CN115858150A - Network function energy-saving method based on Openstack virtualization - Google Patents

Abstract

The invention discloses a network function energy-saving method based on Openstack virtualization, which comprises the following steps of: counting the resource consumption of the current cloud data center to obtain the utilization rate of a CPU (central processing unit) and an internal memory of the virtual host; step 2: when the cloud data center receives a batch of tasks S, determining the distribution scheme of each task according to the pre-distribution algorithm to obtain a pre-distribution scheme T _pre And optimality of this scheme G; and step 3: for each task in the task S, respectively distributing the tasks; and 4, step 4: continuing to detect the tasks received in the cloud data center, and executing the step 2 if a new batch of tasks are received currently; and if no new task is received currently, waiting for the task. The invention uses the migration algorithm and the scheduling algorithm to analyze different task types in detail, and has higher resource utilization rate, less server quantity and better energy-saving effect compared with other NFV energy-saving algorithms.

Description

Network function energy-saving method based on Openstack virtualization

Technical Field

The invention relates to the field of computer edge computing resource scheduling, in particular to a network function energy-saving method based on Openstack virtualization.

Background

In the NFV method, various network functions such as NAT, firewall, intrusion detection, DNS, cache, etc. are changed into independent software mainly by means of cloud computing and virtualization technologies in the current IT industry, and they use virtualization resources provided by devices such as x86 servers, storage and switches, etc. which are lower-layer industry standards, and can implement rapid elastic expansion and contraction according to actual needs at any time. According to the typical NFV use case, NFV services of multiple enterprises can share resources provided by the same infrastructure layer, and even NFV applications and general cloud computing applications can be run on these devices at the same time, so that resource utilization rate is greatly improved, and cost is saved for the enterprises.

Disclosure of Invention

The invention provides a network function energy-saving method based on Openstack virtualization, which aims at researching and analyzing energy consumption of a cloud data center and providing an energy-saving scheduling algorithm of Network Function Virtualization (NFV), and aims to maximize resource utilization rate and reduce the number of servers used so as to achieve the purpose of saving energy by firstly designing a method for acquiring the use conditions of resources of a physical server and a virtual machine and then checking and obtaining energy consumption models of the server and the virtual machine by using acquired data from the beginning of monitoring and analyzing the energy consumption conditions of the resources of the data center. Compared with other similar NFV energy-saving scheduling algorithms, the NFV energy-saving scheduling algorithm has the advantages of being higher in resource utilization rate, smaller in server number and better in energy-saving effect.

The invention adopts the following technical scheme:

a network function energy-saving method based on Openstack virtualization comprises the following steps:

step 1: counting the resource consumption of the current cloud data center to obtain the utilization rate of a CPU (central processing unit) and an internal memory of the virtual host; initializing an algorithm, monitoring the resource utilization rate of data of the physical server and the virtual machine in real time, and allocating and scheduling subsequent tasks;

step 2: when the cloud data center receives a batch of tasks S, determining the distribution scheme of each task according to the pre-distribution algorithm to obtain a pre-distribution scheme T _pre And optimality of this scheme G;

and step 3: for each task in the task S, respectively distributing the tasks;

and 4, step 4: continuing to detect the tasks received in the cloud data center, and executing the step 2 if a new batch of tasks are received currently; and if no new task is received currently, waiting for the task.

Further, the step 3 comprises:

step 3.1: judging whether idle virtual hosts exist currently or not, wherein the number of the idle virtual hosts is L, if so, skipping to the step 3.2, and if not, skipping to the step 3.5;

step 3.2: according to a pre-allocation scheme T _pre And the number M of the required virtual hosts, if the pre-allocation scheme is the optimal allocation scheme, skipping to the step 3.3; otherwise, if the pre-allocation scheme is not the optimal allocation scheme, skipping to step 3.4;

step 3.3: if L is not less than M, the M virtual hosts are used according to the pre-allocation scheme T _pre Performing an allocation task; if L is less than M, opening up M-L virtual host machines, then making all idle virtual host machine M according to preallocation scheme T _pre Performing an allocation task;

