CN114760306B - Task scheduling method for cloud and fog edge collaborative environment based on blockchain - Google Patents
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Abstract
The invention discloses a task scheduling method based on a cloud and mist edge cooperative environment by a block chain, which relates to the technical field of distributed systems and comprises the following steps: pre-constructing a distributed trust frame, constructing a trust verification blockchain and constructing a transaction behavior blockchain; constructing a position awareness fair scheduling model with enhanced signaling in a cloud and fog edge computing system, and calibrating model elements; and carrying out hierarchical service scheduling. The invention realizes the matching of the processing mobile terminal and the access point and the matching of the task and the resource in the local pool, and simultaneously comprehensively considers the task scheduling algorithm of the user mobility, the load balancing and the trust degree, helps the scheduler determine where to deploy and realize the service, and better realizes the resource balancing and the low service delay.
Description
Technical Field
The invention relates to the technical field of distributed systems, in particular to a task scheduling method based on a cloud and mist edge cooperative environment by a block chain.
Background
The cloud edge computing model provides a superior framework for distributed coordination of resources and timely processing of tasks. However, due to the different levels of heterogeneity and resource asymmetry, possible cross-layer task offloading, and the dynamics and mobility of edge nodes, the three-layer hybrid architecture faces a more serious crisis in terms of service reliability and reasonable resource allocation and scheduling. On the other hand, the hybrid service providing environment comprising the edge layer or the internet of things layer has more abundant, random and diversified application scenes than the traditional cloud computing, and the task scheduling strategy is required to be more adaptive and robust.
At present, the traditional centralized cloud service mode has all tasks processed in the center, and has great challenges in practical application, including high delay, network dependence, single-point fault and fault scale effect, and cannot adapt to the instant transaction scene. And researchers have proposed many valuable solutions for efficient task scheduling in distributed systems. However, existing strategies fail to achieve full functionality in a cloud-edge environment for the following reasons: 1) The centralized trust management model cannot process identity authentication and behavior management under heterogeneous and decentralized architectures; 2) Existing distributed or decentralized trust models cannot provide enough trusted trust evidence to convince entities in the same domain or across domains; 3) The relative fairness and global rationality of resource allocation in scheduling is difficult to guarantee.
For the problems in the related art, no effective solution has been proposed at present.
Disclosure of Invention
Aiming at the problems in the related art, the invention provides a task scheduling method based on a cloud and mist edge cooperative environment by a block chain, which aims to overcome the technical problems in the prior art.
The technical scheme of the invention is realized as follows:
a task scheduling method based on a cloud and mist edge collaborative environment by a block chain comprises the following steps:
step S1, a distributed trust frame is pre-constructed, wherein the step comprises the following steps: constructing a trust verification blockchain and a transaction behavior blockchain;
s2, constructing a position awareness fair scheduling model with enhanced signaling in a cloud and fog edge computing system, and calibrating model elements;
step S3, carrying out hierarchical service scheduling, which comprises the following steps:
step S301, a user scheduler searches for an appropriate access point of a specific user according to the current position and recommends according to the distance and the trust level;
step S302, after acquiring equipment recommending execution tasks, a user requests trust service from a tab and makes a decision after acquiring trust data;
step S303, the access point starts a task scheduler to select specific resources for a user;
step S304, a task scheduler selects a proper virtual machine to execute tasks according to the principles of service fairness and load balancing;
step S305, the user obtains service and carries out service evaluation, and transaction data enter TBB;
in step S306, the TBB periodically performs trust evaluation and forwards the trust block into the tab.
The trust verification blockchain is used for storing trust data of the cloud and fog edge mixed transaction system and assisting trust transaction decision.
The transaction behavior blockchain comprises transaction data and evaluation data and is used for generating and storing the transaction data.
