CN113515351A - Resource scheduling implementation method based on energy consumption and QoS (quality of service) cooperative optimization - Google Patents

Resource scheduling implementation method based on energy consumption and QoS (quality of service) cooperative optimization Download PDF

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CN113515351A
CN113515351A CN202111041530.7A CN202111041530A CN113515351A CN 113515351 A CN113515351 A CN 113515351A CN 202111041530 A CN202111041530 A CN 202111041530A CN 113515351 A CN113515351 A CN 113515351A
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virtual machine
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data center
qos
cloud task
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CN113515351B (en
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刘发贵
王彬
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South China University of Technology SCUT
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    • G06—COMPUTING; CALCULATING OR COUNTING
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    • G06F9/00—Arrangements for program control, e.g. control units
    • G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
    • G06F9/44—Arrangements for executing specific programs
    • G06F9/455—Emulation; Interpretation; Software simulation, e.g. virtualisation or emulation of application or operating system execution engines
    • G06F9/45533—Hypervisors; Virtual machine monitors
    • G06F9/45558—Hypervisor-specific management and integration aspects
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F9/00—Arrangements for program control, e.g. control units
    • G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
    • G06F9/46—Multiprogramming arrangements
    • G06F9/48—Program initiating; Program switching, e.g. by interrupt
    • G06F9/4806—Task transfer initiation or dispatching
    • G06F9/4843—Task transfer initiation or dispatching by program, e.g. task dispatcher, supervisor, operating system
    • G06F9/4881—Scheduling strategies for dispatcher, e.g. round robin, multi-level priority queues
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F9/00—Arrangements for program control, e.g. control units
    • G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
    • G06F9/46—Multiprogramming arrangements
    • G06F9/50—Allocation of resources, e.g. of the central processing unit [CPU]
    • G06F9/5005—Allocation of resources, e.g. of the central processing unit [CPU] to service a request
    • G06F9/5027—Allocation of resources, e.g. of the central processing unit [CPU] to service a request the resource being a machine, e.g. CPUs, Servers, Terminals
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F9/00—Arrangements for program control, e.g. control units
    • G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
    • G06F9/46—Multiprogramming arrangements
    • G06F9/50—Allocation of resources, e.g. of the central processing unit [CPU]
    • G06F9/5083—Techniques for rebalancing the load in a distributed system
    • G06F9/5088—Techniques for rebalancing the load in a distributed system involving task migration
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    • G06F—ELECTRIC DIGITAL DATA PROCESSING
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    • G06F9/44—Arrangements for executing specific programs
    • G06F9/455—Emulation; Interpretation; Software simulation, e.g. virtualisation or emulation of application or operating system execution engines
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Abstract

The invention discloses a resource scheduling implementation method based on energy consumption and QoS (quality of service) cooperative optimization. The method comprises the following steps: constructing a cloud task arrival queuing model of multiple virtual machines in a cloud computing data center environment; extracting QoS characteristics of a data center by using a stacking noise reduction automatic encoder technology to obtain a matrix describing QoS characteristic information after dimension reduction, and solving the maximum response time of the current virtual machine so as to perfect the constraint condition of a collaborative optimization objective function; and combining the cloud task arrival queuing model, the collaborative optimization objective function and the Lyapunov optimization method to obtain a resource scheduling algorithm based on the Lyapunov optimization theory, and adopting the resource scheduling algorithm to realize resource scheduling based on energy consumption and QoS collaborative optimization. The invention effectively reduces the energy consumption of the data center while guaranteeing the QoS, and overcomes the interference of the fluctuation of the cloud task arrival to the optimization problem solution in the real scene of the cloud computing data center.

Description

Resource scheduling implementation method based on energy consumption and QoS (quality of service) cooperative optimization
Technical Field
The invention belongs to the field of energy-saving scheduling of cloud computing, and particularly relates to a resource scheduling implementation method based on energy consumption and QoS (quality of service) cooperative optimization.
Background
Cloud computing has long been a popular research project in the global IT field by virtue of ITs ultra-large scale service capability. With the continuous development of cloud computing technology, more and more data centers are emerging in the global scope, and the energy consumption generated by the infrastructure also presents an exponential growth situation. The carbon emission of the current global IT industry accounts for 3-5% of the total carbon emission of the world. According to recent reports, Google data centers consume nearly 3 hundred million watts, while Facebook data centers consume 6000 million watts. Data centers consume more power than high energy manufacturing. The international agency for research, mckentin, investigated 70 large data centers and found that the average power consumption for computer operation was only 6% to 12%. With the continuous increase of the scale of the data centers, the influence of the large amount of electric energy consumed by the data centers on the environment is increasingly prominent.
The virtual machine migration technology can improve the resource utilization rate of the physical nodes, and the total energy consumption value of the cloud computing system can be reduced by closing the servers in the idle state of the data center. Conventional energy saving solutions of this type also have significant adverse effects. When the CPU utilization rate approaches 100%, the performance of the virtual machine is significantly reduced, which means that there is inevitably a conflict relationship between two optimization goals, i.e., energy saving and quality of service (QoS). Excessive pursuit of any of these metrics can hinder the optimization of the other metric. In an actual data center scenario, due to load fluctuation and uncertainty factors, dual objectives of energy saving and QoS guarantee become more complex. Previous optimization methods required all entities (users, cloud tasks, service providers, etc.) to meet a single QoS constraint throughout the cloud computing scheduling process, which is impractical in a practical cloud computing environment. And the solving process of the optimization methods is complex, the convergence speed is low, and the real-time scheduling requirement of the large-scale cloud computing data center is difficult to meet.
Disclosure of Invention
In order to realize better balance between energy consumption and QoS optimization, the invention provides a resource scheduling realization method based on energy consumption and QoS cooperative optimization, and energy consumption generated by a data center is minimized on the premise of guaranteeing QoS of a user. The invention constructs a cloud task arrival queuing model of multiple Virtual Machines (VMs), constructs an energy consumption model and a target optimization function of a data center on the basis, and solves a collaborative optimization problem in each scheduling time slice by combining an optimization method of Lyapunov (Lyapunov) stability theory, so that a cloud task queue of the virtual machines can reach an allowable maximum value, the processing capacity of the virtual machines can be fully exerted, and the utilization of resources is realized.
The purpose of the invention is realized by at least one of the following technical solutions.
A resource scheduling implementation method based on energy consumption and QoS collaborative optimization comprises the following steps:
s1, constructing a cloud task arrival queuing model of multiple Virtual Machines (VMs) in a cloud computing data center environment;
s2, extracting QoS characteristics of the data center by using a stacking noise reduction automatic encoder technology to obtain a matrix describing QoS characteristic information after dimension reduction, and obtaining the maximum response time of the current virtual machine through the matrix to perfect the constraint condition of a collaborative optimization objective function;
s3, combining a cloud task arrival queuing model, a collaborative optimization objective function and a Lyapunov (Lyapunov) optimization method, obtaining a cloud task queue length condition which should be possessed by a virtual machine meeting energy consumption and QoS (quality of service) optimization scheduling, obtaining a resource scheduling algorithm based on the Lyapunov (Lyapunov) optimization theory, and solving the resource scheduling problem of energy consumption and QoS collaborative optimization into each time slice by adopting the resource scheduling algorithm to realize resource scheduling based on energy consumption and QoS collaborative optimization.
Further, in step S1, the cloud task arrival queuing model is formed by connecting a host queuing model and a Virtual Machine (VM) queuing model in series, and is used to optimize a relationship between a virtual machine cloud task queue backlog length and system energy consumption;
in the host queuing model, after the cloud task is submitted to the data center, the data center adopts a load balancing strategy of a least load (least loaded) rule to preferentially distribute the cloud task to the host with the least number of uncompleted cloud task requests, and thus the cloud task is formed to the host with the least number of uncompleted cloud task requestsQueuing model with exponentially distributed inter-arrival time and exponentially distributed cloud task execution time (a)
Figure DEST_PATH_IMAGE001
A queuing model);
in the Virtual Machine (VM) queuing model, each Virtual Machine (VM) instance forms a cloud task with the interval time of arrival of exponential distribution and the execution time of exponential distribution, and the number of virtual machines is
Figure DEST_PATH_IMAGE002
Virtual machine capacity of
Figure DEST_PATH_IMAGE003
Queue model of (1: (
Figure DEST_PATH_IMAGE004
Queuing model), the cloud task is submitted to the virtual machine queuing model by the data center after being distributed by the host queuing model, and then is processed in a first-come-first-serve (FCFS) mode.
