CN110928683B - Edge computing resource allocation method based on two types of intensive virtual machines - Google Patents

Edge computing resource allocation method based on two types of intensive virtual machines Download PDF

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CN110928683B
CN110928683B CN201911112202.4A CN201911112202A CN110928683B CN 110928683 B CN110928683 B CN 110928683B CN 201911112202 A CN201911112202 A CN 201911112202A CN 110928683 B CN110928683 B CN 110928683B
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彭成
唐朝晖
桂卫华
周晓红
陈青
赵龙乾
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Hunan University of Technology
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Abstract

Based on the two-type intensive virtual machine edge computing resource allocation method, the requirements of IO intensive and CPU intensive virtual machines in an edge server on resource allocation are comprehensively considered, the virtual machine state, the uploading data type weight, the system acceleration ratio, the task completion time and the like are respectively introduced into the two-type virtual machine resource allocation computing algorithm, the IO intensive virtual machine priority queue and the number of CPU cores required by the CPU intensive virtual machine are dynamically generated, the reasonable allocation of the edge server resources can be realized, and the influence on cost and time delay caused by intensive computing, high resource consumption and the like is effectively reduced. The problem that the traditional heuristic method, cost function and the like have great influence on the setting human factors of the priority and are not beneficial to the edge server to carry out resource allocation is solved, and the resource allocation efficiency is finally improved.

Description

Edge computing resource allocation method based on two types of intensive virtual machines
Technical Field
The invention belongs to the technical field of edge computing resource allocation, and particularly relates to an edge computing resource allocation method based on two types of intensive virtual machines.
Background
In the industrial manufacturing process, the process is more and more complex, the acquired basic data has multiple sources and heterogeneity, and meanwhile, because the industrial software and hardware platform has stronger independence and closure, the edge calculation deployed at the end close to the manufacturing equipment faces great challenges in data analysis and storage capacity. Therefore, the server resources are fully utilized through the reasonable allocation of the edge server resources, and the speed and efficiency of data storage and calculation are further improved. The calculation of the virtual machine priority is a crucial link for resource allocation of the edge server, and whether the virtual machine priority setting reasonably and directly affects the accuracy of resource allocation of the edge server.
The traditional edge computing resource allocation method mostly assumes that the computing capacity and the storage capacity of an edge computing server are in an unlimited state, the existing resource allocation only considers computing resources, the storage resource allocation problem is less involved, and the setting of the priority usually adopts a cost function or a random method and is greatly influenced by human factors. In fact, the calculation of the priority is closely related to network performance, data characteristics, task types, calculation capacity, storage efficiency and the like, which increases the difficulty of determining the edge calculation resource allocation threshold under the cloud-end collaborative computing architecture. Meanwhile, because the manufactured physical unit has dynamic flexibility, the requirement of the edge server on resources is difficult to accurately reflect by directly using the traditional priority setting method. The above disadvantages may cause that resource allocation is difficult to be adjusted in time during the operation of the edge server, and then the efficiency of edge calculation is affected.
Disclosure of Invention
In order to overcome the defects in the prior art, the invention provides a method for distributing computing resources based on the edges of two types of intensive virtual machines, which comprises the steps of respectively processing an IO intensive virtual machine and a CPU intensive virtual machine, and generating an IO intensive virtual machine priority queue by combining a second-order difference method and the like aiming at the IO intensive virtual machine; aiming at the CPU intensive virtual machine, the real-time requirements of the CPU intensive virtual machine are calculated according to the influence of the virtual machine on the physical machine performance, the task completion time and the like, and the real-time requirements are used for edge computing resource allocation, so that the data storage efficiency and the computing speed of an edge end server are improved, the time delay is reduced, and the cost is saved.
In order to achieve the purpose, the technical scheme adopted by the invention is as follows:
the edge computing resource allocation method based on two types of intensive virtual machines comprises the following specific steps:
A. calculating a certain IO intensive virtual machine ViAt sampling time tiReceiving data tau transmitted from terminal equipmenti,
Figure BDA0002273054250000021
Wherein, N represents the number of terminal devices sending data to the edge server, N represents the number of data types uploaded by a certain terminal device, and alphajkThe size of the data scale corresponding to the uploaded data type is epsilon, and the epsilon represents errors generated by sampling and calculation;
B. for IO intensive virtual machine ViCarrying out second-order difference on the received data to obtain a data uploading rate increment delta v:
suppose IO intensive virtual machine ViAverage velocity of processed data is viThen, time t 'is required'iWill tiThe data transmitted at any moment are all stored in a database, i.e.
