A method, device, medium, equipment and product for decentralized resource-aware fusion

Abstract

The present invention discloses a decentralized resource perception and fusion method, apparatus, medium, equipment and product in the field of decentralized computing technology, aiming to solve the problems of limited perception range, weak perception capability and poor fusion of current technologies. The method comprises: performing resource modeling based on the computing resources, storage resources and communication resources of network heterogeneous computing devices to obtain a resource model; performing task modeling using a directed acyclic graph based on the basic attributes of network separable tasks, the dependencies between tasks and the execution constraints of tasks to obtain a task model; adopting a bottom-up clustering fusion method to dynamically perceive and adjust the usage status of resources in the resource model; adopting a local multi-hop fusion method to dynamically perceive during the task execution process based on the task requirements in the task model and the usage status of the resources, and to fuse the perceived resources. The present invention solves the problems of information fragmentation and redundancy in decentralized resources and improves the processing efficiency of tasks.

Description

Technical Field

[0001] The present invention relates to the field of distributed computing technology, and in particular to a distributed resource perception fusion method, device, medium, equipment and product. Background Art

[0002] Distributed computing is a computing paradigm that relies on modern information infrastructure and devices. It distributes task requirements across spatially distributed heterogeneous computing device nodes through self-organizing dynamic networking based on real-time network characteristics. It leverages available computing resources along communication network paths to jointly optimize computing costs and communication overhead, achieving more efficient, faster, and more accurate task processing. Distributed computing stems from the needs of rescue and disaster relief missions. When disaster sites are far from urban command centers, communication and computing resources are limited, the situation in the disaster area changes rapidly, and network connections are unstable.

[0003] In a distributed computing environment, a single heterogeneous computing device has a limited perception range, weak perception capabilities, and different models of heterogeneous devices. It is necessary to integrate different heterogeneous devices into the same architecture to cooperate. It is necessary to propose a distributed environment heterogeneous computing device perception framework that allows heterogeneous devices with different perception algorithms to cooperate in perception. Without sacrificing the perception capabilities of a single device, the perception range and perception accuracy of all intelligent agents can be logically improved.

[0004] The purpose of decentralized resource awareness is to collect and integrate computing resource information from diverse heterogeneous data sources, providing critical computing device information collection and maintenance capabilities for decentralized resource scheduling and task load balancing models. Through awareness, a single heterogeneous computing device can understand the status, performance, and load of each computing device, enabling the rational allocation and scheduling of tasks based on demand. Furthermore, a unified modeling approach is needed to integrate this perceived resource information into a unified framework for subsequent analysis and processing. Summary of the Invention

[0005] The purpose of the present invention is to overcome the deficiencies in the prior art and provide a distributed resource perception fusion method, device, medium, equipment and product that can solve the problems of information fragmentation and redundancy in distributed resources and improve task processing efficiency.

[0006] In order to solve the above technical problems, the present invention is implemented by adopting the following technical solutions:

[0007] The present invention provides a decentralized resource perception fusion method, comprising:

[0008] Perform resource modeling based on computing resources, storage resources, and communication resources of network heterogeneous computing devices to obtain a resource model;

[0009] According to the basic properties of network separable tasks, the dependencies between tasks and the execution constraints of tasks, a directed acyclic graph is used to model tasks and obtain a task model.

[0010] A bottom-up clustering fusion method is used to dynamically perceive and adjust the usage status of computing resources, storage resources, and communication resources in the resource model, thereby realizing resource-driven decentralized resource perception fusion.

[0011] According to the task requirements in the task model and the usage status of computing resources, storage resources and communication resources in the resource model, a local multi-hop fusion method is adopted to dynamically perceive resources during task execution, and the perceived resources are fused to achieve task-driven decentralized resource perception fusion.

[0012] Optionally, the resource modeling includes:

[0013] The computing resources, storage resources and communication resources of each heterogeneous computing device are represented in the form of tuple feature vectors to obtain the tuple definition of the resource model. , a single heterogeneous computing device The resource representation is ;

[0014] in, represents a vertex set, where the vertices represent heterogeneous computing devices, Represents an edge set, where an edge represents the connection between two heterogeneous computing devices. represents the set of computing speeds of heterogeneous computing devices processing tasks, Represents the maximum capacity set that heterogeneous computing devices can store, represents the set of communication speeds of heterogeneous computing devices, Indicates the Heterogeneous computing devices, Indicates the The computing speed of tasks processed by heterogeneous computing devices, Indicates the The maximum storage capacity of a heterogeneous computing device, Indicates the The communication speed of heterogeneous computing devices.