Wherein the calibration model element comprises:
the nominal task scheduling fairness level SJ, which represents the degree of agreement between the actually obtained user QoS and the expected QoS, is expressed as:
wherein θ is a balance coefficient, OS i For the quality of service, ES, actually obtained by the ith user i The obtained service quality is required for the ith user;
the calibration user model ST, expressed as:
ST＝{st 0 ,st 1 ,…,st k-1 }；
st i ＝{st ID ,st Name ,st Location ,st TaskSet ,st Trust }；
wherein st i For the ith user, st ID User ID, st Name User name, st Location User geographic location, st TaskSet Task set submitted by user, st Trust A credit requirement level for the user;
the calibration task model T, expressed as:
T＝{t 0 ,t 1 ,…,t n-1 }；
ti＝{t ID ,t State ,t RRes ,t ORSet ,t DeadLine }；
where ti is the ith task, t ID For task ID, t State T is the current state of the task RRes Resources requested for a task, t ORSet Resources acquired and occupied for a task, t DeadLine Is the latest completion time of the task;
the calibration service providing model AP is used for providing an entrance of cloud service, fog service and edge service, and is expressed as:
AP＝{ap 0 ,ap 1 ,…,ap k-1 }；
ap i ＝{ap ID ,ap Name ,ap Resouceset ,ap Load ,ap Location }；
wherein ap i Providing an entry, ap, for the ith service ID For service provider ID, ap Name For service provider name, ap Resouceset Resources managed by a service provider, ap Load For the current load of the service provider, ap Location Geographic location for the service provider;
the calibrated resource model R is used for running basic resources of the service, and is expressed as:
R＝{r 0 ,r 1 ,…,r m-1 }；
r i ＝{r ID ,r Name ,r Provider ,r Cap }；
wherein r is i For the ith resource, r ID Is the ID of the resource, r Name R is the name of the resource Provider R is the provider of the resource Cap Is the capacity of the resource;
calibrating a user scheduling model USM, representing binding a user to a feasible nearest access node, expressed as:
USM＝(ST,AP,ACS)；
wherein ACS means selecting an access point ap for st using a specific policy;
calibrating a task scheduling model TSM, wherein the task issued by a user is matched with resources managed by an access point, and the task is expressed as follows:
TSM＝(T,R,RCS)；
wherein RCS means selecting resource r for task t using a specific policy.
The hierarchical service scheduling comprises user scheduling and task scheduling, wherein the hierarchical service scheduling comprises user scheduling and task scheduling;
the user schedule is used for obtaining the most suitable st i Selecting the most appropriate access node for the user;
the task scheduling is used for t i And selecting the most suitable scheduling resource node for the task submitted by the user.
Wherein, the user scheduling includes the following steps:
step S307, user scheduler is from being able to override st i Selecting candidate subset APs from the APs meeting the minimum trust requirement of the candidate subset APs;
step S308, calculating ap and st i The distance between the two is selected, and the nearest ap is selected;
step S309, connect and schedule st i And ap.
The task scheduling method comprises the following steps:
step S310, selecting resource nodes with load values in a reasonable range, and adding the resource nodes to a candidate resource set;
in step S311 of the process of the present invention,calculating t i The service fairness index of each resource node is used for selecting the most suitable resource node according to the principle of maximizing SJ;
step S312, schedule t i To the corresponding resource node and updates the load situation of the scheduled resource.
The invention has the beneficial effects that:
the task scheduling method based on the cloud and fog edge collaborative environment of the block chain builds a distributed trust frame in advance, builds a position awareness fair scheduling model with enhanced trust in a cloud and fog edge computing system, calibrates model elements and carries out a hierarchical service scheduling model, so that the problem of service reliability is solved, matching of a processing mobile terminal and an access point and matching of tasks and resources in a local pool are realized, and simultaneously task scheduling algorithms of user mobility, load balancing and trust are comprehensively considered, a scheduler is helped to decide where to deploy and realize services, and resource balancing and low service delay are better realized.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are needed in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow diagram of a task scheduling method based on a blockchain-to-cloud edge collaborative environment in accordance with an embodiment of the present invention;
FIG. 2 is a decentralized trust framework service transaction schematic of a task scheduling method based on a blockchain-to-cloud edge collaborative environment according to an embodiment of the invention;
FIG. 3 is a schematic diagram of a trust authentication blockchain data structure of a task scheduling method based on a blockchain-to-cloud edge collaborative environment according to an embodiment of the invention;
fig. 4 is a schematic diagram of a transaction behavior blockchain data structure of a task scheduling method based on a blockchain-to-cloud edge collaborative environment according to an embodiment of the invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which are derived by a person skilled in the art based on the embodiments of the invention, fall within the scope of protection of the invention.
