阅读说明：本技术 基于诱饵效应的雾节点任务卸载方法、系统、介质及设备 (Method, system, medium and equipment for unloading task of fog node based on bait effect ) 是由 李登 谭萤 刘佳琦 于 2020-04-07 设计创作，主要内容包括：本发明公开了一种基于诱饵效应的雾节点任务卸载方法、系统、介质及设备,本发明考虑了诱饵效应对用户的激励作用,建立了能够影响雾节点行为决策的任务发布环境,并向环境中卸载任务提供了任务吸引力值,通过任务发布和任务吸引力值有指向性地引导雾节点决策；通过设置诱饵任务,提高了目标任务的客观吸引力值；在此基础上引入偏好系数,反映雾节点的真实决策行为,诱饵任务的加入提高了部分雾节点主观偏好值,使更多雾节点参与任务阈值得到满足,提高了雾节点的参与数量。同时,相较于对比的机制,本方案不需要提高任务报酬就使更多的任务被选择了,提高了移动设备的总效用,本方案能有更实际和更有效的激励效果。(The invention discloses a method, a system, a medium and equipment for unloading a fog node task based on a bait effect, which take the incentive effect of the bait effect on a user into consideration, establish a task issuing environment capable of influencing the behavior decision of the fog node, provide a task attraction value for unloading the task in the environment, and guide the fog node decision in a directive manner through the task issuing and the task attraction value; the objective attraction value of the target task is improved by setting the bait task; on the basis, a preference coefficient is introduced to reflect the real decision-making behavior of the fog nodes, the subjective preference value of part of fog nodes is improved by adding the bait task, so that the threshold value of more fog nodes participating in the task is met, and the participation quantity of the fog nodes is improved. Meanwhile, compared with a contrast mechanism, the scheme enables more tasks to be selected without increasing the task return, improves the total utility of the mobile equipment, and can have a more practical and more effective incentive effect.)

1. A method for unloading a fog node task based on a bait effect is characterized by comprising the following steps:

classifying the tasks to be unloaded, and dividing all the tasks to be unloaded into three classes according to the predicted average reward and average time provided by all the tasks to be unloaded on all the mobile devices: high reward long time class tasks, low reward short time class tasks and other class tasks;

designing a bait task item by taking a high-reward long-time task as a target task and a low-reward short-time task as a competitive task of the target task;

determining a specific bait task, combining the bait task item and the average reward and average time proposed by all tasks to be unloaded, so as to obtain a reward bait factor and a time bait factor of the bait task item when a bait effect value obtains a maximum value in a forward bait effect range, and determining the specific reward and time of the bait task;

task issuing, namely updating and issuing all tasks to be unloaded after the remuneration and time of other tasks are modified into the remuneration and time of the decoy task;

the method comprises the steps that a fog node selects tasks to be unloaded, the classes of the tasks to be unloaded are selected according to reward and time preference of the fog node for task unloading, each task to be unloaded in the selected classes of tasks is bid, and the task to be unloaded with the lowest bid is selected;

and the mobile equipment selects a fog node, and if a certain task to be unloaded is selected by a plurality of fog nodes, the fog node with the lowest bid price is selected as the task to be unloaded sent by the corresponding mobile equipment to unload the task according to the high-low order of the task bid price of the fog node.

2. The method according to claim 1, wherein the bait effect value is obtained by calculation of attraction force under a basic reference point and a bait transfer reference point using a high reward long time class task, a low reward short time class task, respectively;

