CN107087019B - Task scheduling method and device based on end cloud cooperative computing architecture - Google Patents
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Abstract
The invention discloses a terminal cloud cooperative computing architecture and a task scheduling device and method, and aims to solve the problems that in the cloud application era of rapid development, the load of the existing edge cloud platform is large, and the cost of submitting tasks to remote communication is large. Different from the traditional cloud computing platform, the terminal with certain computing capacity in the security certification access system is added into the computing resource pool to form a terminal cluster, the terminal cluster has strong expansibility, the expansibility of the cloud is increased, the terminal cluster and the service node cluster in the static cloud jointly complete tasks required by the single cloud platform, the traditional cloud platform is optimized, and the load of the cloud is effectively reduced. The framework has the real-time and quick characteristics of edge cloud, and can provide more powerful and stable services for big data applications such as the Internet of things.
Description
Technical Field
The invention relates to the technical field of computers, in particular to a terminal cloud cooperative computing architecture and a task scheduling device and method.
Background
The cloud era has led to the rapid development of various services and various intelligent applications. As many industries advance to digitalization, storage and calculation increasingly depend on the cloud, and the cloud becomes a hub for constructing an information society. In the big data era, if all data are processed uniformly by a cloud data center, not only too much load is added to the cloud, but also certain communication overhead exists when tasks are submitted to a remote cloud center, and the traditional cloud platform faces many problems, so that an edge cloud taking the data as the center is generated. In the era of rapidly developing cloud applications, the load of the edge cloud computing platform is gradually overlarge, and some nearby intelligent terminals and equipment need to be added to expand resources in real time, so that the stability and the high efficiency of the edge cloud are ensured, and the normal service provision of big data applications such as the internet of things is ensured, thereby generating an end cloud cooperative computing architecture under the edge cloud computing platform.
The cloud computing is a resource usage mode developed based on a network technology and a virtualization technology and aiming at improving the utilization rate of resources such as computing, storage and the like. Task scheduling directly determines the effective utilization of resources, and is one of the most important and critical problems of cloud computing.
The terminal cloud cooperation is different from a traditional single cloud computing platform, and terminals providing certain computing power are added into a resource pool to jointly complete computing tasks. At the moment, due to the heterogeneity of resources, the difference of the computing capacity of terminal resources and the cloud, the complexity of task scheduling is greatly increased.
At present, more researches on task scheduling algorithms of cloud computing are based on grid computing, and the traditional classical algorithms are improved to adapt to a cloud computing platform, but due to the heterogeneity of resources, the terminal resource computing capability and the cloud difference are not suitable for end cloud cooperative computing under an edge cloud computing platform.
Disclosure of Invention
In view of the foregoing defects in the prior art, the technical problem to be solved by the present invention is to provide a peer cloud cooperative computing architecture, a task scheduling apparatus and a task scheduling method, so as to reduce the useless overhead of the traditional virtualization and improve the resource utilization rate and allocation efficiency.
In order to achieve the above object, the present invention provides an end cloud cooperative computing architecture and a task scheduling device, including:
the cloud cluster comprises service nodes and virtual machine nodes in a static cloud under the edge cloud, the service nodes in the static cloud under the edge cloud generally comprise virtual machine computing nodes, the virtual machine computing nodes are virtual machines dynamically generated in a cloud management server, and the virtual machine nodes can be dynamically generated, managed and destroyed by the cloud management server;
the terminal cloud collaborative middleware is in charge of trusted security authentication access of terminal resources on one hand, and provides reasonable task allocation and scheduling strategies for the terminal cloud collaborative computing architecture on the other hand, so that the task is reasonably and efficiently collaboratively computed between the terminal and the cloud end;
and the terminal cluster consists of various hardware terminals, is accessed to the cloud platform in a safe manner, and provides certain computing resources.
The end cloud collaborative computing architecture and the task scheduling device are characterized in that the end cloud collaborative middleware comprises an automatic discovery and expansion module, a terminal resource monitoring and synchronization module and an end cloud task scheduling module.
The terminal cloud collaborative computing architecture and the task scheduling device are characterized in that various hardware terminals of the terminal cluster are divided into various terminal devices which are connected through wires, connected through wireless, and Android as a main operating system.
An end cloud cooperative computing architecture and a task scheduling method are characterized by comprising the following steps:
step one, a user submits a task to a task queue and puts forward the priority of the task;
enqueuing the tasks submitted by the users according to the priorities of the tasks and the real-time performance of the tasks, selecting the dequeued tasks through queue strategy control, directly sending the tasks to service nodes in a static cloud if the dequeued tasks are real-time, and sending the tasks to an end cloud cooperative server if the tasks are non-real-time;
dividing non-real-time tasks sent by the task queue into divisible tasks and nondividable tasks by the end cloud cooperative server, then scheduling the tasks, distributing the nondividable tasks to a single service node in the static cloud for processing, and distributing the divisible tasks to a service node cluster and a terminal cluster in the static cloud for processing according to an end cloud cooperative computing task distribution algorithm;
and fourthly, establishing a heartbeat mechanism by the service node cluster and the terminal cluster in the static cloud and the end cloud cooperative server, and reporting the task completion condition, the CPU rate, the node load, the idle resources and the network transmission rate to the end cloud cooperative server by the service node cluster and the terminal cluster in the static cloud so as to distribute and schedule the resources according to the current resource state when the tasks are distributed.