Figure BDA0002273054250000022
Then the IO intensive virtual machine ViTreatment time t'iIs divided into three parts of t'i,t′i+1,t′i+2It means that three stages are required to store the data in the database to obtain the data uploading rate increment deltav,
Δv＝τi+2-2τi+1+τi
wherein, taui+2，τi+1，τiAre respectively corresponding to t'i+2,t′i+1,t′iTime virtual machine ViThe amount of data stored in the database;
C. judging IO intensive virtual machine V according to upload rate increment delta ViAll possible states are represented by the set status ═ {1,2,3,4,5,6}, where 1 is the no data state, 2 is the decrease state, 3 is the steady state, 4 is the increase state, 5 is the early warning state, and 6 is the excess state;
D. compute IO intensive virtual machine ViProbability that current state is the same as previous state
Figure BDA0002273054250000031
Wherein ξiNum represents the number of times that the current state of the virtual machine is the same as the previous stateiRepresenting virtual machine V as a proper subset of state set statusiA set of state changes during a work session;
E. compute IO intensive virtual machine ViPriority p of stored datai，
Figure BDA0002273054250000032
Wherein S isi preRepresenting IO intensive virtual machine ViSoft allocated pre-stored space size, Si postRepresenting virtual machine ViAt tiThe size of data stored in a storage node at any moment;
F. compute controller-to-IO intensive virtual machines V in control nodesiPriority P for resource allocationi，PiThe larger the value, the more computing resources need to be allocated to the virtual machine in time, to increase the data storage rate,
Figure BDA0002273054250000033
wherein, W is a weight set corresponding to different types of data, and W ═ W1,w2,...,wnN represents a terminal deviceThe number of uploaded data types;
G. assigning a priority P to each IO intensive virtual machine in an edge serveriEstablishing a maximum priority queue P, correspondingly updating the resource allocation of the IO intensive virtual machine through the change of the priority queue P, optimizing and reasonably allocating storage resources;
H. calculating a certain CPU intensive virtual machine ViAll tasks in' execute the required Time under parallel pseudo-distributed conditions,
Figure BDA0002273054250000041
wherein, time represents the average time of executing a single task, C is the number of CPU cores, and T is the CPU intensive virtual machine Vi' the number of tasks to be executed simultaneously;
I. compute CPU intensive virtual machine ViThe acceleration ratio sp of' is such that,
Figure BDA0002273054250000042
wherein R isnFor CPU-intensive virtual machines Vi' minimum execution time for completing task under parallel pseudo-distributed condition, RdFor CPU-intensive virtual machines Vi' minimum execution time to complete a task under parallel distributed conditions;
J. when the number of the virtual machines exceeds the CPU core number C of the host machine, the physical machine performance is gradually reduced along with the increase of the number of the virtual machines, the influence factor is theta, the relationship between the number G of the virtual machines and the influence factor theta is obtained,
Figure BDA0002273054250000043
wherein G is the number of virtual machines;
K. defining CPU intensive virtual machine Vi'Time required for all tasks to execute under parallel distribution condition',
Figure BDA0002273054250000051
wherein sp is the acceleration ratio, theta is the influence factor, and Time is the CPU-intensive virtual machine Vi' all tasks in the system are executed under the condition of parallel pseudo distribution for required time;
l. setting a CPU intensive virtual machine ViIn the method, the upper Time limit Req _ Time ' required to be completed by all tasks is Time _ com + Time _ cor, where Time _ com is the actual completion Time of the task, Time _ cor is the communication delay Time, all tasks can be completed smoothly, and it is necessary to satisfy that Time ' is less than or equal to Req _ Time ', and if not, it is necessary to allocate the computing resources, i.e. the number of CPU cores, and the specific process is as follows: suppose that d tasks b 'of the b tasks that are not completed on time are { b'1,b′2,...,b′dAt execution time of
Figure BDA0002273054250000052
The corresponding request is completed at an upper time limit of
Figure BDA0002273054250000053
Then in the present case, the CPU intensive virtual machine Vi' the computational resources to be allocated are as follows:
Figure BDA0002273054250000054
wherein the content of the first and second substances,
Figure BDA0002273054250000055
for CPU-intensive virtual machines Vi' number of CPU cores to be allocated.
The invention has the beneficial effects that:
the invention comprehensively considers the requirements of IO intensive and CPU intensive virtual machines in the edge server on resource allocation, respectively introduces the virtual machine state, the uploading data type weight, the system acceleration ratio, the task completion time and the like into the two types of virtual machine resource allocation priority calculation algorithms, can realize reasonable allocation of the edge server resources, and thereby effectively reduces the influence on cost and time delay caused by intensive calculation, high resource consumption and the like. The problem that the traditional random method, cost function and the like have great influence on the setting human factors of the priority and are not beneficial to the edge server to carry out resource allocation is solved, and the resource allocation efficiency is finally improved.
