CN112615919B - Resource allocation method, resource allocation device and block chain - Google Patents
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Abstract
The invention provides a resource allocation method, a resource allocation device and a block chain, belongs to the technical field of communication, and can at least partially solve the problem that the existing resource allocation method is difficult to realize flexible allocation of multi-access edge computing server resources in different areas for edge computing services. The resource allocation method of the embodiment of the invention comprises the following steps: receiving resource allocation requests sent by a plurality of multi-access edge application arrangement systems; determining a resource allocation request as a first resource allocation request from a plurality of resource allocation requests; determining a node as a main node from a plurality of nodes of a block chain according to a preset model and a first resource allocation request, determining a second orchestrator from a plurality of multi-access edge application orchestration systems except the first orchestrator, and determining a second edge computing server from a plurality of multi-access edge computing servers corresponding to the second orchestrator; and allocating the resources borne by the second edge computing server to the task of the resources to be allocated through the main node.
Description
Technical Field
The invention belongs to the technical field of communication, and particularly relates to a resource allocation method, a resource allocation device and a block chain.
Background
With the gradual commercial and large-scale deployment of MEC (Multi-access Edge Computing), large-scale Edge Computing services such as autopilot and smart cities need more professional and agile service orchestration systems.
The meco (multiple access edge application orchestration) system may deploy large-scale Network services between distributed edge clouds, collect various messages from basic components corresponding to the MEC servers, such as edge applications and virtual infrastructures based on containers or VNFs (Network Functions Virtualization), manage a plurality of MEC servers in a region, and flexibly allocate MEC server resources in the region for edge computing services (such as various edge applications and services).
However, due to the strict requirements for message synchronization between multiple meas systems, such as the requirement for security of message exchange, it is difficult to perform message synchronization between multiple meas systems, and it is also difficult to implement flexible allocation of MEC server resources in different areas for edge computing services.
Disclosure of Invention
The invention at least partially solves the problem that the conventional resource allocation method is difficult to realize the flexible allocation of the MEC server resources in different areas for the edge computing service, and provides a resource allocation method capable of flexibly allocating the MEC server resources in different areas for the edge computing service.
A first aspect of the present invention provides a resource allocation method, including:
receiving resource allocation requests sent by a plurality of multi-access edge application arrangement systems, wherein each multi-access edge application arrangement system corresponds to a plurality of multi-access edge computing servers bearing edge computing resources, and the resource allocation requests comprise resource tasks to be allocated;
determining a resource allocation request as a first resource allocation request from a plurality of resource allocation requests, wherein a multi-access edge application orchestration system sending the first resource allocation request is a first orchestrator;
determining a node as a main node from a plurality of nodes of a block chain according to a preset model and the first resource allocation request, determining a second orchestrator from a plurality of multi-access edge application orchestration systems except the first orchestrator, and determining a second edge computing server from a plurality of multi-access edge computing servers corresponding to the second orchestrator;
and allocating the resources borne by the second edge computing server to the task of the resources to be allocated through the main node.
Optionally, the preset model is a competition depth Q learning model.
Further optionally, the state space of the preset model is s (t) ═ { h (t), r (t), v (t) }, where h (t) represents the confidence level of the block chain node at time t, r (t) represents the confidence level of the multi-access edge application orchestration system at time t, and v (t) represents the available resources of the multi-access edge computing server at time t.
Optionally, the allocating, by the master node, the resource borne by the second edge computing server to the task of the resource to be allocated includes: sending a response message to the first orchestrator; sending a verification message to all multi-access edge application arrangement systems, wherein the verification message is a message generated by the main node according to the resource task to be distributed and the second edge computing server; and sending the resource task to be distributed to the second edge computing server.
Further optionally, the sending the verification message to all multi-access edge application orchestration systems includes: and controlling the main node to send a preparation message to other secondary nodes except the main node of the block chain, and sending a verification message to all multi-access edge application arranging systems after all the secondary nodes confirm the preparation message.
