CN112084026A - Low-energy-consumption edge computing resource deployment system and method based on particle swarm - Google Patents
Low-energy-consumption edge computing resource deployment system and method based on particle swarm Download PDFInfo
- Publication number
- CN112084026A CN112084026A CN202010909367.0A CN202010909367A CN112084026A CN 112084026 A CN112084026 A CN 112084026A CN 202010909367 A CN202010909367 A CN 202010909367A CN 112084026 A CN112084026 A CN 112084026A
- Authority
- CN
- China
- Prior art keywords
- energy consumption
- particle
- network
- network resource
- resource deployment
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000005265 energy consumption Methods 0.000 title claims abstract description 154
- 239000002245 particle Substances 0.000 title claims abstract description 151
- 238000000034 method Methods 0.000 title claims description 30
- 238000004364 calculation method Methods 0.000 claims description 44
- 238000005457 optimization Methods 0.000 claims description 28
- 230000005540 biological transmission Effects 0.000 claims description 20
- 150000001875 compounds Chemical class 0.000 claims description 6
- 238000013468 resource allocation Methods 0.000 abstract description 10
- 230000008569 process Effects 0.000 description 8
- 238000004891 communication Methods 0.000 description 5
- 230000007246 mechanism Effects 0.000 description 4
- 238000011160 research Methods 0.000 description 4
- 230000003068 static effect Effects 0.000 description 4
- 238000004458 analytical method Methods 0.000 description 3
- 238000010586 diagram Methods 0.000 description 3
- 238000005516 engineering process Methods 0.000 description 3
- 235000008694 Humulus lupulus Nutrition 0.000 description 2
- 238000007726 management method Methods 0.000 description 2
- 238000013508 migration Methods 0.000 description 2
- 230000005012 migration Effects 0.000 description 2
- 239000003595 mist Substances 0.000 description 2
- 238000013500 data storage Methods 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000002474 experimental method Methods 0.000 description 1
- 238000005206 flow analysis Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 230000009467 reduction Effects 0.000 description 1
- 230000004044 response Effects 0.000 description 1
- 238000010845 search algorithm Methods 0.000 description 1
- 239000002904 solvent Substances 0.000 description 1
- 238000012795 verification Methods 0.000 description 1
Images
Classifications
-
- 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
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D10/00—Energy efficient computing, e.g. low power processors, power management or thermal management
Landscapes
- Engineering & Computer Science (AREA)
- Software Systems (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Data Exchanges In Wide-Area Networks (AREA)
Abstract
The invention provides a low-energy-consumption edge computing resource deployment system based on particle swarm, which comprises a network resource energy consumption model storage module, a network resource deployment model storage module and a network resource deployment module, and is used for obtaining the optimal number of computing nodes and/or the optimal number of storage nodes in edge computing resources. The invention provides a low-energy-consumption edge computing resource deployment system based on particle swarm by designing an edge computing service model based on SDN and an objective function for minimizing the energy consumption of the whole network, which is based on the constraint relation among various resources and optimizes a resource allocation scheme on the basis, thereby achieving the purpose of saving the energy consumption of the whole network.
Description
Technical Field
The invention relates to the technical field of communication resource allocation, in particular to a low-energy-consumption edge computing resource deployment system based on particle swarm; meanwhile, the invention also relates to a low-energy-consumption edge computing resource deployment method based on the particle swarm.
Background
In a 5G network environment, to reduce the delay of user traffic, edge computing techniques have been successfully applied to solve this problem. With the increase of user services, the resource demand of edge computing is larger and larger, and how to reduce the energy consumption of the whole network as much as possible on the premise of meeting the user demands becomes a problem which needs to be solved urgently. The existing research mainly adopts methods such as virtual machine migration, game theory, optimization theory and the like to solve the problems of low service execution efficiency and energy consumption reduction.
For example, mishra.s.k and the like propose a Sustainable Service scheme of a mist Server node aiming at improving the execution efficiency of user tasks in a susteable Service Allocation Using a metallurgical technical in a Fog Server for Industrial Applications, and reduce the energy consumption of the mist node and the execution duration of the user tasks. In Hybrid method for minimizing service delay in edge computing through VM routing and transmission power control, T.G provides a service response model for high-speed transmission and fast processing based on fast migration of virtual machines and network flow control technology, and better solves the problem of low performance of edge computing servers. Meng X and the like in Delay-constrained hybrid computing with bound and fog computing use the user task Delay requirement as constraint, carry out combined modeling on the calculated amount and the communication amount of the task, and provide a scheduling mechanism for minimizing the user task Delay, thereby reducing the calculation and communication Delay of the user task. CHEN Xu. and the like put forward an optimization algorithm for user task calculation demand unloading based on a game theory by taking user task execution time limit as constraint in Decentralized calculation and streaming for mobile closed calculation, and the problem of low user task execution efficiency is solved well. The Xu.J and the like provide a Resource Allocation mechanism for cooperation of a cloud service provider and an Edge service provider in the Utility-aware Resource Allocation for Edge Computing with the aim of reducing Resource consumption, reduce the total Resource overhead under the condition of meeting the constraint of user task execution time delay, and verify the expandability and reliability of the Allocation mechanism. In A bandwidth allocation scheme to media flow requirements in mobile edge computing, Ito.Y and the like aim to maximize task transmission rate, an information transmission bandwidth resource allocation mechanism in a mobile edge computing network is designed, a bandwidth resource allocation scheme for providing corresponding services according to specific requirements of users is realized, and different requirements of different users are met.
Through the analysis of the existing research, a lot of research results have been obtained in the field of resource allocation in the edge computing environment. However, the existing research mainly aims at solving different problems of time delay, rate, resource amount and the like, and ignores the constraint relation among various resources.
Therefore, on the premise of meeting the user requirements, a low-energy-consumption edge calculation method based on the constraint relation among various resources is developed, the energy consumption of the whole network is saved, and the problem to be solved by technical personnel in the field is urgently needed.
