^*。

Said change v_iMeasuring F_i(v_i) At v_iTaking other values, the results are specifically:

v_iincrease exponentially, judge F_i(v) The overall trend and the interval of the maximum value of (c);

v_ilinearly traversing the interval where the maximum value is positioned, and determining F_i(v_i) Maximum value of (2) and F_i(v_i) The law of variation around the maximum.

Setting the step length, changing α, lambda and mu, and repeatedly measuring to obtain a comprehensive scheduling efficiency function H_Q(v) Selecting a comprehensive scheduling efficiency function H_Q(v) α ', λ ', μ ' corresponding to the maximum value are specifically:

fixing α and lambda optimizing mu to obtain a measured comprehensive scheduling efficiency function H_Q(v) Optimal μ'; fixingα and mu optimize lambda, determine the comprehensive scheduling efficiency function H_Q(v) Obtaining optimum lambda', fixing lambda and mu optimization α, determining comprehensive scheduling efficiency function H_Q(v) Optimal α 'is obtained, thus α', λ ', μ' are obtained.

And (3) carrying out local optimality verification on the optimal parameters (α ', lambda', mu '), comparing the adjacent 26 groups (α, lambda, mu) with (α', lambda ', mu'), and if (α ', lambda', mu ') is better than 26 groups (α, lambda, mu), satisfying the local optimality, and if one or more groups are better than (α', lambda ', mu'), selecting H_Q(v) The highest corresponding (α, λ, μ) is the optimal (α ', λ ', μ ').

The comprehensive scheduling efficiency function H_Q(v) The calculation method comprises the following steps:

and m is the number of interfaces.

The technical scheme provided by the embodiment of the invention has the following beneficial effects:

based on the characteristics of the service capability of single equipment, the invention researches and establishes an adaptive scheduling algorithm model for improving the scheduling efficiency of the equipment cluster, and simultaneously considers the influence of parameters for controlling a receiving window on the algorithm. The specific implementation mode is to maintain a receiving window for each device, control the number of requests processed by the devices at the same time, and simultaneously feed back and adjust the size of the receiving window according to the request processing result to realize the self-adaption of flow control.

By W_j，iIndirectly describing service capabilities of a single device F_i(v) By C_i，iDescribing the load of the device; ensure C_j，i≤W_j，iTo achieve flow control; selecting

Minimal equipment to achieve traffic averaging without the need to accurately monitor instantaneous request traffic to the equipment; automatically adjusting W based on success or failure of interface invocation_j，iTo achieve automatic adaptation v^*An unknown interface.

The invention finds that the service capacity of single equipment is highly sensitive to flow overload through experiments, and therefore provides a method for calculating an ideal value of the service capacity of the single equipment based on flow control conditions.

The invention tests and optimizes the adaptive scheduling algorithm on the data service cluster. Compared with a single device without flow control, the self-adaptive scheduling algorithm is verified to be capable of effectively controlling the flow. And performing independent optimization experiment and analysis on each parameter of the algorithm to obtain an optimal parameter combination.

Drawings

The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and together with the description, serve to explain the principles of the invention.

FIG. 1 is a flow chart of an adaptive scheduling method for a data service cluster according to the present invention;

fig. 2 is a measurement result of the service capability of the interface of a single device in the second embodiment;

FIG. 3 is an ideal value of the interface service capability of a single device part under the condition of flow control in the second embodiment;

fig. 4 is a measurement result of the service capability of the interface of a single device in the second embodiment;

FIG. 5 is a data structure used by the adaptive scheduling algorithm according to the second embodiment;

fig. 6 shows the effect of the adaptive scheduling algorithm in controlling the flow rate in the second embodiment.