step 3.4: if L is not less than M-1, obtaining an actual distribution scheme T by the M-1 virtual hosts through an actual distribution algorithm, and distributing tasks according to the actual distribution scheme T; if L is smaller than M-1, additionally opening up M-1-L virtual hosts to obtain an actual distribution scheme T through an actual distribution algorithm, and distributing tasks according to the actual distribution scheme T;

step 3.5: if the current pre-allocation scheme T _pre If the optimal distribution scheme is adopted, skipping to the step 3.6, otherwise skipping to the step 3.7;

step 3.6: newly opening up M virtual hosts and according to T _pre Carrying out task allocation;

step 3.7: and newly opening up M-1 virtual hosts, obtaining an actual distribution scheme T through an actual distribution algorithm, and distributing tasks according to the actual distribution scheme T.

Further, in step 2, the pre-allocation algorithm is as follows:

for theta batch tasks S = { S = { S = } ₁ ,S ₂ ,S ₃ ,…,S _i ,…,S _θ Independent and independent of each other, each task S _i Has n _i Subtasks, and the resource consumption of each subtask to CPU is

The consumption of the memory is

The CPU idle load after the start of each virtual host is alpha _cpu The upper limit of the load is beta _cpu With a memory empty load of alpha _memory The upper limit of the load is beta _memory ；

The following algorithm is designed to determine the actual selection of the ith task S _i Number of virtual hosts that need to be deployed:

each virtual host can accommodate at most

A task, the task uses>

The method comprises the steps that each virtual host can finish batch tasks only by 1 task consumption time, C tasks run on each virtual host simultaneously, the number of deployed virtual hosts is set to be m, the number of distributable virtual hosts at most is set to be N, and the number of the distributable virtual hosts is taken

Executes the depletion pick>

Completing batch tasks within one task time, wherein the additional energy consumption is alpha tau (m-1); in the formula>

Means rounding up the result of dividing a by b; />

Is to remove aRounding down with the result of b;

compared with the task of executing the batch by using only one virtual host, when a plurality of virtual hosts are used, the additional energy consumption of the virtual hosts is as follows: w _{Is additionally provided with} = α τ (m-1), the time earned is:

setting an optimization function f (m) = W _{Is additionally provided with} /T _{Earn money} ；

Therefore, it is

When there is i e [1, phi ]]When n% iC =0, f (i) is the minimum value, and the number of virtual hosts to be deployed is considered to be

The allocation scheme obtained in this case is the optimal allocation scheme, and when τ is 1, additionally satisfied ^ er>

The scheme at this time is the optimal distribution scheme; wherein a% b =0 means that a is divisible by b;

traverse all tasks S = { S = } ₁ ,S ₂ ,S ₃ ,…,S _i ,…,S _θ Get the number of virtual host machines M = { M = required by each task ₁ ,m ₂ ,m ₃ ,…,m _i ,…,m _θ For a task S _i If m is _i The obtained distribution scheme is the optimal distribution scheme G _i =1, its pre-allocation scheme

Deployment is at m _i Deploying tasks on the virtual host, and paralleling C subtasks on each host, at this time m _i The platform virtual hosts are all in a full load condition; if m _i The resulting allocation scheme is not the optimal allocation scheme G _i =0, its pre-allocation scheme =>

Deployment is at m _i Deployment of tasks on the desktop virtual host, and m _i Parallel C subtasks on 1 host, in this case m _i -1 virtual machines are all in a full load condition, one virtual machine remaining partially loaded;

so that all tasks S = { S } can be finally obtained ₁ ,S ₂ ,S ₃ ,…,S _i ,…,S _θ Pre-allocation scheme of

And optimality of the pre-allocation scheme G = { G = { (G) ₁ ,G ₂ ,G ₃ ,…,G _i ,…,G _θ }。

Further, in step 3, the actual allocation algorithm is:

for the pre-allocation scheme of the current non-optimal allocation scheme, tasks are allocated to m-1 virtual hosts according to the pre-allocation scheme; the CPU occupancy for the virtual host in which there is a remaining, less full load is set to ρ _cpu The occupancy rate of the memory is set as rho _memory (ii) a Notation q = { ρ = _cpu ,ρ _memory }