Disclosure of Invention
In view of this, the present invention aims to provide a low-energy-consumption edge computing resource deployment system based on particle swarm, so as to reduce the number of storage nodes and computing nodes, optimize a resource allocation scheme, and further achieve the purpose of saving energy consumption of the whole network.
In order to achieve the purpose, the technical scheme of the invention is realized as follows:
a low-energy-consumption edge computing resource deployment system based on particle swarm is used for obtaining the optimal number of computing nodes and/or the optimal number of storage nodes in the edge computing resources; comprises that
The network resource energy consumption model storage module is used for storing a network resource energy consumption model comprising a total energy consumption calculation formula and a whole network energy consumption minimization objective function;
the system comprises a network resource deployment model storage module, a particle swarm optimization algorithm and a particle deployment model generation module, wherein the network resource deployment model storage module is used for storing a network resource deployment model constructed by the particle swarm optimization algorithm, and the particle swarm optimization algorithm takes a deployment scheme of network resources as the positions of particles and takes an optimization mode of the network resource deployment scheme as the moving speed of the particles;
and the network resource deployment module is used for calling the network resource energy consumption model and the network resource deployment model, carrying out iterative computation on the network resource deployment model by taking the network resource energy consumption model as a judgment condition, and outputting a network resource deployment scheme, wherein the network resource deployment scheme comprises the optimal number of the storage nodes and/or the optimal number of the computing nodes.
Further, the total energy consumption calculation formula is as follows
Wherein F is total node energy consumption, ka∈KAServing the request for content, kb∈KBIn order to compute the request for a service,
in order to store the energy consumption of the nodes,
in order to dynamically calculate the energy consumption,
in order to calculate the energy consumption statically,
the energy consumption for the transmission of the content is,
to calculate the transmission energy consumption.
Further, the objective function for minimizing the energy consumption of the whole network is as follows
In the formula (I), the compound is shown in the specification,
for the optimal number of storage nodes to be used,
for the optimal number of compute nodes, N is the maximum number of storage nodes, and M is the maximum number of compute nodes.
Further, the iterative computation comprises the following steps:
step one, initializing parameters; the initialized parameters comprise iteration number MG, particle swarm size O and initial position X of randomly generated particlesiInitial velocity V of randomly generated particlesi；
Step two, calculating an initial position; obtaining the minimum total network energy consumption according to the initialized parameters obtained in the step one and the total energy consumption calculation formula of each node; obtaining a deployment scheme of network resources and an optimal particle initial position X according to the minimized whole network energy consumptioniSetting the optimal initial position XiSet as the global optimum initial position XgbThe initial position X of each particleiSet as the individual optimum initial position Xpb；
Step three, respectively updating the particle speed and the particle position; judging whether the initial position of each particle meets the constraint condition, if so, respectively updating the particle speed and the particle position; if not, randomly generating a new particle position and a new particle speed;
respectively updating the new global optimal initial position and the individual optimal initial position; when f (X)i)＜f(Xpb) When, set up Xpb＝Xi(ii) a When f (X)pb)＜f(Xgb) When, set up Xgb＝Xpb；
Step five, judging whether an ending condition is reached; if yes, outputting the optimal XiAnd if not, returning to the step three.
Further, the constraint condition is the maximum number of storage nodes and the maximum number of calculation nodes.
Meanwhile, the invention also provides a low-energy-consumption edge computing resource deployment method based on the particle swarm.
In order to achieve the purpose, the technical scheme of the invention is realized as follows:
a low-energy-consumption edge computing resource deployment method based on particle swarm obtains the optimal number of computing nodes and/or the optimal number of storage nodes in the edge computing resources; comprises the following steps
a. Storing a network resource energy consumption model comprising a total energy consumption calculation formula and a whole network energy consumption minimization objective function;
b. storing a network resource deployment model constructed by a particle swarm optimization algorithm, wherein the deployment scheme of network resources is used as the positions of particles in the particle swarm optimization algorithm, and the optimization mode of the network resource deployment scheme is used as the moving speed of the particles;
c. and calling the network resource energy consumption model and the network resource deployment model, carrying out iterative computation on the network resource deployment model by taking the network resource energy consumption model as a judgment condition, and outputting a network resource deployment scheme, wherein the network resource deployment scheme comprises the optimal number of storage nodes and/or the optimal number of computing nodes.
Further, the total energy consumption calculation formula is as follows
Wherein F is total node energy consumption, ka∈KAServing the request for content, kb∈KBIn order to compute the request for a service,
in order to store the energy consumption of the nodes,
in order to dynamically calculate the energy consumption,
in order to calculate the energy consumption statically,
the energy consumption for the transmission of the content is,
to calculate the transmission energy consumption.
Further, the objective function for minimizing the energy consumption of the whole network is as follows
In the formula (I), the compound is shown in the specification,
for the optimal number of storage nodes to be used,
for the optimal number of compute nodes, N is the maximum number of storage nodes, and M is the maximum number of compute nodes.
Further, the iterative computation comprises the following steps:
c1, initializing parameters; the initialized parameters comprise iteration number MG, particle swarm size O and initial position X of randomly generated particlesiInitial velocity V of randomly generated particlesi；
c2, calculating an initial position; obtaining the minimum total network energy consumption according to the initialized parameters obtained in the step c1 and the total energy consumption calculation formula of each node; obtaining a deployment scheme of network resources and an optimal particle initial position X according to the minimized whole network energy consumptioniSetting the optimal initial position XiSet as the global optimum initial position XgbThe initial position X of each particleiSet as the individual optimum initial position Xpb；
c3, respectively updating the particle speed and the particle position; judging whether the initial position of each particle meets the constraint condition, if so, respectively updating the particle speed and the particle position; if not, randomly generating a new particle position and a new particle speed;
c4, updating the new global optimal initial position and the individual optimal initial position respectively; when f (X)i)＜f(Xpb) When, set up Xpb＝Xi(ii) a When f (X)pb)＜f(Xgb) When, set up Xgb＝Xpb；
c5, judging whether the ending condition is reached; if yes, outputting the optimal XiIf not, return to step c 3.