A second aspect of the present invention provides a resource allocation apparatus, the apparatus comprising:
the system comprises a receiving module, a resource allocation module and a resource allocation module, wherein the receiving module is used for receiving resource allocation requests sent by a plurality of multi-access edge application arrangement systems, each multi-access edge application arrangement system corresponds to a plurality of multi-access edge computing servers bearing edge computing resources, and the resource allocation requests comprise resource tasks to be allocated;
the system comprises a selection module, a first scheduler and a second scheduler, wherein the selection module is used for determining a resource allocation request as a first resource allocation request from a plurality of resource allocation requests, and the multi-access edge application scheduling system sending the first resource allocation request is a first scheduler;
the deep learning module is used for determining one node as a main node from a plurality of nodes of a block chain according to a preset model and the first resource allocation request, determining a second orchestrator from a plurality of multi-access edge application orchestration systems except the first orchestrator, and determining a second edge computing server from a plurality of multi-access edge computing servers corresponding to the second orchestrator;
and the allocation module is used for allocating the resources borne by the second edge computing server to the task of the resources to be allocated through the main node.
Optionally, the preset model is a competition depth Q learning model.
Further optionally, the state space of the preset model is s (t) ═ { h (t), r (t), v (t) }, where h (t) represents the confidence level of the block chain node at time t, r (t) represents the confidence level of the multi-access edge application orchestration system at time t, and v (t) represents the available resources of the multi-access edge computing server at time t.
Optionally, the allocating module further includes: the response unit is used for sending response information to the first orchestrator; a sending unit, configured to send a verification message to all multi-access edge application orchestration systems, where the verification message is a message generated by the master node according to the resource task to be allocated and the second edge computing server; and the allocation unit is used for sending the resource task to be allocated to the second edge computing server.
A third aspect of the present invention provides a blockchain, the blockchain comprising:
a plurality of nodes;
and any one of the above resource allocation apparatuses.
In the resource allocation method, the resource allocation device and the block chain of the embodiment of the invention, the interaction between different MEAO systems is realized through the block chain system by utilizing the characteristics of invariable message records and reliable message synchronization of the block chain system, and the management and allocation of MEC server resources managed by the MEAO systems in different areas are realized.
Drawings
Fig. 1 is a flowchart illustrating a resource allocation method according to an embodiment of the present invention;
FIG. 2 is a flowchart illustrating another resource allocation method according to an embodiment of the present invention;
FIG. 3 is a block diagram illustrating a resource allocation apparatus according to an embodiment of the present invention;
FIG. 4 is a block diagram illustrating a configuration of an allocation unit of a resource allocation apparatus according to an embodiment of the present invention;
fig. 5 is a block diagram illustrating a block chain according to an embodiment of the present invention.
Detailed Description
In order to make the technical solutions of the present invention better understood, the present invention will be described in further detail with reference to the accompanying drawings and specific embodiments.
It is to be understood that the specific embodiments and figures described herein are merely illustrative of the invention and are not limiting of the invention.
It is to be understood that the embodiments and features of the embodiments can be combined with each other without conflict.
It is to be understood that, for the convenience of description, only parts related to the present invention are shown in the drawings of the present invention, and parts not related to the present invention are not shown in the drawings.
It should be understood that each unit and module related in the embodiments of the present invention may correspond to only one physical structure, may also be composed of multiple physical structures, or multiple units and modules may also be integrated into one physical structure.
It will be understood that, without conflict, the functions, steps, etc. noted in the flowchart and block diagrams of the present invention may occur in an order different from that noted in the figures.
It is to be understood that the flowchart and block diagrams of the present invention illustrate the architecture, functionality, and operation of possible implementations of systems, apparatus, devices and methods according to various embodiments of the present invention. Each block in the flowchart or block diagrams may represent a unit, module, segment, code, which comprises executable instructions for implementing the specified function(s). Furthermore, each block or combination of blocks in the block diagrams and flowchart illustrations can be implemented by a hardware-based system that performs the specified functions or by a combination of hardware and computer instructions.