Traversing the remaining availability of the CPUs and the memories of all the non-idle virtual hosts at present, Q = { Q = ₁ ,Q ₂ ,…,Q _i ,…,Q _t T is the number of the non-idle virtual hosts, and the CPU residual of the ith non-idle virtual host is set as

The remaining available condition of memory is->

I.e. is>

Traversing all the non-idle virtual hosts to obtain the ith station non-idleThe idle virtual host receives the remaining available condition Q' = Q-Q after the current remaining task is received;

find all the satisfies

Is/is>

And is

Is denoted as Γ, the f value of each element in Γ is calculated from f (cpu, memory) =0.5cpu +0.5memory, taken +>

Allocating the tasks which are not fully loaded in the pre-allocation scheme to the ith non-idle virtual host; on the contrary, if the gamma is an empty set, a new virtual machine is opened for storing the underloaded tasks in the pre-allocation scheme.

Further, the step 3 further includes:

step 3.8: using a migration algorithm to count the resource consumption of the executing task, and migrating the specific virtual machine;

the method comprises the following steps:

step 3.8.1: migration probability calculation is carried out on the load conditions of all the virtual hosts to obtain a server queue needing migration;

step 3.8.2: if the queue is empty, then no migration is currently required; otherwise, the queue is not empty and needs to be migrated;

step 3.8.3: migrating and selecting a target virtual host, and migrating all tasks on the source virtual host to the target host if the CPU and the memory consumption of the target virtual host are still within the upper limit of the load after all migration tasks of the source target host are received by the target virtual host; otherwise, the virtual host is not migrated temporarily;

step 3.8.4: through system detection, if the resource utilization rates of the CPUs and the memories of some virtual hosts are found to be in an idle state for a long time, the virtual hosts can be closed to save energy consumption.

Further, in step 3.8.1, a migration probability function F = F is designed _cpu (cpu)+f _memory (memory) wherein

Compared with the prior art, the invention has the beneficial effects that:

the invention classifies the virtual host tasks on the server, scientifically migrates the old tasks by using the migration algorithm and the allocation algorithm, allocates the new tasks, maximizes the resource utilization rate and reduces the number of the required servers as much as possible. The invention uses the migration algorithm and the scheduling algorithm to carry out detailed analysis on different task types, and has higher resource utilization rate, less server quantity and better energy-saving effect compared with other NFV energy-saving algorithms.

Drawings

Fig. 1 is a flowchart of a network function energy saving method based on Openstack virtualization.

Detailed Description

The following detailed description of the preferred embodiments of the present invention, taken in conjunction with the accompanying drawings, will make the advantages and features of the invention easier to understand by those skilled in the art, and thus will clearly and clearly define the scope of the invention.

Referring to fig. 1, a network function energy saving method based on Openstack virtualization is used for counting resource consumption of a task being executed by performing statistical analysis on resource consumption conditions of a cloud data center and combining a proposed migration algorithm and a proposed allocation algorithm, and performing migration and new task allocation on a specific virtual machine.

The method comprises the following steps:

step 1: counting the resource consumption of the current cloud data center to obtain the utilization rate of a CPU (central processing unit) and an internal memory of the virtual host; an initialization algorithm is used for monitoring the resource utilization rate of the data of the physical server and the virtual machine in real time and allocating and scheduling subsequent tasks;

step 2: current number of cloudsWhen a batch of tasks S is received by the data center, determining the distribution scheme of each task according to the pre-distribution algorithm to obtain a pre-distribution scheme T _pre And optimality of this scheme G;

and step 3: for each task in the task S, respectively distributing the tasks;

step 3.1: judging whether idle virtual hosts exist currently or not, wherein the number of the idle virtual hosts is L, if so, skipping to the step 3.2, and if not, skipping to the step 3.5;

step 3.2: according to a pre-allocation scheme T _pre And the number M of the required virtual hosts, if the pre-allocation scheme is the optimal allocation scheme, skipping to the step 3.3; otherwise, if the pre-allocation scheme is not the optimal allocation scheme, skipping to step 3.4;

step 3.3: if L is not less than M, the M virtual hosts are used according to the pre-allocation scheme T _pre Performing an allocation task; if L is less than M, opening up M-L virtual host machines, then making all idle virtual host machines (M machines) according to preallocation scheme T _pre Performing an allocation task;