[0015] Optionally, the task modeling includes:

[0016] (a1) Based on the basic properties of network separable tasks, we define them as follows:

[0017]

[0018] Among them, each task is divided into a finite number of node tasks. Representation Program The size of the data stored and processed by the task is a set of Representation Program The set of task workloads, Representation Program The maximum processing time set that the task can accept, Representation Program The number of node tasks, Representation Program No. The amount of data stored and processed by each node task, Representation Program No. The task workload of each node task, Representation Program No. The maximum processing time that a node task can accept;

[0019] (b1) Define the amount of output data to be transferred , represents the data information carried by the communication between adjacent node tasks with sequential dependencies;

[0020] (c1) Use a directed acyclic graph (DAG) to describe the task set. Each task includes several node tasks, and the following expression is obtained.

[0021]

[0022] in, represents the task graph, Representing a task graph The set of finite node tasks in , Representing a task graph The set of directed edges in Representation Program No. Node tasks, Representation Program From the first Node tasks to There are directed edges of node tasks, and node tasks For node tasks Parent task, node task The execution priority is higher than the node task ;

[0023] Combining the definitions and descriptions in (a1) to (c1), we get the tuple definition of the task model. .

[0024] Optionally, the dynamically sensing and adjusting the usage status of resources in the resource model using a bottom-up clustering and fusion method includes:

[0025] The heterogeneous computing devices are associated by regularly broadcasting heartbeat packets, which contain resource representations of the heterogeneous computing devices.

[0026] Divide the heterogeneous computing devices into several clusters based on their sensing ranges; any two heterogeneous computing devices in a cluster have overlapping sensing ranges and can sense each other.

[0027] The computing resources, storage resources, and communication resources of each heterogeneous computing device in each cluster are comprehensively scored according to different weights, and the heterogeneous computing device with the highest comprehensive score is used as the cluster head device of the cluster;

[0028] Generate or update the resource adjacency table of all heterogeneous computing devices in the cluster until all heterogeneous computing devices are perceived to form a global virtual resource pool; wherein the resource adjacency table is a one-dimensional array, and the elements in the group are the numbers of other heterogeneous computing devices accessible to the current heterogeneous computing device, representing the set of other heterogeneous computing devices accessible to the current heterogeneous computing device. The resource adjacency table of the cluster head device stores the resource representation of all heterogeneous computing devices in the cluster.

[0029] Optionally, the comprehensive score is calculated using the following formula:

[0030]

[0031] in, Indicates the The comprehensive score of heterogeneous computing devices, Indicates the weight of computing resources. Indicates the weight of storage resources. Indicates the weight of communication resources.

[0032] Optionally, a local multi-hop fusion method is used to dynamically sense resources during task execution and fuse the sensed resources, including:

[0033] According to the task requirements in the task model, determine whether the computing resources, storage resources, and communication resources of the heterogeneous computing device currently receiving the task meet the task requirements. If they do, directly process them; otherwise, trigger recursive fusion within the one-hop perception range:

[0034] According to the resource adjacency table of the heterogeneous computing device currently receiving the task, the first-level perception device of the heterogeneous computing device currently receiving the task is obtained; the computing resources, storage resources, and communication resources of the heterogeneous computing device currently receiving the task and the first-level perception device of the heterogeneous computing device currently receiving the task are integrated to obtain the first-level integrated resources; determine whether the first-level integrated resources meet the task requirements, and if so, directly process them; otherwise, trigger recursive integration within the two-hop perception range:

[0035] According to the resource adjacency list of other heterogeneous computing devices accessible to the heterogeneous computing device currently receiving the task, the secondary perception device of the heterogeneous computing device currently receiving the task is obtained; the computing resources, storage resources and communication resources of the secondary perception device of the heterogeneous computing device currently receiving the task are secondarily integrated with the first-level fused resources to obtain the second-level fused resources; it is determined whether the second-level fused resources meet the task requirements, and if so, they are directly processed; otherwise, the current task is assigned to any heterogeneous computing device in the resource model except the heterogeneous computing device currently receiving the task and its first-level perception device and second-level perception device;

[0036] Repeat the above steps until the task is completed, or all heterogeneous computing devices in the resource model cannot meet the task requirements and the task processing fails.

[0037] In a second aspect, the present invention provides a distributed resource perception and fusion device, comprising:

[0038] The resource modeling module is used to: perform resource modeling based on the computing resources, storage resources, and communication resources of network heterogeneous computing devices to obtain a resource model;

[0039] The task modeling module is used to: perform task modeling using a directed acyclic graph based on the basic properties of network-dividable tasks, the dependencies between tasks, and the execution constraints of tasks to obtain a task model;

[0040] A resource-driven module is used to dynamically perceive and adjust the usage status of computing resources, storage resources, and communication resources in the resource model using a bottom-up clustering and fusion method, thereby realizing resource-driven decentralized resource perception and fusion.