According to the embodiment of the invention, a task scheduling method based on a cloud and mist edge cooperative environment by a block chain is provided.
1-2, a task scheduling method based on a cloud and mist edge cooperative environment based on a block chain according to an embodiment of the invention comprises the following steps:
step S1, a distributed trust frame is pre-constructed, wherein the step comprises the following steps: constructing a trust verification blockchain and a transaction behavior blockchain;
the technical scheme is as shown in fig. 3, and the trust verification block chain (TAB) is constructed and used for storing trust data of the cloud-fog edge hybrid transaction system and assisting trust transaction decision. TAB employs a coalition chain structure in which blocks store trust data for nodes in a transaction system. TAB is responsible for managing trust data in the cloud services market and providing trust evaluation results to other nodes. Each block contains two parts of data: identity trust data and behavioral trust data. When the node is initially added, only the identification part is written; however, over time, the behavior is written in part as transactions proceed. Authentication is accomplished by a small number of regulatory personnel, who may be either ordinary users or some special nodes selected by the market authority. The user is responsible for storing and verifying trust data and ensuring the consistency of the data by some specially designed consistency mechanism. When nodes apply for entry into a transaction network, they must pay a fee to run the smart contract for initial authentication. In addition, when it is desired to obtain trust data of other nodes, a fee is paid. The fund provides incentive fees to the user.
Further, as shown in fig. 4, the blocks in their transaction behavior blockchain (TBB, transactional behavior blockchain) contain transaction data and evaluation data, which facilitate the generation of behavior trust data. The TBB is responsible for generating and storing transaction data. In TBB, the miner has two tasks, one is to generate a new transaction block based on the latest transaction results, and the other is to evaluate behavioral trust, generate a trust block, and then forward it to TAB. The corresponding trust block will be validated by the user and stored in the tab.
In addition, the technical scheme is adopted, so that the tab is written into by the identity trust of the entity when the entity registers for the first time; as transactions proceed, transaction records between entities will be written one by one into the TBB, and then behavioral trust is evaluated in the TBB and forwarded to TAB; when TAB gradually grasps enough trust data of an entity in a transaction chain, the entity can be helped to make trust decisions more accurately, and the security and the reliability and success rate of communication network interaction are improved; by taking advantage of the dual block chain structure, efficient parallel computing is achieved. Since the trust value is provided by TAB and the large-scale calculation or evaluation is performed at the TBB end, the delay caused by trust management is effectively reduced, and the application of the blockchain in a real-time and high-reliability scene is possible.