step 3.4: if L is not less than M-1, obtaining an actual distribution scheme T by the M-1 virtual hosts through an actual distribution algorithm, and distributing tasks according to the actual distribution scheme T; if L is smaller than M-1, additionally opening up M-1-L virtual hosts to obtain an actual distribution scheme T through an actual distribution algorithm, and distributing tasks according to the actual distribution scheme T;

step 3.5: if the current pre-allocation scheme T _pre If the optimal distribution scheme is adopted, skipping to the step 3.6, otherwise skipping to the step 3.7;

step 3.6: newly opening up M virtual hosts and according to T _pre Carrying out task allocation;

step 3.7: newly opening up M-1 virtual hosts, obtaining an actual distribution scheme T through an actual distribution algorithm, and distributing tasks according to the actual distribution scheme T;

and 4, step 4: continuing to detect the tasks received in the cloud data center, and executing the step 2 if a new batch of tasks are received currently; and if no new task is received currently, waiting for the task.

In addition, in the working process of the whole cloud data center, the resource consumption condition of each virtual host needs to be monitored in real time. The migration algorithm can be used for migrating tasks on the virtual host with low resource utilization rate and closing the current virtual host so as to increase the overall resource utilization rate with the data center, reduce energy consumption and achieve the energy-saving effect.

Further, in step 2, the pre-allocation algorithm is as follows:

for theta batch tasks S = { S = { S = } ₁ ,S ₂ ,S ₃ ,…,S _i ,…,S _θ Independent and independent of each other, each task S _i Has n _i Subtasks, and the resource consumption of each subtask to CPU is

The consumption of the memory is

The CPU idle load after the start of each virtual host is alpha _cpu The upper limit of the load is beta _cpu With a memory empty load of alpha _memory The upper limit of the load is beta _memory ；

The following algorithm is designed to determine the actual selection of the ith task S _i Number of virtual hosts that need to be deployed:

each virtual host can accommodate at most

A task, the task uses>

Each virtual host can complete batch tasks by only consuming 1 task time, C tasks run on each virtual host simultaneously, the number of deployed virtual hosts is set to be m, the number of virtual hosts which can be distributed at most is set to be N, and the number of deployed virtual hosts is taken as

Executes the depletion pick>

Completing batch tasks within one task time, wherein the additional energy consumption is alpha tau (m-1); in the formula>

Means rounding up the result of dividing a by b; />

Means rounding down the result of dividing a by b;

compared with the task of executing the batch by using only one virtual host, when a plurality of virtual hosts are used, the additional energy consumption of the virtual hosts is as follows: w _{Is additionally provided with} = α τ (m-1), the time earned is:

setting an optimization function f (m) = W _{Is additionally provided with} /T _{Earn money} ；

Therefore, it is

In particular:

when there is i e [1, phi ]]When n% iC =0, f (i) is the minimum value, and the number of virtual hosts to be deployed is considered to be

The resulting allocation scheme is now an optimal allocation scheme (τ is not 1), which additionally is satisfied for ^ R when τ is 1>

The scheme at this time is the optimal distribution scheme; where a% b =0 means that a is divisible by b.

Traverse all tasks S = { S = } ₁ ,S ₂ ,S ₃ ,…,S _i ,…,S _θ Get the number of virtual host machines M = { M = required by each task ₁ ,m ₂ ,m ₃ ,…,m _i ,…,m _θ For a task S _i If m is _i The resulting allocation scheme is the optimal allocation scheme (G) _i = 1), then its pre-allocation scheme

Deployment is at m _i Deploying tasks on the virtual host, and paralleling C subtasks on each host, wherein m is the moment _i The platform virtual hosts are all in a full load condition; if m _i The resulting allocation scheme is not the optimal allocation scheme (G) _i = 0), then its pre-allocation scheme £ is asserted>

Deployment is at m _i Deployment of tasks on the desktop virtual host, and m _i Parallel C subtasks on 1 host, in this case m _i -1 virtual machines are all in a full load condition, one virtual machine remaining partially loaded; />