Further, the constraint condition is the maximum number of storage nodes and the maximum number of calculation nodes.
Compared with the prior art, the invention has the following advantages:
1. the invention designs a resource allocation scheme under the constraint of energy consumption and bandwidth by taking the minimization of the whole network energy consumption and the occupation amount of network bandwidth as a combined target. Firstly, designing an edge computing service model based on an SDN (software defined network) and an objective function for minimizing the energy consumption of the whole network, and providing a low-energy-consumption edge computing resource deployment system based on particle swarm, wherein a resource allocation scheme is optimized on the basis of constraint relations among various resources, so that the aim of saving the energy consumption of the whole network is fulfilled; secondly, experiments verify that the system reduces the number of storage nodes and computing nodes and saves the energy consumption of the whole network; finally, the application analysis of the system shows that the method has better application effect and performance, and better solves the problem of high energy consumption of network resources in the environment of different service request arrival rates.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts. In the drawings:
fig. 1 is a schematic structural diagram of a low-energy-consumption edge computing resource deployment system based on particle swarm in embodiment 1 of the present invention;
fig. 2 is a schematic flowchart of a low-energy-consumption edge computing resource deployment method based on particle swarm in embodiment 2 of the present invention;
fig. 3 is a schematic diagram illustrating comparison of the number of storage nodes in the low-energy-consumption edge computing method based on particle swarm in embodiment 3 of the present invention;
fig. 4 is a schematic diagram illustrating comparison of the number of storage nodes in the low-energy-consumption edge calculation method based on particle swarm in embodiment 3 of the present invention.
Detailed Description
The technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. It is to be understood that the described embodiments are merely a few embodiments of the invention, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments of the present invention without making any creative effort, shall fall within the protection scope of the present invention.
In the present invention, it is to be understood that the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", "clockwise", "counterclockwise", and the like indicate orientations or positional relationships based on those shown in the drawings, and are used merely for convenience of description and for simplicity of description, and do not indicate or imply that the device or element so referred to must have a particular orientation, be constructed in a particular orientation, and be operated, and thus should not be considered as limiting the present invention.
In the present invention, unless otherwise expressly specified or limited, the terms "mounted," "connected," "secured," and the like are to be construed broadly and can, for example, be fixedly connected, detachably connected, or integrally connected; can be mechanically or electrically connected; the connection may be direct or indirect via an intermediate medium, and may be a communication between the two elements. The specific meanings of the above terms in the present invention can be understood by those skilled in the art according to specific situations.
The present invention will be described in detail below with reference to the embodiments with reference to the attached drawings.
Example 1
The embodiment relates to a low-energy-consumption edge computing resource deployment system based on particle swarm, which is used for obtaining the optimal number of computing nodes and/or the optimal number of storage nodes in the edge computing resource; as shown in fig. 1, comprises
And the network resource energy consumption model storage module is used for storing a network resource energy consumption model comprising a total energy consumption calculation formula and a whole network energy consumption minimization objective function. Currently, SDN-based edge computing technology has become a major technology and development trend. In an edge computing architecture of an SDN, the SDN includes three devices, a controller, a repeater, and a remote server. The controller adopts a main and standby redundancy mode to manage the resources of the whole network. The repeater realizes the functions of calculation, storage and transmission of the data of the whole network under the management of the controller. The repeater comprises three types of network nodes, storage nodes and computing nodes. The storage node provides a network transmission function and a data storage function, and the computing node provides a network transmission function and a service computing function.
The service using process based on the SDN edge computing architecture mainly comprises three processes, namely a control process, a user using storage resources process and a user using computing resources process.
(1) The control flow comprises the following steps: the controller interacts with the transponder and the remote server to register and configure the resource status.
(2) The user uses the storage resource flow: a user initiates a content access request to a storage node closest to the user; the storage node checks whether content resources required by the user exist or not; if not, requesting the content resource from the remote server and storing the returned content resource; the storage node returns the resource it requested to the user.
(3) User usage computing resource flow: and the user puts forward a calculation request to the calculation node closest to the user, and the calculation node calculates the calculation task of the user and then returns the result to the user.
As can be known from service usage flow analysis under an edge computing architecture based on an SDN, in order to meet user requirements, the number of deployed storage nodes and computing nodes needs to be increased in a network.
Under the SDN-based edge computing architecture, the service types include a content service request and a computing service request. Wherein the content service requests use of ka∈KAIndicating that computing service requests use of kb∈KBAnd (4) showing. In terms of content service requests, use
The number of service requests arriving within the time t is indicated, and the size of the stored content of each content service request is indicated. In terms of computing service requests, use
Indicating the number of service requests arriving within a time t, the task execution duration usage of each service request
Indicating required traffic usage during service
And (4) showing.
In the edge computing network environment, the energy consumption comprises three types of energy consumption of storage, energy consumption of calculation and energy consumption of transmission. The storage energy consumption is mainly consumed and used by the storage nodes
The calculation method is shown as formula (1).
In the formula, pcaThe unit is J/(bits) for average energy consumption of storage.
The computing energy consumption is mainly consumed by computing nodes and comprises dynamic computing energy consumption
And static calculation of energy consumption
Two kinds. The calculation method for dynamically calculating the energy consumption is shown as the formula (2). The calculation method for statically calculating the energy consumption is shown as the formula (3).
In the formula, pactiveThe average power consumption of the virtual machine copy for executing the computing task under the computing dynamic state is expressed by J/(bits gs).
Is the number of service requests arriving within time t.
The duration of task execution for the service request.
Is the number of VMs in a static environment. p is a radical ofstaticThe average power consumption of the virtual machine copies for executing the computing tasks is in the unit of J/s in a static state.
The transmission energy consumption is mainly consumed by the network node and comprises two types of content transmission energy consumption and calculation transmission energy consumption. Energy consumption for content transmission
Calculated using equation (4). Calculating transmission energy consumption
Calculated using equation (5).
In the formula (I), the compound is shown in the specification,
for storing the content size of the service request, plinkThe unit is J/bit, which is the energy consumption parameter of the link. p is a radical ofnodeThe unit is J/bit, which is the energy consumption parameter of the network node.