[0041] The task-driven module is used to: dynamically perceive resources during task execution using a local multi-hop fusion method based on the task requirements in the task model and the usage status of computing resources, storage resources, and communication resources in the resource model, and fuse the perceived resources to achieve task-driven decentralized resource perception fusion.

[0042] In a third aspect, the present invention provides a computer-readable storage medium having computer instructions stored thereon. When the computer instructions are executed by a processor, the steps of the decentralized resource perception fusion method described in the first aspect are implemented.

[0043] In a fourth aspect, the present invention provides a computer device, comprising:

[0044] Memory, for storing computer instructions;

[0045] A processor is used to execute the computer instructions to implement the steps of the decentralized resource awareness fusion method described in the first aspect.

[0046] In a fifth aspect, the present invention provides a computer program product comprising computer instructions, characterized in that when the computer instructions are executed by a processor, the steps of the distributed resource perception fusion method described in the first aspect are implemented.

[0047] Compared with the prior art, the present invention has the following beneficial effects:

[0048] 1. The distributed resource-aware fusion method proposed in this invention models device resources and tasks, enabling the association of independent resources and tasks. When decomposing and allocating tasks, it is sufficient to simply find resource vectors that meet the node task requirements. This allows for an intuitive understanding of resource status and task allocation through an embedded graph. Furthermore, the proposed distributed resource- and task-driven fusion method can flexibly adapt to different situations. In the presence of task requirements, the resource-driven fusion method can effectively manage computing device resources and form a virtual resource pool. When task requirements arrive, the task-driven fusion method can quickly respond to tasks, meeting the high latency sensitivity requirements of tasks in distributed environments.

[0049] 2. The distributed resource perception and fusion device provided by this invention, by providing a resource modeling module, a task modeling module, a resource driving module, and a task driving module, jointly achieves distributed resource perception and fusion. This can solve the problems of information fragmentation and redundancy in distributed resources and improve task processing efficiency. It has practical significance and promising application prospects.

[0050] 3. The computer-readable storage medium, computer device / equipment / system and computer program product provided by the present invention can execute the steps of the distributed resource perception fusion method provided by the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 Flowchart of a decentralized resource awareness fusion method according to an embodiment of the present invention;

[0052] Figure 2 A flowchart of resource-driven decentralized resource perception fusion according to an embodiment of the present invention;

[0053] Figure 3A flowchart of task-driven decentralized resource perception fusion according to an embodiment of the present invention;

[0054] Figure 4 A schematic diagram of a clustering example provided according to an embodiment of the present invention;

[0055] Figure 5 This is a schematic diagram of an example of task-driven decentralized resource awareness fusion according to an embodiment of the present invention. DETAILED DESCRIPTION

[0056] The technical solution of the present invention is described in detail below through the accompanying drawings and specific embodiments. It should be understood that the embodiments of the present application and the specific features in the embodiments are detailed descriptions of the technical solution of the present application, rather than limitations on the technical solution of the present application. Unless there is a conflict, the embodiments of the present application and the technical features in the embodiments can be combined with each other.

[0057] It should be noted that the term "and / or" in this document simply describes an association between related objects, indicating that three possible relationships exist. For example, "A and / or B" can represent: A exists alone, A and B exist simultaneously, or B exists alone. Furthermore, the character " / " in this document generally indicates that the related objects are in an "or" relationship.

[0058] Example 1:

[0059] The embodiment of the present invention discloses a decentralized resource perception fusion method, referring to Figure 1 As shown, the specific steps include:

[0060] S1, perform resource modeling based on the computing resources, storage resources, and communication resources of network heterogeneous computing devices to obtain a resource model;

[0061] S2, based on the basic properties of network separable tasks, the dependencies between tasks and the execution constraints of tasks, use directed acyclic graphs to model tasks and obtain a task model;

[0062] S3, using a bottom-up cluster fusion method to dynamically perceive and adjust the usage status of computing resources, storage resources, and communication resources in the resource model, to achieve resource-driven decentralized resource perception and fusion;

[0063] S4, according to the task requirements in the task model and the usage status of computing resources, storage resources and communication resources in the resource model, a local multi-hop fusion method is used to dynamically perceive resources during task execution, and the perceived resources are integrated to achieve task-driven decentralized resource perception fusion.