Drawings
FIG. 1 is a flow chart of the present invention;
FIG. 2 is a diagram of a resource allocation result;
FIG. 3 is a timing comparison diagram of the multitask concurrent execution and other methods of the present invention.
Detailed Description
The present invention is described in further detail below with reference to the attached drawing figures.
Referring to fig. 1, the method for distributing edge computing resources based on two types of intensive virtual machines includes the following steps:
A. calculating a certain IO intensive virtual machine Vi∈V＝{V1,V2,…,VQQ is the number of virtual machines at sampling time tiReceiving data tau transmitted from terminal equipmenti,
Figure BDA0002273054250000061
Wherein, N represents the number of terminal devices sending data to the edge server, and N represents the data type set S uploaded by a certain terminal devicej＝{sj1,sj2,…,sjnNumber of elements in }, αjk∈αj＝{αj1,αj2,...αjk,...αjnThe size of the data scale corresponding to the type of the uploaded data is used, and epsilon represents errors generated by sampling and calculation;
B. for IO intensive virtual machine ViCarrying out second order difference on the received data to obtain a numberData upload rate increment Δ v:
suppose a virtual machine ViAverage velocity of processed data is viThen, time t 'is required'iWill tiThe data transmitted at any moment are all stored in a database, i.e.
Figure BDA0002273054250000071
Then the IO intensive virtual machine ViTreatment time t'iIs divided into three parts of t'i,t′t+1,t′i+2It means that three stages are required to store the data in the database to obtain the data uploading rate increment deltav,
Δv＝τi+2-2τi+1+τi
wherein, taui+2，τi+1，τiAre respectively corresponding to t'i+2,t′i+1,t′iTime virtual machine ViThe amount of data stored in the database;
C. judging IO intensive virtual machine V according to upload rate increment delta ViIs represented by the set status ═ {1,2,3,4,5,6}, where 1 is the no data state, 2 is the decrease state, 3 is the steady state, 4 is the increase state, 5 is the early warning state, and 6 is the excess state; setting: when Δ v ≈ τiWhen the terminal equipment receives the data, the virtual machine does not receive the data sent by the terminal equipment in the time period, and the virtual machine is in a data-free state; when-taui<Δv<When 0, the data uploaded by the terminal equipment in the time slot is reduced, and the data is in a reduced state; when the delta v is approximately equal to 0, the data uploaded by the terminal equipment in the time period basically keeps unchanged and is in a stable state; when Δ v<M0When the time is up, the data uploaded by the terminal equipment in the time slot is increased and is in an increased state; when M is0≤Δv<M1When the time is up, the data uploaded by the terminal equipment in the time period exceeds the early warning value M0The state is an early warning state; when Δ v.gtoreq.M1When the data uploaded by the terminal equipment exceeds the actual physical storage capacity of the storage node in the time period, the data is in an excess state and needs to be addedHard disk space or optimized data storage scheme to address the rate of terminal device data upload, where M1The actual storage size of the storage space of the edge server.
D. Compute IO intensive virtual machine ViProbability that current state is the same as previous state
Figure BDA0002273054250000081
Wherein ξiNum represents the number of times that the current state of the virtual machine is the same as the previous stateiRepresenting virtual machine V as a proper subset of state set statusiA set of state changes over a period of operation;
E. compute IO intensive virtual machine ViPriority p of stored datai，
Figure BDA0002273054250000082
Wherein S isi preRepresenting virtual machine ViSoft allocated pre-stored space size, Si postRepresenting virtual machine ViAt tiThe size of data stored in a storage node at any moment;
F. computing a controller-to-virtual machine V in a control nodeiPriority P for resource allocationi，PiThe larger the value, the more computing resources need to be allocated to the virtual machine in time, to increase the data storage rate,
Figure BDA0002273054250000083
wherein, W is a weight set corresponding to different types of data, and W ═ W1,w2,...,wn}；
G. Assigning a priority P to each IO intensive virtual machine in an edge serveriAnd establishes a maximum priority queue P by priorityThe change of the level queue P correspondingly updates the resource configuration of the IO intensive virtual machine, optimizes and reasonably distributes storage resources;
H. calculating a certain CPU intensive virtual machine ViAll tasks in' execute the required Time under parallel pseudo-distributed conditions,
Figure BDA0002273054250000084
wherein, time represents the average time of executing a single task, C is the number of CPU cores, and T is the CPU intensive virtual machine Vi' the number of tasks to be executed simultaneously;
I. compute CPU intensive virtual machine ViThe acceleration ratio sp of' is such that,
Figure BDA0002273054250000091
wherein R isnFor CPU-intensive virtual machines Vi' minimum execution time for completing task under parallel pseudo-distributed condition, RdFor CPU-intensive virtual machines Vi' minimum execution time to complete a task under parallel distributed conditions;