Further, when the cloud task enters the host queuing model, if at least one available Virtual Machine (VM) is on the host, the data center allocates the cloud task to the Virtual Machine (VM) currently in the idle state for execution; when all the virtual machines on the host computer are occupied by the cloud tasks and fully loaded, the newly arrived cloud tasks enter a queue buffer area of a virtual machine queuing model, and the queue buffer area is a first-in first-out (FIFO) queue specially used for storing the cloud tasks waiting to be executed; after the cloud task is executed and leaves a certain Virtual Machine (VM), the data center distributes the cloud task at the head of the queue buffer area to the Virtual Machine (VM); after the cloud task obtains the access right to the Virtual Machine (VM), the cloud task can be immediately provided with cloud service;
considering the running time of the data center as being composed of a plurality of continuous time slices, wherein the length of each time slice is defined as t; therefore, in time slot t, the queuing model of a single virtual machine: (
Figure DEST_PATH_IMAGE005
Queuing model) is defined by the following equation:
Figure DEST_PATH_IMAGE006
wherein the content of the first and second substances,
Figure DEST_PATH_IMAGE007
and
Figure DEST_PATH_IMAGE008
is the queue length of the kth Virtual Machine (VM) in the jth and jth +1 time slices;
Figure DEST_PATH_IMAGE009
is shown in the jth time slice
Figure DEST_PATH_IMAGE010
Middle k virtual machine
Figure DEST_PATH_IMAGE011
The number of cloud tasks processed;
Figure DEST_PATH_IMAGE012
is the jth time slice
Figure DEST_PATH_IMAGE013
Middle k virtual machine
Figure 306441DEST_PATH_IMAGE011
The number of cloud tasks in the queue; when in use
Figure DEST_PATH_IMAGE014
When the queue length in a Virtual Machine (VM) is less than its processing power
Figure 32821DEST_PATH_IMAGE009
The queue length of the Virtual Machine (VM) may reach a minimum value of 0;
the length of a Virtual Machine (VM) queuing model can directly influence the running state of a host in a data center; the operating states of the host can be classified into the following two types:
1) active state: a cloud task queue of a Virtual Machine (VM) on a host is not empty, and a cloud task is waiting to be processed;
2) an idle state: a cloud task queue of a Virtual Machine (VM) on a host is empty, and the host in an idle state can be regarded as being in a sleep mode and in a low power consumption state, and generally speaking, the power consumption generated by the host in the idle state is a constant value;
the power consumption model of a data center can therefore be described in the form:
Figure DEST_PATH_IMAGE015
in the formula (I), the compound is shown in the specification,
Figure DEST_PATH_IMAGE016
representing the total energy consumption of the current data center, wherein the first item on the right side of the energy consumption equation represents the total power consumption generated by the physical host machine in the current active state; p is the number of physical hosts in operation,
Figure DEST_PATH_IMAGE017
is the first
Figure DEST_PATH_IMAGE018
Power consumption of individual physical hosts in an active state; q is the number of physical hosts in the idle state,
Figure DEST_PATH_IMAGE019
is the first
Figure DEST_PATH_IMAGE020
Power consumption of each physical host in an idle state; the power consumption in the idle state can be regarded as a constant; m is the maximum number of Virtual Machines (VMs) currently available in the data center;
when the resource utilization rate is higher, the number of the physical hosts in the idle state is less, and the power consumption is also less; thus equation (2) can be expressed in the following form:
Figure DEST_PATH_IMAGE021
Figure DEST_PATH_IMAGE022
wherein
Figure DEST_PATH_IMAGE023
Is as follows
Figure DEST_PATH_IMAGE024
The power consumption of the individual physical hosts in the active state,
Figure DEST_PATH_IMAGE025
，
Figure DEST_PATH_IMAGE026
is a constant power consumption of the host in the idle state,
Figure DEST_PATH_IMAGE027
is in the jth time slice
Figure DEST_PATH_IMAGE028
Queue length of individual virtual machines.
Further, in step S2, processing resource occupation history data of the virtual machine in the data center by using a stacking noise reduction automatic encoder technology, and extracting feature information affecting a QoS index of the data center through robustness of a stacking noise reduction encoder;
using a high-dimensional matrix composed of a parameter set of a virtual machine as data of a network input layer, constructing a target function of a stacking noise reduction automatic encoder, and performing dimension reduction and data reconstruction on the high-dimensional matrix through the target function to obtain optimized characteristics;
in the process of minimizing the loss function of the encoder, the complexity of the stacked noise reduction automatic encoder is also used as one index to participate in the training process and used as the loss functionA constraint term for a number; adding regular terms based on weight attenuation after loss function to improve generalization effect of the stacked noise reduction automatic encoder to avoid overfitting, and adopting
Figure DEST_PATH_IMAGE029
And
Figure DEST_PATH_IMAGE030
cross entropy of
Figure DEST_PATH_IMAGE031
As a loss function, the robustness of the stacked noise reduction auto-encoder is further enhanced.
Further, in the cloud environment, the requirements of tasks submitted by different users are different, some tasks tend to be completed within the shortest time, and some tasks only need to be completed within a period of time. For tasks with longer completion time limit, the tasks can be considered to be distributed to the virtual machine with weaker performance to be executed, and the virtual machine with stronger performance preferentially ensures the tasks with shorter completion time limit, so that the benefits of all users can be guaranteed as much as possible. The traditional scheduling method requires all entities in the whole cloud computing environment to meet a single QoS constraint target, which often fails to meet the requirements of a real cloud computing environment.
The invention provides a QoS service capability discovery method based on a stacking noise reduction automatic encoder, which is used for constructing a QoS constraint condition in an objective function. The automatic encoder is an unsupervised neural network model, and the basic idea is to enable an encoding layer (a hidden layer) to learn the implicit characteristics of input data, and the learned new characteristics can be used for reconstructing original input data through a decoding layer. Therefore, the automatic encoder performs the work of feature dimension reduction and feature learning.
The stacked noise reduction automatic encoder (SDAE) is an improvement on the basis of an automatic encoder, and aims to learn more robust features; except for a conventional encoding stage and a conventional decoding stage, a noise reduction automatic encoder carries out random damage processing on data information before encoding, and the damage processing mainly adds noise into original input data; obtaining an uncontaminated true input by adding noise to the training data and causing the encoder to learn to remove this noise; this therefore forces the encoder to learn and extract more robust features in the input data.
In the process of executing the cloud task, when the response time of the cloud task
Figure DEST_PATH_IMAGE032
When the length is too long, SLA vision can be caused; in the process of training the stacking noise reduction automatic encoder, a high-dimensional matrix formed by a parameter set of the virtual machine is used as data of a network input layer of the stacking noise reduction automatic encoder, and dimension reduction and data reconstruction are carried out on the high-dimensional matrix through the stacking noise reduction automatic encoder to obtain optimized QoS characteristic information;
the parameter set of the virtual machine comprises cloud task response time, queue length, virtual machine CPU utilization rate, bandwidth, CPU number, memory size, memory occupancy rate, virtual machine migration times, virtual machine migration time overhead and SLA violation rate of the virtual machine at the previous moment; when the data center is provided with
Figure DEST_PATH_IMAGE033
When the virtual machine is in a starting state, in each scheduling period interval, the parameter set of the virtual machine is combined to form
Figure DEST_PATH_IMAGE034
High dimensional matrix of
Figure DEST_PATH_IMAGE035
；
The network structure of the stacked noise reduction automatic encoder comprises an input layer, a damaged input layer, a hidden layer and an output layer; in addition to the conventional encoding and decoding stages, the stacked noise reduction auto-encoder performs a random corruption process on the data before encoding in order to add noise to the training data, thereby forcing the encoder to learn and extract better QoS profile information from the input layer data; order to
Figure DEST_PATH_IMAGE036
Representing a sample of the original input data,
Figure DEST_PATH_IMAGE037
representing corrupted data after the addition of gaussian noise,
Figure DEST_PATH_IMAGE038
and
Figure DEST_PATH_IMAGE039
representing the weights of the encoder and the decoder respectively,
Figure DEST_PATH_IMAGE040
and
Figure DEST_PATH_IMAGE041
representing the bias term, the coding function of the stacked noise reduction automatic encoder (SDAE) encodes the original input to obtain a new feature representation
Figure DEST_PATH_IMAGE042
The encoding process is as follows:
Figure DEST_PATH_IMAGE043
wherein
Figure DEST_PATH_IMAGE044
Is a function of the sigmoid and is,
Figure DEST_PATH_IMAGE045
it is used as a non-linear deterministic mapping; similarly, the decoding function will derive a characterization from the hidden layer
Figure DEST_PATH_IMAGE046
Input reconstruction into original input
Figure DEST_PATH_IMAGE047
The decoding process is as follows:
Figure DEST_PATH_IMAGE048
the goal of training a stacked noise reduction autoencoder is to optimize the parameter set