[0064] Specifically,

[0065] In step S1, this embodiment characterizes the resources of a single heterogeneous computing device in the form of a tuple feature vector, and forms a large resource network graph of a distributed computing network composed of several heterogeneous computing devices. The definition of resource structure includes the basic attributes of resources, the dynamic characteristics of resources, and the constraints of resources. The basic attributes of resources include the following aspects: Resource UID represents the unique identifier of the resource, which is used to distinguish different resources; Resource type represents multiple types of resources. In this embodiment, only computing resources, storage resources, and communication resources are considered; Computing resources are the core resources required for processing tasks and provide computing power support; Storage resources determine the data storage capacity of the device; Communication resources determine the data transmission capacity of the device; In summary, the resource modeling includes:

[0066] Each heterogeneous computing device resource is represented by a tuple feature vector to obtain the tuple definition of the resource model , a single heterogeneous computing device The resource representation is ;

[0067] in, represents a vertex set, where vertices represent heterogeneous computing devices. Represents an edge set, where an edge represents the connection between two heterogeneous computing devices. represents the set of computing speeds of heterogeneous computing devices processing tasks, Represents the maximum capacity set that heterogeneous computing devices can store, represents the set of communication speeds of heterogeneous computing devices, Indicates the Heterogeneous computing devices, Indicates the The computing speed of tasks processed by heterogeneous computing devices, Indicates the The maximum storage capacity of a heterogeneous computing device, Indicates the The communication speed of heterogeneous computing devices.

[0068] In step S2, the definition of the task structure includes the basic attributes of the task, the dependencies between tasks, and the execution constraints of the task. The basic attributes of the task include the following aspects: the task ID represents the unique identifier of the task, which is used to distinguish different tasks; the resource requirement represents the resources required for task execution, which are consistent with the basic attributes described in the resource model; the execution time represents the expected execution time of the task; the priority is used to determine the importance and urgency of the task. In summary, the task modeling includes:

[0069] (a1) Based on the basic properties of network separable tasks, we define them as follows:

[0070]

[0071] Among them, each task can be divided into a finite number of node tasks. Representation Program The size of the data stored and processed by the task is a set of Representation Program The set of task workloads, Representation Program The maximum processing time set that the task can accept, Representation Program The number of node tasks, Representation Program No. The amount of data stored and processed by each node task, Representation Program No. The task workload of each node task, Representation Program No. The maximum processing time that a node task can accept;

[0072] (b1) Define the amount of output data to be transferred , which means that the communication between adjacent node tasks with sequential dependencies will carry a small amount of data information, that is, the input information of the latter will be obtained from the output information of the previous one;

[0073] (c1) Use a directed acyclic graph (DAG) to describe the task set. Each task can have several node tasks. A single task and its node tasks are decomposed into a DAG graph, resulting in the following expression;

[0074]

[0075] in, represents the task graph, Representation Program No. Node tasks, Representing a task graph The set of finite node tasks in , Representing a task graph The set of directed edges in Representation Program From the first Node tasks to There are directed edges between node tasks, and tasks for The parent task of the task The execution priority is higher than the task ;

[0076] Combining the definitions and descriptions in (a1) to (c1), we get the tuple definition of the task model. .

[0077] In step S3, this embodiment uses a bottom-up clustering and fusion method with distributed resources as the core; heterogeneous computing devices regularly broadcast heartbeat packets every 60 seconds to associate computing devices with other computing devices in the neighborhood; the heartbeat packets contain resource representations of the resources of each heterogeneous computing device; several computing devices are clustered according to the sensing range, and cluster heads are selected within the cluster to fuse each other's resources; refer to Figure 2 As shown in the resource-driven decentralized resource perception and fusion flow chart, the bottom-up clustering fusion method is used to dynamically perceive and adjust the usage status of resources in the resource model, including:

[0078] The heterogeneous computing devices are associated by regularly broadcasting heartbeat packets, which contain resource representations of the heterogeneous computing devices.

[0079] Divide the heterogeneous computing devices into several clusters based on their sensing ranges; any two heterogeneous computing devices in a cluster have overlapping sensing ranges and can sense each other.

[0080] The computing resources, storage resources, and communication resources of each heterogeneous computing device in each cluster are comprehensively scored according to different weights, and the heterogeneous computing device with the highest comprehensive score is used as the cluster head device of the cluster;

[0081] Generate or update the resource adjacency table of all heterogeneous computing devices in the cluster until all heterogeneous computing devices are perceived to form a global virtual resource pool; wherein the resource adjacency table is a one-dimensional array, and the elements in the group are the numbers of other heterogeneous computing devices accessible to the current heterogeneous computing device, representing the set of other heterogeneous computing devices accessible to the current heterogeneous computing device. The resource adjacency table of the cluster head device stores the resource representation of all heterogeneous computing devices in the cluster.

[0082] In this embodiment, the comprehensive score is calculated using the following formula:

[0083]

[0084] in, Indicates the The comprehensive score of heterogeneous computing devices, Indicates the weight of computing resources. Indicates the weight of storage resources. Indicates the weight of communication resources.