An average number of hops to the serving node is requested for the content service.
To calculate the traffic required in the service process.
To calculate the average number of hops of a service request to a service node.
Based on the above analysis, the total energy consumption is defined as formula (6).
Therefore, the present invention defines the objective function of minimizing the power consumption as equation (7).
In the formula (I), the compound is shown in the specification,
for the optimal number of storage nodes to be used,
for the optimal number of compute nodes, N is the maximum number of storage nodes, and M is the maximum number of compute nodes.
The network resource energy consumption model comprises the above equation (6) and equation (7) and the operation and execution processes thereof.
And the network resource deployment model storage module is used for storing a network resource deployment model constructed by a particle swarm optimization algorithm, wherein the particle swarm optimization algorithm takes a deployment scheme of network resources as the positions of particles and takes an optimization mode of the network resource deployment scheme as the moving speed of the particles. The particle swarm optimization algorithm is a high-efficiency global random search algorithm and is successfully applied to the optimization problem solution. Preferably, the nodes of this embodiment include storage nodes and computing nodes, that is, the network resources to be deployed include storage nodes and computing nodes. Therefore, the optimal number of storage nodes and the optimal number of computing nodes are derived by the particle swarm optimization.
The particle swarm optimization algorithm has the main ideas that: describing a deployment scheme of network resources as the position of a particle; the deployment scheme of the network resources comprises the number of storage nodes and the number of computing nodes. The particles then move in these solution spaces in the direction of the movement from the historical optimum position X of the particlepbAnd neighborhood history XgbAnd (4) determining the optimal position. Therefore, the key content comprises four aspects of position, speed, position update and speed update. As described in detail below.
(1) Position: the deployment scheme of the network resources is taken as the particle location. Assuming that the deployment scenario of the ith network resource includes n tasks to be completed, the particle position of the solution is expressed as
Wherein the elements
The number of the network resource representing the jth task acquisition resource.
(2) Speed: and taking the optimization mode of the deployment scheme of the network resources as the moving speed of the particles. Suppose the deployment scheme of the ith network resource is
The particle movement velocity of the solution is expressed as
Wherein the elements
Values of 1 and 0. When in use
And indicating that the current task needs to update the number of the acquired network resource. When in use
Indicating that the current task does not need to update the number of the acquired network resource.
(3) And (3) updating the position: and updating the position based on the position at the last moment and the speed at the current moment, wherein the calculation method is as shown in a formula (8). Equation (8) represents position Xi+1Is the last time position XiVelocity V with current timei+1The product of (c). Wherein the product operation
Is a velocity Vi+1In
Is in position XiWhich needs to be adjusted.
(4) And (3) updating the speed: and updating the speed based on the position and the speed at the last moment, wherein the calculation method is as shown in a formula (9). Equation (9) represents velocity Vi+1Is P1V、P2(XpbΘXi)、P3(XgbΘXi) The sum of the three values. Wherein, P1、P2、P3Is three constants representing the probability of whether three values are updated, and P1+P2+P31. And operation of
Refers to the addition of corresponding elements at a specified probability. XpbΘXiAnd XgbΘXiThe operator Θ in between refers to a subtraction operation, which is used to compare the dissimilarity of two particle positions. When the elements at the same positions of the two particles are the same, the subtraction result is 1; when elements on the same position of two particlesWhen the elements are different, the subtraction result is 0.
And the network resource deployment module is used for calling the network resource energy consumption model and the network resource deployment model, carrying out iterative computation on the network resource deployment model by taking the network resource energy consumption model as a judgment condition, and outputting a network resource deployment scheme, wherein the network resource deployment scheme comprises the optimal number of the storage nodes and/or the optimal number of the computing nodes. The network resource deployment model comprises the above equation (8) and equation (9) and the operation and execution processes thereof. The iterative computation comprises the following five steps of initializing parameters, computing initial positions, updating particle speeds and particle positions, updating global optimal initial positions and individual optimal initial positions, and judging whether end conditions are met. In particular, the amount of the solvent to be used,
the method comprises the steps of inputting a task number N, a maximum number N of storage nodes, a maximum number M of calculation nodes, an iteration number MG and a particle swarm size O, and outputting an optimal particle position Xi. And the iteration number MG and the particle swarm size O are self-defined. The value of the iteration number MG is generally between 20 and 30. The particle swarm size N is generally 5-10. The method comprises the following specific steps:
step one, initializing parameters. The initialized parameters comprise iteration number MG, particle swarm size O and initial position X of randomly generated particlesiInitial velocity V of randomly generated particlesi. Since the size of the particle group is O, O "initial positions X of randomly generated particles" can be obtained in the first stepiInitial velocity V of "and O" randomly generated particlesi”。
And step two, calculating an initial position. According to the above-mentioned "regarding the deployment scheme of the network resource as the particle position", therefore, the initial position X of the particle is randomly generated from the O "in the first stepi"there are O available" deployment scenarios for network resources ". In this embodiment, the network resource can be called through the following stepsConsumption model: and the network resource deployment module reads the network resource energy consumption model from the network resource energy consumption model storage module, reads O 'deployment schemes of the network resources' according to instructions contained in the network resource energy consumption model, and acquires O whole network energy consumptions according to the calculation strategy described by the formula (6). After O pieces of whole network energy consumption are obtained by calling a network resource energy consumption model, setting a 'deployment scheme of network resources' corresponding to the minimized minimum whole network energy consumption as an optimal particle initial position Xi. Setting the optimal initial position XiSet as the global optimum initial position XgbThe initial position X of each particleiSet as the individual optimum initial position Xpb。
And step three, respectively updating the particle speed and the particle position. Firstly, judging whether the positions of the particles meet constraint conditions or not, and carrying out corresponding operation. Preferably, the constraint condition is an optimal number of compute nodes and an optimal number of storage nodes. In this embodiment, taking the network resource energy consumption model as a determination condition means: judging whether the initial positions of the updated particles meet the optimal number of the storage nodes or not
And the optimal number of compute nodes
Namely, the objective function formulas (6) and (7) of minimizing the energy consumption are taken as the judgment conditions. If the constraint conditions are met, updating the particle speed and the particle position respectively by using a formula (8) and a formula (9); if the constraint condition is not met, new particle positions and particle speeds are randomly generated. In this embodiment, the network resource deployment model is invoked by the following steps: the network resource deployment module reads the network resource deployment model from the network resource deployment model storage module, and reads the initial positions X of O' randomly generated particles according to the instructions contained in the network resource energy consumption modeliInitial velocity V of "and O" randomly generated particlesi", for the particle velocity and particle position, respectively, according to the calculation strategies described by equations (8) and (9)And (5) updating. And step three, obtaining the updated particle position, wherein the updated particle position corresponds to a new network resource deployment scheme.