Figure DEST_PATH_IMAGE049
To minimize
Figure DEST_PATH_IMAGE050
A reconstruction error therebetween; order to
Figure DEST_PATH_IMAGE051
Representing loss function of stacked noise reduction auto-encoder (SDAE), optimization objective function of noise reduction auto-encoder
Figure DEST_PATH_IMAGE052
Can be expressed as:
Figure DEST_PATH_IMAGE053
in the process of minimizing the loss function, the complexity of the stacking noise reduction automatic encoder needs to be considered, and an over-fitting is easily caused by an excessively complex model, so that the complexity of the model is also used as one index to participate in the training process, and the model is further constrained; adding a weight attenuation-based regularization term (L2 Norm) after the loss function to improve the generalization effect of the model to avoid overfitting; the weight attenuation is a coefficient placed in front of the regular term, and the influence of the complexity of the model on the loss function can be adjusted;
by using
Figure DEST_PATH_IMAGE054
And
Figure DEST_PATH_IMAGE055
cross entropy of
Figure DEST_PATH_IMAGE056
As a loss function;
Figure DEST_PATH_IMAGE057
(ii) a Wherein
Figure DEST_PATH_IMAGE058
For stacked noise reduction auto-encodersjAt one time slice, the firstiA squared loss function value of a weight vector of samples, wherein,
Figure DEST_PATH_IMAGE059
， Tis the total number of time slices;
Figure DEST_PATH_IMAGE060
d is the total number of samples of the input layer of the stacked noise reduction automatic encoder;
Figure DEST_PATH_IMAGE061
a weight adjustment coefficient that is a regularization term;
Figure DEST_PATH_IMAGE062
(ii) a The resulting loss function is thus obtained as follows:
Figure DEST_PATH_IMAGE063
wherein cross entropy loss function
Figure DEST_PATH_IMAGE064
Can be expressed as:
Figure DEST_PATH_IMAGE065
according to the above equation, the loss optimization function of the stacked noise reduction auto-encoder can be expressed as:
Figure DEST_PATH_IMAGE066
the optimization of the objective function can be solved by using a quasi-Newton method for estimating the parameters
Figure DEST_PATH_IMAGE067
By using the obtained estimated parameters as the parameters of the network model of the stacking noise reduction automatic encoder, a low-dimensional matrix reflecting the QoS characteristic information of the virtual machine can be obtained
Figure DEST_PATH_IMAGE068
；
Maximum response time with cloud task
Figure DEST_PATH_IMAGE069
As an evaluation criterion of the data center service quality; order to
Figure DEST_PATH_IMAGE070
Representing a maximum response time of a Virtual Machine (VM);
Figure DEST_PATH_IMAGE071
represents a maximum response time specified by a Service Level Agreement (SLA);
Figure DEST_PATH_IMAGE072
QoS characteristic information matrix representing a stacked noise reduction auto-encoder (SDAE) through a current Virtual Machine (VM)
Figure 663342DEST_PATH_IMAGE068
And
Figure DEST_PATH_IMAGE073
calculating the maximum response time of the cloud task on the virtual machine according to the cosine similarity; in order to ensure the quality of service of the cloud task,
Figure DEST_PATH_IMAGE074
should be less than
Figure DEST_PATH_IMAGE075
And
Figure 803121DEST_PATH_IMAGE073
a minimum value in between; thus, overall co-optimization objectivesThe scalar function can be written as:
Figure DEST_PATH_IMAGE076
Figure DEST_PATH_IMAGE077
Figure DEST_PATH_IMAGE078
Figure DEST_PATH_IMAGE079
further, in step S3, analyzing the queue length of the cloud task reaching the queuing model by using Lyapunov (Lyapunov) optimization theory, and fitting the cloud task execution time at the current time by using QoS characteristic information to improve the time constraint condition of data center resource scheduling;
when in use
Figure DEST_PATH_IMAGE080
In time, the resource scheduling algorithm should reduce the number of non-empty task queues of a Virtual Machine (VM) to the maximum extent to reduce the energy consumption generated by a data center; at the same time, when
Figure DEST_PATH_IMAGE081
The length of the cloud task queue should be kept large enough to preferentially make the utilization rate of the virtual machine as full as possible, and preferentially make the utilization rate of the virtual machine as full as possible, so as to fully release the processing capacity of the virtual machine.
Further, Lyapunov (Lyapunov) equation is commonly used to analyze the stability of the queuing model, and the Lyapunov (Lyapunov) equation expression may be expressed in the form of:
Figure DEST_PATH_IMAGE082
wherein
Figure DEST_PATH_IMAGE083
Is the queue length of the kth virtual machine in the jth time slice,
Figure DEST_PATH_IMAGE085
is the value of the optimization objective function of the stacking noise reduction automatic encoder in the jth time slice;
Figure DEST_PATH_IMAGE087
to stack the lyapunov equation values of the optimization objective function of the noise reduction auto-encoder in the jth time slice,
Figure DEST_PATH_IMAGE088
，Tis the total number of time slices,
Figure DEST_PATH_IMAGE089
， Nfor the maximum number of virtual machines available in the current data center, in each time slice
Figure DEST_PATH_IMAGE090
The lyapunov drift function is defined as follows:
Figure DEST_PATH_IMAGE091
in order to maintain the stability of the cloud task queue in the cloud computing fluctuation environment, the result of equation (12) should be converged and the drift value should be maintained
Figure DEST_PATH_IMAGE092
The smaller the value of the final result is, the more stable the cloud task queue is;
on the premise of ensuring the service quality of the cloud task, in order to further reduce the energy consumption of the physical host, namely, the number of the virtual machines running in the virtual machine queuing model is reduced to the minimum; the objective function of the co-optimization problem can be rewritten as:
Figure DEST_PATH_IMAGE093
wherein V is a unit conversion coefficient which is used for converting the energy consumption into corresponding cloud task queue length units; according to the Lyapunov optimization method, a resource scheduling collaborative optimization objective function of the data center is obtained from a formula (13):
Figure DEST_PATH_IMAGE094
the solution to this function is as follows:
Figure DEST_PATH_IMAGE095
transforming equation (15) to obtain an equivalent equation of the form:
Figure DEST_PATH_IMAGE096
wherein
Figure DEST_PATH_IMAGE097
Is the first
Figure DEST_PATH_IMAGE098
The penalty value of the lyapunov drift plus penalty function for each virtual machine, the first solution of equation (16) is:
Figure DEST_PATH_IMAGE099
when in use
Figure DEST_PATH_IMAGE100
Equation (16) is solved by a quadratic equation:
Figure DEST_PATH_IMAGE101
as shown in the formula (18),
Figure DEST_PATH_IMAGE102
(ii) a When in use
Figure 208904DEST_PATH_IMAGE100
In time, the resource scheduling algorithm must satisfy the following conditions:
Figure DEST_PATH_IMAGE103
(ii) a Thus when
Figure DEST_PATH_IMAGE104
In time, the resource scheduling algorithm should reduce the number of non-empty task queues of the VM to the maximum extent; at the same time, when
Figure DEST_PATH_IMAGE105
In time, the resource scheduling algorithm preferentially enables the utilization rate of the virtual machine to tend to or be in a full load state, so that the processing capacity of the virtual machine is fully released.
Further, a resource scheduling algorithm based on Lyapunov (Lyapunov) optimization theory is executed by the data center in each preset scheduling cycle of the cloud environment, and the execution process of the resource scheduling algorithm is as follows:
if there is
Figure DEST_PATH_IMAGE106
The cloud task arrives at the data center within the period,
Figure DEST_PATH_IMAGE107
is the maximum response time of the cloud task,
Figure DEST_PATH_IMAGE108
virtual machine
Figure DEST_PATH_IMAGE109
The processing power of (a) is set,
Figure DEST_PATH_IMAGE110
for virtual machines
Figure 729561DEST_PATH_IMAGE109
The current cloud task queuing queue length; queuing a queue for a current cloud task
Figure DEST_PATH_IMAGE111
Finding the queue with the shortest queue length
Figure 240696DEST_PATH_IMAGE110
(ii) a If it is not
Figure DEST_PATH_IMAGE112
And then:
Figure DEST_PATH_IMAGE113
Figure DEST_PATH_IMAGE114
wherein
Figure DEST_PATH_IMAGE115
For the number of cloud tasks currently pending,
Figure DEST_PATH_IMAGE116
to be made available to a virtual machine
Figure DEST_PATH_IMAGE117
The number of cloud tasks to process;
if it is not
Figure DEST_PATH_IMAGE118
Then give an order
Figure DEST_PATH_IMAGE119
(ii) a And slave queues that have been assigned to virtual machines
Figure DEST_PATH_IMAGE120
In the process of (1), removing the substrate,repeating the steps until the length of the cloud task queuing queue is 0; the resource scheduling algorithm is to arrange the resource utilization rates of the virtual machines with the non-empty queues in an ascending order, so that the virtual machines of the queues meeting the Lyapunov optimization condition are selected from the virtual machines with the non-empty queues to be migrated, the virtual machine with the lowest utilization rate reaches the maximum resource utilization rate limit, and the resource scheduling algorithm can realize the balanced utilization of resources and reduce the number of overloaded hosts.
Compared with the prior art, the invention has the following advantages and technical effects:
1. the resource scheduling framework for energy consumption and QoS collaborative optimization in the cloud computing environment is provided, energy consumption generated by a data center can be minimized on the premise of guaranteeing QoS of a user, the solution of the collaborative optimization problem is specifically carried out in each time slice, and the energy efficiency problem caused by fluctuation of cloud task arrival can be more efficiently dealt with.
2. A multi-virtual-machine cloud task arrival queuing model is constructed, the host queuing model and the virtual machine queuing model are connected in series through an M/M/∞ + M/M/C/N queuing structure, and the efficiency of scheduling and distributing after the cloud task arrives at a data center can be improved.