[0085] In computing-intensive scenarios, computing resources, as the core resources of the device, account for the largest proportion, followed by the need to save corresponding information within the node task. Therefore, the weight of storage resources is greater than that of communication resources. Therefore, the weight distribution can be referred to The above weight distribution is only for reference. Different weights should be allocated according to different scenario requirements. For example, in scenarios that are sensitive to latency, consider increasing the proportion of communication resources.

[0086] In step S4, this embodiment takes task requirements as the core and adopts a local multi-hop fusion method; it dynamically optimizes and utilizes resources according to task requirements and resource status, emphasizing dynamic perception during task execution; when a heterogeneous computing device receives a task processing request, it first decides whether to process the task based on the computing power of the node task itself; if there is a node task that has all the resources to process the task and meets the task constraints, it will quickly respond to the task; otherwise, it will be fused with other node tasks within the range to collaboratively process the task; refer to Figure 3 As shown in Figure 1, a local multi-hop fusion method is used to dynamically sense resources during task execution and fuse the sensed resources, including:

[0087] According to the task requirements in the task model, determine whether the computing resources, storage resources, and communication resources of the heterogeneous computing device currently receiving the task meet the task requirements. If they do, directly process them; otherwise, trigger recursive fusion within the one-hop perception range:

[0088] According to the resource adjacency table of the heterogeneous computing device currently receiving the task, the first-level perception device of the heterogeneous computing device currently receiving the task is obtained; the computing resources, storage resources, and communication resources of the heterogeneous computing device currently receiving the task and the first-level perception device of the heterogeneous computing device currently receiving the task are integrated to obtain the first-level integrated resources; determine whether the first-level integrated resources meet the task requirements, and if so, directly process them; otherwise, trigger recursive integration within the two-hop perception range:

[0089] According to the resource adjacency list of other heterogeneous computing devices accessible to the heterogeneous computing device currently receiving the task, the secondary perception device of the heterogeneous computing device currently receiving the task is obtained; the computing resources, storage resources and communication resources of the secondary perception device of the heterogeneous computing device currently receiving the task are secondarily integrated with the first-level fused resources to obtain the second-level fused resources; it is determined whether the second-level fused resources meet the task requirements, and if so, they are directly processed; otherwise, the current task is assigned to any heterogeneous computing device in the resource model except the heterogeneous computing device currently receiving the task and its first-level perception device and second-level perception device;

[0090] Repeat the above steps until the task is completed, or all heterogeneous computing devices in the resource model cannot meet the task requirements and the task processing fails.

[0091] To help understand the specific operation method of this embodiment, the following operation examples are given:

[0092] refer to Figure 4 The clustering example diagram shown here assumes that a decentralized environment exists. 、 、 、 、 、 Six heterogeneous computing devices, each with different resource attributes and perception ranges.

[0093] Heterogeneous computing devices The perception range has heterogeneous computing devices ;

[0094] Heterogeneous computing devices The perception range has heterogeneous computing devices , heterogeneous computing devices and heterogeneous computing devices ;

[0095] Heterogeneous computing devices The perception range has heterogeneous computing devices and heterogeneous computing devices ;

[0096] Heterogeneous computing devices The perception range has heterogeneous computing devices and heterogeneous computing devices ;

[0097] Heterogeneous computing devices The perception range has heterogeneous computing devices and heterogeneous computing devices ;

[0098] Heterogeneous computing devices Not within the perception range of any node task.

[0099] When performing perception fusion, heterogeneous computing devices and Fusion is performed, and the fused resource adjacency table is saved in each node task, so that heterogeneous computing devices and Fusion, while on heterogeneous computing devices and The fused resource adjacency table is stored in the memory; thus heterogeneous computing devices There are two fused resource adjacency tables in the system. You can consider updating them when merging. Finally, heterogeneous computing devices The resource adjacency table stored in the system contains all the heterogeneous computing devices. 、 and The resource attributes of the resource adjacency table are then updated to the heterogeneous computing device during the next heartbeat detection. and Achieve local unification. Then, use the same method to pass the intermediate heterogeneous computing device Heterogeneous computing devices and heterogeneous computing devices Make an association.

[0100] This fusion method starts from the most marginal distributed devices and integrates resources in the form of clusters, like a tree structure, and finally achieves global resource unification; however, the problem is that there will eventually be a super-central node task in the node task network. This node task stores a large number of resource attributes of distributed devices. If the node task fails, data loss will occur. The reason for this problem is that the resource adjacency table is only stored in a single node task. After fusion, the local resource adjacency table can be updated to other node tasks within the range during the next heartbeat detection to achieve redundant backup and improve performance by sacrificing some storage resources; because the storage of the adjacency table only takes up a small amount of memory attributes, this method is feasible.