So that all tasks S = { S } can be finally obtained ₁ ,S ₂ ,S ₃ ,…,S _i ,…,S _θ Preallocation scheme of

And optimality of the pre-allocation scheme G = { G = { (G) ₁ ,G ₂ ,G ₃ ,…,G _i ,…,G _θ }。

Further, in step 3, the actual allocation algorithm is:

for the pre-allocation scheme of the current non-optimal allocation scheme, tasks are allocated to m-1 virtual hosts according to the pre-allocation scheme; the CPU occupancy for the virtual host in which there is a remaining underfill is set to ρ _cpu The occupancy rate of the memory is set as rho _memory (ii) a Notation q = { ρ = _cpu ,ρ _memory }；

Traversing all the remaining available (excluding the upper limit of the load beta) cases of the CPUs and the memories of the non-idle virtual hosts (assuming t stations) at present, Q = { Q = ₁ ,Q ₂ ,…,Q _i ,…,Q _t }, setCPU of the ith non-idle virtual host remains available as

The remaining available condition of memory is->

I.e. based on>

Traversing all the non-idle virtual hosts to obtain the condition Q' = Q-Q of the i-th non-idle virtual host after receiving the current residual tasks (not including the load upper limit beta);

find all the satisfies

Is/is>

And is provided with

Is denoted as Γ, the f value of each element in Γ is calculated from f (cpu, memory) =0.5cpu +0.5memory, taken +>

Allocating the tasks which are not fully loaded in the pre-allocation scheme to the ith non-idle virtual host; on the contrary, if the gamma is an empty set, a new virtual machine is opened for storing the underloaded tasks in the pre-allocation scheme.

Furthermore, the migration algorithm mainly aims at migrating all tasks on the virtual host with the resource utilization rate close to the idle load to other virtual hosts, trying to migrate the virtual host to be idle, and releasing the virtual host to save energy consumption. If there are not enough resources on the remaining VMs to accommodate all tasks on the source VM, then no migration occurs.

Designing a migration probability function

Wherein->

The steps of the migration algorithm are as follows:

1. migration probability calculation is carried out on the load conditions of all the virtual hosts to obtain a server queue needing migration;

2. if the queue is empty, then no migration is currently required; otherwise, the queue is not empty and needs to be migrated;

3. migrating and selecting a target virtual host, and migrating all tasks on the source virtual host to the target host if the CPU and the memory consumption of the target virtual host are still within the upper limit of the load after all migration tasks of the source target host are received by the target virtual host; otherwise, the virtual host is not migrated for the moment;

4. through system detection, if the resource utilization rates of the CPUs and the memories of some virtual hosts are found to be in an idle state for a long time, the virtual hosts can be closed to save energy consumption.

Example 1

The method comprises the steps of currently receiving batch tasks S1, S2 and S3, wherein S1 comprises 100 subtasks, and each subtask occupies 10% of a CPU and 3% of a memory. S2 comprises 50 subtasks, each subtask occupies 15% of the CPU, and occupies 5% of the memory. S3 comprises 20 subtasks, wherein each subtask occupies 20% of the CPU and 20% of the memory; the idle CPU utilization rate is 20% when the virtual host is started, the memory utilization rate is 15%, the CPU utilization rate is 80% when the virtual host is at the highest load, the memory utilization rate is 90%, and one task can only be distributed on 15 virtual hosts at most once.

Step 1, counting the resource consumption of the current cloud data center to obtain the utilization rate of a CPU and an internal memory of the virtual host. And initializing an algorithm, monitoring the resource utilization rate of the data of the physical server and the virtual machine in real time, and allocating and scheduling subsequent tasks.

And 2, according to a pre-allocation algorithm, calculating to obtain 9, 13 and 7 numbers of the allocated virtual hosts of the S1, the S2 and the S3 respectively, wherein the consumed unit task time is 2,1 and 1, and the three pre-allocation schemes are not optimal allocation schemes.

Step 3, for task S1:

step 3.1, no idle virtual host exists at present, and the step 3.5 is skipped;

step 3.5, the current pre-allocation scheme is not the optimal allocation scheme, and the step 3.7 is skipped;

step 3.7 opens up 8 virtual hosts, executes the actual distribution algorithm, no non-idle virtual host exists at present,

so 1 virtual host needs to be opened up to allocate the task.