3. The efficient data center QoS feature extraction method is provided, more feature information influencing the QoS of the data center is excavated through the robustness of the stacked noise reduction encoder, and the cloud task execution time at the current moment is obtained through fitting, so that the time constraint condition of data center resource scheduling is completed.
4. The method combines a queuing model, a deep noise reduction automatic encoder network and a Lyapunov (Lyapunov) stability theory, and provides a resource scheduling algorithm based on the Lyapunov (Lyapunov) optimization theory, so that the QoS is guaranteed, the energy consumption of a data center can be effectively reduced, and the total time overhead of data center scheduling is greatly optimized.
Drawings
FIG. 1 is a framework diagram of collaborative optimization of a cloud computing platform according to an embodiment of the present invention;
fig. 2 is a diagram of a QoS feature extraction framework of a noise reduction-based automatic encoder in a data center according to an embodiment of the present invention.
Detailed Description
In the following description, technical solutions are set forth in conjunction with specific figures in order to provide a thorough understanding of the present invention. This application is capable of embodiments in many different forms than those described herein and it is intended that similar embodiments will be apparent to those skilled in the art without the use of inventive faculty, and that the invention will be within its scope.
The terminology used in the description is for the purpose of describing particular embodiments only and is not intended to be limiting of the description. As used in one or more embodiments of the present specification and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It should also be understood that the term "and/or" as used in one or more embodiments of the present specification refers to and encompasses any and all possible combinations of one or more of the associated listed items.
It should be understood that although the terms first, second, etc. may be used herein to describe various information in one or more embodiments of the specification, these information should not be limited by these terms, which are used only for distinguishing between similar items and not necessarily for describing a sequential or chronological order of the features described in one or more embodiments of the specification. Furthermore, the terms "having," "including," and similar referents, are intended to cover a non-exclusive scope, such that a process, method, system, article, or apparatus that comprises a list of steps or modules is not necessarily limited to the particular details set forth, but may include other inherent information not expressly listed for such steps or modules.
Example (b):
a resource scheduling implementation method based on energy consumption and QoS collaborative optimization comprises the following steps:
s1, constructing a cloud task arrival queuing model of multiple Virtual Machines (VMs) in a cloud computing data center environment;
as shown in fig. 1, the cloud task arrival queuing model is formed by connecting a host queuing model and a Virtual Machine (VM) queuing model in series, and is used for optimizing the relationship between the virtual machine cloud task queue backlog length and the system energy consumption;
in the host queuing model, after the cloud task is submitted to the data center, the data center adopts a load balancing strategy of a minimum load (least loaded) rule to preferentially distribute the cloud task to the host with the minimum number of uncompleted cloud task requests, and thereby, a queuing model (a queuing model with exponentially distributed cloud task arrival interval time and exponentially distributed cloud task execution time) is formed
Figure DEST_PATH_IMAGE121
A queuing model);
in the Virtual Machine (VM) queuing model, each Virtual Machine (VM) instance forms a cloud task with the interval time of arrival of exponential distribution and the execution time of exponential distribution, and the number of virtual machines is
Figure DEST_PATH_IMAGE122
Virtual machine capacity of
Figure DEST_PATH_IMAGE123
Queue model of (1: (
Figure DEST_PATH_IMAGE124
Queuing model), the cloud task is submitted to the virtual machine queuing model by the data center after being distributed by the host queuing model, and then is processed in a first-come-first-serve (FCFS) mode.
When a cloud task enters a host queuing model, if at least one available Virtual Machine (VM) is arranged on a host, the data center allocates the cloud task to the Virtual Machine (VM) in an idle state at present to execute; when all the virtual machines on the host computer are occupied by the cloud tasks and fully loaded, the newly arrived cloud tasks enter a queue buffer area of a virtual machine queuing model, and the queue buffer area is a first-in first-out (FIFO) queue specially used for storing the cloud tasks waiting to be executed; after the cloud task is executed and leaves a certain Virtual Machine (VM), the data center distributes the cloud task at the head of the queue buffer area to the Virtual Machine (VM); after the cloud task obtains the access right to the Virtual Machine (VM), the cloud task can be immediately provided with cloud service;
considering the running time of the data center as being composed of a plurality of continuous time slices, wherein the length of each time slice is defined as t; therefore, in time slot t, the queuing model of a single virtual machine: (
Figure 792938DEST_PATH_IMAGE124
Queuing model) is defined by the following equation:
Figure DEST_PATH_IMAGE125
wherein the content of the first and second substances,
Figure DEST_PATH_IMAGE126
and
Figure DEST_PATH_IMAGE127
is the queue length of the kth Virtual Machine (VM) in the jth and jth +1 time slices;
Figure DEST_PATH_IMAGE128
is shown in the jth time slice
Figure 954740DEST_PATH_IMAGE010
Middle k virtual machine
Figure DEST_PATH_IMAGE129
The number of cloud tasks processed;
Figure DEST_PATH_IMAGE130
is the jth time slice
Figure 684544DEST_PATH_IMAGE010
Middle k virtual machine
Figure DEST_PATH_IMAGE131
The number of cloud tasks in the queue; when in use
Figure DEST_PATH_IMAGE132
When the queue length in a Virtual Machine (VM) is less than its processing power
Figure 575881DEST_PATH_IMAGE128
The queue length of the Virtual Machine (VM) may reach a minimum value of 0;
the length of a Virtual Machine (VM) queuing model can directly influence the running state of a host in a data center; the operating states of the host can be classified into the following two types:
1) active state: a cloud task queue of a Virtual Machine (VM) on a host is not empty, and a cloud task is waiting to be processed;
2) an idle state: a cloud task queue of a Virtual Machine (VM) on a host is empty, and the host in an idle state can be regarded as being in a sleep mode and in a low power consumption state, and generally speaking, the power consumption generated by the host in the idle state is a constant value;
the power consumption model of a data center can therefore be described in the form:
Figure DEST_PATH_IMAGE133
in the formula (I), the compound is shown in the specification,
Figure DEST_PATH_IMAGE134
representing the total energy consumption of the current data center, wherein the first item on the right side of the energy consumption equation represents the total power consumption generated by the physical host machine in the current active state; p is the number of physical hosts in operation,
Figure DEST_PATH_IMAGE135
is the first
Figure DEST_PATH_IMAGE136
Power consumption of individual physical hosts in an active state; q is the number of physical hosts in the idle state,
Figure DEST_PATH_IMAGE137
is the first
Figure DEST_PATH_IMAGE138
Power consumption of each physical host in an idle state; the power consumption in the idle state can be regarded as a constant; m is the maximum number of Virtual Machines (VMs) currently available in the data center;
when the resource utilization rate is higher, the number of the physical hosts in the idle state is less, and the power consumption is also less; thus equation (2) can be expressed in the following form:
Figure DEST_PATH_IMAGE139
Figure 957228DEST_PATH_IMAGE022
wherein
Figure DEST_PATH_IMAGE140
Is as follows
Figure DEST_PATH_IMAGE141
The power consumption of the individual physical hosts in the active state,
Figure DEST_PATH_IMAGE142
，
Figure DEST_PATH_IMAGE143
is a constant power consumption of the host in idle state.
S2, extracting QoS characteristics of the data center by using a stacking noise reduction automatic encoder technology to obtain a matrix describing QoS characteristic information after dimension reduction, and obtaining the maximum response time of the current virtual machine through the matrix to perfect the constraint condition of a collaborative optimization objective function;
processing resource occupation historical data of a virtual machine in a data center by using a stacking noise reduction automatic encoder technology, and extracting characteristic information influencing QoS indexes of the data center through robustness of a stacking noise reduction encoder;
using a high-dimensional matrix composed of a parameter set of a virtual machine as data of a network input layer, constructing a target function of a stacking noise reduction automatic encoder, and performing dimension reduction and data reconstruction on the high-dimensional matrix through the target function to obtain optimized characteristics;
in the process of minimizing the loss function of the encoder, the complexity of the stacked noise reduction automatic encoder is also used as one index to participate in the training process and is used as a constraint term of the loss function; adding regular terms based on weight attenuation after loss function to improve generalization effect of the stacked noise reduction automatic encoder to avoid overfitting, and adopting
Figure 74847DEST_PATH_IMAGE029
And
Figure DEST_PATH_IMAGE144
cross entropy of
Figure DEST_PATH_IMAGE145
As a loss function, the robustness of the stacked noise reduction auto-encoder is further enhanced.
