CN112363829A - Dynamic user resource allocation method based on elastic scale aggregation - Google Patents
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Abstract
The invention discloses a dynamic user resource allocation method based on elastic scale aggregation. The invention calculates the elastic scale according to the hot degree and the variation trend of the user task, dynamically aggregates the service resources of the edge server, and meets the requirements of the user in a cooperative mode, thereby effectively reducing the response delay of the user application and optimizing the experience of the user. Compared with the existing resource dynamic allocation method, the method has stronger operability, can effectively reduce the interaction delay when the user accesses the service, and provides high-quality user experience.
Description
Technical Field
The invention belongs to the technical field of edge computing and service computing, relates to a user resource dynamic allocation method, and particularly relates to a user resource dynamic allocation method based on elastic scale aggregation.
Background
With the continuous popularization of the 5G technology, a plurality of intelligent Web applications are deployed in mobile equipment, great convenience is brought to the fields of life, entertainment, social contact and the like of people by the applications, and the life style of the current people is redefined. But these applications do not run smoothly on these devices due to the low performance of current mobile devices. Therefore, many Web application services borrow the widely popular edge computing framework to offload the tasks of the user application to the edge server at the near end, and get rid of the performance limitation of the mobile device by allocating enough service resources.
Such a strategy exposes a number of disadvantages in the actual operation process. For example, in a dense area, excessive task offloading requirements may result in insufficient service resources of nearby edge servers, and thus the requests of users cannot be responded to in time, resulting in increased delay. The edge servers in the area with rare remote users have idle resources due to lack of sufficient user requests.
In addition, users in a certain geographic range are dynamically changed, tasks of different user groups are different, and more types of service resources need to be added to nearby edge servers to meet the requirements of new users. In fact, the types of service resources of edge servers in a user area are limited, and more new demands can cause resource contention, so that task congestion is caused, and user experience is greatly influenced.
Therefore, there is a need to provide an improved dynamic resource allocation method to overcome the above-mentioned drawbacks.
Disclosure of Invention
In order to solve the technical problem, the invention provides a dynamic user resource allocation method based on elastic scale aggregation. The invention calculates the elastic scale according to the hot degree and the variation trend of the user task, dynamically aggregates the service resources of the edge server, and meets the requirements of the user in a cooperative mode, thereby effectively reducing the response delay of the user application and optimizing the experience of the user.
The technical scheme adopted by the invention is as follows: a dynamic user resource allocation method based on elastic scale aggregation is characterized by comprising the following steps:
step 1: establishing a tracking file of each edge server, and collecting all historical user service resource demand records in an area which is responsible for each edge server;
step 2: calculating the total task request quantity of each edge server in a given time interval T;
and step 3: initializing and calculating an elastic scale according to the total task request quantity, aggregating all edge servers and constructing an aggregation set;
and 4, step 4: initializing and distributing service resources;
and 5: calculating the importance of each aggregation set and the change trend of the task demand of each edge server in a given time interval t;
step 6: optimizing and calculating an elastic scale based on the importance degree, and re-aggregating each edge server;
and 7: dynamically adjusting the resource allocation based on the change trend of the task demand;
and 8: and repeating the steps 5-7, and continuously and dynamically allocating the resources.
The invention provides a dynamic user resource allocation method based on elastic scale aggregation by considering the hot degree and the variation trend of a user task. Compared with the existing resource dynamic allocation method, the method has stronger operability, can effectively reduce the interaction delay when the user accesses the service, and provides high-quality user experience.
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FIG. 1 is a flow chart of an embodiment of the present invention;
FIG. 2 is a diagram illustrating elastic scale-based construction of aggregate aggregates in an embodiment of the present invention;
FIG. 3 is a data result 1 comparing the method of the present invention (DBLR) with the existing methods (genetic algorithm GA, simulated annealing SA, Random algorithm and Greedy algorithm) according to an embodiment of the present invention;
FIG. 4 is a data result 2 comparing the method of the present invention (DBLR) with the existing methods (genetic algorithm GA, simulated annealing SA, Random algorithm Random, and Greedy algorithm Greeny) in an embodiment of the present invention.
Detailed Description
In order to facilitate the understanding and implementation of the present invention for those of ordinary skill in the art, the present invention is further described in detail with reference to the accompanying drawings and examples, it is to be understood that the embodiments described herein are merely illustrative and explanatory of the present invention and are not restrictive thereof.