Detailed Description

Example one

As shown in fig. 1, the present invention provides an adaptive scheduling method for a data service cluster, including:

the API sends out calling requests, and the API calling requests are arranged behind the existing requests according to a first-in first-out (FIFO) sequence;

reading the most front request, analyzing the request, and reading an interface i' of the request;

traverse W_j，iAnd C_j，iCorresponding column in which W_n×mTo maintain the receive window matrix, element W_j，iMaximum number of interfaces i on recording device j that can be called simultaneously, C_n×mAs a concurrency matrix, element C_j，iRecording the current concurrency number (the number of requests being processed) of the interface i on the device j; obtaining a set of candidate devices (i.e. devices with current concurrency smaller than the receiving window) D' (D is a set of all devices) D ═ j | C_j，i′＜W_j，i′，j∈D}

When D' is an empty set, rearranging the API call requests in the request queue according to the sequence;

when D ' is not an empty set, selecting the device j ' with the lowest load from the set D ':

c is to be_j′，i′Adding 1, assigning the request to the device j' for execution, and if the update time is reached, updating V_j′，i′，V_j′，i′Record the rate, V, at which requests invoking interface i' arrive at device j_j′，i′Frequent updates (e.g., every minute) are not required;

when the execution of the device j' is overtime or fails, F is set_j′，i′Adding 1, F_j′，i′To maintain the failure request count matrix, determine whether F is satisfied_j′，i′≥λV_j′，i′When it is true, λ is a rational number between 0 and 1, which may be 0.5, and when it is true, let

α is a rational number between 0 and 1, and may be 0.5, S_j′，i′＝0，F _j′，i′0; then returning to execute the next instruction; f_j′，i′≥λV_j′，i′If not, returning to execute the next instruction;

when the device j' successfully executes, S_j′，i′Adding 1, S_m×nTo execute the successful request count matrix, S_j′，i′To invoke the counting of successful execution of interface i 'at device j', a determination is madeWhether or not S is satisfied_j′，i′≥μV_j′，i′If it is true, μ is a rational number between 0 and 1, which may be 0.5, and if true, let W_j′，i′，S_j′，i′＝0，F_j′，i′When the instruction is equal to 0, returning to execute the next instruction; s_j′，i′≥μV_j′，i′If not, returning to execute the next instruction;

preferably, the values of the parameters α, λ, μ are optimized, specifically:

at a velocity v_i＝v₀Requesting the calling interface i, v from the cluster gateway₀Measuring a rate w' at which the request is completed for setting the rate; preferably, the operation is repeated a plurality of times to obtain a mean value of w

Then

G_Q，i(v₀) Rate of requests completed for the cluster;

selecting v_iIncrease exponentially (e.g. v ═ 1, 2)²，2³，...，2ⁿ) In the same way, G can be measured_Q，i(v_i)；

Calculating a scheduling efficiency function

Wherein F ″)_i(x) rate of completion of requests from a single device under flow control conditions F_iIdeal value of (& gtF'_i(ii) an approximate representation of (a),

wherein v is_i ^*Is to follow v_iIncrease assay F_i(v_i) Increasing to an overload threshold; f_i(v_i ^*) At a velocity v_i ^*Rate of completion of a single device request；

Changing to request the interface i from the cluster gateway until all the interfaces go through, and repeatedly calculating to obtain a scheduling efficiency function H_Q，i(v_i) (ii) a Defining a comprehensive scheduling efficiency function H of m interfaces on a cluster Q_Q(v)：

Setting step length, changing α, lambda and mu, repeatedly measuring to obtain comprehensive scheduling efficiency function H_Q(v) Selecting a comprehensive scheduling efficiency function H_Q(v) α ', λ' and μ 'corresponding to the maximum values are the optimal parameters α', λ 'and μ'.

Preferably, α and λ can be fixed and optimized, making α ═ 0.5, λ ═ 0.5, μ ∈ {0.2, 0.4, 0.6,

0.8, optimum μ ' was obtained, fixed α and μ optimum λ were obtained, α was 0.5, μ was 0.5, λ ∈ 0.2, 0.4, 0.6, 0.8, and optimum λ ' was obtained, fixed λ and μ optimum α, λ was 0.5, μ was 0.5, α∈ 0.2, 0.4, 0.6, 0.8, and optimum α ' was obtained.