[0101] Reference Figure 5 The following is a diagram of an example of task-driven decentralized resource perception fusion. 、 、 、 and The current task is decomposed into five node tasks that are executed sequentially. 、 、 、 、 and For six distributed heterogeneous computing devices, the distributed resource awareness fusion process includes:

[0102] First, according to heterogeneous computing devices If the resource cannot be processed, it will be judged by the heterogeneous computing device within the sensing range. Fusion, after fusion, heterogeneous computing devices and heterogeneous computing devices Collaborative task processing; because tasks arrive at each computing device in parallel, all idle computing devices in the environment can process the task request in parallel, and finally select a node task that meets the constraints and has the best response time for processing.

[0103] Similarly, when heterogeneous computing devices When receiving a task request, if it cannot be processed, a one-hop perception range fusion is performed through heterogeneous computing devices. and heterogeneous computing devices If the minimum resource requirements for processing the task cannot be met through collaborative processing, then consider increasing the number of collaborative node tasks through secondary perception, so that heterogeneous computing devices can be used. 、 、 Co-processing, which obviously goes beyond heterogeneous computing devices Although the one-hop perception range can bring more resource attributes, the complexity of the system will increase exponentially and the delay will be greater.

[0104] To sum up, the distributed resource perception fusion method proposed in this embodiment models device resources and tasks so that independent resources and tasks can be associated. When tasks are decomposed and allocated, it is only necessary to find resource vectors that meet the node task requirements. The resource status and task allocation situation can be intuitively understood through the embedded graph. In addition, the proposed distributed resource perception fusion method based on resource and task driving can flexibly adapt to different situations. In the case of task requirements, the resource-driven perception fusion method can effectively manage the resources of the computing device and form a virtual resource pool. When the task requirements arrive, the task-driven resource perception fusion method can respond quickly to the task and meet the high sensitivity of tasks to latency in a distributed environment.

[0105] Example 2:

[0106] Based on the same inventive concept as the first embodiment, this embodiment of the present invention discloses a distributed resource perception and fusion device, including:

[0107] The resource modeling module is used to: perform resource modeling based on the computing resources, storage resources, and communication resources of network heterogeneous computing devices to obtain a resource model;

[0108] The task modeling module is used to: perform task modeling using a directed acyclic graph based on the basic properties of network-dividable tasks, the dependencies between tasks, and the execution constraints of tasks to obtain a task model;

[0109] A resource-driven module is used to dynamically perceive and adjust the usage status of computing resources, storage resources, and communication resources in the resource model using a bottom-up clustering and fusion method, thereby realizing resource-driven decentralized resource perception and fusion.

[0110] The task-driven module is used to: dynamically perceive resources during task execution using a local multi-hop fusion method based on the task requirements in the task model and the usage status of computing resources, storage resources, and communication resources in the resource model, and fuse the perceived resources to achieve task-driven decentralized resource perception fusion.

[0111] The specific functional implementation of each of the above modules can be found in the relevant content of the method in Example 1 and will not be elaborated on here.

[0112] Example 3:

[0113] This embodiment provides a computer-readable storage medium having computer instructions stored thereon. When the computer instructions are executed by a processor, the steps of the decentralized resource awareness fusion method described in the first embodiment are implemented.

[0114] Example 4:

[0115] This embodiment provides a computer device, including:

[0116] Memory, for storing computer instructions;

[0117] The processor is configured to execute the computer instructions to implement the steps of the decentralized resource awareness fusion method described in the first embodiment.

[0118] Embodiment 5:

[0119] This embodiment provides a computer program product, including computer instructions, characterized in that when the computer instructions are executed by a processor, the steps of the decentralized resource awareness fusion method described in the first embodiment are implemented.

[0120] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0121] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0122] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0123] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0124] The embodiments of the present invention are described above in conjunction with the accompanying drawings, but the present invention is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the guidance of the present invention, ordinary technicians in this field can also make many forms without departing from the scope of protection of the purpose of the present invention and the claims, which are all protected by the present invention.