Referring to fig. 1, the method for dynamically allocating user resources based on elastic scale aggregation provided by the present invention includes the following steps:
step 1: establishing a tracking file of each edge server, and collecting all historical user service resource demand records in an area which is responsible for each edge server;
the tracking file of the edge server in this embodiment includes the total amount of bandwidth resources, the longitude and latitude coordinates of the server, and the average data transmission rate; the historical user service resource demand record comprises the total number of users, the total number of task requests and the minimum amount of service resources required by each task request.
The minimum amount of service resources required for each task request of the present embodiment includes a minimum amount of computing resources required for each task request and a minimum amount of storage resources required for each task request.
Step 2: calculating the total task request quantity of each edge server in a given time interval T;
in this embodiment, the total task request amount of each edge server in the time interval T is calculated by the following formula;
wherein the content of the first and second substances,
represents the total task request amount of the edge server i in the period T, M represents the total number of task types,
representing the total amount of requests of task type m in edge server i during T period. .
And step 3: initializing and calculating an elastic scale according to the total task request quantity, aggregating all edge servers and constructing an aggregation set;
in this embodiment, the elastic scale is calculated by the following formula;
wherein the content of the first and second substances,
representing the total task request quantity of the edge server i in the T period, n representing the total number of the edge servers, lambda being a weight coefficient, and the value range being (0, 1)]。
Referring to fig. 2, in the present embodiment, each edge server is aggregated to construct an aggregation set; the specific implementation comprises the following substeps:
step 3.1: sorting in descending order according to the total task request quantity of each edge server, and selecting front CluTThe edge server with the highest rank serves as an aggregation center; wherein, CluTRepresents the elastic scale; see S1 in fig. 2;
step 3.2: respectively calculating the geographic distance between each edge server and different aggregation centers for the rest edge servers, selecting the nearest aggregation center to join the aggregation set, and finally forming the CluTAn aggregation set; see S2 and S3 in fig. 2.
The calculation formula of the geographic distance is as follows:
Distanceij＝R*cos-1[sin(Mlati)*sin(Mlatj)*cos(Mloni-Mlonj)+cos(Mlati)*cos(Mlatj)]*π÷180；
Distanceijrepresenting the distance between the edge server i and the aggregation center j, R represents the average radius of the earth, and has a value of 6371.004km, and pi represents the circumferential ratio; mlatiRepresenting the calculated latitude value of the edge server i, if the geographic location is the northern hemisphere, then Mlati＝90-lati(ii) a If the geographic location is the southern hemisphere, then Mlati＝90+lati(ii) a Wherein, latiFor the true latitude value of the edge server i, obtained from GPS data, MlatjMethod for computing (D) and MlatiThe consistency is achieved;
Mlonirepresenting the calculated longitude value of the edge server i, if the geographic location is eastern hemisphere, Mloni＝loni(ii) a Mlon if the geographic location is the western hemispherei＝-loni(ii) a Wherein, loniFor the true longitude value of the edge server i, obtained from GPS data, MlonjIs calculated by the method (2) and MloniAnd (5) the consistency is achieved.
And 4, step 4: initializing and distributing service resources;
the specific implementation of the embodiment includes the following sub-steps:
step 4.1: for each aggregation set, calculating the total request quantity of each type of user task in the aggregation subset, and performing descending sequencing;
the total request quantity calculation formula of each type of user task is as follows:
wherein the content of the first and second substances,
represents the total request quantity of the task types m in the aggregation set j in the T period, K represents the total number of the edge servers in the aggregation set j,
representing the total request amount of the task type m in the aggregation set j in the T period.
Step 4.2: selecting the user task type with the highest ranking, and determining the number of edge servers required to be allocated by service resources corresponding to the user task of the type according to the average bandwidth resource total amount of the edge servers in the aggregation subset;
the calculation method comprises the following steps:
step 4.3: randomly selecting edge servers meeting the quantity requirement from the aggregation subset to distribute service resources;
step 4.4: for the rest edge servers which are not allocated with the service, calculating the time delay between each edge server and the aggregation center, and selecting the edge server with the minimum delay to allocate the service resource;
the time delay calculation method comprises the following steps:
tij＝0.5+0.1*Distanceij
tijrepresenting the time delay between the edge server i and the aggregation center j.
Step 4.5: the next ranked user task is selected and steps 4.2-4.4 are repeated until all types of user tasks are selected.
And 5: calculating the importance of each aggregation set and the change trend of the task demand of each edge server in a given time interval t;
in this embodiment, the importance of each aggregation set in the time interval t is calculated by the following formula;
wherein the content of the first and second substances,
representing the importance degree of the polymerization set j in the time interval t;
represents the total number of users of aggregate set j in time interval t,
representing the total number of users of the edge server k in the aggregation set j in the time interval t; k represents the total number of edge servers in the aggregation set j;
representing the aggregate set j overall task request volume within the period t.