It is to be understood that the units and modules involved in the embodiments of the present invention may be implemented by software, and may also be implemented by hardware, for example, the units and modules may be located in a processor.
Example 1:
referring to fig. 1, the present embodiment provides a resource allocation method.
Specifically, the resource allocation method of the present embodiment may be used in a multi-access edge computing service collaborative orchestration system architecture.
The multi-access edge computing service collaborative arrangement system architecture is divided into three layers from top to bottom, and the three layers comprise an edge computing service and application layer, a block chain-based collaborative arrangement system layer and an MEC edge ICT facility resource layer.
The edge computing service and application layer comprises edge computing services such as automatic driving, smart cities, manufacturing-only and cloud games.
The MEC edge ICT (information and communications technology) facility resource layer includes network facilities of multiple access technologies, such as a 4G or 5G mobile network U-GW/DP/UPF, a WiFi access network AP, or an ONU/ONT of an optical access network, and also includes a plurality of MEC servers carrying edge computing resources of services and applications, and MEPs (MEC platforms) deployed on these servers, and forms a converged network and an edge computing resource pool in each area by virtualizing these ICT resources.
The collaborative orchestration system layer based on the blockchain includes a system consisting of K MEAO systems, MEPMs (mobile edge platform managers) corresponding to the MEAO systems, an orchestrator NFVO (network function virtualization orchestrator) of the NFV, and a blockchain system having M nodes.
In order to reduce the information delay and the network overhead of sending information of the large-scale and widely deployed MEAO systems in a geographic area, the K MEAO systems are located in the K areas, each MEAO system manages all MEC servers in the area, and through the resource allocation method and the block chain of the embodiment, the K MEAO systems can perform information synchronization and manage and allocate resources in an edge computing resource pool of an ICT facility resource layer at the edge of the MEC.
The resource allocation method of the embodiment specifically includes:
s101, receiving resource allocation requests sent by a plurality of multi-access edge application arrangement systems, wherein each multi-access edge application arrangement system corresponds to a plurality of multi-access edge computing servers bearing edge computing resources, and the resource allocation requests comprise resource tasks to be allocated.
The MEAO system (multiple access edge application orchestration system) in the blockchain based collaborative orchestration system layer collects local messages within its administrative scope and then separates these messages into a series of transaction transactions.
All the MEAO systems send resource allocation requests to the blockchain system, wherein the resource allocation requests include resource tasks to be allocated, and specifically may be a transaction, which is used for requesting to allocate a multi-access edge computing server (MEC server) managed by the MEAO systems located in other areas for the resource tasks to be allocated, that is, resources corresponding to the MEC servers corresponding to the MEAO systems.
S102, determining a resource allocation request from a plurality of resource allocation requests as a first resource allocation request, and using a multi-access edge application orchestration system for sending the first resource allocation request as a first orchestrator.
After receiving a plurality of resource allocation requests, the blockchain system selects a resource allocation request as a first resource allocation request, and the MEAO system that sends the first resource allocation request is the first orchestrator.
S103, determining one node as a main node from a plurality of nodes of the block chain according to the preset model and the first resource allocation request, determining a second orchestrator from a plurality of multi-access edge application orchestration systems except the first orchestrator, and determining a second edge computing server from a plurality of multi-access edge computing servers corresponding to the second orchestrator.
Since the blockchain system is decentralized and has no central management node, after determining the first resource allocation request, the collaborative orchestration system layer based on the blockchain determines one node as a primary node and the other nodes as secondary nodes from the plurality of nodes of the blockchain system according to a preset model, determines a second orchestrator of the MEAO system satisfying the first resource allocation request from the plurality of MEAO systems other than the first orchestrator, and determines a server as a second edge computing server from the MEC servers managed by the second orchestrator.
Optionally, the preset model is a competition depth Q learning model.
Further optionally, the state space of the preset model is s (t) ═ { h (t), r (t), v (t) }, where h (t) represents the confidence level of the block chain node at time t, r (t) represents the confidence level of the multi-access edge application orchestration system at time t, and v (t) represents the available resources of the multi-access edge computing server at time t.