Step 3, for task S2:

step 3.1, no idle host computer exists at present, and the step 3.5 is skipped to;

step 3.5, the current pre-allocation scheme is not the optimal allocation scheme, and the step 3.7 is skipped;

and 3.7, opening up 12 virtual hosts, executing an actual distribution algorithm, and opening up 1 virtual host to distribute tasks because no non-idle virtual host exists currently.

Step 3. For task S3:

step 3.1, no idle host computer exists at present, and the step 3.5 is skipped to;

step 3.5, the current pre-allocation scheme is not the optimal allocation scheme, and the step 3.7 is skipped;

and 3.7, opening up 6 virtual hosts, executing an actual distribution algorithm, and opening up 1 virtual host to distribute tasks because no non-idle virtual host exists currently.

And 4, continuing to detect the tasks received in the cloud data center, if a new batch of tasks are received currently, executing the step 2, and if no new tasks are received currently, waiting for the tasks.

Example 2

On the basis of embodiment 1, task 1 has calculated one unit of task time, assuming that task 2 and task 3 have completed the calculation and their assigned virtual hosts have not been released.

At this time, the tasks S4, S5, and S6 are received, where the task S4 includes 60 subtasks, each subtask occupies 10% of the CPU and occupies 3% of the memory, the task S5 includes 5 subtasks, each subtask occupies 30% of the CPU and occupies 20% of the memory, the task S6 includes 15 subtasks, each subtask occupies 10% of the CPU and occupies 30% of the memory, the empty CPU utilization rate when the virtual host is started is 20%, the memory utilization rate is 15%, the CPU utilization rate when the virtual host is at the highest load is 80%, the memory utilization rate is 90%, and one task can be allocated to 15 virtual hosts at most once.

Step 1, counting the resource consumption of the current cloud data center to obtain the utilization rate of a CPU and a memory of the virtual host. And initializing an algorithm, monitoring the resource utilization rate of the data of the physical server and the virtual machine in real time, and allocating and scheduling subsequent tasks.

And 2, according to a pre-allocation algorithm, calculating to obtain the numbers of the allocated virtual hosts of S4, S5 and S6 which are respectively 10,3 and 8, wherein the consumed unit task time is 1,1 and 1, and only the pre-allocation scheme of S4 is the optimal allocation scheme.

Step 3, for task S4:

step 3.1, currently, an idle virtual host L =21, and step 3.2 is skipped;

step 3.2 the scheme is an optimal distribution scheme, M =10, and step 3.3 is skipped;

step 3.3L is not less than M, and virtual hosts are distributed according to a pre-distribution scheme;

step 3, for task S5:

step 3.1, currently, an idle virtual host L =11, and skipping to step 3.2;

step 3.2 the allocation scheme is not the optimal allocation scheme, M =3, step 3.4 is skipped;

and 3.4L is not less than M-1, at the moment, 2 virtual hosts are distributed according to a pre-distribution scheme, 18 virtual hosts are currently distributed according to an actual distribution algorithm, the current residual tasks need 30% of CPU resources and 20% of memory resources, the most appropriate virtual host is matched, and the task S1 is one virtual host which is not fully loaded.

Step 3, for task S5:

step 3.1, currently, an idle virtual host L =9, and step 3.2 is skipped;

step 3.2, the allocation scheme is not the optimal allocation scheme, M =8, and step 3.4 is skipped;

and 3.4L is not less than M-1, at the moment, 7 virtual hosts are allocated according to a pre-allocation scheme, 20 virtual machines are currently not idle according to an actual allocation algorithm, the current residual tasks need 10% of CPU resources and 30% of memory resources, but no virtual machine capable of storing the current tasks is available, and therefore 1 virtual host needs to be newly developed to store the residual tasks.

And 4, continuing to detect the tasks received in the cloud data center, if a new batch of tasks are received currently, executing the step 2, and if no new tasks are received currently, waiting for the tasks.