In the embodiment, the change trend of the task demand of each edge server in the time interval t is calculated through the following formula;
wherein the content of the first and second substances,
representing the variation trend of the task m in the polymerization set j in the time interval t;
representing the variation trend of the task m on the edge server k in the aggregation set j in the time interval t; k represents the total number of edge servers in the aggregation set j; alpha and beta are weight coefficients;
representing the total amount of requests of task type m in aggregate set j during the period t,
representing the total request quantity of the task types m in the aggregation set j in the T period，
Showing the trend of the change of the task m in the aggregation set j in the time zone T.
Step 6: optimizing and calculating an elastic scale based on the importance degree, and re-aggregating each edge server;
in the embodiment, the elastic scale is calculated by optimization through the following formula;
wherein, CluT+tRepresents the optimized elastic scale in the time interval t,
representing the importance of the aggregate set j within the time interval t,
indicating the importance of the aggregate set j within the time interval T.
In this embodiment, the reassembling of each edge server specifically includes the following sub-steps:
step 6.1: sorting in descending order according to the total number of users of each edge server, and selecting front CluT+tThe edge server with the highest rank is used as an aggregation center to form the CluT+tA new aggregate set;
step 6.2: and respectively calculating the geographical distance between each edge server and different aggregation centers for the rest edge servers, and selecting the closest aggregation center to join the aggregation set.
And 7: dynamically adjusting the resource allocation based on the change trend of the task demand;
in this embodiment, step 7 specifically includes the following sub-steps:
step 7.1: for each new aggregation set, calculating the overall variation trend of each type of user task in the set, and sequencing in a descending order;
step 7.2: selecting the tasks with the positive change trend and theta before ranking to form a task set to be updated according to the sequencing result;
step 7.3: calculating the number of edge servers of service resources required by each task in a task set to be updated;
step 7.4: randomly selecting a satisfied number of edge servers from within the aggregation set;
step 7.5: for the selected edge server, if the existing user task has a negative change trend, recovering corresponding service resources from the server; if the user task in the task set to be updated is served in the server, reserving corresponding service resources; and if the user tasks in the task set to be updated are not served by the server, adding corresponding service resources.
And 8: and repeating the steps 5-7, and continuously and dynamically allocating the resources.
The invention can effectively reduce the interaction delay when the user accesses the service and provide high-quality user experience. The data results of the method of the invention (DBLR) were compared with the data results of the existing methods (genetic algorithm GA, simulated annealing SA, Random algorithm and Greedy algorithm). Fig. 3 shows that the present invention can obtain the minimum average user interaction delay under different aggregation numbers compared with the existing method. Fig. 4 shows that as the total number of edge servers increases, the method of the present invention is hardly affected, the average interaction delay of the user is the lowest, and the effect is obviously better than the other four methods, which proves the superiority of the method of the present invention.
It should be understood that parts of the specification not set forth in detail are well within the prior art.
It should be understood that the above description of the preferred embodiments is given for clarity and not for any purpose of limitation, and that various changes, substitutions and alterations can be made herein without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (10)
1. A dynamic user resource allocation method based on elastic scale aggregation is characterized by comprising the following steps:
step 1: establishing a tracking file of each edge server, and collecting all historical user service resource demand records in an area which is responsible for each edge server;
step 2: calculating the total task request quantity of each edge server in a given time interval T;
and step 3: initializing and calculating an elastic scale according to the total task request quantity, aggregating all edge servers and constructing an aggregation set;
and 4, step 4: initializing and distributing service resources;
and 5: calculating the importance of each aggregation set and the change trend of the task demand of each edge server in a given time interval t;
step 6: optimizing and calculating an elastic scale based on the importance degree, and re-aggregating each edge server;
and 7: dynamically adjusting the resource allocation based on the change trend of the task demand;
and 8: and repeating the steps 5-7, and continuously and dynamically allocating the resources.
2. The method for dynamically allocating user resources based on flexible scale aggregation according to claim 1, wherein: the tracking file of the edge server in the step 1 comprises the total bandwidth resource amount, longitude and latitude coordinates of the server and the average data transmission rate; the historical user service resource demand record comprises the total number of users, the total number of task requests and the minimum service resource amount required by each task request.
3. The method for dynamically allocating user resources based on flexible scale aggregation according to claim 2, wherein: the minimum amount of service resources required by each task request comprises a minimum amount of computing resources required by each task request and a minimum amount of storage resources required by each task request.