Specifically, assume that there are C MEC servers, which are represented by {1, …, C }, there are N nodes in the blockchain system, which are represented by {1, …, N }, and M measo systems, which are represented by {1, …, M }, and the confidence level transition probability matrix of the blockchain nodes and the measo systems is defined as PMAnd PNThe MEC resource state transition matrix is Pc.
In the byzantine fault model, when there are N nodes in total in the blockchain system, there are F ═ N-1)/3 incorrect nodes at most.
At each time step, a MEAO system is selected to interface with the blockchain system, a master blockchain node is selected, and an MEC server is selected to perform the computing task.
When selecting an MEC server to perform a computation task, the operation cost of the block chain system and the MEC server used by the computation task needs to be considered.
Both the reception of resource allocation requests by the blockchain system from the MEAO system and the communication between the various nodes of the blockchain system require the MEC server to provide network resources, consuming link bandwidth.
When a computing task is deployed on a MEC server, computing and storage resources are also needed to meet its needs. Meanwhile, for example, verifying a signature, generating a MAC (message authentication code), and the like need to consume computing resources of the MEC server.
Consuming the resources of the MEC server naturally results in operating costs of the MEC server.
In order to meet the efficiency of resource allocation of the block chain system, three aspects of access delay, processing time and operation cost need to be considered when one MEC server is selected for performing calculation tasks.
The access delay refers to the data transmission time of the computing task, the processing time refers to the processing time of the computing task, the processing time is related to the network resources and the position of the selected MEC server and is a function of the hop count, the hop count is related to the link bandwidth, and the operation cost is the cost of the computing resources and the network resources consumed by the MEC server.
Specifically, the preset model of this embodiment is a Dueling DQN (competitive deep Q learning) model, that is, the confidence transition probability matrix P of the block chain node and the MEAO system is obtained through the Dueling DQN training modelMAnd PNAnd an MEC resource state transition matrix Pc.
Specifically, the state space during the preset model training in this embodiment may be s (t) ═ h (t), r (t), v (t) }, where h (t) represents the confidence level of the block chain node at time t, r (t) represents the confidence level of the multi-access edge application orchestration system at time t, and v (t) represents the available resources of the multi-access edge computing server at time t.
The state space comprises the trust between different block chain nodes, the trust between a plurality of MEAO systems and available resources of the MEC server, so that the model can learn from interaction between actions and the environment, and an optimal model is obtained through data training.
Since the selection of master nodes in the blockchain system, the selection of the MEAO systems to be connected to the blockchain, and the selection of MEC servers meeting the resource requirements need to be done at each time step. Thus, the action in each time step t can be expressed as: a (t) { a ═ an(t),am(t),ac(t) }, in which, an(t) is selectedNode of the block chain of (a)m(t) is a selected MEAO system, ac(t) is the selected MEC server.
The reward function is a weighted sum of blockchain throughput with access latency, processing time and operating cost, where the weight coefficient of blockchain throughput is >0 and the other weight coefficients are all <0.
In one training process of the model, the MEAO system and the blockchain master node and the MEC server are selected, and an instant reward is obtained and recorded as r (t). Furthermore, the system state will be converted to s (t +1), including the confidence between the blockchain node and the MEAO system, and the MEC server resources for the next step.
And S104, distributing the resources borne by the second edge computing server to the task of the resources to be distributed through the main node.
And the block chain system allocates the resources borne by the second edge computing server to the resource tasks to be allocated through the main node, namely, the main node enables the second edge computing server to process the resource tasks to be allocated.
Optionally, referring to fig. 2, allocating, by the master node, resources carried by the second edge computing server to a task to be allocated with resources (S104), includes:
and S1041, sending response information to the first orchestrator.
The node responds to the resource allocation request of the selected MEAO system (i.e. the first orchestrator) and generates a signature and a Message Authentication Code (MAC) according to the received task to be allocated and itself.
S1042, sending verification information to all multi-access edge application arrangement systems, wherein the verification information is generated by the main node according to the resource tasks to be distributed and the second edge computing server.