M(τ_tt|r)＝P_tt(v_tt|v_ave)+Q_tt(t_tt|t_ave)

wherein, M (τ)_ttR) represents a certain class of tasks τ_ttAttraction force, P, based on reference point r_tt(v_tt|v_ave) Representing tt-like tasks based on v_aveThe reward appeal of; q_tt(t_tt|t_ave) Representing tt-like tasks based on t_aveTime cost attractiveness of (t) ∈ { A, B, C }, A, B, C respectively indicate indicia of a high reward long time class task, a low reward short time class task and other classes of tasks, [ tau ]_goalDenotes the main push target task, τ_competeRepresenting a competing task;

wherein alpha and beta represent the concave-convex degree of the value function of the benefit and loss interval, and the value range is (0, 1); λ represents the degree of loss aversion, λ > 1;

r denotes a basic reference point, which refers to the predicted average reward v given by all the tasks to be offloaded_aveAnd an average time t_aveAnd r ' represents a bait transfer reference point, and refers to a predicted average reward v ' presented by a high reward long-time task, a low reward short-time task and a bait task '_aveAnd average time t'_ave。

3. The method according to claim 2, wherein the specific process of selecting the task to be unloaded according to the reward and the time preference of the fog node for task unloading is as follows:

constructing a preference function of the fog nodes on the consideration and time of various unloading tasks;

W_i,tt＝_iP_tt(v_tt|v_ave)+(1-_i)Q_tt(t_tt|t_ave)

wherein, W_i,ttIndicating the overall preference value of the fog node i for the tt class task,_ipreference coefficient for representing the reward of the fog node i to the task 1-_iRepresenting the preference coefficient of the fog node to the task time;

from all W's greater than a set global preference threshold_i,ttIn (1), the largest is selected

the bid of the task j to be unloaded selected by the fog node i is calculated according to the following formula:

wherein, b_i,jBid, C, representing selection of task to be offloaded j by fog node i_i,j(t_j,f_i) Represents the cost of the selected task j of the fog node i, andt_jrepresenting the time required for the task j to be unloaded, f_iRepresenting the clock frequency of the fog node i; tt is a Chinese character^*Indicating a selected task class mark, T ═ { a, B, decoy }, decoy indicating a decoy task, T-tt^*For other types of tasks;

4. The method according to claim 3, wherein a matching matrix of the fog nodes and the tasks with the unloading is constructed according to the tasks to be unloaded selected by the fog nodes, and before the tasks to be unloaded are selected by the fog nodes, the matching matrix A ═ a_i,j}_I×JAll elements are zero;

wherein, a_i,jThe corresponding matrix element between the fog node i and the task j to be unloaded is represented as a_i,jIf the fog node i bids on the task j to be unloaded, a_i,j＝1。

5. The method according to claim 4, characterized in that the matching matrix is traversed, if only 1 element per column of the matching matrix is 1 at most, the matching matrix is output, otherwise, the lowest-bid fog node is selected as the executor of the task unloading finally implemented in each column for each task to be unloaded.

6. The method of claim 1, wherein if the number of other tasks is 0 before the task is released, a third-party service provider is used for a decoy task, and after the newly-created decoy task is added to the task to be unloaded, the task is released.

7. The method of claim 2, wherein α - β -0.88 and λ -2.25.

8. A decoy effect based fog node task offloading system, comprising:

the task classification module to be unloaded is used for classifying all the tasks to be unloaded into three categories according to the predicted average reward and average time provided by all the tasks to be unloaded: high reward long time class tasks, low reward short time class tasks and other class tasks;

the bait task item design module is used for designing a bait task item by taking a high-reward long-time task as a target task and taking a low-reward short-time task as a competitive task of the target task;

the specific bait task determining module is used for combining the bait task item and the estimated average reward and average time proposed by all tasks to be unloaded so as to obtain a reward bait factor and a time bait factor of the bait task item when the bait effect value obtains the maximum value in the forward bait effect range and determine the specific reward and time of the bait task;

the task issuing module is used for updating and issuing all tasks to be unloaded after the remuneration and the time of other tasks are modified into the remuneration and the time of the decoy task;

the method comprises the steps that a fog node selects a task module to be unloaded, a task class to be unloaded is selected according to reward and time preference of task unloading of the fog node, each task to be unloaded in the selected class of tasks is bid, and the task to be unloaded with the lowest bid is selected;

and the mobile equipment selection fog node module is used for judging whether a certain task to be unloaded is selected by a plurality of fog nodes or not, and selecting the fog node with the lowest bid as the corresponding task to be unloaded sent by the mobile equipment to unload the task according to the high-low order of the fog nodes for bidding the task.