4. The method for dynamically allocating user resources based on flexible scale aggregation according to claim 1, wherein: in step 2, calculating the total task request quantity of each edge server in the time interval T by the following formula;
wherein the content of the first and second substances,
represents the total task request amount of the edge server i in the period T, M represents the total number of task types,
representing the total amount of requests of task type m in edge server i during T period.
5. The method for dynamically allocating user resources based on flexible scale aggregation according to claim 1, wherein: in step 3, calculating the elastic scale by the following formula;
wherein the content of the first and second substances,
representing the total task request quantity of the edge server i in the T period, n representing the total number of the edge servers, lambda being a weight coefficient, and the value range being (0, 1)]。
6. The method for dynamically allocating user resources based on flexible scale aggregation according to claim 1, wherein: aggregating each edge server in the step 3 to construct an aggregation set; the specific implementation comprises the following substeps:
step 3.1: sorting in descending order according to the total task request quantity of each edge server, and selecting front CluTThe edge server with the highest rank serves as an aggregation center; wherein, CluTRepresenting the elastic scale in the T period;
step 3.2: respectively calculating the geographic distance between each edge server and different aggregation centers for the rest edge servers, selecting the nearest aggregation center to join the aggregation set, and finally forming the CluTAn aggregate set.
7. The method for dynamically allocating user resources based on elastic scale aggregation according to claim 1, wherein the step 4 is implemented by the following sub-steps:
step 4.1: for each aggregation set, calculating the total request quantity of each type of user task in the aggregation subset, and performing descending sequencing;
step 4.2: selecting the user task type with the highest ranking, and determining the number of edge servers required to be allocated by service resources corresponding to the user task of the type according to the average bandwidth resource total amount of the edge servers in the aggregation subset;
step 4.3: randomly selecting edge servers meeting the quantity requirement from the aggregation subset to distribute service resources;
step 4.4: for the rest edge servers which are not allocated with the service, calculating the time delay between each edge server and the aggregation center, and selecting the edge server with the minimum delay to allocate the service resource;
step 4.5: the next ranked user task is selected and steps 4.2-4.4 are repeated until all types of user tasks are selected.
8. The method for dynamically allocating user resources based on flexible scale aggregation according to claim 1, wherein: in step 5, calculating the importance of each aggregation set in the time interval t by the following formula;
wherein the content of the first and second substances,
representing the importance degree of the polymerization set j in the time interval t;
represents the total number of users of aggregate set j in time interval t,
representing the total number of users of the edge server k in the aggregation set j in the time interval t; k represents the total number of edge servers in the aggregation set j;
representing the total task request quantity of the aggregation set j in the t period;
step 5, calculating the change trend of the task demand of each edge server in the time interval t by the following formula;
wherein the content of the first and second substances,
representing the variation trend of the task m in the polymerization set j in the time interval t;
representing the variation trend of the task m on the edge server k in the aggregation set j in the time interval t; k represents the total number of edge servers in the aggregation set j; alpha and beta are weight coefficients;
representing the total amount of requests of task type m in aggregate set j during the period t,
representing the total amount of requests of task type m in aggregate set j during the period T,
showing the trend of the change of the task m in the aggregation set j in the time zone T.
9. The method for dynamically allocating user resources based on flexible scale aggregation according to claim 1, wherein: in step 6, the elastic scale is optimized and calculated through the following formula;
wherein, CluT+tRepresents the optimized elastic scale in the time interval t,
representing the importance of the aggregate set j within the time interval t,
representing the importance degree of the polymerization set j in the time interval T;
the reassembling of the edge servers in the step 6 specifically includes the following substeps:
step 6.1: according to the use of each edge serverSorting the total number of users in descending order, and selecting the front CluT+tThe edge server with the highest rank is used as an aggregation center to form the CluT+tA new aggregate set;
step 6.2: and respectively calculating the geographical distance between each edge server and different aggregation centers for the rest edge servers, and selecting the closest aggregation center to join the aggregation set.
10. The method for dynamically allocating user resources based on elastic scale aggregation according to any one of claims 1 to 9, wherein step 7 specifically comprises the following sub-steps:
step 7.1: for each new aggregation set, calculating the overall variation trend of each type of user task in the set, and sequencing in a descending order;
step 7.2: selecting the tasks with the positive change trend and theta before ranking to form a task set to be updated according to the sequencing result;
step 7.3: calculating the number of edge servers of service resources required by each task in a task set to be updated;
step 7.4: randomly selecting a satisfied number of edge servers from within the aggregation set;
step 7.5: for the selected edge server, if the existing user task has a negative change trend, recovering corresponding service resources from the server; if the user task in the task set to be updated is served in the server, reserving corresponding service resources; and if the user tasks in the task set to be updated are not served by the server, adding corresponding service resources.
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