In the process of executing the cloud task, when the response time of the cloud task
Figure 914616DEST_PATH_IMAGE032
When the length is too long, SLA vision can be caused; in the process of training the stacking noise reduction automatic encoder, a high-dimensional matrix formed by a parameter set of the virtual machine is used as data of a network input layer of the stacking noise reduction automatic encoder, and dimension reduction and data reconstruction are carried out on the high-dimensional matrix through the stacking noise reduction automatic encoder to obtain optimized QoS characteristic information;
the parameter set of the virtual machine comprises cloud task response time, queue length, virtual machine CPU utilization rate, bandwidth, CPU number, memory size, memory occupancy rate, virtual machine migration times, virtual machine migration time overhead and SLA violation rate of the virtual machine at the previous moment; when the data center is provided with
Figure 159392DEST_PATH_IMAGE033
When the station virtual machine is in a startup state,in each scheduling period interval, the parameter set of the virtual machine is combined to form
Figure DEST_PATH_IMAGE146
High dimensional matrix of
Figure DEST_PATH_IMAGE147
；
The structure of the stacked noise reduction automatic encoder is shown in fig. 2, and the network structure of the encoder comprises an input layer, a damaged input layer, a hidden layer and an output layer; in addition to the conventional encoding and decoding stages, the stacked noise reduction auto-encoder performs a random corruption process on the data before encoding in order to add noise to the training data, thereby forcing the encoder to learn and extract better QoS profile information from the input layer data; order to
Figure DEST_PATH_IMAGE148
Representing a sample of the original input data,
Figure DEST_PATH_IMAGE149
representing corrupted data after gaussian noise is added, W1 and W2 represent encoder and decoder weights respectively,
Figure DEST_PATH_IMAGE150
and
Figure DEST_PATH_IMAGE151
representing the bias term, the encoding function of the stacked noise reduction automatic encoder (SDAE) encodes the original input to obtain a new feature representation, and the encoding process is as follows:
Figure 634903DEST_PATH_IMAGE043
wherein
Figure DEST_PATH_IMAGE152
Is a function of the sigmoid and is,
Figure DEST_PATH_IMAGE153
it is used as a non-linear deterministic mapping; similarly, the decoding function reconstructs the characterizing input from the hidden layer to the original input, the decoding process is as follows:
Figure 625835DEST_PATH_IMAGE048
the goal of training a stacked noise reduction autoencoder is to optimize the parameter set
Figure DEST_PATH_IMAGE154
To minimize
Figure DEST_PATH_IMAGE155
A reconstruction error therebetween; order to
Figure DEST_PATH_IMAGE156
Representing loss function of stacked noise reduction auto-encoder (SDAE), optimization objective function of noise reduction auto-encoder
Figure DEST_PATH_IMAGE157
Can be expressed as:
Figure 783150DEST_PATH_IMAGE053
in the process of minimizing the loss function, the complexity of the stacking noise reduction automatic encoder needs to be considered, and an over-fitting is easily caused by an excessively complex model, so that the complexity of the model is also used as one index to participate in the training process, and the model is further constrained; adding a weight attenuation-based regularization term (L2 Norm) after the loss function to improve the generalization effect of the model to avoid overfitting; the weight attenuation is a coefficient placed in front of the regular term, and the influence of the complexity of the model on the loss function can be adjusted;
by using
Figure DEST_PATH_IMAGE158
And
Figure DEST_PATH_IMAGE159
cross entropy of
Figure DEST_PATH_IMAGE160
As a loss function;
Figure DEST_PATH_IMAGE161
；
Figure DEST_PATH_IMAGE162
a weight adjustment coefficient that is a regularization term;
Figure DEST_PATH_IMAGE163
(ii) a The resulting loss function can thus be obtained as follows:
Figure 88915DEST_PATH_IMAGE063
wherein cross entropy loss function
Figure 410834DEST_PATH_IMAGE145
Can be expressed as:
Figure DEST_PATH_IMAGE164
according to the above equation, the loss optimization function of the stacked noise reduction auto-encoder can be expressed as:
Figure DEST_PATH_IMAGE165
the optimization of the objective function can be solved by using a quasi-newton method, in this embodiment, a quasi-newton optimization algorithm based on linear search is used for estimating parameters
Figure DEST_PATH_IMAGE166
By using the obtained estimated parameters as the parameters of the network model of the stacking noise reduction automatic encoder, a low-dimensional matrix reflecting the QoS characteristic information of the virtual machine can be obtained
Figure 268029DEST_PATH_IMAGE068
；
Maximum response time with cloud task
Figure DEST_PATH_IMAGE167
As an evaluation criterion of the data center service quality; order to
Figure DEST_PATH_IMAGE168
Representing a maximum response time of a Virtual Machine (VM);
Figure DEST_PATH_IMAGE169
represents a maximum response time specified by a Service Level Agreement (SLA);
Figure DEST_PATH_IMAGE170
QoS characteristic information matrix representing a stacked noise reduction auto-encoder (SDAE) through a current Virtual Machine (VM)
Figure DEST_PATH_IMAGE171
And
Figure DEST_PATH_IMAGE172
calculating the maximum response time of the cloud task on the virtual machine according to the cosine similarity; in order to ensure the quality of service of the cloud task,
Figure DEST_PATH_IMAGE173
should be less than
Figure DEST_PATH_IMAGE174
And
Figure DEST_PATH_IMAGE175
a minimum value in between; thus, the overall collaborative optimization objective function can be written as:
Figure DEST_PATH_IMAGE176
Figure 763413DEST_PATH_IMAGE077
Figure 844690DEST_PATH_IMAGE078
Figure 811020DEST_PATH_IMAGE079
s3, combining a cloud task arrival queuing model, a collaborative optimization objective function and a Lyapunov (Lyapunov) optimization method, obtaining a cloud task queue length condition which should be possessed by a virtual machine meeting energy consumption and QoS (quality of service) optimization scheduling, obtaining a resource scheduling algorithm based on the Lyapunov (Lyapunov) optimization theory, and solving the resource scheduling problem of energy consumption and QoS collaborative optimization into each time slice by adopting the resource scheduling algorithm to realize resource scheduling based on energy consumption and QoS collaborative optimization.
Analyzing the queue length of the cloud task reaching a queuing model by utilizing a Lyapunov (Lyapunov) optimization theory, fitting the cloud task execution time at the current moment by utilizing QoS (quality of service) characteristic information, and improving the time constraint condition of data center resource scheduling;
when in use
Figure DEST_PATH_IMAGE177
In time, the resource scheduling algorithm should reduce the number of non-empty task queues of a Virtual Machine (VM) to the maximum extent to reduce the energy consumption generated by a data center; at the same time, when
Figure 172221DEST_PATH_IMAGE081
The length of the cloud task queue should be kept large enough to preferentially make the utilization rate of the virtual machine as full as possible, so as to fully release the processing capacity of the virtual machine.
The Lyapunov (Lyapunov) equation is commonly used to analyze the stability of the queuing model, and the Lyapunov (Lyapunov) equation expression may be expressed in the form:
Figure 207090DEST_PATH_IMAGE082
wherein at each time slice
Figure DEST_PATH_IMAGE178
In general, the Lyapunov (Lyapunov) drift function may be defined as follows:
Figure DEST_PATH_IMAGE179
in order to maintain the stability of the cloud task queue in the cloud computing fluctuation environment, the result of equation (12) should be converged and the drift value should be maintained
Figure DEST_PATH_IMAGE180
The smaller the value of the final result is, the more stable the cloud task queue is; in this embodiment, the stability of the cloud task queue model under the condition of the formula (10) is firstly proved, that is, the maximum value of the formula (12) can be converged; the following expression can be obtained by equation (12):
Figure DEST_PATH_IMAGE181
based on the following inequality relationships:
Figure DEST_PATH_IMAGE182
Figure DEST_PATH_IMAGE183
the maximum cloud task queue length that a virtual machine can accommodate may be expressed in the form:
Figure DEST_PATH_IMAGE184
wherein t represents the execution time of a single cloud task; integrating the formula, when the constraint condition of the collaborative optimization objective function needs to be satisfied, the number of the cloud tasks scheduled on the virtual machine needs to satisfy the following condition:
Figure DEST_PATH_IMAGE185
thus, equation (13) can be rewritten in the form:
Figure DEST_PATH_IMAGE186
the first term to the right of the inequality number of formula (17) is a constant term; the second term is a convex quadratic function, which means that the function can obtain a finite maximum value; therefore, it is proved that the Lyapunov (Lyapunov) drift function can make the cloud computing environment maintain stability under the condition constraint of the formula (10).
On the premise of ensuring the service quality of the cloud task, in order to further reduce the energy consumption of the physical host, namely, the number of the virtual machines running in the virtual machine queuing model is reduced to the minimum; the objective function of the co-optimization problem can be rewritten as:
Figure DEST_PATH_IMAGE187
Figure DEST_PATH_IMAGE188
wherein V is a unit conversion coefficient which is used for converting the energy consumption into corresponding cloud task queue length units; according to the Lyapunov (Lyapunov) optimization method, the resource scheduling objective optimization function of the data center can be obtained from formula (18):
Figure DEST_PATH_IMAGE189
the solution process of the optimization function is as follows:
Figure DEST_PATH_IMAGE190
by performing a transformation on equation (20), an equivalent equation of the form:
Figure DEST_PATH_IMAGE191
wherein
Figure DEST_PATH_IMAGE192
Is the penalty value of the Lyapunov (Lyapunov) drift-plus-penalty function, the first solution of equation (21) is:
Figure DEST_PATH_IMAGE193
when in use
Figure DEST_PATH_IMAGE194
Then, equation (21) can be solved by a quadratic equation:
Figure DEST_PATH_IMAGE195
as shown in the formula (23),
Figure DEST_PATH_IMAGE196
(ii) a When in use
Figure DEST_PATH_IMAGE197
In time, the resource scheduling algorithm must satisfy the following conditions:
Figure DEST_PATH_IMAGE198
(ii) a Thus can be properly obtained
Figure DEST_PATH_IMAGE199
In time, the resource scheduling algorithm should reduce the number of non-empty task queues of the VM to the maximum extent so as to reduce the energy consumption generated by the data center;at the same time, when
Figure 745747DEST_PATH_IMAGE081
The resource scheduling algorithm will preferentially make the virtual machine utilization as fully loaded as possible, so that the processing power of the virtual machine is fully released.