Claims

1. A distributed resource perception fusion method, characterized in that: include: Perform resource modeling based on computing resources, storage resources, and communication resources of network heterogeneous computing devices to obtain a resource model; According to the basic properties of network separable tasks, the dependencies between tasks and the execution constraints of tasks, a directed acyclic graph is used to model tasks and obtain a task model. A bottom-up clustering fusion method is used to dynamically perceive and adjust the usage status of computing resources, storage resources, and communication resources in the resource model, thereby realizing resource-driven decentralized resource perception fusion. According to the task requirements in the task model and the usage status of computing resources, storage resources and communication resources in the resource model, a local multi-hop fusion method is used to dynamically perceive resources during task execution, and the perceived resources are integrated to achieve task-driven decentralized resource perception fusion; The resource modeling includes: The computing resources, storage resources and communication resources of each heterogeneous computing device are represented in the form of tuple feature vectors to obtain the tuple definition of the resource model. , a single heterogeneous computing device The resource representation is ; in, represents a vertex set, where vertices represent heterogeneous computing devices. Represents an edge set, where an edge represents the connection between two heterogeneous computing devices. represents the set of computing speeds of heterogeneous computing devices processing tasks, Represents the maximum capacity set that heterogeneous computing devices can store, represents the set of communication speeds of heterogeneous computing devices, Indicates the Heterogeneous computing devices, Indicates the The computing speed of tasks processed by heterogeneous computing devices, Indicates the The maximum storage capacity of a heterogeneous computing device, Indicates the Communication speed of heterogeneous computing devices; The bottom-up clustering and fusion method is used to dynamically perceive and adjust the usage status of resources in the resource model, including: The heterogeneous computing devices are associated by regularly broadcasting heartbeat packets, which contain resource representations of the heterogeneous computing devices. Divide the heterogeneous computing devices into several clusters based on their sensing ranges; any two heterogeneous computing devices in a cluster have overlapping sensing ranges and can sense each other. The computing resources, storage resources, and communication resources of each heterogeneous computing device in each cluster are comprehensively scored according to different weights, and the heterogeneous computing device with the highest comprehensive score is used as the cluster head device of the cluster; Generate or update the resource adjacency table of all heterogeneous computing devices in the cluster until all heterogeneous computing devices are sensed to form a global virtual resource pool; the resource adjacency table is a one-dimensional array, and the elements in the group are the numbers of other heterogeneous computing devices accessible to the current heterogeneous computing device, representing the set of other heterogeneous computing devices accessible to the current heterogeneous computing device. The resource adjacency table of the cluster head device stores the resource representation of all heterogeneous computing devices in the cluster; The local multi-hop fusion method is used to dynamically perceive resources during task execution and fuse the perceived resources, including: According to the task requirements in the task model, determine whether the computing resources, storage resources, and communication resources of the heterogeneous computing device currently receiving the task meet the task requirements. If they do, directly process them; otherwise, trigger recursive fusion within the one-hop perception range: According to the resource adjacency table of the heterogeneous computing device currently receiving the task, the first-level perception device of the heterogeneous computing device currently receiving the task is obtained; the computing resources, storage resources, and communication resources of the heterogeneous computing device currently receiving the task and the first-level perception device of the heterogeneous computing device currently receiving the task are integrated to obtain the first-level integrated resources; determine whether the first-level integrated resources meet the task requirements, and if so, directly process them; otherwise, trigger recursive integration within the two-hop perception range: According to the resource adjacency list of other heterogeneous computing devices accessible to the heterogeneous computing device currently receiving the task, the secondary perception device of the heterogeneous computing device currently receiving the task is obtained; the computing resources, storage resources and communication resources of the secondary perception device of the heterogeneous computing device currently receiving the task are secondarily integrated with the first-level fused resources to obtain the second-level fused resources; it is determined whether the second-level fused resources meet the task requirements, and if so, they are directly processed; otherwise, the current task is assigned to any heterogeneous computing device in the resource model except the heterogeneous computing device currently receiving the task and its first-level perception device and second-level perception device; Repeat the above steps until the task is completed, or all heterogeneous computing devices in the resource model cannot meet the task requirements and the task processing fails.

2. The distributed resource perception fusion method according to claim 1, characterized in that: The task modeling includes: (a1) Based on the basic properties of network separable tasks, we define them as follows: ， Among them, each task is divided into a finite number of node tasks. Representation Program The size of the data stored and processed by the task is a set of Representation Program The set of task workloads, Representation Program The maximum processing time set that the task can accept, Representation Program The number of node tasks, Representation Program No. The amount of data stored and processed by each node task, Representation Program No. The task workload of each node task, Representation Program No. The maximum processing time that a node task can accept; (b1) Define the amount of output data to be transferred , represents the data information carried by the communication between adjacent node tasks with sequential dependencies; (c1) Use a directed acyclic graph (DAG) to describe the task set. Each task includes several node tasks, and the following expression is obtained. ， in, represents the task graph, Representing a task graph The set of finite node tasks in , Representing a task graph The set of directed edges in Representation Program No. Node tasks, Representation Program From the first Node tasks to There are directed edges of node tasks, and node tasks For node tasks Parent task, node task The execution priority is higher than the node task ; Combining the definitions and descriptions in (a1) to (c1), we get the tuple definition of the task model. .

3. The distributed resource perception fusion method according to claim 1, characterized in that: The comprehensive score is calculated by the following formula: ， in, Indicates the The comprehensive score of heterogeneous computing devices, Indicates the weight of computing resources. Indicates the weight of storage resources. Indicates the weight of communication resources.