9. A computer-readable storage medium, on which a computer program is stored, which program, when being executed by a processor, is adapted to carry out the method for decoy-effect based task offloading of a fog node according to any of claims 1 to 7.

10. A decoy-effect based fog node task off-loading facility, comprising: a processor and a memory;

the memory is adapted to store a computer program, and the processor is adapted to execute the computer program stored in the memory to cause the decoy-effect based fog node task offloading device to perform the decoy-effect based fog node task offloading method of any of claims 1-7.

Technical Field

The invention belongs to the technical field of mist computing unloading, and particularly relates to a mist node task unloading method, system, medium and equipment based on a bait effect.

Background

The fog calculation is a platform for providing calculation, storage and network service between the terminal equipment and the cloud center through a fog node. Devices with limited resources such as vehicles, base stations and access points, and devices with rich resources such as computer clusters can be used as Fog nodes (Fog nodes). Compared with cloud computing, the fog computing has a series of advantages of low time delay, location perception, real-time interaction, mobile support and the like.

Due to the privacy of the fog nodes, owners of the fog nodes are reluctant to provide task offload services proactively, resulting in unacceptable delays and even service outages. And the fog computing network is based on opportunity-based offloading, so a reasonable incentive mechanism needs to be designed according to different application requirements to encourage the fog nodes to actively participate in the computing offloading task.

The incentive mechanisms in the present stage of computing offloading can be mainly divided into three types of incentive mechanisms based on entertainment, services and money. With the monetary mechanism being the most direct incentive. The money-based incentive mechanisms can be divided into two categories, namely auction-based and game theory-based, and the auction mechanism is most widely applied in the calculation unloading. However, existing computational offload incentive mechanisms are based on traditional economic design, and it is generally believed that adding one type of task to a task set reduces the probability of selection for each type of task before, because the denominator increases and the numerator does not change. Moreover, the task remuneration of the mechanisms is made according to the task time cost, which means that if the unloading rate of the high-time-cost task is improved, the task can be achieved only by improving the remuneration of the task, so that the utility of the mobile equipment is inevitably reduced, and the technical development direction is not met. However, the research on the behavioral economics bait effect shows that adding a specific bait item to a selected set does not only separate the probability of selection of an existing option, but rather makes an old option more attractive. Then, in real life, the preferences of the fog nodes are different, and the difference in preferences has an influence on the selection thereof. For example, some fog nodes prefer low cost tasks, which are more sensitive to time cost attributes; some tasks are more preferable to high reward tasks, which are more sensitive to reward attributes; there are also fog nodes that do not have higher expectations of their relative attributes due to increased rewards or time, and their preference is neutral.

In view of the above, there is a need for a more efficient method for stimulating a cloud node to participate in computational offloading that considers the influence of the decoy effect on user behavior decisions.

Disclosure of Invention

In order to solve the technical problems, the invention provides a mist node task unloading method, a mist node task unloading system, a mist node task unloading medium and mist node task unloading equipment based on a bait effect.

The invention provides the following technical scheme:

in one aspect, a method for unloading a task of a fog node based on a bait effect comprises the following steps:

classifying the tasks to be unloaded, and dividing all the tasks to be unloaded into three classes according to the predicted average reward and average time provided by all the tasks to be unloaded on all the mobile devices: high reward long time class tasks, low reward short time class tasks and other class tasks;

designing a bait task item by taking a high-reward long-time task as a target task and a low-reward short-time task as a competitive task of the target task;

determining a specific bait task, combining the bait task item and the average reward and average time proposed by all tasks to be unloaded, so as to obtain a reward bait factor and a time bait factor of the bait task item when a bait effect value obtains a maximum value in a forward bait effect range, and determining the specific reward and time of the bait task;