The end cloud cooperative computing architecture and the task scheduling method are characterized in that the third inseparable task is inseparable or dependent relationships exist among subtasks and serial execution is required.
The terminal cloud cooperative computing architecture and the task scheduling method are characterized in that in the third step, the tasks sent to the terminal cluster are backed up at the cloud end, and due to the fact that the network and resource conditions of the terminals are complex, if the terminal computing is in a problem, the tasks can be processed at the service nodes in the static cloud.
The invention has the beneficial effects that:
the invention provides a terminal cloud cooperative computing architecture, a task scheduling device and a task scheduling method under an edge cloud computing platform, and aims to solve the problems that the existing edge cloud platform is large in load and the communication overhead from task submission to a far end is large in a cloud application era with rapid development. Different from the traditional cloud computing platform, the terminal with certain computing capacity in the security certification access system is added into the computing resource pool to form a terminal cluster, the terminal cluster has strong expansibility, the expansibility of the cloud is increased, the terminal cluster and the service node cluster in the static cloud jointly complete tasks required by the single cloud platform, the traditional cloud platform is optimized, and the load of the cloud is effectively reduced. The framework has the real-time and quick characteristics of edge cloud, and can provide more powerful and stable services for big data applications such as the Internet of things.
The conception, the specific structure and the technical effects of the present invention will be further described with reference to the accompanying drawings to fully understand the objects, the features and the effects of the present invention.
Drawings
FIG. 1 is a diagram of a peer-cloud collaborative computing architecture
FIG. 2 is a flow chart of end cloud collaborative computing
FIG. 3 is a flowchart of a scheduling method of end cloud cooperative computing tasks according to the present invention
Detailed Description
As shown in fig. 1, an end cloud cooperative computing architecture and a task scheduling device are characterized by comprising:
cloud clustering: the cloud-based cloud service system comprises service nodes and virtual machine nodes in a static cloud under an edge cloud, wherein the service nodes in the static cloud under the edge cloud are generally composed of virtual machine computing nodes, and the virtual machine nodes can be dynamically generated, managed and destroyed by a cloud management server.
End cloud collaboration middleware: the end cloud collaborative middleware comprises an automatic discovery and expansion module, a terminal resource monitoring and synchronization module and an end cloud task scheduling module. The terminal cloud collaborative middleware is in charge of trusted security authentication access of terminal resources on one hand, and provides reasonable task allocation and scheduling strategies for the terminal cloud collaborative computing framework on the other hand, so that the task is reasonably and efficiently collaboratively computed between the terminal and the cloud.
Terminal clustering: the terminal cluster is composed of various hardware terminals, the terminals can be divided into wired connected PCs, wireless connected PCs, various terminal devices such as mobile phones and tablets and the like which take Android as a main operating system, the number of the terminals is large, the terminals can be expanded anytime and anywhere, and meanwhile the terminals are accessed to a cloud platform in a safe mode to provide certain computing resources.
The terminal cloud collaboration middleware mainly comprises the following functional modules:
1. auto discovery and extension module
The end cloud cooperative computing architecture is established on the basis of the local edge cloud, and the problems of cross-domain and cross-network access and the like do not exist. The terminal in the area can interact with the whole cloud in a wired and wireless network connection mode.
The automatic discovery is a basic function of the end cloud collaboration server, and is a dynamic method for expanding terminal resources by a cloud platform. The method for actively detecting is used by the end cloud cooperative server and used for discovering available terminal resources in time. The terminal cloud cooperation server actively sends a corresponding data packet to a network accessed by the terminal at regular time, and at the moment, if the terminal capable of providing the computing resources receives the data packet, the terminal uses a corresponding security authentication protocol and then uses a data packet with a certain format to transmit the data packet to the terminal cloud cooperation server for trusted authentication of the resources. And the end cloud coordinator receives the data packet and then analyzes the data packet according to a specified data format to judge whether the authentication is successful. If the authentication is successful, the collaboration server immediately regards the terminal as a trusted computing resource node, adds the trusted computing resource node into a real-time computing resource pool, records a MacAddr field in a data packet as a unique identifier of the trusted terminal, and if the IP address changes and the connection is disconnected due to instability of a wireless network of the terminal, the system can perform an ARP request through the recorded MacAddr of the terminal, acquire the network address of the terminal and perform reconnection.