Further optionally, sending a verification message to all multi-access edge application orchestration systems includes: and controlling the main node to send a preparation message to other auxiliary nodes except the main node of the block chain, and after all the auxiliary nodes confirm the preparation message, sending a verification message to all the multi-access edge application arrangement systems.
The primary node sends a prepare message to all secondary nodes and generates MACs (message authentication codes) for all replica nodes.
Then, all the replica nodes respectively send a preparation message to all the block link points, and after the sending is completed, all the nodes send submission messages to other nodes.
Finally, the block chain node replies to all the MEAO systems with a verified message (i.e. a message verified with a message verification code), i.e. a verification message, which may comprise the identity of the second edge calculation server, which the second composer may obtain from the verification message after receiving the verification message, and which may access the master node.
And S1043, sending the resource task to be distributed to the second edge computing server.
Through the master node, the first orchestrator may send data of the tasks to be assigned to the second edge computing server, so that the second edge computing server processes the data.
In the block chain technology, a cryptography method is used, each message block has a hash sequence of the previous message block, each block reserves the record of all the existing blocks, and the resource task to be allocated is sent to the second edge computing server through the node of the block chain, so that the non-tamper property of information transmission is ensured, and the reliability of the resource allocation process is improved.
It should be emphasized that after one resource allocation procedure is finished, the next resource allocation procedure may not be performed immediately, and two different resource allocation procedures need to be kept isolated to avoid mutual interference.
The resource allocation method of this embodiment utilizes the characteristics of invariable message records and reliable message synchronization of the blockchain system to realize interaction between different memo systems through the blockchain system, thereby realizing management and allocation of MEC server resources managed by the memo systems in different areas.
Example 2:
referring to fig. 3, the present embodiment provides a resource allocation apparatus, including:
the system comprises a receiving module, a resource distributing module and a resource distributing module, wherein the receiving module is used for receiving resource distributing requests sent by a plurality of multi-access edge application arranging systems, each multi-access edge application arranging system corresponds to a plurality of multi-access edge computing servers bearing edge computing resources, and the resource distributing requests comprise resource tasks to be distributed;
the selection module is used for determining one resource allocation request from a plurality of resource allocation requests as a first resource allocation request, and the multi-access edge application arrangement system which sends the first resource allocation request is a first orchestrator;
the deep learning module is used for determining one node as a main node from a plurality of nodes of the block chain according to a preset model and a first resource allocation request, determining a second orchestrator from a plurality of multi-access edge application orchestration systems except the first orchestrator, and determining a second edge computing server from a plurality of multi-access edge computing servers corresponding to the second orchestrator;
and the allocation module is used for allocating the resources borne by the second edge computing server to the task of the resources to be allocated through the main node.
Optionally, the preset model is a competition depth Q learning model.
Optionally, the state space of the preset model is s (t) ═ { h (t), r (t), v (t) }, where h (t) represents the confidence level of the block chain node at time t, r (t) represents the confidence level of the multi-access edge application orchestration system at time t, and v (t) represents the available resources of the multi-access edge computing server at time t.
Optionally, referring to fig. 4, the allocating module further includes:
the response unit is used for sending response information to the first orchestrator;
the sending unit is used for sending verification information to all the multi-access edge application arrangement systems, wherein the verification information is generated by the main node according to the resource task to be distributed and the second edge computing server;
and the allocation unit is used for sending the resource task to be allocated to the second edge computing server.
In the resource allocation apparatus of this embodiment, by using the characteristics of invariable message records and reliable message synchronization of the blockchain system, interaction between different memo systems is realized through the blockchain system, and management and allocation of MEC server resources managed by memo systems in different areas are realized.
Example 3:
referring to fig. 5, the present embodiment provides a blockchain including a plurality of nodes and any one of the above resource allocation apparatuses.
In the block chain of this embodiment, by using the characteristics of invariable message records and reliable message synchronization of the block chain system, interaction between different memo systems is realized through the block chain system, and management and allocation of MEC server resources managed by the memo systems in different areas are realized.