task issuing, namely updating and issuing all tasks to be unloaded after the remuneration and time of other tasks are modified into the remuneration and time of the decoy task;

the method comprises the steps that a fog node selects tasks to be unloaded, the classes of the tasks to be unloaded are selected according to reward and time preference of the fog node for task unloading, each task to be unloaded in the selected classes of tasks is bid, and the task to be unloaded with the lowest bid is selected;

and the mobile equipment selects a fog node, and if a certain task to be unloaded is selected by a plurality of fog nodes, the fog node with the lowest bid price is selected as the task to be unloaded sent by the corresponding mobile equipment to unload the task according to the high-low order of the task bid price of the fog node.

According to theory and simulation experiment data, the number of target tasks and total tasks which are successfully unloaded by adopting the scheme is larger than the number of tasks when a comparison mechanism (PMMRA mechanism, PMMRA is a classical incentive mechanism in a calculation unloading system) is adopted, and the objective attraction value of the target tasks (tasks to be unloaded) is improved by setting decoy tasks; on the basis, a preference coefficient is introduced to reflect the real decision-making behavior of the fog nodes, the subjective preference value of part of fog nodes is improved by adding the bait task, so that the threshold value of more fog nodes participating in the task is met, and the participation quantity of the fog nodes is improved. Meanwhile, compared with a comparison mechanism (the task prediction reward is determined according to the market supply and demand relationship), the scheme enables more tasks to be selected without increasing the task reward, and improves the total utility of the mobile device.

The mobile equipment and the fog node are an unloading service requester and an executing party;

the reward decoy factor of the decoy task item is less than 1 and the time decoy factor is greater than 1;

further, the bait effect value is obtained by calculating the attraction force of the high-reward long-time task and the low-reward short-time task under the basic reference point and the bait transfer reference point respectively;

M(τ_tt|r)＝P_tt(v_tt|v_ave)+Q_tt(t_tt|t_ave)

wherein, M (τ)_ttR) represents a certain class of tasks τ_ttAttraction force, P, based on reference point r_tt(v_tt|v_ave) Representing tt-like tasks based on v_aveThe reward appeal of; q_tt(t_tt|t_ave) Representing tt-like tasks based on t_aveTime cost attractiveness of (t) ∈ { A, B, C }, A, B, C respectively indicate indicia of a high reward long time class task, a low reward short time class task and other classes of tasks, [ tau ]_goalDenotes the main push target task, τ_competeRepresenting a competing task;

wherein alpha and beta represent the concave-convex degree of the value function of the benefit and loss interval, and the value range is (0, 1); λ represents the degree of loss aversion, λ > 1;

r denotes a basic reference point, which refers to the predicted average reward v given by all the tasks to be offloaded_aveAnd an average time t_aveAnd r ' represents a bait transfer reference point, and refers to a predicted average reward v ' presented by a high reward long-time task, a low reward short-time task and a bait task '_aveAnd average time t'_ave。

The value ranges of alpha and beta are (0,1), which means that the values brought by the alpha and the beta are marginally decreased no matter the alpha and the beta are obtained or lost; λ greater than 1 indicates a loss aversion degree, which indicates that the loss interval is steeper than the gain interval; according to the experimental results of carniman and tewolski, α ═ β ═ 0.88 and λ ═ 2.25.

The calculation of the attraction of a certain type of tasks under the bait transfer reference point is similar to the calculation formula of the attraction under the basic reference point, namely the consideration and the time under the bait transfer reference point are correspondingly replaced;

further, the specific process of selecting the task to be unloaded according to the reward and the time preference of the task unloading performed by the fog node is as follows:

constructing a preference function of the fog nodes on the consideration and time of various unloading tasks;

W_i,tt＝_iP_tt(v_tt|v_ave)+(1-_i)Q_tt(t_tt|t_ave)

wherein, W_i,ttIndicating the overall preference value of the fog node i for the tt class task,_ipreference coefficient for representing the reward of the fog node i to the task 1-_iRepresenting the preference coefficient of the fog node to the task time;

_ipreference users for reward > 0.5;_iless than 0.5 is a cost preference user;_i0.5 is a non-preferred user.