S2, constructing a position awareness fair scheduling model with enhanced signaling in a cloud and fog edge computing system, and calibrating model elements, wherein the method comprises the following steps of:
a nominal task scheduling fairness level (SJ), representing the degree of agreement between the actual acquired user QoS and the expected QoS, is expressed as:
wherein θ is a balance coefficient, OS i For the quality of service, ES, actually obtained by the ith user i The obtained service quality is required for the ith user;
therefore, SJ in the entire system is expressed as:
wherein,scheduling a fair level of weight coefficients for the calibration tasks;
applying the two formulas above to a specific resource yields the following two formulas, respectively:
wherein CR is i ＝(C cpu ,C ram ,C bd ) CPU, memory and bandwidth capabilities, ER, for resources i ＝(E cpu ,E ram ,E bd ) CPU, memory and bandwidth capabilities for the ith resource desired by the user, P i ＝(P cpu ,P ram ,P bd ) For the weight of the user's ith task to the required resources, SJ ij A level of fairness is scheduled for tasks for which the ith task is scheduled over the jth resource;
calibrating a user model (Smart Thing Model, ST), i.e. the entity requesting the service, wherein the set of ST is expressed as:
ST＝{st 0 ,st 1 ,…,st k-1 }；
st i ＝{st ID ,st Name ,st Location ,st TaskSet ,st Trust }；
wherein st i For the ith user, st ID User ID, st Name User name, st Location User geographic location, st TaskSet Task set submitted by user, st Trust A credit requirement level for the user;
the Task Model (T) is calibrated. I.e., the task that each entity issues, is expressed as:
T＝{t 0 ,t 1 ,…,t n-1 }；
ti＝{t ID ,t State ,t RRes ,t ORSet ,t DeadLine }；
where ti is the ith task, t ID For task ID, t State T is the current state of the task RRes Resources requested for a task, t ORSet Resources acquired and occupied for a task, t DeadLine Is the latest completion time of the task;
the provisioning service Model (AP). Provision portals for cloud services, fog services, edge services, expressed as:
AP＝{ap 0 ,ap 1 ,...,ap k-1 }；
ap i ＝{ap ID ,ap Name ,ap Resouceset ,ap Load ,ap Location }；
wherein ap i Providing an entry, ap, for the ith service ID For service provider ID, ap Name For service provider name, ap Resouceset Resources managed by a service provider, ap Load For the current load of the service provider, ap Locat i on Geographic location for the service provider;
a Resource Model (R) is used to run the basic resources of the service, expressed as:
R＝{r 0 ,r 1 ,...,r m-1 }；
r i ＝{r ID ,r Name ,r Provider ,r Cap }；
wherein r is i For the ith resource, r ID Is the ID of the resource, r Name R is the name of the resource Provider R is the provider of the resource Cap Is the capacity of the resource;
calibrating a user scheduling model (User Scheduling Model, USM), representing binding users to feasible nearest access nodes, expressed as:
USM＝(ST,AP,ACS)；
wherein ACS means selecting an access point ap for st using a specific policy;
a scaled task scheduling model (Task Scheduling Model, TSM) that represents matching of tasks issued by users with resources managed by access points, expressed as:
TSM＝(T,R,RCS)；
wherein RCS means selecting resource r for task t using a specific policy.
Step S3, carrying out hierarchical service scheduling, which comprises the following steps:
in step S301, the user scheduler searches for an appropriate access point for a specific user/smart device according to the current location and makes a recommendation according to the distance and the confidence level.
Step S302, when a device recommending execution of a task is acquired, the user/smart item requests a trust service from the tab, and makes a decision after acquiring trust data,
if the transaction is agreed, continuing step S303;
otherwise, it requests the scheduler to resume recommendation and returns to step S301;
step S303, the access point (gateway) starts a task scheduler to select specific resources for the user;
step S304, a task scheduler selects a proper virtual machine to execute tasks according to the principles of service fairness and load balancing;
step S305, the user obtains service and carries out service evaluation, and transaction data enter TBB;
in step S306, the TBB periodically performs trust evaluation and forwards the trust block into the tab.
In addition, the hierarchical service schedule comprises user schedule and task schedule, and specifically comprises the following steps:
wherein, the user schedule is used for obtaining the most suitable st i Selecting a most suitable access node for a user, comprising the steps of:
step S307, user scheduler is from being able to override st i And meet itSelecting a candidate subset AP from the AP with the lowest trust requirement;
step S308, calculating ap and st i The distance between the two is selected, and the nearest ap is selected;
step S309, connect and schedule st i And ap.
Wherein the task scheduling is used for t i The selected resource node r selects the most suitable scheduling resource node for the task submitted by the user, and comprises the following steps:
step S310, selecting resource nodes with load values in a reasonable range, and adding the resource nodes to a candidate resource set;
step S311, calculating t i The service fairness index of each resource node is used for selecting the most suitable resource node according to the principle of maximizing SJ;
step S312, schedule t i To the corresponding resource node and updates the load situation of the scheduled resource.