The resource scheduling algorithm based on the Lyapunov (Lyapunov) optimization theory is executed by the data center in each preset scheduling period of the cloud environment, and the execution process of the resource scheduling algorithm is as follows:
if there is
Figure 911406DEST_PATH_IMAGE106
The cloud task arrives at the data center within the period,
Figure DEST_PATH_IMAGE200
is the maximum response time of the cloud task,
Figure DEST_PATH_IMAGE201
virtual machine
Figure DEST_PATH_IMAGE202
The processing power of (a) is set,
Figure DEST_PATH_IMAGE203
for virtual machines
Figure DEST_PATH_IMAGE204
The current cloud task queuing queue length; queuing a queue for a current cloud task
Figure DEST_PATH_IMAGE205
Finding the queue with the shortest queue length
Figure DEST_PATH_IMAGE206
(ii) a If it is not
Figure DEST_PATH_IMAGE207
And then:
Figure DEST_PATH_IMAGE208
Figure DEST_PATH_IMAGE209
wherein
Figure DEST_PATH_IMAGE210
For the number of cloud tasks currently pending,
Figure DEST_PATH_IMAGE211
to be made available to a virtual machine
Figure 263641DEST_PATH_IMAGE117
The number of cloud tasks to process;
if it is not
Figure DEST_PATH_IMAGE212
Then give an order
Figure DEST_PATH_IMAGE213
(ii) a And slave queues that have been assigned to virtual machines
Figure 709379DEST_PATH_IMAGE205
Removing the queue and repeating the steps until the queue length is 0; the resource scheduling algorithm is to arrange the resource utilization rates of the virtual machines with the non-empty queues in an ascending order, so that the virtual machines of the queues meeting the Lyapunov optimization condition are selected from the virtual machines with the non-empty queues to be migrated, the virtual machine with the lowest utilization rate reaches the maximum resource utilization rate limit, and the resource scheduling algorithm can realize the balanced utilization of resources and reduce the number of overloaded hosts.
In this embodiment, there is
Figure DEST_PATH_IMAGE214
A virtual machine, an order
Figure DEST_PATH_IMAGE215
Indicating by performing on each time slice tThe strategy obtained by the line drift penalty function,
Figure DEST_PATH_IMAGE216
the unit conversion coefficient is expressed and used for converting the system energy consumption into corresponding cloud task queue length units;
Figure DEST_PATH_IMAGE217
and
Figure DEST_PATH_IMAGE218
respectively representing a Lyapunov (Lyapunov) drift value and a penalty value obtained by performing a drift-plus-penalty function; by analyzing the co-optimization objective function, the following equation can be derived:
Figure DEST_PATH_IMAGE219
wherein the content of the first and second substances,
Figure DEST_PATH_IMAGE220
is a time slice
Figure DEST_PATH_IMAGE221
To middle
Figure DEST_PATH_IMAGE222
The length of the queue for each virtual machine,
Figure DEST_PATH_IMAGE223
as time slices
Figure DEST_PATH_IMAGE224
The k-th virtual machine drift value,
Figure DEST_PATH_IMAGE225
as a mapping function of the drift value and the drift policy for the kth virtual machine,
Figure DEST_PATH_IMAGE226
is a constant value, and is characterized in that,
Figure DEST_PATH_IMAGE227
；
Figure DEST_PATH_IMAGE228
stacking parameter sets of the noise reduction automatic encoder for the time t;
meanwhile, expected values are taken from two sides of the formula, and according to an expected standard calculation mode, the following results can be obtained:
Figure DEST_PATH_IMAGE229
since the approximate value of the optimal solution is obtained by solving the quasi-Newton method, the optimal solution is obtained
Figure DEST_PATH_IMAGE230
The optimal estimation strategy obtained for implementing the drift penalty function,
Figure DEST_PATH_IMAGE231
the function E is a solving function for the expected value, which is constant.
Accumulating the time slices according to the above deduction conclusion, so as to obtain:
Figure DEST_PATH_IMAGE232
wherein
Figure DEST_PATH_IMAGE233
Is an estimate of the lyapunov drift plus a penalty function penalty value,
Figure DEST_PATH_IMAGE234
is the total number of time slices,
Figure DEST_PATH_IMAGE235
for optimizing the objective function during a time slice T
Figure DEST_PATH_IMAGE236
The value of the lyapunov equation of (a),
Figure DEST_PATH_IMAGE237
optimization objective function for stacking noise reduction auto-encoders at time t
Figure 391880DEST_PATH_IMAGE236
The value of (a). Since L (Θ (T)). gtoreq.0, one can find:
Figure DEST_PATH_IMAGE238
observing the right end of the formula, only one adjustable parameter V can be found, and the rest are fixed values; thus, for the above formula, the objective optimization function for cloud environment energy consumption can be expressed as:
Figure DEST_PATH_IMAGE239
wherein
Figure DEST_PATH_IMAGE240
Is a mapping function between the energy consumption and the conversion coefficient V;
similarly, analyzing the average length of the cloud task queue, the following inequality may be obtained:
Figure DEST_PATH_IMAGE242
，
in the above-mentioned formula,
Figure DEST_PATH_IMAGE243
represents a constant obtained according to the constant term of the formula (17) in the embodiment when the average length of the cloud task queue is calculated,
Figure 100923DEST_PATH_IMAGE244
representing the Lyapunov expectation and penalty scaling factor,
Figure DEST_PATH_IMAGE245
representing the maximum penalty value achieved after execution of the drift penalty function,
Figure 28207DEST_PATH_IMAGE246
represents the minimum penalty value obtained after the execution of the drift penalty function;
v is used as a unique variable parameter to determine the backlog length of the cloud task queue; v determining the speed at which the time-averaged constraint of the original problem converges to a non-negative number; thus, the cloud task queue backlog length for the average duration may be represented by o (v); according to the conclusion obtained by the analysis, it is obvious that the unit conversion coefficient V has opposite influence on the energy consumption optimization of the cloud system and the length of the cloud task queue backlog; this illustrates that there is a trade-off between algorithm execution time (number of iterations) and algorithm efficiency (solution accuracy) [ O (V), O (1/V) ]. Because the Lyapunov optimization method belongs to the model-free learning category, and the optimization problem can be solved and completed in each independent time slice, the processing capacity of the virtual machine can be fully exerted, the QoS can be guaranteed, the energy consumption of the data center can be effectively reduced, and the time overhead of data center scheduling can be greatly reduced.
The above-mentioned procedures are preferred embodiments of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention shall be covered by the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims (10)

1. A resource scheduling implementation method based on energy consumption and QoS collaborative optimization is characterized by comprising the following steps:
s1, constructing a cloud task arrival queuing model of the multiple virtual machines in a cloud computing data center environment;
s2, extracting QoS characteristics of the data center by using a stacking noise reduction automatic encoder technology to obtain a matrix describing QoS characteristic information after dimension reduction, and obtaining the maximum response time of the current virtual machine through the matrix to perfect the constraint condition of a collaborative optimization objective function;
s3, combining the cloud task arrival queuing model, the collaborative optimization objective optimization function and the Lyapunov optimization method, obtaining the cloud task queue length condition which should be possessed by the virtual machine meeting the energy consumption and QoS optimization scheduling, obtaining a resource scheduling algorithm based on the Lyapunov optimization theory, and solving the resource scheduling problem of the energy consumption and QoS collaborative optimization into each time slice by adopting the resource scheduling algorithm to realize the resource scheduling based on the energy consumption and QoS collaborative optimization.
2. The method for implementing resource scheduling based on energy consumption and QoS collaborative optimization according to claim 1, wherein in step S1, the cloud task arrival queuing model is composed of a host queuing model and a virtual machine queuing model in series, and is used for optimizing a relationship between a virtual machine cloud task queue backlog length and system energy consumption;
in the host queuing model, after the cloud tasks are submitted to the data center, the data center adopts a load balancing strategy of a minimum load criterion to preferentially distribute the cloud tasks to the hosts with the minimum unfinished cloud task request number, and thus, a queuing model with the cloud task arrival interval time in exponential distribution and the cloud task execution time in exponential distribution is formed;
in the virtual machine queuing model, each virtual machine instance forms an exponential distribution of the cloud task arrival interval time and the cloud task execution time, and the number of the virtual machines is
Figure 517966DEST_PATH_IMAGE001
Virtual machine capacity of
Figure 497550DEST_PATH_IMAGE002
According to the queuing model, the cloud tasks are distributed by the host queuing model and then submitted to the virtual machine queuing model by the data center, and then are processed in a first-come-first-serve mode.