Preferably, local optimality verification is performed, the optimal parameters are combined to be (α ', λ', μ '), the adjacent 26 groups (α, λ, μ) are compared with (α', λ ', μ'), if (α ', λ', μ ') is better than 26 groups (α, λ, μ), the local optimality is satisfied, and if one or more groups are better than (α', λ ', μ'), H is selected_Q(v) The highest corresponding (α, λ, μ) is the optimal (α ', λ ', μ ').

Example two

Single device service capability measurement

Step 1: controlled variable

(1) Software and hardware configuration of the equipment: the measurement is carried out on equipment with the same manufacturer, the same model and the same Android version, all the applications allowed to be unloaded are unloaded, and only the applications and the interfaces to be measured are installed. And the equipment is connected to a power supply to keep the full-charged state of the battery.

(2) Network environment: the device is connected to a stable Wi-Fi access point, and measurements are taken during off-peak hours.

Step 2: measurement F_i(v)

At a rate v ═ v₀Sending a request for calling an interface i to the equipment, and measuring the rate w' of the completion of the request; repeating the operation for multiple times to obtain the mean value of w

Then

By the same token, F can be measured_i(v) Results when v takes other values. Due to measurement of a single F_i(v) The operation of (a) is time-consuming, the value range of v is large, and if v is not properly selected, the total measurement time is hard to bear. To determine F within a reasonable time_i(v) The overall trend, the maximum value and the change rule around the maximum value of the formula are as follows:

(1) first, v increases exponentially (e.g., v ═ 1, 2)²，2³，...，2ⁿ) Judgment of F_i(v) The overall trend and the interval in which the maximum value lies.

(2) Then, v linearly traverses the interval where the maximum value is located, and determines F_i(v) Maximum value of (2) and F_i(v) The law of variation around the maximum.

Preferably, the completion rate of a single device is determined by:

to determine F within a reasonable time_i(v) The overall trend, the maximum value and the change rule around the maximum value,

(1) first, v_iIncrease exponentially (e.g. v)_i＝1，2，2²，2³，...，2ⁿ) Judgment of F_i(v_i) The overall trend and the interval in which the maximum value lies.

(2) Then, v_iLinearly traversing the interval where the maximum value is positioned, and determining F_i(v_i) Maximum value of (2) and F_i(v_i) The law of variation around the maximum.

Preferably, in the measurement, the control variables are required: software and hardware configuration of the equipment: the measurement is carried out on equipment with the same manufacturer, the same model and the same Android version, all the applications allowed to be unloaded are unloaded, and only the applications and the interfaces to be measured are installed. Connecting the equipment into a power supply and keeping the battery in a full-charged state; selected network environment: the device is connected to a stable Wi-Fi access point, and measurements are taken during off-peak hours.

And measuring the service capability of the partial behavior reflection interface on the single equipment. The experimental equipment is a Changhong S07 mobile phone, and the Android version is 6.0. The selected interface was open from seven common applications (see table 1). Except for the interface for acquiring the new product list of the 'split-many' application, other interfaces have parameters. To prevent that a possible application local cache affects the accuracy of the measurement values, a randomly chosen parameter is used for each request. The 'key words' are selected from Chinese general word stock, and the 'stock names or codes' and 'city codes' come from special word stock.

TABLE 1 measured behavioral reflex interface

As shown in FIG. 2, is a measurement of the exponential increase of v, showing F_i(v) The overall trend of (c):

(1) with increasing v, F_i(v) Increasing and then decreasing, and finally approaching 0; f_i(v) The variation of (c) corresponds to the typical "idle-saturation-overload" procedure.

(2)F_i(v) Image of growing phase (before maximum is reached) and F_i(v) The higher the image (dotted grey line) overlap at v, indicating that the interface is more stable to service before being saturated by the requested traffic.

(3) F of different interfaces_i(v) The maximum values are different and may vary widely, reflecting differences in service capabilities. For example, "search offer information for" Mei Tuo "applications"interface max F_i(v) Less than 5, and the 'search stock information' interface max F of 'the classic edition of the' Yilian playsman_i(v) Is more than 100, the difference between the two is more than 20 times.