Further, when applied, it deploys a blockchain-based framework defining transaction-related classes and trust-related classes. Its transaction related class, transaction data structure. Transaction data of the transaction system is stored, from which trust values of the principal for the delegate can be obtained, for maintaining the transaction data and preserving its traceability and tamper resistance. Specifically, the method comprises the steps of generating a transaction and adding the transaction, wherein the transaction is generated;
wherein generating a transaction comprises the steps of: verifying the signature; calculating a hash value; generating a trust value according to the transaction information; a transaction output is generated.
Wherein, add the transaction, including the following steps: verifying the transaction; executing the transaction; adding a transaction;
in addition, the trust related class, the trust block is used for storing the trust value of a certain node.
In addition, the block generation in the process of scheduling execution comprises the following steps:
the node initiates a transaction request trust service from a trust blockchain tab;
after paying a certain fee, the trustworthiness of the candidate provider (access server or application server) is obtained, thereby helping it to make a trustworthy decision;
after each transaction, transaction related data (transaction type, content and assessment data) will be used to generate a transaction block and added to the transaction blockchain TBB after agreement;
the TBB periodically starts trust evaluation transaction, pushes a new trust block to the tab, and calculates the trust degree of a specific transaction node by adopting a lightweight method, wherein the trust degree is expressed as follows:
wherein T is i Representing the trust value of node i, θ represents the evaluation weight of node k, V ki Representing the trust evaluation value from node k to node i.
In summary, by means of the technical scheme, the distributed trust framework is pre-constructed, the position awareness fair scheduling model with enhanced trust in the cloud and fog edge computing system is constructed, model elements are calibrated, and the hierarchical service scheduling model is performed, so that the service reliability problem is solved, matching of the mobile terminal and the access point and matching of tasks and resources in a local pool are achieved, meanwhile, task scheduling algorithms of user mobility, load balancing and trust are comprehensively considered, a scheduler is helped to decide where to deploy and realize services, and resource balancing and low service delay are better realized.
The foregoing is merely a preferred embodiment of the present invention and is not intended to limit the present invention, and other embodiments of the present disclosure will be readily apparent to those skilled in the art after considering the disclosure herein in the specification and examples. This application is intended to cover any adaptations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.
It is to be understood that the present disclosure is not limited to the precise arrangements and instrumentalities shown in the drawings, and that various modifications and changes may be effected without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.
Claims (4)
1. A task scheduling method based on a cloud and mist edge cooperative environment by a block chain is characterized by comprising the following steps:
pre-building a decentralized trust framework, which comprises: constructing a trust verification blockchain and a transaction behavior blockchain TBB;
constructing a position awareness fair scheduling model with enhanced signaling in a cloud and fog edge computing system, and calibrating model elements, wherein the method comprises the following steps of:
the nominal task scheduling fairness level SJ, which represents the degree of agreement between the actually obtained user QoS and the expected QoS, is expressed as:
wherein θ is a balance coefficient, OS i For the quality of service, ES, actually obtained by the ith user i The obtained service quality is required for the ith user;
therefore, SJ in the entire system is expressed as:
wherein,scheduling a fair level of weight coefficients for the calibration tasks;
applying the two formulas above to a specific resource yields the following two formulas, respectively:
wherein CR is i ＝(C cpu ,C ram ,C bd ) CPU, memory and bandwidth capabilities, ER, for resources i ＝(E cpu ,E ram ,E bd ) CPU, memory and bandwidth capabilities for the ith resource desired by the user, P i ＝(P cpu ,P ram ,P bd ) For the weight of the user's ith task to the required resources, SJ i j is the job scheduling fairness level for which the ith job is scheduled onto the jth resource;