3. The method for realizing resource scheduling based on energy consumption and QoS collaborative optimization according to claim 2, wherein when a cloud task enters a host queuing model, if at least one virtual machine is used on a host, the data center allocates the cloud task to the virtual machine currently in an idle state for execution; when all the virtual machines on the host are occupied by the cloud tasks and fully loaded, the newly arrived cloud tasks enter a queue buffer area of a virtual machine queuing model, and the queue buffer area is a first-in first-out queue specially used for storing the cloud tasks waiting to be executed; after the cloud task is executed and leaves a certain virtual machine, the data center distributes the cloud task at the head of the queue buffer area to the virtual machine; after the cloud task obtains the access right to the virtual machine, the cloud task can immediately provide cloud service for the virtual machine;
considering the running time of the data center as being composed of a plurality of continuous time slices, wherein the length of each time slice is defined as t; therefore, in the time slot t, the queuing model of the single virtual machine is defined by the following formula:
Figure 299080DEST_PATH_IMAGE003
wherein the content of the first and second substances,
Figure 518224DEST_PATH_IMAGE004
and
Figure 548139DEST_PATH_IMAGE005
is the queue length of the kth Virtual Machine (VM) in the jth and jth +1 time slices;
Figure 153034DEST_PATH_IMAGE006
is shown in the jth time slice
Figure 465912DEST_PATH_IMAGE007
Middle k virtual machine
Figure 721920DEST_PATH_IMAGE008
The number of cloud tasks processed;
Figure 424873DEST_PATH_IMAGE009
is the jth time slice
Figure 644079DEST_PATH_IMAGE007
Middle k virtual machine
Figure 601010DEST_PATH_IMAGE008
The number of cloud tasks in the queue; when in use
Figure 399391DEST_PATH_IMAGE010
The queue length in the virtual machine is less than its processing capacity
Figure 255346DEST_PATH_IMAGE006
The queue length of the virtual machine can reach a minimum value of 0.
4. The method for realizing resource scheduling based on energy consumption and QoS collaborative optimization according to claim 3, wherein the length of the virtual machine queuing model directly affects the running state of a host in the data center; the operating states of the host are classified into the following two types:
1) active state: the cloud task queue of the virtual machine on the host is not empty, and the cloud task is waiting to be processed;
2) an idle state: the cloud task queue of the virtual machine on the host is empty, the host in an idle state can be regarded as being in a sleep mode and in a low power consumption state, and the power consumption generated by the host in the idle state is a constant value;
the power consumption model of a data center is described in the form:
Figure 815043DEST_PATH_IMAGE011
in the formula (I), the compound is shown in the specification,
Figure 74200DEST_PATH_IMAGE012
representing the total energy power consumption of the current data center, with the energy consumption equation to the rightThe first item represents the total power consumption generated by the physical host currently in the active state; p is the number of physical hosts in operation,
Figure 949591DEST_PATH_IMAGE013
is the first
Figure 756401DEST_PATH_IMAGE014
Power consumption of individual physical hosts in an active state; q is the number of physical hosts in the idle state,
Figure 435901DEST_PATH_IMAGE015
is the first
Figure 882157DEST_PATH_IMAGE016
Power consumption of each physical host in an idle state; the power consumption in the idle state is regarded as a constant; m is the maximum number of virtual machines currently available in the data center;
equation (2) is expressed in the following form:
Figure 456140DEST_PATH_IMAGE017
wherein
Figure 422209DEST_PATH_IMAGE018
Is as follows
Figure 312501DEST_PATH_IMAGE019
The power consumption of the individual physical hosts in the active state,
Figure 534219DEST_PATH_IMAGE020
，
Figure 341203DEST_PATH_IMAGE021
is a constant power consumption of the host in the idle state,
Figure 574776DEST_PATH_IMAGE022
is in the jth time slice
Figure 120074DEST_PATH_IMAGE023
Queue length of individual virtual machines.
5. The method for realizing resource scheduling based on energy consumption and QoS collaborative optimization according to claim 1, wherein in step S2, historical data of resource occupation of virtual machines in a data center are processed by using a stacking noise reduction automatic encoder technology, and feature information affecting QoS indexes of the data center is extracted through robustness of a stacking noise reduction encoder;
using a high-dimensional matrix composed of a parameter set of a virtual machine as data of a network input layer, constructing a target function of a stacking noise reduction automatic encoder, and performing dimension reduction and data reconstruction on the high-dimensional matrix through the target function to obtain optimized characteristics;
in the minimization process of the loss function of the encoder, the complexity of the stacked noise reduction automatic encoder is also used as one index to participate in the training process and is used as a constraint term of the loss function; adding regular terms based on weight attenuation after loss function to improve generalization effect of the stacked noise reduction automatic encoder to avoid overfitting, and adopting
Figure 57866DEST_PATH_IMAGE024
And
Figure 345456DEST_PATH_IMAGE025
cross entropy of
Figure 654990DEST_PATH_IMAGE026
As a loss function, the robustness of the stacked noise reduction auto-encoder is further enhanced.
6. The method as claimed in claim 5, wherein the method is implemented in cloud computing, and is characterized in that the method is implemented in cloud computingIn the process of executing the service, when the response time of the cloud task
Figure 602623DEST_PATH_IMAGE027
When the length is too long, SLA vision can be caused; in the process of training the stacking noise reduction automatic encoder, a high-dimensional matrix formed by a parameter set of the virtual machine is used as data of a network input layer of the stacking noise reduction automatic encoder, and dimension reduction and data reconstruction are carried out on the high-dimensional matrix through the stacking noise reduction automatic encoder to obtain optimized QoS characteristic information;
the parameter set of the virtual machine comprises cloud task response time, queue length, virtual machine CPU utilization rate, bandwidth, CPU number, memory size, memory occupancy rate, virtual machine migration times, virtual machine migration time overhead and SLA violation rate of the virtual machine at the previous moment; when the data center is provided with
Figure 163488DEST_PATH_IMAGE028
When the virtual machine is in a starting state, in each scheduling period interval, the parameter set of the virtual machine is combined to form
Figure 897869DEST_PATH_IMAGE029
High dimensional matrix of
Figure 394143DEST_PATH_IMAGE030
。
7. The method for implementing resource scheduling based on energy consumption and QoS collaborative optimization of claim 6, wherein the network structure of the stacked noise reduction automatic encoder comprises an input layer, a damaged input layer, a hidden layer and an output layer; the stacking noise reduction automatic encoder performs random damage processing on data before encoding, and adds noise into training data, so that the encoder is forced to learn and extract better QoS characteristic information from input layer data; order to
Figure 652573DEST_PATH_IMAGE031
Representing the original inputInto a data sample, wherein
Figure 474252DEST_PATH_IMAGE032
And D is the total number of samples of the input layer of the stacked noise reduction automatic encoder;
Figure 345031DEST_PATH_IMAGE033
representing corrupted data after the addition of gaussian noise,
Figure 621993DEST_PATH_IMAGE034
and
Figure 492353DEST_PATH_IMAGE035
representing the weights of the encoder and decoder, respectively;
Figure 880175DEST_PATH_IMAGE036
and
Figure 198935DEST_PATH_IMAGE037
representing the bias term, the coding function of the stacked noise reduction automatic encoder (SDAE) encodes the original input to obtain a new feature representation
Figure 272897DEST_PATH_IMAGE038
The encoding process is as follows:
Figure 643936DEST_PATH_IMAGE039
wherein
Figure 206892DEST_PATH_IMAGE040
Is a function of the sigmoid and is,
Figure 454118DEST_PATH_IMAGE041
it is used as a non-linear deterministic mapping; decoding function characterizes from hidden layer
Figure 489387DEST_PATH_IMAGE042
Input reconstruction into original input
Figure 178822DEST_PATH_IMAGE043
The decoding process is as follows:
Figure 293196DEST_PATH_IMAGE044
the goal of training a stacked noise reduction autoencoder is to optimize the parameter set
Figure 820298DEST_PATH_IMAGE046
To minimize
Figure 150034DEST_PATH_IMAGE047
A reconstruction error therebetween; order to
Figure 389875DEST_PATH_IMAGE048
Representing loss function of stacked noise-reducing automatic encoder, optimized objective function of noise-reducing automatic encoder
Figure 308337DEST_PATH_IMAGE050
Expressed as:
Figure 825901DEST_PATH_IMAGE051