And step four, respectively updating the new global optimal initial position and the individual optimal initial position. When f (X)i)＜f(Xpb) When, set up Xpb＝Xi(ii) a The network resource deployment module reads the network resource deployment model from the network resource deployment model storage module, reads a new network resource deployment scheme corresponding to the updated particle position according to an instruction contained in the network resource energy consumption model, and acquires the whole network energy consumption f (X) corresponding to the new network resource deployment scheme according to a calculation strategy described by the formula (6)i). Comparing the whole network energy consumption f (X) corresponding to the new network resource deployment schemei) With the individual optimum initial position XpbCorresponding total network energy consumption f (X)pb) If f (X)i)＜f(Xpb) Setting the updated particle position as the individual optimal initial position Xpb. Then, when f (X)pb)＜f(Xgb) When, set up Xgb＝XpbHere f (X)pb) That is, the total network energy consumption f (X) corresponding to the new network resource deployment scheme in this stepi) Comparing it with the optimal initial position X of the whole networkgbCorresponding total network energy consumption f (X)gb) If f (X)pb)＜f(Xgb) Setting the updated particle position as the global optimal initial position Xgb。
Step five, judging the global optimal initial position X obtained in the step fourgbWhether an end condition is reached. The end condition is the maximum number of iterations MG. If yes, the global optimal initial position X obtained in the step four is usedgbAs the optimum XiOutput is performed, where X is optimaliThat is, the updated particle positions obtained in the third step correspond to the new network resource deployment scheme, so that the optimal network resource deployment scheme, that is, the optimal number of storage nodes, can be output
And optimization of computing nodesNumber of
If not, returning to the step three, and continuing to iterate until the optimal X is outputiThe optimal number of storage nodes can be obtained
And the optimal number of compute nodes
Preferably, the method may further include the network resource deployment allocation module: after the network resource deployment allocation module receives the network resource deployment scheme (i.e., the optimal number of storage nodes and/or the optimal number of computing nodes) output by the network resource deployment module, the network resource deployment allocation module executes the allocation of the network resource deployment.
Example 2
The embodiment relates to a low-energy-consumption edge computing resource deployment method based on particle swarm, as shown in fig. 2, comprising the following steps:
a. storing a network resource energy consumption model comprising a total energy consumption calculation formula and a whole network energy consumption minimization objective function;
b. storing a network resource deployment model constructed by a particle swarm optimization algorithm, wherein the deployment scheme of network resources is used as the positions of particles in the particle swarm optimization algorithm, and the optimization mode of the network resource deployment scheme is used as the moving speed of the particles;
c. and calling the network resource energy consumption model and the network resource deployment model, carrying out iterative computation on the network resource deployment model by taking the network resource energy consumption model as a judgment condition, and outputting a network resource deployment scheme, wherein the network resource deployment scheme comprises the optimal number of storage nodes and/or the optimal number of computing nodes.
It should be noted that, the method of this embodiment may be implemented by using the corresponding modules, devices, and the like in the low-energy-consumption edge computing resource deployment system based on particle swarm in embodiment 1, and those skilled in the art may refer to the technical solution of the system to implement the step flow of the method, that is, the implementation manner in the system may be understood as a preferred example for implementing the method, and details are not repeated here.
Example 3
The present embodiment relates to performance verification of the low-energy-consumption edge computing resource deployment system based on particle swarm in embodiment 1, and specifically uses US64[12 ]]Simulating a network topology environment (see Choi N, Guan K, Kipper D C, et al. in-network capacitance impact on optimal energy management in content-centralized network [ C ]]//2012IEEE International conference on communications (ICC). IEEE,2012:2889 and 2894). US64 is a typical topology of a business network, and can effectively describe the characteristics of network nodes in the business network. In terms of performance parameters of the network nodes, the power consumption of the static computing resources is set to 50W, and the power consumption of the storage nodes is set to 3 x 10-7J/bit, setting the energy consumption of link resource to 1 × 10- 7J/bit, setting the energy consumption of the network node to 2 x 10-7J/bit. In terms of the number of service requests, the number of service requests arriving per second is set to 1 to 25. The maximum number of storage nodes is set to 80, and the maximum number of compute nodes is set to 20.
To verify that the system of the present invention is solving for storage nodes
And a computing node
The Deployment quantity of the system RDAoPS is superior to that of the traditional Algorithm RDAoRN (resource Deployment Algorithm based on Request number). The RDAoRN algorithm is used for deploying storage nodes and computing nodes according to the characteristics of network topology and the number of service requests. In the embodiment, under the environment of different service request arrival rates, the number of storage nodes and the number of calculation nodes of two algorithms are verified. The comparison results are shown in fig. 3 and 4.
In fig. 3, the X-axis represents the service request arrival rate increasing from 5 to 30 per second, and the Y-axis represents the number of storage nodes. It can be seen from the figure that as the service request arrival rate increases, the number of storage nodes increases under both algorithms. This is because as the service demand increases, more storage nodes need to be deployed to meet the service demand. Comparing the results of the two algorithms, it can be seen that the number of deployed storage nodes tends to be stable when the service request arrival rate reaches 20 per second. This indicates that the storage node location deployed by the present invention is reasonable.