2. Terminal resource monitoring and synchronizing module
The end cloud cooperation server allocates and schedules resources according to the resource state and the task attribute in the current system when distributing the task, so that the end cloud cooperation server needs to maintain a resource pool for real-time computing and record computing resource information of each cloud end or terminal node which is added to the cloud platform at present.
And after the terminal is successfully accessed to the cloud platform, the terminal resource monitoring module calls corresponding services and sends the computing resource configuration information to the end cloud cooperation server. The terminal cloud cooperative server can issue a resource information request to the terminal at regular time, and the terminal receives the request and continues to call the resource monitoring module to feed back the resource information request to the terminal cloud cooperative server in time, so that the resource information request is ensured to be synchronous with records in the real-time computing resource pool as far as possible.
3. End cloud task scheduling module
And the end cloud task scheduling module is completed by matching the task queue and the end cloud cooperative server. The method comprises the steps that firstly, a task queue is classified according to task attributes or priorities, then a corresponding task needing to be processed is taken out by an end cloud cooperative server, and the task and the resource are scheduled according to current real-time resource information and a certain task allocation algorithm. And finally, the reasonable distribution of the tasks to the cloud and the terminal computing is ensured, and the direct computing cooperation of the tasks in the cloud and the load balance of the whole system are kept.
An end cloud cooperative computing architecture and a task scheduling method are characterized by comprising the following steps:
step one, a user submits a task to a task queue and puts forward the priority of the task.
And step two, enqueuing the tasks submitted by the users according to the priorities of the tasks and the real-time performance of the tasks, selecting the dequeued tasks through queue strategy control, directly sending the tasks to service nodes in the static cloud if the dequeued tasks are real-time, and sending the tasks to the end cloud cooperative server if the tasks are non-real-time.
And thirdly, dividing the non-real-time tasks sent by the task queue into divisible tasks and inseparable tasks by the end cloud cooperative server, then scheduling the tasks, wherein the inseparable tasks are inseparable or dependency relationships exist among subtasks and need to be executed in series, the inseparable tasks are distributed to a single service node in a static cloud for processing, the divisible tasks are distributed to a service node cluster and a terminal cluster in the static cloud for processing according to an end cloud cooperative computing task distribution algorithm, and for the tasks sent to the terminal cluster, backup is carried out at a cloud end firstly, and due to the fact that network and resource conditions of terminals are complex, if terminal computing has problems, the tasks can be processed at the service node in the static cloud.
And fourthly, establishing a heartbeat mechanism by the service node cluster and the terminal cluster in the static cloud and the end cloud cooperative server, and reporting task completion conditions, CPU (central processing unit) rates, node loads, idle resources, network transmission rates and the like to the end cloud cooperative server by the service node cluster and the terminal cluster in the static cloud so as to distribute and schedule resources according to the current resource state during task distribution.
As shown in fig. 2 and 3, which are flowcharts of the scheduling method for the end cloud collaborative computing task of the present invention, the whole process is as follows:
1. enqueuing the tasks according to the real-time performance of the tasks and the priority of the tasks proposed by the users, and then determining the dequeuing sequence of the tasks in the queue through queue strategy control.
2. And carrying out real-time judgment on the dequeuing task.
a. And if so, immediately sending the data to the service node in the static cloud.
b. And if the dequeuing is not real-time, sending the dequeuing non-real-time task to the end cloud cooperative server.
3. And carrying out separability judgment on the non-real-time tasks.
a. And if the task is not separable, sending the task to a service node in the static cloud.
b. And if the tasks can be divided, calculating the task amount to be distributed by each node according to a task distribution algorithm, then distributing the task amount to the service nodes and the terminal clusters in the static cloud for processing, and if the tasks are sent to the nodes of the terminal clusters, carrying out safe backup at the cloud end for recovery when errors occur.
The task assignment algorithm is as follows:
definition 1: the task scheduling is composed of scheduling nodes, networks, terminals or cloud computing nodes. The end and the cloud computing nodes are regarded as a whole to be processed uniformly, the set of all the computing nodes is M, the computing node of the resource is d, wherein M is { d ═ d1，d2，…，dnAnd n is the total number of the computing nodes. Computing node diThe transmission time and the calculation time of the unit task are respectively biAnd ci。
Definition 2: the total size of tasks submitted by users needing to be processed currently is A, and the purpose of the algorithm is to divide the tasks with the total size of A into n subtasks (the number of all computing nodes) and divide the subtasks into each computing node to run. These subtasks are independent tasks and have no dependencies with other tasks.
Definition 3: computing node diProcessing sub-task aiHas a completion time of Ti. The shortest time for completing the task is the time for completing all the subtasks simultaneously, namely T1＝T2＝…＝TnIf the shortest time is not that all subtasks are run and completed at the same time, the running computing node is not completedThe assignment of the task to the computing node that has completed running may result in a shorter running time.