The foregoing merely represents preferred embodiments of the invention, which are described in some detail and detail, and therefore should not be construed as limiting the scope of the invention. It should be noted that, for those skilled in the art, various changes, modifications and substitutions can be made without departing from the spirit of the present invention, and these are all within the scope of the present invention. Therefore, the protection scope of the present patent shall be subject to the appended claims.

Claims (6)

1. A network function energy-saving method based on Openstack virtualization is characterized by comprising the following steps:

step 1: counting the resource consumption of the current cloud data center to obtain the utilization rate of a CPU (central processing unit) and an internal memory of the virtual host; an initialization algorithm is used for monitoring the resource utilization rate of the data of the physical server and the virtual machine in real time and allocating and scheduling subsequent tasks;

step 2: when the cloud data center receives a batch of tasks S, determining the distribution scheme of each task according to the pre-distribution algorithm to obtain a pre-distribution scheme T _pre And optimality of this scheme G;

and step 3: for each task in the task S, respectively distributing the tasks;

and 4, step 4: continuing to detect the tasks received in the cloud data center, and executing the step 2 if a new batch of tasks are received currently; and if no new task is received currently, waiting for the task.

2. The Openstack virtualization-based network function power saving method as claimed in claim 1, wherein the step 3 comprises:

step 3.1: judging whether idle virtual hosts exist currently or not, wherein the number of the idle virtual hosts is L, if so, skipping to the step 3.2, and if not, skipping to the step 3.5;

step 3.2: according to a pre-allocation scheme T _pre And the number M of the required virtual hosts, if the pre-allocation scheme is the optimal allocation scheme, skipping to the step 3.3; otherwise, if the pre-allocation scheme is not the optimal allocation scheme, skipping to step 3.4;

step 3.3: if L is not less than M, the M virtual hosts are used according to the pre-allocation scheme T _pre Carrying out distribution tasks; if L is less than M, opening up M-L virtual host machines, then making all idle virtual host machine M according to preallocation scheme T _pre Performing an allocation task;

step 3.4: if L is not less than M-1, obtaining an actual distribution scheme T by the M-1 virtual hosts through an actual distribution algorithm, and distributing tasks according to the actual distribution scheme T; if L is smaller than M-1, additionally opening up M-1-L virtual hosts to obtain an actual distribution scheme T through an actual distribution algorithm, and distributing tasks according to the actual distribution scheme T;

step 3.5: if the current pre-allocation scheme T _pre If the optimal distribution scheme is adopted, skipping to the step 3.6, otherwise skipping to the step 3.7;

step 3.6: newly opening up M virtual hosts and according to T _pre Carrying out task allocation;

step 3.7: and newly opening up M-1 virtual hosts, obtaining an actual distribution scheme T through an actual distribution algorithm, and distributing tasks according to the actual distribution scheme T.

3. The Openstack virtualization-based network function power saving method according to claim 1 or 2, wherein in step 2, the pre-allocation algorithm is:

for theta batch tasks S = { S = { S = } ₁ ,S ₂ ,S ₃ ,…,S _i ,…,S _θ Independent and independent of each other, each task S _i Has n _i Subtasks, and the resource consumption of each subtask to CPU is

The consumption of the memory is

The CPU idle load after the start of each virtual host is alpha _cpu The upper limit of the load is beta _cpu With a memory empty load of alpha _memroy The upper limit of the load is beta _memory ；

The following algorithm is designed to determine the actual selection of the ith task S _i Number of virtual hosts that need to be deployed:

each virtual host can accommodate at most

A task, the task uses>

Each virtual host can complete a batch of tasks with only 1 task consuming time, C tasks are run simultaneously on each virtual host, setting the number of the deployed virtual hosts as m and the maximum number of the distributable virtual hosts as N, and taking the number of the distributable virtual hosts as N>

Executes the depletion pick>

Completing batch tasks within one task time, wherein the additional energy consumption is alpha tau (m-1); in the formula>

Means forRounding up the result of dividing a by b; />

Means rounding down the result of dividing a by b;

compared with the task of executing the batch by using only one virtual host, when a plurality of virtual hosts are used, the additional energy consumption of the virtual hosts is as follows: w _{Is additionally provided with} = α τ (m-1), the time earned is:

setting an optimization function f (m) = W _{Addition of} /T _{Earn money} ；

Therefore, it is

When there is i e [1, phi ]]When n% iC =0, f (i) is the minimum value, and the number of virtual hosts to be deployed is considered to be