J. when the number of the virtual machines exceeds the CPU core number C of the host machine, the physical machine performance is gradually reduced along with the increase of the number of the virtual machines, the influence factor is theta, the relationship between the number G of the virtual machines and the influence factor theta is obtained,
Figure BDA0002273054250000092
wherein G is the number of virtual machines;
K. defining CPU intensive virtual machine Vi'Time required for all tasks to execute under parallel distribution condition',
Figure BDA0002273054250000093
wherein sp is the acceleration ratio, theta is the influence factor, and Time is the CPU-intensive virtual machine Vi' all tasks in the system are executed under the condition of parallel pseudo distribution for required time;
l. setting a CPU intensive virtual machine ViIn the method, the upper Time limit Req _ Time ' required to be completed by all tasks is Time _ com + Time _ cor, where Time _ com is the actual completion Time of the task, Time _ cor is the communication delay Time, all tasks can be completed smoothly, and it is necessary to satisfy that Time ' is less than or equal to Req _ Time ', and if not, it is necessary to allocate the computing resources, i.e. the number of CPU cores, and the specific process is as follows: suppose that d tasks b 'of the b tasks that are not completed on time are { b'1,b′2,...,b′dAt execution time of
Figure BDA0002273054250000101
The corresponding request is completed at an upper time limit of
Figure BDA0002273054250000102
Then in the present case, the CPU intensive virtual machine Vi' the computational resources to be allocated are as follows:
Figure BDA0002273054250000103
wherein the content of the first and second substances,
Figure BDA0002273054250000104
for CPU-intensive virtual machines Vi' number of CPU cores to be allocated;
m. to demonstrate the effectiveness of the method of the invention, it is compared with different methods, as further described. The virtual environment of the edge server is created through the VMware VSphere virtualization platform, and comprises 1 Controller node, 4 IO intensive virtual machines and 3 CPU intensive virtual machines.
Firstly, respectively running client services of a resource distributor on 4 IO intensive virtual machines, monitoring IO performance of the virtual machines, obtaining priority through calculation and sending the priority to a Controller node, running server services of the resource distributor on the Controller node, collecting the priority of the IO intensive virtual machines, and responding to resource requests of the IO intensive virtual machines according to a maximum priority list. Compared with the current popular heuristic algorithm, namely a genetic algorithm, the algorithm adopted by the invention can distribute resources for the optimal response of the IO intensive virtual machine under the condition of ensuring sufficient sampling width. The genetic algorithm has uncertainty due to the adoption of random numbers, and the resource response is not reasonable enough, for example, between 7s and 23s, the resource allocation time is too compact, so that part of resources can be repeatedly allocated; the allocation of resources at 60s does not take into account the possibility that data continues to rise at 59s and is not robust enough. Therefore, the algorithm of the present invention can adaptively allocate resources to the IO-intensive virtual machine according to the business requirements, and the specific result is shown in fig. 2.
Next, the Hadoop and Spark distributed storage and computation frameworks were run on the 3 CPU-intensive virtual machines, respectively. Aiming at common CPU intensive application programs such as WordCount, Sort, TeraSort, RandomWriter and the like, test data sets with different scales are generated through HiBench and stored in an HDFS file system and submitted to Spark to execute corresponding tasks. Compared with the current popular genetic algorithm, the completion time of the experiment after the 6 tasks are executed 100 times concurrently is shown in figure 3, wherein, (a) is Task-1WordCount execution time comparison, (b) is Task-2 Sort execution time comparison, (c) is Task-3 TeraSort execution time comparison, (d) is Task-4 RandomWriter execution time comparison, (e) is Task-5 Grep execution time comparison, and (f) is Task-6 TestDFSIO execution time comparison, and the method adopted by the invention has higher operation efficiency.

Claims (1)

1. An edge computing resource allocation method based on two types of intensive virtual machines comprises the following specific steps:
A. calculating a certain IO intensive virtual machine ViAt sampling time tiReceiving data tau transmitted from terminal equipmentiI.e. by
Figure FDA0002273054240000011
Wherein, N represents the number of terminal devices sending data to the edge server, N represents the number of data types uploaded by a certain terminal device, and alphajkThe size of the data scale corresponding to the uploaded data type is epsilon, and the epsilon represents errors generated by sampling and calculation;
B. for IO intensive virtual machine ViCarrying out second-order difference on the received data to obtain a data uploading rate increment delta v:
first assume IO intensive virtual machine ViAverage velocity of processed data is viThen, it takes time ti' will tiThe data transmitted at any moment are all stored in a database, i.e.