4. A distributed resource perception and fusion device, characterized in that: include: The resource modeling module is used to: perform resource modeling based on the computing resources, storage resources, and communication resources of network heterogeneous computing devices to obtain a resource model; The task modeling module is used to: perform task modeling using a directed acyclic graph based on the basic properties of network-dividable tasks, the dependencies between tasks, and the execution constraints of tasks to obtain a task model; A resource-driven module is used to dynamically perceive and adjust the usage status of computing resources, storage resources, and communication resources in the resource model using a bottom-up clustering and fusion method, thereby realizing resource-driven decentralized resource perception and fusion. A task-driven module is configured to: dynamically sense resources during task execution using a local multi-hop fusion method based on task requirements in the task model and the usage status of computing resources, storage resources, and communication resources in the resource model, and fuse the sensed resources to achieve task-driven decentralized resource sensing and fusion; The resource modeling includes: The computing resources, storage resources and communication resources of each heterogeneous computing device are represented in the form of tuple feature vectors to obtain the tuple definition of the resource model. , a single heterogeneous computing device The resource representation is ; in, represents a vertex set, where vertices represent heterogeneous computing devices. Represents an edge set, where an edge represents the connection between two heterogeneous computing devices. represents the set of computing speeds of heterogeneous computing devices processing tasks, Represents the maximum capacity set that heterogeneous computing devices can store, represents the set of communication speeds of heterogeneous computing devices, Indicates the Heterogeneous computing devices, Indicates the The computing speed of tasks processed by heterogeneous computing devices, Indicates the The maximum storage capacity of a heterogeneous computing device, Indicates the Communication speed of heterogeneous computing devices; The bottom-up clustering and fusion method is used to dynamically perceive and adjust the usage status of resources in the resource model, including: The heterogeneous computing devices are associated by regularly broadcasting heartbeat packets, which contain resource representations of the heterogeneous computing devices. Divide the heterogeneous computing devices into several clusters based on their sensing ranges; any two heterogeneous computing devices in a cluster have overlapping sensing ranges and can sense each other. The computing resources, storage resources, and communication resources of each heterogeneous computing device in each cluster are comprehensively scored according to different weights, and the heterogeneous computing device with the highest comprehensive score is used as the cluster head device of the cluster; Generate or update the resource adjacency table of all heterogeneous computing devices in the cluster until all heterogeneous computing devices are sensed to form a global virtual resource pool; the resource adjacency table is a one-dimensional array, and the elements in the group are the numbers of other heterogeneous computing devices accessible to the current heterogeneous computing device, representing the set of other heterogeneous computing devices accessible to the current heterogeneous computing device. The resource adjacency table of the cluster head device stores the resource representation of all heterogeneous computing devices in the cluster; The local multi-hop fusion method is used to dynamically perceive resources during task execution and fuse the perceived resources, including: According to the task requirements in the task model, determine whether the computing resources, storage resources, and communication resources of the heterogeneous computing device currently receiving the task meet the task requirements. If they do, directly process them; otherwise, trigger recursive fusion within the one-hop perception range: According to the resource adjacency table of the heterogeneous computing device currently receiving the task, the first-level perception device of the heterogeneous computing device currently receiving the task is obtained; the computing resources, storage resources, and communication resources of the heterogeneous computing device currently receiving the task and the first-level perception device of the heterogeneous computing device currently receiving the task are integrated to obtain the first-level integrated resources; determine whether the first-level integrated resources meet the task requirements, and if so, directly process them; otherwise, trigger recursive integration within the two-hop perception range: According to the resource adjacency list of other heterogeneous computing devices accessible to the heterogeneous computing device currently receiving the task, the secondary perception device of the heterogeneous computing device currently receiving the task is obtained; the computing resources, storage resources and communication resources of the secondary perception device of the heterogeneous computing device currently receiving the task are secondarily integrated with the first-level fused resources to obtain the second-level fused resources; it is determined whether the second-level fused resources meet the task requirements, and if so, they are directly processed; otherwise, the current task is assigned to any heterogeneous computing device in the resource model except the heterogeneous computing device currently receiving the task and its first-level perception device and second-level perception device; Repeat the above steps until the task is completed, or all heterogeneous computing devices in the resource model cannot meet the task requirements and the task processing fails.

5. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the decentralized resource perception fusion method according to any one of claims 1 to 3 are implemented.

6. A computer device, characterized in that: include: Memory, for storing computer instructions; A processor is configured to execute the computer instructions to implement the steps of the decentralized resource awareness fusion method according to any one of claims 1 to 3.

7. A computer program product comprising computer instructions, characterized in that When the computer instructions are executed by a processor, the steps of the decentralized resource awareness fusion method described in any one of claims 1 to 3 are implemented.

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