The allocation scheme obtained in this case is the optimal allocation scheme, which is satisfied additionally when τ is 1>

The scheme at this time is the optimal distribution scheme; wherein a% b =0 means that a can be divided exactly by b;

traverse all tasks S = { S = } ₁ ,S ₂ ,S ₃ ,…,S _i ,…,S _θ Get the number of virtual host machines M = { M = required by each task ₁ ,m ₂ ,m ₃ ,…,m _i ,…,m _θ For a task S _i If m is _i The obtained distribution scheme is the optimal distribution scheme G _i =1, its pre-allocation scheme

Deployment is at m _i Desktop virtual host topDeploying the tasks, and paralleling C subtasks on each host, wherein m is the moment _i The platform virtual hosts are all in a full load condition; if m _i The resulting allocation scheme is not the optimal allocation scheme G _i =0, its pre-allocation scheme =>

Deployment is at m _i Deployment of tasks on the desktop virtual host, and m _i Parallel C subtasks on 1 host, in this case m _i -1 virtual machines are all in a full load condition, one virtual machine remaining partially loaded;

so that all tasks S = { S } can be finally obtained ₁ ,S ₂ ,S ₃ ,…,S _i ,…,S _θ Pre-allocation scheme of

And optimality of pre-allocation scheme G = { G = ₁ ,G ₂ ,G ₃ ,…,G _i ,…,G _θ }。

4. The Openstack virtualization-based network function energy-saving method as claimed in claim 3, wherein in step 3, the actual allocation algorithm is:

for the pre-allocation scheme of the current non-optimal allocation scheme, tasks are allocated to m-1 virtual hosts according to the pre-allocation scheme; the CPU occupancy for the virtual host in which there is a remaining underfill is set to ρ _cpu The occupancy rate of the memory is set as rho _pmemory (ii) a Notation q = { ρ = _cpu ,ρ _memory }

Traversing the remaining availability Q = { Q ] of the CPUs and the memories of all the non-idle virtual hosts at present ₁ ,Q ₂ ,…,Q _i ,…,Q _t And f, t is the number of the non-idle virtual hosts, and the residual CPU of the ith non-idle virtual host is set as

The remaining available condition of memory is->

I.e. is>

Traversing all the non-idle virtual hosts to obtain the remaining available condition Q' = Q-Q of the ith non-idle virtual host after the ith non-idle virtual host receives the current remaining tasks;

find all the satisfies

Is/is>

And is

Is denoted as Γ, the f value of each element in Γ is calculated from f (cpu, memory) =0.5cpu +0.5memory, taken +>

Allocating the tasks which are not fully loaded in the pre-allocation scheme to the ith non-idle virtual host; on the contrary, if the gamma is an empty set, a new virtual machine is opened for storing the underloaded tasks in the pre-allocation scheme.

5. The Openstack virtualization-based network function power saving method according to claim 4, wherein the step 3 further comprises:

step 3.8: using a migration algorithm to count the resource consumption of the executing task, and migrating the specific virtual machine;

the method comprises the following steps:

step 3.8.1: migration probability calculation is carried out on the load conditions of all the virtual hosts to obtain a server queue needing migration;

step 3.8.2: if the queue is empty, then no migration is currently required; otherwise, the queue is not empty and needs to be migrated;

step 3.8.3: migrating and selecting a target virtual host, and migrating all tasks on the source virtual host to the target host if the CPU and the memory consumption of the target virtual host are still within the upper limit of the load after all migration tasks of the source target host are received by the target virtual host; otherwise, the virtual host is not migrated for the moment;

step 3.8.4: through system detection, if the resource utilization rates of the CPUs and the memories of some virtual hosts are found to be in an idle state for a long time, the virtual hosts can be closed to save energy consumption.

6. The Openstack virtualization-based network function power saving method as claimed in claim 5, wherein in step 3.8.1, a migration probability function F = F is designed _cpu (cpu)+f _memory (memory) wherein

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