It will be understood that the above embodiments are merely exemplary embodiments taken to illustrate the principles of the present invention, which is not limited thereto. It will be apparent to those skilled in the art that various modifications and improvements can be made without departing from the spirit and substance of the invention, and these modifications and improvements are also considered to be within the scope of the invention.
Claims (6)
1. A method for resource allocation, the method comprising:
receiving resource allocation requests sent by a plurality of multi-access edge application arrangement systems, wherein each multi-access edge application arrangement system corresponds to a plurality of multi-access edge computing servers bearing edge computing resources, and the resource allocation requests comprise resource tasks to be allocated;
determining a resource allocation request as a first resource allocation request from a plurality of resource allocation requests, wherein a multi-access edge application orchestration system sending the first resource allocation request is a first orchestrator;
determining a node as a main node from a plurality of nodes of a block chain according to a preset model and the first resource allocation request, determining a second orchestrator from a plurality of multi-access edge application orchestration systems except the first orchestrator, and determining a second edge computing server from a plurality of multi-access edge computing servers corresponding to the second orchestrator;
distributing the resources borne by the second edge computing server to the resource tasks to be distributed through the main node;
the preset model is a competition depth Q learning model;
the state space of the preset model is s (t) { h (t), r (t), v (t) }, wherein h (t) represents the trust degree of the block chain node at the time t, r (t) represents the trust degree of the multi-access edge application arrangement system at the time t, and v (t) represents the available resources of the multi-access edge computing server at the time t.
2. The method of claim 1, wherein the allocating, by the master node, the resource carried by the second edge computing server to the task of the resource to be allocated comprises:
sending a response message to the first orchestrator;
sending a verification message to all multi-access edge application arrangement systems, wherein the verification message is a message generated by the main node according to the resource task to be distributed and the second edge computing server;
and sending the resource task to be distributed to the second edge computing server.
3. The method of claim 2, wherein sending a validation message to all multiple access edge application orchestration systems comprises:
and controlling the main node to send a preparation message to other secondary nodes except the main node of the block chain, and sending a verification message to all multi-access edge application arranging systems after all the secondary nodes confirm the preparation message.
4. An apparatus for resource allocation, the apparatus comprising:
the system comprises a receiving module, a resource allocation module and a resource allocation module, wherein the receiving module is used for receiving resource allocation requests sent by a plurality of multi-access edge application arrangement systems, each multi-access edge application arrangement system corresponds to a plurality of multi-access edge computing servers bearing edge computing resources, and the resource allocation requests comprise resource tasks to be allocated;
the system comprises a selection module, a first scheduler and a second scheduler, wherein the selection module is used for determining a resource allocation request as a first resource allocation request from a plurality of resource allocation requests, and the multi-access edge application scheduling system sending the first resource allocation request is a first scheduler;
the deep learning module is used for determining one node as a main node from a plurality of nodes of a block chain according to a preset model and the first resource allocation request, determining a second orchestrator from a plurality of multi-access edge application orchestration systems except the first orchestrator, and determining a second edge computing server from a plurality of multi-access edge computing servers corresponding to the second orchestrator;
the allocation module is used for allocating the resources borne by the second edge computing server to the task of the resources to be allocated through the main node;
the preset model is a competition depth Q learning model;
the state space of the preset model is s (t) { h (t), r (t), v (t) }, wherein h (t) represents the trust degree of the block chain node at the time t, r (t) represents the trust degree of the multi-access edge application arrangement system at the time t, and v (t) represents the available resources of the multi-access edge computing server at the time t.
5. The apparatus of claim 4, wherein the assignment module further comprises:
the response unit is used for sending response information to the first orchestrator;
a sending unit, configured to send a verification message to all multi-access edge application orchestration systems, where the verification message is a message generated by the master node according to the resource task to be allocated and the second edge computing server;
and the allocation unit is used for sending the resource task to be allocated to the second edge computing server.
6. A blockchain, the blockchain comprising:
a plurality of nodes;
and the resource allocation apparatus of claim 4 or 5.
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