From all W's greater than a set global preference threshold_i,ttIn (1), the largest is selectedCorresponding tt^*Class tasks and compute fog node i to select selected tt^*The method comprises the steps that bids of tasks to be unloaded in class tasks are selected for a fog node i according to the lowest bid;

the bid of the task j to be unloaded selected by the fog node i is calculated according to the following formula:

wherein, b_i,jBid, C, representing selection of task to be offloaded j by fog node i_i,j(t_j,f_i) Represents the cost of the selected task j of the fog node i, andt_jrepresenting the time required for the task j to be unloaded, f_iRepresenting the clock frequency of the fog node i; tt denotes the selected task class mark, T ═ { a, B, decoy }, decoy denotes the decoy task, T-tt^*For other types of tasks;

to selectThe preference satisfaction of the task is the maximum value of the difference value between the preference value of the task and the preference values of other tasks.

The fog node bids the task with cost + preference satisfaction, namely if the preference satisfaction is more than 0, the fog node bids with lower cost, otherwise, the fog node bids with higher cost. (bid equals cost in PMMRA mechanism);

the attraction value and the preference value correspond to the task and the fog node, respectively. Similar to the goods in the market, the merchant will first make prices according to the objective attributes of the goods, such as low-quality goods with low price and high-quality goods with high price, and the addition of a general quality good with high price can increase the sales volume of the high-price goods when the two are difficult to decide. However, there is a subjective factor when the user actually selects, that is, the user's individual preferences for price and quality, and the user's actual selection can be reflected by adding this factor.

The objective attraction values of the two types of tasks after adding the preference factors are transformed into subjective preference values for each fog node. The fog node selects the type of task with high preference and bids on the task according to the bidding formula.

Further, according to the task to be unloaded selected by the fog node, a matching matrix of the fog node and the task with the unloading is constructed, and before the task to be unloaded is selected by the fog node, the matching matrix A is { a ═ a }_i,j}_I×JAll elements are zero;

wherein, a_i,jThe corresponding matrix element between the fog node i and the task j to be unloaded is represented as a_i,jIf the fog node i bids on the task j to be unloaded, a_i,j＝1。

And further, traversing the matching matrix, if only 1 element in each column of the matching matrix is 1 at most, outputting the matching matrix, and otherwise, selecting the fog node with the lowest bid for each task to be unloaded in each column as an executor finally implementing task unloading.

If more than 1 element in a certain column in the matching matrix is 1, the unloading task (mobile device) represented by the column selects a fog node, and the successful fog node and the task bargain price are determined in a Vickrey auction mode in a specific example. In particular, the mobile device acts as a buyer, and the projected reward offered is the reward that it is willing to give using the offload service; the fog node acts as a seller and offers a bid to be paid for its desire to perform an offloading task.

The Vickrey auction, also known as a second price auction. The aim is to make bidders participate in the auction at the lowest price as close to the truest themselves as possible. Because the lowest price is directly obtained, the bidders usually offer a little higher price than the psychological price for guaranteeing the benefits of the bidders.

Both the reward due to mission expectations and the offer of fog nodes are related to their own utility. For a task, the expected reward reflects the reward it is expected to give, above which the money to unload the task may be paid higher than its locally calculated cost, but this is not necessarily so, as the mobile device is also subjective and it is not necessarily perfectly rational for the expected reward set equal to its locally calculated cost. But this part is not considered because the task classification of the first step actually screens out the tasks belonging to the abnormal tasks, and the remaining two types of tasks basically accord with the expected reward to approximate the time cost.