in the process of minimizing the loss function, the complexity of the stacking noise reduction automatic encoder is considered, the complexity of the model is also used as one index to participate in the training process, and then the model is constrained; adding a weight attenuation-based regularization term after the loss function to improve the generalization effect of the model to avoid overfitting; the weight attenuation is a coefficient placed in front of the regular term and is used for adjusting the influence of the complexity of the model on the loss function;
by using
Figure 916886DEST_PATH_IMAGE024
And
Figure 471797DEST_PATH_IMAGE025
cross entropy of
Figure 762324DEST_PATH_IMAGE026
As a loss function;
Figure 112148DEST_PATH_IMAGE053
(ii) a Wherein
Figure 258189DEST_PATH_IMAGE054
For stacked noise reduction auto-encodersjAt one time slice, the firstiA squared loss function value of a weight vector of samples, wherein,
Figure 965695DEST_PATH_IMAGE055
， Tis the total number of time slices;
Figure 795280DEST_PATH_IMAGE056
d is the total number of samples of the input layer of the stacked noise reduction automatic encoder;
Figure 328497DEST_PATH_IMAGE014
a weight adjustment coefficient that is a regularization term;
Figure 880135DEST_PATH_IMAGE057
(ii) a The resulting loss function is thus obtained as follows:
Figure 704515DEST_PATH_IMAGE058
wherein cross entropy loss function
Figure 533058DEST_PATH_IMAGE026
Watch capable of showingShown as follows:
Figure 562994DEST_PATH_IMAGE059
according to the above equation, the loss optimization function of the stacked noise reduction auto-encoder can be expressed as:
Figure 160899DEST_PATH_IMAGE060
solving for estimating parameters by using quasi-Newton method
Figure 597209DEST_PATH_IMAGE061
Wherein
Figure 253200DEST_PATH_IMAGE062
Is an estimate of the encoder weight(s),
Figure 682260DEST_PATH_IMAGE063
is an estimate of the decoder weights,
Figure 395082DEST_PATH_IMAGE064
is a bias term
Figure 794502DEST_PATH_IMAGE065
Is determined by the estimated value of (c),
Figure 109119DEST_PATH_IMAGE066
is a bias term
Figure 798020DEST_PATH_IMAGE067
Obtaining a low-dimensional matrix reflecting the QoS characteristic information of the virtual machine by using the obtained estimation parameters as the parameters of the network model of the stacking noise reduction automatic encoder
Figure 556560DEST_PATH_IMAGE068
；
Maximum response time with cloud task
Figure 531167DEST_PATH_IMAGE027
As an evaluation criterion of the data center service quality; order to
Figure 625717DEST_PATH_IMAGE069
Representing a maximum response time of the virtual machine;
Figure 984950DEST_PATH_IMAGE070
represents a maximum response time specified by a service level agreement;
Figure 352823DEST_PATH_IMAGE071
QoS characteristic information matrix representing passage of stacking noise reduction automatic encoder through current virtual machine
Figure 789153DEST_PATH_IMAGE068
And
Figure 469010DEST_PATH_IMAGE070
calculating the maximum response time of the cloud task on the virtual machine according to the cosine similarity;
Figure 879039DEST_PATH_IMAGE027
should be less than
Figure 635250DEST_PATH_IMAGE071
And
Figure 127250DEST_PATH_IMAGE070
a minimum value in between; the overall collaborative optimization objective function is as follows:
Figure 686970DEST_PATH_IMAGE072
Figure 128920DEST_PATH_IMAGE073
Figure 11690DEST_PATH_IMAGE074
Figure 22496DEST_PATH_IMAGE075
。
8. the method for realizing resource scheduling based on energy consumption and QoS collaborative optimization according to claim 7, wherein in step S3, a Lyapunov optimization theory is used to analyze the queue length of a cloud task to a queuing model, and QoS characteristic information is used to fit the cloud task execution time at the current moment, so as to improve the time constraint condition of data center resource scheduling;
when in use
Figure 266660DEST_PATH_IMAGE076
In time, the resource scheduling algorithm should reduce the number of non-empty task queues of a Virtual Machine (VM) to the maximum extent to reduce the energy consumption generated by a data center; at the same time, when
Figure 830159DEST_PATH_IMAGE077
The length of the cloud task queue should remain large enough to allow the processing power of the virtual machine to be fully freed.
9. The method for realizing resource scheduling based on energy consumption and QoS collaborative optimization of claim 8, wherein the Lyapunov equation is expressed in the form of:
Figure 987088DEST_PATH_IMAGE078
wherein
Figure 678397DEST_PATH_IMAGE079
Is the queue length of the kth virtual machine in the jth time slice,
Figure 276178DEST_PATH_IMAGE080
is the value of the optimization objective function of the stacking noise reduction automatic encoder in the jth time slice;
Figure 114960DEST_PATH_IMAGE081
to stack the lyapunov equation values of the optimization objective function of the noise reduction auto-encoder in the jth time slice,
Figure 223613DEST_PATH_IMAGE082
，Tis the total number of time slices,
Figure 618862DEST_PATH_IMAGE083
， Nfor the maximum number of virtual machines available in the current data center, in each time slice
Figure 454095DEST_PATH_IMAGE084
The lyapunov drift function is defined as follows:
Figure 84198DEST_PATH_IMAGE085
wherein the content of the first and second substances,
Figure 849454DEST_PATH_IMAGE086
for the size of the drift value, the stability of the cloud task queue needs to be maintained under the environment of cloud computing fluctuation, the result of the formula (12) is required to be converged, and the drift value
Figure 938908DEST_PATH_IMAGE087
The smaller the value of the final result is, the more stable the cloud task queue is;
on the premise of ensuring the service quality of the cloud task, the energy consumption of the physical host is further reduced, namely the number of the virtual machines running in the virtual machine queuing model is reduced to the minimum; the objective function of the collaborative optimization problem is rewritten as:
Figure 628904DEST_PATH_IMAGE088
wherein V is a unit conversion coefficient which is used for converting the energy consumption into corresponding cloud task queue length units; according to the Lyapunov optimization method, a resource scheduling collaborative optimization objective function of the data center is obtained from a formula (13):
Figure 438678DEST_PATH_IMAGE089
the solution to this function is as follows:
Figure 99994DEST_PATH_IMAGE090
transforming equation (15) to obtain an equivalent equation of the form:
Figure 576494DEST_PATH_IMAGE091
wherein
Figure 266638DEST_PATH_IMAGE092
Is the first
Figure 591181DEST_PATH_IMAGE093
The penalty value of the lyapunov drift plus penalty function for each virtual machine, the first solution of equation (16) is:
Figure 23206DEST_PATH_IMAGE094
when in use
Figure 676135DEST_PATH_IMAGE095
Equation (16) is solved by a quadratic equation:
Figure 602068DEST_PATH_IMAGE096
as shown in the formula (18),
Figure 938451DEST_PATH_IMAGE097
(ii) a When in use
Figure 407482DEST_PATH_IMAGE095
In time, the resource scheduling algorithm must satisfy the following conditions:
Figure 584923DEST_PATH_IMAGE098
(ii) a Thus when
Figure 76205DEST_PATH_IMAGE099
In time, the resource scheduling algorithm should reduce the number of non-empty task queues of the VM to the maximum extent; at the same time, when
Figure 422612DEST_PATH_IMAGE100
In time, the resource scheduling algorithm preferentially enables the utilization rate of the virtual machine to tend to or be in a full load state, so that the processing capacity of the virtual machine is fully released.
10. The method for realizing resource scheduling based on energy consumption and QoS collaborative optimization according to any one of claims 1 to 9, wherein a resource scheduling algorithm based on the Lyapunov optimization theory is executed by the data center in each preset scheduling cycle of the cloud environment, and the execution process of the resource scheduling algorithm is as follows:
if there is
Figure 77116DEST_PATH_IMAGE101
The cloud task arrives at the data center within the period,
Figure 611910DEST_PATH_IMAGE102
is the maximum response time of the cloud task,
Figure 299552DEST_PATH_IMAGE103
virtual machine
Figure 770164DEST_PATH_IMAGE104
The processing power of (a) is set,
Figure 26005DEST_PATH_IMAGE105
for virtual machines
Figure 642837DEST_PATH_IMAGE104
The current cloud task queuing queue length; queuing a queue for a current cloud task
Figure 605941DEST_PATH_IMAGE106
Finding the queue with the shortest queue length
Figure 327529DEST_PATH_IMAGE105
(ii) a If it is not
Figure 221449DEST_PATH_IMAGE107
And then:
Figure 546814DEST_PATH_IMAGE108
Figure 357875DEST_PATH_IMAGE109
wherein
Figure 402888DEST_PATH_IMAGE110
For the number of cloud tasks currently pending,
Figure 156598DEST_PATH_IMAGE111
to be made available to a virtual machine
Figure 654272DEST_PATH_IMAGE112
The number of cloud tasks to process;
if it is not
Figure 469158DEST_PATH_IMAGE113
Then give an order
Figure 313089DEST_PATH_IMAGE114
(ii) a And slave queues that have been assigned to virtual machines
Figure 331695DEST_PATH_IMAGE115
Removing the cloud task queue, and repeating the steps until the length of the cloud task queue is 0; the resource scheduling algorithm is to arrange the resource utilization rates of the virtual machines with the non-empty queues in an ascending order, so that the virtual machines of the queues meeting the Lyapunov optimization conditions are selected from the virtual machines with the non-empty queues to be migrated, and the virtual machine with the lowest utilization rate reaches the maximum resource utilization rate limit.
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