Figure FDA0002273054240000012
Then the IO intensive virtual machine ViIs processed for a time ti'divided into three portions t'i,t′i+1,t′i+2It means that three stages are required to store the data in the database to obtain the data uploading rate increment deltav,
Δv＝τi+2-2τi+1+τi
wherein, taui+2，τi+1，τiAre respectively corresponding to t'i+2,t′i+1,ti' time IO intensive virtual machine ViThe amount of data stored in the database;
C. judging IO intensive virtual machine V according to upload rate increment delta ViAll possible states are represented by the set status ═ {1,2,3,4,5,6}, where 1 is a no-data state,2 is a decrease state, 3 is a steady state, 4 is an increase state, 5 is an early warning state, and 6 is an excess state;
D. compute IO intensive virtual machine ViProbability that current state is the same as previous state
Figure FDA0002273054240000021
Wherein ξiNum represents the number of times that the current state of the virtual machine is the same as the previous stateiRepresenting virtual machine V as a proper subset of state set statusiA set of state changes during a work session;
E. computer virtual machine ViPriority p of stored datai，
Figure FDA0002273054240000022
Wherein S isi preRepresenting IO intensive virtual machine ViSoft allocated pre-stored space size, Si postRepresenting virtual machine ViAt tiThe size of data stored in a storage node at any moment;
F. compute controller-to-IO intensive virtual machines V in control nodesiPriority P for resource allocationi，PiThe larger the value, the more computing resources need to be allocated to the virtual machine in time, to increase the data storage rate,
Figure FDA0002273054240000023
wherein, W is a weight set corresponding to different types of data, and W ═ W1,w2,...,wn}；
G. Assigning a priority P to each IO intensive virtual machine in an edge serveriAnd establishing a maximum priority queue P, and changing the queue P according to the priorityThe resource allocation of the IO intensive virtual machine is correspondingly updated, and storage resources are optimized and reasonably allocated;
H. calculating a certain CPU intensive virtual machine ViAll tasks in' execute the required Time under parallel pseudo-distributed conditions,
Figure FDA0002273054240000031
wherein, time represents the average time of executing a single task, C is the number of CPU cores, and T is the CPU intensive virtual machine Vi' the number of tasks to be executed simultaneously;
I. compute CPU intensive virtual machine ViThe acceleration ratio sp of' is such that,
Figure FDA0002273054240000032
wherein R isnFor CPU-intensive virtual machines Vi' minimum execution time for completing task under parallel pseudo-distributed condition, RdFor CPU-intensive virtual machines Vi' minimum execution time to complete a task under parallel distributed conditions;
J. when the number of the virtual machines exceeds the CPU core number C of the host machine, the physical machine performance is gradually reduced along with the increase of the number of the virtual machines, the influence factor is theta, the relationship between the number G of the virtual machines and the influence factor theta is obtained,
Figure FDA0002273054240000033
wherein G is the number of virtual machines;
K. defining CPU intensive virtual machine Vi'Time required for all tasks to execute under parallel distribution condition',
Figure FDA0002273054240000034
wherein sp is the acceleration ratio, theta is the influence factor, and Time is the CPU-intensive virtual machine Vi' all tasks in the system are executed under the condition of parallel pseudo distribution for required time;
l. setting a CPU intensive virtual machine ViIn the method, the upper Time limit Req _ Time ' required to be completed by all tasks is Time _ com + Time _ cor, where Time _ com is the actual completion Time of the task, Time _ cor is the communication delay Time, all tasks can be completed smoothly, and it is necessary to satisfy that Time ' is less than or equal to Req _ Time ', and if not, it is necessary to allocate the computing resources, i.e. the number of CPU cores, and the specific process is as follows: suppose that d tasks b 'of the b tasks that are not completed on time are { b'1,b′2,...,b′dAt execution time of
Figure FDA0002273054240000041
The corresponding request is completed at an upper time limit of
Figure FDA0002273054240000042
Then in the present case, the CPU intensive virtual machine Vi' the computational resources to be allocated are as follows:
Figure FDA0002273054240000043
wherein the content of the first and second substances,
Figure FDA0002273054240000044
for CPU-intensive virtual machines Vi' number of CPU cores to be allocated.
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