The subjectivity of the fog node is reflected by the preference value, so the bidding price is the influence of cost plus preference, and therefore the bidding price of the fog node is generally lower than the expected reward of the task (the reward is less effective and higher for the task side). The final task bargaining price must be higher than the fog node bid due to the use of the second price auction.

If all elements in a certain column are 0, the task is not selected by the fog node, and unloading fails.

Further, if the number of other tasks is 0 before the task is released, a third-party service provider is used for a bait task, and after the newly-built bait task is added into the task to be unloaded, the task is released.

Further, α ═ β ═ 0.88, and λ ═ 2.25.

In another aspect, a decoy-effect-based fog node task offloading system includes:

the task classification module to be unloaded is used for classifying all the tasks to be unloaded into three categories according to the predicted average reward and average time provided by all the tasks to be unloaded: high reward long time class tasks, low reward short time class tasks and other class tasks;

the bait task item design module is used for designing a bait task item by taking a high-reward long-time task as a target task and taking a low-reward short-time task as a competitive task of the target task;

the specific bait task determining module is used for combining the bait task item and the estimated average reward and average time proposed by all tasks to be unloaded so as to obtain a reward bait factor and a time bait factor of the bait task item when the bait effect value obtains the maximum value in the forward bait effect range and determine the specific reward and time of the bait task;

the task issuing module is used for updating and issuing all tasks to be unloaded after the remuneration and the time of other tasks are modified into the remuneration and the time of the decoy task;

the method comprises the steps that a fog node selects a task module to be unloaded, a task class to be unloaded is selected according to reward and time preference of task unloading of the fog node, each task to be unloaded in the selected class of tasks is bid, and the task to be unloaded with the lowest bid is selected;

and the mobile equipment selection fog node module is used for judging whether a certain task to be unloaded is selected by a plurality of fog nodes or not, and selecting the fog node with the lowest bid as the corresponding task to be unloaded sent by the mobile equipment to unload the task according to the high-low order of the fog nodes for bidding the task.

In another aspect, a computer-readable storage medium has stored thereon a computer program which, when executed by a processor, implements the above-described decoy-effect-based fog node task offloading method.

In yet another aspect, a decoy-effect based fog node task off-load device, comprising: a processor and a memory;

the memory is used for storing computer programs, and the processor is used for executing the computer programs stored by the memory so as to enable the fog node task unloading equipment based on the bait effect to execute the fog node task unloading method based on the bait effect.

Advantageous effects

The invention has proposed a fog node task unloading method, system, medium and apparatus based on bait effect, compared with prior art, the existing incentive mechanism is based on the limited rational assumption design of the traditional economics, and the invention has considered the incentive effect to users of bait effect, has set up the task that can influence the decision-making of fog node behavior, and offer the attraction value of task to unload the task in the environment, guide the decision-making of fog node with the directivity through task issue and attraction value of task; by setting the bait task, the objective attraction value of the target task (task to be unloaded) is improved; on the basis, a preference coefficient is introduced to reflect the real decision-making behavior of the fog nodes, the subjective preference value of part of fog nodes is improved by adding the bait task, so that the threshold value of more fog nodes participating in the task is met, and the participation quantity of the fog nodes is improved. Meanwhile, compared with a comparison mechanism (the task prediction reward is determined according to the market supply and demand relationship), the scheme enables more tasks to be selected without increasing the task reward, improves the total utility of the mobile equipment, and can have a more practical and more effective incentive effect.

Drawings

FIG. 1 is a schematic flow chart of the technical solution of the present invention;

FIG. 2 is a flow diagram of the attribute computation mechanism of FIG. 1;

FIG. 3 is a graph illustrating a comparison of the performance of the number of tasks that a fog node participates in offloading tasks using an embodiment of the present invention and a representative method of the prior art under the same data;

fig. 4 is a graph comparing the total utility of a mobile device using embodiments of the present invention with a representative method of the prior art under the same data.

Detailed Description

The invention will be further described with reference to the accompanying drawings and examples.