In fig. 4, the X-axis also represents service request arrival rate and the Y-axis represents the number of compute nodes. It can be seen from the figure that as the service request arrival rate increases, the number of computing nodes under both algorithms increases. This is because as service demands increase, more compute nodes need to be deployed to meet the service demands. From the comparison of the results of the two algorithms, the number of deployed computing nodes tends to be stable when the service request arrival rate reaches 25 per second. This indicates that the invention is more reasonable in the location of the deployed computing nodes.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.
Claims (10)
1. A low-energy-consumption edge computing resource deployment system based on particle swarm is characterized in that: the method comprises the steps of obtaining the optimal number of computing nodes and/or the optimal number of storage nodes in the edge computing resource; comprises that
The network resource energy consumption model storage module is used for storing a network resource energy consumption model comprising a total energy consumption calculation formula and a whole network energy consumption minimization objective function;
the system comprises a network resource deployment model storage module, a particle swarm optimization algorithm and a particle deployment model generation module, wherein the network resource deployment model storage module is used for storing a network resource deployment model constructed by the particle swarm optimization algorithm, and the particle swarm optimization algorithm takes a deployment scheme of network resources as the positions of particles and takes an optimization mode of the network resource deployment scheme as the moving speed of the particles;
and the network resource deployment module is used for calling the network resource energy consumption model and the network resource deployment model, carrying out iterative computation on the network resource deployment model by taking the network resource energy consumption model as a judgment condition, and outputting a network resource deployment scheme, wherein the network resource deployment scheme comprises the optimal number of the storage nodes and/or the optimal number of the computing nodes.
2. The particle swarm-based low energy edge computing resource deployment system of claim 1, wherein: the total energy consumption calculation formula is as follows
Wherein F is total node energy consumption, ka∈KAServing the request for content, kb∈KBIn order to compute the request for a service,
in order to store the energy consumption of the nodes,
in order to dynamically calculate the energy consumption,
in order to calculate the energy consumption statically,
the energy consumption for the transmission of the content is,
to calculate the transmission energy consumption.
3. The particle swarm-based low energy edge computing resource deployment system of claim 1, wherein: the energy consumption minimization objective function of the whole network is as follows
In the formula (I), the compound is shown in the specification,
for the optimal number of storage nodes to be used,
for the optimal number of compute nodes, N is the maximum number of storage nodes, and M is the maximum number of compute nodes.
4. The particle swarm-based low energy edge computing resource deployment system of any of claims 1-3, wherein: the iterative computation comprises the following steps:
step one, initializing parameters; the initialized parameters comprise iteration number MG, particle swarm size O and initial position X of randomly generated particlesiInitial velocity V of randomly generated particlesi；
Step two, calculating an initial position; obtaining the minimum total network energy consumption according to the initialized parameters obtained in the step one and the total energy consumption calculation formula of each node; obtaining a deployment scheme of network resources and an optimal particle initial position X according to the minimized whole network energy consumptioniSetting the optimal initial position XiSet as the global optimum initial position XgbThe initial position X of each particleiSet as the individual optimum initial position Xpb；
Step three, respectively updating the particle speed and the particle position; judging whether the initial position of each particle meets the constraint condition, if so, respectively updating the particle speed and the particle position; if not, randomly generating a new particle position and a new particle speed;
respectively updating the new global optimal initial position and the individual optimal initial position; when f (X)i)＜f(Xpb) When, set up Xpb＝Xi(ii) a When f (X)pb)＜f(Xgb) When, set up Xgb＝Xpb；
Step five, judging whether an ending condition is reached; if yes, outputting the optimal XiAnd if not, returning to the step three.
5. The particle swarm-based low energy edge computing resource deployment system of claim 4, wherein: the constraint conditions are the maximum number of storage nodes and the maximum number of calculation nodes.
6. A low-energy-consumption edge computing resource deployment method based on particle swarm is characterized in that: obtaining the optimal number of computing nodes and/or the optimal number of storage nodes in the edge computing resources; comprises the following steps
a. Storing a network resource energy consumption model comprising a total energy consumption calculation formula and a whole network energy consumption minimization objective function;
b. storing a network resource deployment model constructed by a particle swarm optimization algorithm, wherein the deployment scheme of network resources is used as the positions of particles in the particle swarm optimization algorithm, and the optimization mode of the network resource deployment scheme is used as the moving speed of the particles;
c. and calling the network resource energy consumption model and the network resource deployment model, carrying out iterative computation on the network resource deployment model by taking the network resource energy consumption model as a judgment condition, and outputting a network resource deployment scheme, wherein the network resource deployment scheme comprises the optimal number of storage nodes and/or the optimal number of computing nodes.
7. The particle swarm-based low-energy-consumption edge computing resource deployment method of claim 6, wherein: the total energy consumption calculation formula is as follows
Wherein F is total node energy consumption, ka∈KAServing the request for content, kb∈KBIn order to compute the request for a service,
in order to store the energy consumption of the nodes,
in order to dynamically calculate the energy consumption,
in order to calculate the energy consumption statically,
the energy consumption for the transmission of the content is,
to calculate the transmission energy consumption.
8. The particle swarm-based low-energy-consumption edge computing resource deployment method of claim 6, wherein: the energy consumption minimization objective function of the whole network is as follows
In the formula (I), the compound is shown in the specification,
for the optimal number of storage nodes to be used,
for the optimal number of compute nodes, N is the maximum number of storage nodes, and M is the maximum number of compute nodes.