FIG. 4 shows a linear traversal of v for F_i(v) The measurement result of the interval in which the maximum value is located. The abscissa, ordinate and gray dashed line have the same meaning as in fig. 2. The image on the left side of each row is the same as the corresponding curve in fig. 2 (v grows exponentially), and the highlight part is the interval where the maximum value is located; the right image is F_i(v) Details of the variation around the maximum (v increases linearly). Display of images, F_i(v) If v continues to increase after the maximum value is reached, F_i(v) The fluctuation of (a) is significantly increased; the interface is sensitive to the increase of the request traffic after being saturated by the request traffic.

From the above measurements, it can be speculated that if some flow control mechanism is used to reject part of the requests when v is too large, so that the rate v' at which the device accepts requests satisfies the following formula, the service capacity degradation caused by overload can be avoided.

As shown in FIG. 3, F is defined as the flow control condition_i(v) Is preferably F'_i(v)：

v＜v^*When F_i(v) Can be approximately expressed as F_i(v) V, thus F'_i(v) May be approximately represented as F ″)_i(v)：

The "swallow cloud" data service cluster suitable for the adaptive scheduling algorithm should satisfy the following assumptions:

(1) the cluster consists of n devices (with the same software and hardware configuration (same manufacturer, model and Android version) (

numbers

1, 2.., n)).

(2) The network environment where the cluster is located is stable.

(3) The m interfaces (numbered 1,2, D), the device number set of deployment interface i is D_i。

(4) The same interface service capability on each device is the same, using function F_i(v) And (4) showing.

(5) The different interfaces deployed on the same equipment have no influence on each other, i.e. function F_i(v) Independent of other interfaces deployed on the device.

(6) The interface calling request is stateless, and the gateway can forward the request to any equipment in the cluster, wherein the equipment is provided with the corresponding interface for processing.

A "swallow cloud" data service cluster Q satisfying the above assumptions may be represented by a 2m +2 tuple:

Q＝(n，m，D₁，D₂，...，D_m，F₁(v)，F₂(v)，...，F_m(v))

the total service capability of the cluster Q on interface i can be defined as a function G_Q，i(v) V is the rate of arrival of requests to the cluster (in units of times/second), G_Q，i(v) Is the rate (in times/second) at which the cluster completes the request. The request rate assigned by the gateway to device j is v_j(in units of times/second) then there are:

F″_i(v) for flow control conditions F_i(v) Approximate representation of the ideal values, it is easy to prove that:

namely, it is

Given | D_iI and F_i(v) Time G_Q，i(v) The upper limit of (3). Defining a scheduling efficiency function H for an interface i on a cluster Q_Q，i(v)：

Defining a comprehensive scheduling efficiency function H of m interfaces on a cluster Q_Q(v)：

Cluster parameters (i.e., n, m, D)_i，F_i) And scheduling algorithm can influence H_Q(v) When cluster parameters are not changed, H_Q(v) Reflecting the performance of the scheduling algorithm. The goal of the scheduling algorithm is therefore to increase H_Q(v)

To increase H_Q(v) H must be increased_Q，i(v) In that respect To increase H_Q，i(v) It is necessary to make G_Q，i(v) Approaching the upper limit while satisfying:

equation ① requires control of request flow to ensure D_iDoes not exceed a threshold value v^*Equation ② requires that the requested flow be distributed evenly so that D_iEach device in (a) receives requests at an equal rate (i.e., load is equal). The scheduling algorithm only needs to properly control and allocate the requested traffic, so that G is enabled_Q，i(v) Approaching the upper limit, and then H_Q，i(v) Approach 1, finally H_Q(v) Close to 1, the optimization objective is achieved.

However, in order to approximately satisfy the above condition, the scheduling algorithm needs to solve the following problem:

(1) critical value v of different interfaces^*Different, and the interface types that the scheduling algorithm needs to support are not controlled, and the critical values of all the interfaces cannot be measured in advance through experiments.

(2) When the requested traffic to the cluster is large, monitoring the instantaneous traffic to each device faces the contradiction of low delay and high accuracy: in order to more accurately monitor the traffic data, the frequency of updating the data must be increased, but this results in increased delay for the scheduling algorithm.

One possible solution is to adaptively control the traffic, dynamically sense the threshold of the interface and the load of the device as follows:

(1) maintaining a receive window matrix W_n×mElement W_i，iThe maximum number of interfaces i on device j that can be called simultaneously is recorded. W_j，iCan be set to a smaller integer, and W is set during the operation of the algorithm_j，iWill be constantly updated.

(2) Maintaining a current concurrency number matrix C_n×mElement C_j，iThe current concurrency (number of requests being processed) of the interface i on device j is recorded. When the gateway forwards the interface i call request to device j, C_j，iAdding 1; when the device j finishes processing the interface i call request once, no matter whether the execution is successful or not, C_j，iMinus 1.

(3) When the gateway receives the call request of the interface i, the gateway tries to find a target device j' meeting the following conditions:

(4) if the target equipment i' meeting the formula does not exist, executing current limiting operation, namely, processing after the equipment meeting the condition appears, and rejecting the request if the equipment is overtime in the waiting process; and if the target equipment j 'exists, forwarding the request to the equipment j' for execution, and waiting for an execution result.

Determining W_j，iThe adjustment rules of (2) need to solve two key problems: (1) how to judge the success or failure of a large number of requests in a short time; (2) w_j，iThe amount should be increased or decreased. The second problem is easily solved, since the cluster device is very sensitive to request overload, for W_j，iThe adjustment of (1) should follow the principle of 'increasing and decreasing multiplicatively'. The "mass" determination in the first problem may be based on the ratio of the successful or failed request count to the arriving device request traffic, with a truly difficult definition of "short time". The proposed 'competition counting' rule can avoid the direct judgment of 'short time' and achieve the expected effect. The following is "Competition count"Rule:

(1) maintaining a current traffic matrix V_n×mElement V_j，iThe rate at which requests invoking interface i arrive at device j is recorded. V_j，iFrequent updates (e.g., every minute) are not required.

(2) Maintaining a successful request count matrix S_n×mWhenever device j successfully calls interface i, S_j，iAnd adding 1. If the updated S_j，iSatisfies S_j，i≥μV_j，iThen W is_j，iAdding 1, S_j，iAnd F_j，iAnd (6) clearing.

(3) Maintaining a failed request count matrix F_n×mWhenever device j fails to call interface i, F_j，iAnd adding 1. If updated F_j，iSatisfies F_j，i≥λV_j，iThen, then

S_j，iAnd F_j，iAnd (6) clearing.

The key to the "Contention count" rule is S_j，iAnd F_j，iA variable that reaches the threshold value first in the process results in W_j，iIs then S_j，iAnd F_j，iAre all clear, so successful or failed request pair S_j，iAnd F_j，iThe influence of (A) does not last, S_j，iAnd F_j，iThe cluster state in a short time can be more accurately reflected.

The parameters to be determined are α, lambda and mu, the value range is 0 < α, lambda and mu < 1, and the W is updated by using a competition counting rule_j，iThe adaptive scheduling algorithm of (a) can be uniquely represented by a triplet (α, λ, μ).

As shown in fig. 5, the data structures used by the adaptive scheduling algorithm are mainly queues and hash tables.

The queue is used for storing the requests to be processed. The space complexity is O (N), and N is the maximum value of the queue length; the average time complexity of enqueue and dequeue operations is O (1).

Hash table for storing W_j，iWhen global variables are equal, the method realizes access according to 'equipment-interface' by adopting a nested modeAsking for an element, the element being NULL indicates that no corresponding interface is deployed on the device. The space complexity is O (nm), wherein n is the number of devices and m is the number of interfaces. The average temporal complexity of accessing an element is O (1).

The core of the adaptive scheduling algorithm is adaptive flow control, and the key influencing the performance of the algorithm is the adjustment rule and the parameters of a receiving window. Therefore, three steps are required to be completed for realizing the adaptive scheduling algorithm:

(1) in this chapter, a "competition count" rule is selected, and the parameters to be optimized are α, λ and μ.

(2) Verifying the effectiveness of the algorithm and determining whether the algorithm can effectively control the flow to the device.

(3) And aiming at the parameters of the actual data service cluster optimization algorithm, the scheduling efficiency is improved.

And measuring the service capacity of the single device after the self-adaptive scheduling algorithm is used, comparing the result with the service capacity without flow control, and verifying that the self-adaptive scheduling algorithm can effectively control the flow reaching the device to prevent the loss of the service capacity of the device due to overload. The verification method comprises the following steps:

(1) the "race count" rule is chosen to take any legal value for the parameter, where the value of the parameter is (α, λ, μ) ═ 0.5, 0.5, 0.5.

(2) The algorithm is applied to the completely same single equipment, and the service capability of the equipment is measured under the same experimental environment by the same method. As shown in FIG. 6, the abscissa represents the request arrival rate v and the ordinate represents the service capability F_i(v) In that respect The black curve represents the result of using the adaptive scheduling algorithm, and the result shows that the adaptive scheduling algorithm obviously improves the service capability of a single device under high load.

Optimizing algorithm parameters α, lambda, mu on a 'swallow cloud' data service cluster, wherein the optimization aim is to improve the comprehensive scheduling efficiency H_Q(v) The optimization method comprises the following steps:

(1) assuming that the three parameters can be optimized independently, when one of the parameters is optimized, the values of the other two parameters are fixed.

(2) Fixing α and lambda, uniformly selecting k values from the (0, 1) interval as mu candidates (taking k as 4 in experiments, the candidate values are 0.2, 0.4, 0.6 and 0.8), and sequentially testing the comprehensive scheduling efficiency H of different mu pairs of clusters_Q(v) Is selected such that H_Q(v) Overall highest value μ'.

(3) Similarly, fixing α and μ optimizes λ, and fixing λ and μ optimizes α, yields a combination of α, λ, μ optima (α ', λ ', μ ').

(4) The local optimality of (α ', λ ', μ ') was verified, i.e. better than all (α ' + p Δ, λ ' + q Δ, μ ' + r Δ (p, q, r ∈ -1, 0, 1 and not all 0, the experiment was taken as Δ ═ 0.1.) it was necessary to test 26 parameter combinations and compare the results with (α ', λ ', μ ').

The parameters (α, λ, μ) were optimized as described above on cluster Q' consisting of 20 identical devices (software and hardware configuration and deployed behavioral reflex interface).

Fixing α and lambda optimizing mu

When α is 0.5, λ is 0.5, and μ ∈ {0.2, 0.4, 0.6, 0.8}, it is found that the most suitable candidate value of μ is 0.4, and therefore μ' is 0.4.

Fix α and μ optimize λ

When α is 0.5, μ is 0.5, λ ∈ {0.2, 0.4, 0.6, 0.8}, it is shown that λ' is 0.2.

Fixed lambda and mu optimization α

When λ is 0.5, μ is 0.5, and α∈ {0.2, 0.4, 0.6, and 0.8}, α' is 0.8.

Verification of local optimality

The optimal parameter combination is (α ', λ', μ ') (0.8, 0.2, 0.4), and the experimental results show that (α', λ ', μ') is superior to (α, λ, μ ') of 26 groups, and satisfies local optimality, by comparing the adjacent 26 groups (α, λ, μ) with (α', λ ', μ').

The above description is only a preferred embodiment of the application and is illustrative of the principles of the technology employed. It will be appreciated by those skilled in the art that the scope of the invention herein disclosed is not limited to the particular combination of features described above, but also encompasses other arrangements formed by any combination of the above features or their equivalents without departing from the spirit of the invention. For example, the above features may be replaced with (but not limited to) features having similar functions disclosed in the present application.

Claims

1. An adaptive scheduling method for a data service cluster, comprising:

screening candidate equipment meeting the conditions;

2. The adaptive scheduling method of a data service cluster according to claim 1, wherein the request is executed on the candidate device, and if the request exceeds a set time or the request fails to be executed, after the execution fails, a failure condition is determined, and a next instruction is executed, where the failure condition is determined and the next instruction is executed:

judging whether F is satisfied_j′,i′≥λV_j′,i′Is established, F_j′,i′Maintaining a failed count, V, at device j' for calling interface i_j′,i′For the rate at which requests for invoking interface i 'arrive at device j', λ is a real number between 0 and 1, which may be 0.5, and if true, makes the order

W_j′,i′The maximum number of simultaneous calls that can be made to the interface i 'on the recording device j', α being a real number between 0 and 1, S_j′,i′＝0，F_j′,i′＝0，S_j′,i′Performing a successful count at device j 'for the call interface i'; then returning to execute the next instruction; f_j′,i′≥λV_j′,i′If not, returning to execute the next instruction.

3. The adaptive scheduling method of a data service cluster according to claim 1, wherein if the execution is successful, after the execution is successfully recorded, a success condition is determined, and a next instruction is executed, where the determination of the success condition and the execution of the next instruction specifically include:

judging whether S is satisfied_j′,i′≥μV_j′,i′Is true, mu is a rational number between 0 and 1, S_j′,i′To invoke the interface i 'to perform a successful count at device j', V_j′,i′The rate at which requests for call interface i 'reach device j'; when it is established, let W_j′,i′＝0，S_j′,i′＝0，F_j′,i′0, wherein F_j′,i′Maintaining a failed count at the device j 'for the calling interface i', and then returning to execute the next instruction; s_j′,i′≥μV_j′,i′If not, returning to execute the next instruction.

4. The adaptive scheduling method of a data service cluster according to claim 2 or 3, wherein the values of the parameters α, λ, μ are optimized, specifically:

Rate at which interface i cluster completes requests

Selecting different v_iIn the same way, different v can be measured_iRate G of completion request of corresponding interface i cluster_Q,i(v_i)；

Compute interface i scheduling efficiency function H_Q,i(v_i)；

Changing to request the interface i from the cluster gateway until all the interfaces go through, and repeatedly calculating to obtain a scheduling efficiency function H_Q,i(v_i) (ii) a Measuring comprehensive scheduling efficiency function H of m interfaces on cluster Q_Q(v)；

5. The method of claim 4, wherein the scheduling efficiency function H is the scheduling efficiency function_Q，i(v_i) The calculation method comprises the following steps:

6.The method of claim 5, wherein v is the number of bits of the data service cluster_i ^*And F_i(v_i) The measuring method comprises the following steps:

controlling an external variable;

Then

7. The method of claim 6, wherein the change v is a change in a scheduling algorithm_iMeasuring F_i(v_i) At v_iTaking other values, the results are specifically:

8. The adaptive scheduling method of claim 7 wherein the step size is set and the overall scheduling efficiency function H is obtained by repeating the measurement by changing α, λ and μ_Q(v) Selecting a comprehensive scheduling efficiency function H_Q(v) α ', λ ', μ ' corresponding to the maximum value are specifically:

fixing α and lambda optimizing mu to obtain a measured comprehensive scheduling efficiency function H_Q(v) Optimization ofFixing α and optimizing lambda to determine comprehensive scheduling efficiency function H_Q(v) Obtaining optimum lambda', fixing lambda and mu optimization α, determining comprehensive scheduling efficiency function H_Q(v) Optimal α 'is obtained, thus α', λ ', μ' are obtained.

9. The adaptive scheduling method of a data service cluster as claimed in claim 8, wherein the optimal parameters (α ', λ', μ ') are verified for local optimality, the neighboring 26 groups (α, λ, μ) are compared with (α', λ ', μ'), and if (α ', λ', μ ') is better than 26 groups (α, λ, μ), the local optimality is satisfied, and if one or more of them are better than (α', λ ', μ'), H is selected_Q(v) The highest corresponding (α, λ, μ) is the optimal (α ', λ ', μ ').

10. The method for adaptive scheduling of a data service cluster of claim 9,

and m is the number of interfaces.