Step one, determining the optimal scheduling sequence of all the computing nodes, wherein the optimal scheduling sequence of the computing nodes is a descending bandwidth sequence, and tasks can reach the computing nodes to run as soon as possible by scheduling the tasks to the computing nodes with high bandwidth. Because the tasks submitted by the users are directly submitted to the local edge cloud, the cloud network bandwidth is relatively excellent, the cloud computing reliability is higher, and the problems of communication overhead, safety and the like are solved, namely, the tasks are mainly firstly distributed to the cloud tasks and then distributed to the end tasks. Let d1，d2，...，dnAnd the data are well ordered according to the scheduling sequence.
Step two, calculating the node di to process the subtask aiIs Ti, equal to the transmission time of the task plus the computation time, i.e. Ti＝aibi+aiciAnd because the shortest time for completing the task is the time for completing all the subtasks simultaneously, namely T1＝T2＝…＝TnCalculating the relationship a between the task sizes of the calculation nodes according to the above equationi＝βia1Wherein
Step three, dividing the task size according to the relation a between the calculation nodesi＝βia1And the total amount of tasks A ═ a1+a2+…+anCalculating the task size of the first computing node
Step four, dividing the task size into the relationship a according to each computing nodei＝βia1And the first computing node is assigned to the task size
Calculating the task size of each computing node
And distributing the solution to each cloud or terminal computing node in the system for computing according to the solution result.
The foregoing detailed description of the preferred embodiments of the invention has been presented. It should be understood that numerous modifications and variations could be devised by those skilled in the art in light of the present teachings without departing from the inventive concepts. Therefore, the technical solutions available to those skilled in the art through logic analysis, reasoning and limited experiments based on the prior art according to the concept of the present invention should be within the scope of protection defined by the claims.
Claims (4)
1. A task scheduling method based on an end cloud cooperative computing architecture is characterized by comprising the following steps:
step one, a user submits a task to a task queue and puts forward the priority of the task;
enqueuing the tasks submitted by the users according to the priorities of the tasks and the real-time performance of the tasks, selecting the dequeued tasks through queue strategy control, directly sending the tasks to service nodes in a static cloud if the dequeued tasks are real-time, and sending the tasks to an end cloud cooperative server if the tasks are non-real-time;
dividing non-real-time tasks sent by the task queue into divisible tasks and nondividable tasks by the end cloud cooperative server, then scheduling the tasks, distributing the nondividable tasks to a single service node in the static cloud for processing, and distributing the divisible tasks to a service node cluster and a terminal cluster in the static cloud for processing according to an end cloud cooperative computing task distribution algorithm;
step four, a heartbeat mechanism is established between the service node cluster and the terminal cluster in the static cloud and the end cloud cooperative server, and the service node cluster and the terminal cluster in the static cloud report task completion conditions, CPU (central processing unit) rates, node loads, idle resources and network transmission rates to the end cloud cooperative server so as to distribute and schedule resources according to the current resource state when the tasks are distributed;
the third step of inseparable tasks refers to inseparable tasks or tasks which have dependency relationship among subtasks and need to be executed serially;
and step three, for the task sent to the terminal cluster, backup is carried out at the cloud end, and due to the fact that the network and resource conditions of the terminal are complex, if the terminal is in a problem during calculation, the task can be processed in a service node in the static cloud.
2. A task scheduling device based on an end cloud collaborative computing architecture, the device being configured to implement the task scheduling method according to claim 1, wherein the task scheduling device based on the end cloud collaborative computing architecture comprises:
the cloud cluster comprises service nodes and virtual machine nodes in a static cloud under the edge cloud, the service nodes in the static cloud under the edge cloud are formed by virtual machine computing nodes, the virtual machine computing nodes are virtual machines dynamically generated in the cloud management server, and the virtual machine nodes can be dynamically generated, managed and destroyed by the cloud management server;
the terminal cloud collaborative middleware is in charge of trusted security authentication access of terminal resources on one hand, and provides reasonable task allocation and scheduling strategies for the terminal cloud collaborative computing architecture on the other hand, so that the task is reasonably and efficiently collaboratively computed between the terminal and the cloud end;
and the terminal cluster consists of various hardware terminals, is accessed to the cloud platform in a safe manner, and provides certain computing resources.
3. The task scheduling device based on the end cloud collaborative computing architecture as claimed in claim 2, wherein the end cloud collaborative middleware comprises an auto discovery and extension module, a terminal resource monitoring and synchronization module, and an end cloud task scheduling module.
4. The task scheduling device based on the end cloud collaborative computing architecture as claimed in claim 2, wherein various hardware terminals of the terminal cluster are divided into a wired PC, a wireless PC and various terminal devices with Android as a main operating system.
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