9. The particle population-based low energy edge computing resource deployment method of any one of claims 6-8, wherein: the iterative computation comprises the following steps:
c1, initializing parameters; the initialized parameters comprise iteration number MG, particle swarm size O and initial position X of randomly generated particlesiInitial velocity V of randomly generated particlesi；
c2, calculating an initial position; obtaining the minimum total network energy consumption according to the initialized parameters obtained in the step c1 and the total energy consumption calculation formula of each node; obtaining a deployment scheme of network resources and an optimal particle initial position X according to the minimized whole network energy consumptioniSetting the optimal initial position XiSet as the global optimum initial position XgbThe initial position X of each particleiSet as the individual optimum initial position Xpb；
c3, respectively updating the particle speed and the particle position; judging whether the initial position of each particle meets the constraint condition, if so, respectively updating the particle speed and the particle position; if not, randomly generating a new particle position and a new particle speed;
c4, updating the new global optimal initial position and the individual optimal initial position respectively; when f (X)i)＜f(Xpb) When, set up Xpb＝Xi(ii) a When f (X)pb)＜f(Xgb) When, set up Xgb＝Xpb；
c5, judging whether the ending condition is reached; if yes, outputting the optimal XiIf not, return to step c 3.
10. The particle swarm-based low-energy-consumption edge computing resource deployment method of claim 9, wherein: the constraint conditions are the maximum number of storage nodes and the maximum number of calculation nodes.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010909367.0A CN112084026B (en) | 2020-09-02 | 2020-09-02 | Particle swarm-based low-energy-consumption edge computing resource deployment system and method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010909367.0A CN112084026B (en) | 2020-09-02 | 2020-09-02 | Particle swarm-based low-energy-consumption edge computing resource deployment system and method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN112084026A true CN112084026A (en) | 2020-12-15 |
| CN112084026B CN112084026B (en) | 2024-05-17 |
Family
ID=73732419
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010909367.0A Active CN112084026B (en) | 2020-09-02 | 2020-09-02 | Particle swarm-based low-energy-consumption edge computing resource deployment system and method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112084026B (en) |
Cited By (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112822055A (en) * | 2021-01-21 | 2021-05-18 | 国网河北省电力有限公司信息通信分公司 | DQN-based edge computing node deployment algorithm |
| CN112882723A (en) * | 2021-02-24 | 2021-06-01 | 武汉大学 | Edge service deployment method facing parallel micro-service combination |
| CN113079053A (en) * | 2021-05-07 | 2021-07-06 | 广东电网有限责任公司电力调度控制中心 | Virtual resource reconfiguration method and system based on particle swarm theory under network slice |
| CN113179318A (en) * | 2021-04-27 | 2021-07-27 | 上海交通大学 | Industrial network system sensor scheduling method based on edge calculation |
| CN113472844A (en) * | 2021-05-26 | 2021-10-01 | 北京邮电大学 | Edge computing server deployment method, device and equipment for Internet of vehicles |
| CN113934515A (en) * | 2021-12-17 | 2022-01-14 | 飞诺门阵(北京)科技有限公司 | Container group scheduling method and device based on data domain and calculation domain |
| CN114818446A (en) * | 2021-12-22 | 2022-07-29 | 安徽继远软件有限公司 | Power service decomposition method and system facing 5G cloud edge-end cooperation |
| CN117909418A (en) * | 2024-03-20 | 2024-04-19 | 广东琴智科技研究院有限公司 | Deep learning model storage consistency method, computing subsystem and computing platform |
| CN117909418B (en) * | 2024-03-20 | 2024-05-31 | 广东琴智科技研究院有限公司 | Deep learning model storage consistency method, computing subsystem and computing platform |
Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102625324A (en) * | 2012-03-08 | 2012-08-01 | 上海大学 | Wireless optical fiber sensor network deployment method based on particle swarm optimization |
| US20150009857A1 (en) * | 2013-07-03 | 2015-01-08 | Tata Consultancy Services Limited | Network resource optimization in communication networks |
| US20180260200A1 (en) * | 2017-03-09 | 2018-09-13 | Nec Europe Ltd. | System and method for deploying application components on distributed it resources |
| CN109218414A (en) * | 2018-08-27 | 2019-01-15 | 杭州中恒云能源互联网技术有限公司 | A kind of distributed computing method of smart grid-oriented hybrid network framework |
| CN110084522A (en) * | 2019-04-30 | 2019-08-02 | 国网上海市电力公司 | EV charging station location generation method and device based on random particles group's algorithm |
| CN110570075A (en) * | 2019-07-18 | 2019-12-13 | 北京邮电大学 | Power business edge calculation task allocation method and device |
| US20200136906A1 (en) * | 2019-04-30 | 2020-04-30 | Francesc Guim Bernat | Modular i/o configurations for edge computing using disaggregated chiplets |
| CN111314889A (en) * | 2020-02-26 | 2020-06-19 | 华南理工大学 | Task unloading and resource allocation method based on mobile edge calculation in Internet of vehicles |
| US20200220887A1 (en) * | 2019-01-04 | 2020-07-09 | Samsung Electronics Co., Ltd. | Method and apparatus for organizing and detecting swarms in a network |
-
2020
- 2020-09-02 CN CN202010909367.0A patent/CN112084026B/en active Active
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102625324A (en) * | 2012-03-08 | 2012-08-01 | 上海大学 | Wireless optical fiber sensor network deployment method based on particle swarm optimization |
| US20150009857A1 (en) * | 2013-07-03 | 2015-01-08 | Tata Consultancy Services Limited | Network resource optimization in communication networks |
| US20180260200A1 (en) * | 2017-03-09 | 2018-09-13 | Nec Europe Ltd. | System and method for deploying application components on distributed it resources |
| CN109218414A (en) * | 2018-08-27 | 2019-01-15 | 杭州中恒云能源互联网技术有限公司 | A kind of distributed computing method of smart grid-oriented hybrid network framework |