calibrating a user model ST, i.e. the entity requesting the service, wherein the set of ST is expressed as:
ST＝{st 0 ,st 1 ,…,st k-1 }；
st i ＝{st ID ,st Name ,st Location ,st TaskSet ,st Trust }；
wherein st i For the ith user, st ID User ID, st Name User name, st Location User geographic location, st TaskSet Task set submitted by user, st Trust A credit requirement level for the user;
the calibration task model T, i.e. the task issued by each entity, is expressed as:
T＝{t 0 ,t 1 ,…,t n-1 }；
ti＝{t ID ,t State ,t RRes ,t ORSet ,t DeadLine }；
where ti is the ith task, t ID For task ID, t State T is the current state of the task RRes Resources requested for a task, t ORSet Resources acquired and occupied for a task, t DeadLine Is the latest completion time of the task;
the calibration service providing model AP is used for providing an entrance of cloud service, fog service and edge service, and is expressed as:
AP＝{ap 0 ,ap 1 ,…,ap k-1 }；
ap i ＝{ap ID ,ap Name ,ap Resouceset ,ap Load ,ap Location }；
wherein ap i Providing an entry, ap, for the ith service ID For service provider ID, ap Name For service provider name, ap Resouceset Resources managed by a service provider, ap Load For the current load of the service provider, ap Location Geographic location for the service provider;
the calibrated resource model R is used for running basic resources of the service, and is expressed as:
R＝{r 0 ,r 1 ,…,r m-1 }；
r i ＝{r ID ,r Name ,r Provider ,r Cap }；
wherein r is i For the ith resource, r ID Is the ID of the resource, r Name R is the name of the resource Provider R is the provider of the resource Cap Is the capacity of the resource;
calibrating a user scheduling model USM, representing binding a user to a feasible nearest access node, expressed as:
USM＝(ST,AP,ACS)；
wherein ACS means selecting an access point ap for st using a specific policy;
calibrating a task scheduling model TSM, wherein the task issued by a user is matched with resources managed by an access point, and the task is expressed as follows:
TSM＝(T,R,RCS)；
wherein RCS means selecting resource r for task t using a specific policy;
performing hierarchical service scheduling, comprising the steps of:
the user scheduler searches for an appropriate access point of a specific user according to the current position and recommends according to the distance and the trust level;
after acquiring the equipment recommending to execute the task, the user requests trust service from the tab and makes a decision after acquiring trust data;
the access point starts a task scheduler to select specific resources for a user;
the task scheduler selects a proper virtual machine to execute tasks according to the principles of service fairness and load balancing;
the user obtains service and carries out service evaluation, and transaction data enter a transaction behavior blockchain TBB;
the transaction behavior blockchain TBB periodically performs trust evaluation and forwards a trust block to the tab;
the trust verification blockchain is used for storing trust data of the cloud and fog edge mixed transaction system and assisting trust transaction decision; transaction behavior blockchain TBB, including transaction data and assessment data, is used to generate and store transaction data.
2. The task scheduling method based on the blockchain-to-cloud edge collaborative environment according to claim 1, wherein the hierarchical service scheduling includes user scheduling and task scheduling, wherein;
the user schedule is used for obtaining the most suitable st i Selecting the most appropriate access node for the user;
the task scheduling is used for t i And selecting the most suitable scheduling resource node for the task submitted by the user.
3. The task scheduling method based on the blockchain-to-cloud edge collaborative environment according to claim 2, characterized in that the user scheduling includes the following steps:
the user scheduler can never cover st i Selecting candidate subset APs from the APs meeting the minimum trust requirement of the candidate subset APs;
calculating ap and st i The distance between the two is selected, and the nearest ap is selected;
connect and schedule st i And ap.
4. The task scheduling method based on the blockchain-to-cloud edge collaborative environment according to claim 3, wherein the task scheduling comprises the following steps:
selecting resource nodes with load values in a reasonable range, and adding the resource nodes into a candidate resource set;
calculating t i The service fairness index of each resource node is used for selecting the most suitable resource node according to the principle of maximizing SJ;
scheduling t i To the corresponding resource node and updates the load situation of the scheduled resource.
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