| US20200220887A1 (en) * | 2019-01-04 | 2020-07-09 | Samsung Electronics Co., Ltd. | Method and apparatus for organizing and detecting swarms in a network |
| CN110084522A (en) * | 2019-04-30 | 2019-08-02 | 国网上海市电力公司 | EV charging station location generation method and device based on random particles group's algorithm |
| US20200136906A1 (en) * | 2019-04-30 | 2020-04-30 | Francesc Guim Bernat | Modular i/o configurations for edge computing using disaggregated chiplets |
| CN110570075A (en) * | 2019-07-18 | 2019-12-13 | 北京邮电大学 | Power business edge calculation task allocation method and device |
| CN111314889A (en) * | 2020-02-26 | 2020-06-19 | 华南理工大学 | Task unloading and resource allocation method based on mobile edge calculation in Internet of vehicles |
Non-Patent Citations (4)
| Title |
|---|
| CHOI, N., GUAN, K., KILPER, D.C., ET AL: "In-network caching effect on optimal energy consumption in content-centric networking", 2012 IEEE INTERNATIONAL CONFERENCE ON COMMUNICATIONS (ICC), 15 June 2012 (2012-06-15), pages 2889 - 2894, XP032274146, DOI: 10.1109/ICC.2012.6364320 * |
| KENNEDY, J., EBERHART, R.: "Particle swarm optimization", PROCEEDINGS OF ICNN’95- INTERNATIONAL CONFERENCE ON NEURAL NETWORKS, vol. 4, 27 November 1995 (1995-11-27), pages 1942 - 1948, XP010156090, DOI: 10.1109/ICNN.1995.488968 * |
| 李立: "５Ｇ网络架构下的移动云计算优化方法探讨", 单片机与嵌入式系统应用, no. 9, 1 September 2020 (2020-09-01), pages 21 - 27 * |
| 胡文建，等: "基于网络内在特征的电力通信网探测站点选择算法", 河北电力技术, vol. 39, no. 1, 25 February 2020 (2020-02-25), pages 34 - 37 * |
Cited By (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112822055B (en) * | 2021-01-21 | 2023-12-22 | 国网河北省电力有限公司信息通信分公司 | Edge computing node deployment method based on DQN |
| CN112822055A (en) * | 2021-01-21 | 2021-05-18 | 国网河北省电力有限公司信息通信分公司 | DQN-based edge computing node deployment algorithm |
| CN112882723B (en) * | 2021-02-24 | 2023-09-08 | 武汉大学 | Edge service deployment method for parallel micro-service combination |
| CN112882723A (en) * | 2021-02-24 | 2021-06-01 | 武汉大学 | Edge service deployment method facing parallel micro-service combination |
| CN113179318A (en) * | 2021-04-27 | 2021-07-27 | 上海交通大学 | Industrial network system sensor scheduling method based on edge calculation |
| CN113179318B (en) * | 2021-04-27 | 2022-03-15 | 上海交通大学 | Industrial network system sensor scheduling method based on edge calculation |
| CN113079053B (en) * | 2021-05-07 | 2023-01-20 | 广东电网有限责任公司电力调度控制中心 | Virtual resource reconfiguration method and system based on particle swarm theory under network slice |
| CN113079053A (en) * | 2021-05-07 | 2021-07-06 | 广东电网有限责任公司电力调度控制中心 | Virtual resource reconfiguration method and system based on particle swarm theory under network slice |
| CN113472844A (en) * | 2021-05-26 | 2021-10-01 | 北京邮电大学 | Edge computing server deployment method, device and equipment for Internet of vehicles |
| CN113934515A (en) * | 2021-12-17 | 2022-01-14 | 飞诺门阵(北京)科技有限公司 | Container group scheduling method and device based on data domain and calculation domain |
| CN114818446A (en) * | 2021-12-22 | 2022-07-29 | 安徽继远软件有限公司 | Power service decomposition method and system facing 5G cloud edge-end cooperation |
| CN117909418A (en) * | 2024-03-20 | 2024-04-19 | 广东琴智科技研究院有限公司 | Deep learning model storage consistency method, computing subsystem and computing platform |
| CN117909418B (en) * | 2024-03-20 | 2024-05-31 | 广东琴智科技研究院有限公司 | Deep learning model storage consistency method, computing subsystem and computing platform |
Also Published As
| Publication number | Publication date |
|---|---|
| CN112084026B (en) | 2024-05-17 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN112084026A (en) | Low-energy-consumption edge computing resource deployment system and method based on particle swarm | |
| Wang et al. | Multiagent-based resource allocation for energy minimization in cloud computing systems | |
| CN111124662B (en) | Fog calculation load balancing method and system | |
| CN113364850B (en) | Software-defined cloud-edge collaborative network energy consumption optimization method and system | |
| Dong et al. | Virtual machine placement for improving energy efficiency and network performance in iaas cloud | |
| CN107819662A (en) | A kind of service function chain real-time migration method applied to cloud and mist calculating network | |
| CN110087250B (en) | Network slice arranging scheme and method based on multi-objective joint optimization model | |
| WO2022057811A1 (en) | Edge server-oriented network burst load evacuation method | |
| CN108923979B (en) | Software defined network virtual network mapping method | |
| CN113419867B (en) | Energy-saving service supply method in edge-oriented cloud collaborative computing environment | |
| CN113993218A (en) | Multi-agent DRL-based cooperative unloading and resource allocation method under MEC architecture | |
| Zhang et al. | A multi-agent learning approach to online distributed resource allocation | |
| Wang et al. | Achieving energy efficiency in data centers using an artificial intelligence abstraction model | |
| Liang et al. | A location-aware service deployment algorithm based on k-means for cloudlets | |
| Ng et al. | Defragmentation for efficient runtime resource management in NoC-based many-core systems | |
| Cui et al. | A new approach on task offloading scheduling for application of mobile edge computing | |
| Meneguette et al. | An efficient green-aware architecture for virtual machine migration in sustainable vehicular clouds | |
| CN107197039A (en) | A kind of PAAS platform service bag distribution methods and system based on CDN | |
| Wu et al. | Resource allocation optimization in the NFV-enabled MEC network based on game theory | |
| CN113032146A (en) | Robust service supply method for multi-access edge computing environment | |
| CN102427420B (en) | Virtual network mapping method and device based on graph pattern matching | |
| CN110677301B (en) | Software defined transmission control method for single controller with multiple switches in 5G network | |
| CN110308991B (en) | Data center energy-saving optimization method and system based on random tasks | |
| Luo et al. | Traffic-aware VDC embedding in data center: A case study of fattree | |
| Mukherjee et al. | Timed loops for distributed storage in wireless networks |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant |