Resource pool planning method based on computing power network

Abstract

The invention relates to the technical field of big data, and discloses a resource pool planning method based on a computing power network, comprising the following steps: S1, global resource modeling; s2, intention-driven resource slicing; s3, intelligent scheduling and load balancing; s4, predictive fault-tolerant self-healing is carried out; s5, energy efficiency optimization resource recovery; according to the method, a safe and efficient distributed computing power network resource awareness framework is constructed by fusing a dynamic key agreement protocol and a federated learning mechanism; according to the new method, the resource discovery accuracy, the state updating timeliness and the abnormal node recognition capability are remarkably improved, and meanwhile, the privacy leakage risk in cross-domain collaboration is effectively avoided through the national secret algorithm encryption, block chain auditing and zero-knowledge certification technologies; according to the method, greenization and high efficiency of resource allocation are realized through a carbon perception dynamic scheduling strategy and a multi-objective optimization model; tasks are preferentially scheduled to green energy nodes, energy consumption of idle nodes is reduced in combination with a dynamic frequency modulation technology, and system energy consumption and carbon emission are reduced.

Description

Technical Field

[0001] The present invention relates to the field of big data technology, and in particular to a method for planning a resource pool based on a computing power network. Background Art

[0002] Computing network resource pooling technology is the core means to achieve unified management and flexible scheduling of distributed computing resources. It builds a virtualized resource pool by integrating heterogeneous nodes (such as cloud servers, edge devices, and terminal computing power) to provide users with on-demand services.

[0003] Existing technologies primarily rely on centralized architectures or static federated models, implementing task allocation based on periodic resource reporting and centralized decision-making mechanisms. However, as computing networks scale and scenarios become more complex (such as multi-tenant cross-domain collaboration and green computing requirements), traditional solutions face severe challenges in terms of real-time performance, security, and energy efficiency optimization.

[0004] First, at the resource perception level, traditional centralized management requires nodes to periodically report their full status data, resulting in high latency in resource view updates and delayed responses to dynamic load fluctuations. Furthermore, the transmission of sensitive data lacks end-to-end encryption, allowing attackers to steal node hardware features or business load information, leading to privacy leaks. Although some solutions use federated learning to achieve cross-domain data collaboration, they do not combine dynamic key negotiation and hardware trusted verification mechanisms, and the risk of side-channel attacks still exists during model training.

[0005] Second, at the resource scheduling level, existing methods mostly focus on single-dimensional optimization of performance or cost, and lack a global consideration of carbon emission constraints. For example, prioritizing tasks to low-load nodes may lead to computing power overload in high-carbon emission areas, exacerbating energy waste and environmental burdens. In addition, traditional fault-tolerance mechanisms rely on full state backup and passive fault recovery, with low migration efficiency and prone to secondary load imbalance, making it difficult to meet the high availability requirements of real-time services.

[0006] 3. At the energy efficiency management level, current energy-saving technologies mostly adopt static strategies (such as fixed frequency adjustment), which are not linked in real time with the grid's carbon emission factors and green energy supply status, resulting in low clean energy utilization; at the same time, the task scheduling process ignores node-level carbon footprint tracking, making it impossible to accurately quantify and optimize the system's total carbon emissions. Summary of the Invention

[0007] The purpose of the present invention is to provide a planning method for a resource pool based on a computing power network to solve the problems raised in the above background technology.

[0008] To achieve the above object, the present invention provides the following technical solutions:

[0009] A method for planning a resource pool based on a computing power network includes the following steps:

[0010] S1. Global resource modeling:

[0011] Through the security detection agent module, a lightweight protocol with dynamic key negotiation is used to authenticate and detect distributed computing nodes, collecting node hardware configuration, load rate, network bandwidth, and latency data in real time.

[0012] A dynamic resource topology map is constructed based on spatiotemporal correlation analysis. A resource profiling engine that integrates multi-dimensional features generates a node evaluation matrix that includes computing power, stability ratings, and energy efficiency indicators. A federated learning mechanism is used to achieve collaborative updates of cross-domain resource views.

[0013] S2, intent-driven resource slicing:

[0014] Deploy a multimodal demand parsing engine to convert natural language or structured requirements submitted by users into a task description framework that includes service quality constraints, resource requirement vectors, and priority weights;

[0015] A two-layer optimization model is constructed. The upper layer coordinates multi-task resource competition through resource pricing strategies, while the lower layer uses deep reinforcement learning to dynamically adjust resource allocation weights to maximize resource utilization while meeting service level agreement constraints.

[0016] S3, intelligent scheduling and load balancing:

[0017] Design a dynamic weighted evaluation strategy to generate a node suitability score based on real-time node computing power, network latency prediction, energy cost, and carbon emission factors. Generate a scheduling plan based on an intelligent optimization algorithm, monitor node load in real time during task execution, and trigger task redistribution of incremental state migration when the load deviates from the threshold.

[0018] S4. Predictive fault-tolerant self-healing:

[0019] Analyze node sensor data through time series prediction models to predict the probability of failure in future time windows;

[0020] When the predicted value exceeds the threshold, fault-tolerance operations are performed in stages: first, differential snapshot technology is used to save the task state, a backup node is selected based on the fitness score, and finally, the task context is migrated via a high-speed network.

[0021] S5. Energy efficiency optimization and resource recovery:

[0022] Establish a dynamic carbon footprint tracking model that integrates real-time grid carbon emissions data, node energy efficiency ratios, and task energy consumption characteristics;

[0023] Design a multi-objective scheduling strategy to prioritize tasks to green energy nodes and reduce the energy consumption of idle nodes through dynamic frequency modulation technology.

[0024] As a further solution of the present invention: the security detection agent module in S1 adopts the identification encryption technology of the national secret algorithm, the node identity authentication includes the hardware trust measurement value and the geographic location hash value, and the detection data adopts a fragmented multi-path transmission mechanism.

[0025] As a further solution of the present invention: the dynamic key agreement mechanism in S1 specifically includes:

[0026] Temporary session keys are generated between nodes using the elliptic curve encryption algorithm;

[0027] The key validity period is dynamically bound to the node online status, and the key update is automatically triggered when the node is offline for a timeout;

[0028] The detection data transmission adopts a forward security mechanism, and each communication uses an independent encrypted channel.

[0029] As a further solution of the present invention: the federated learning mechanism in S1 adopts differential privacy protection technology, adds noise data when updating cross-domain resource views, and reduces communication overhead through model compression.

[0030] As a further solution of the present invention: the two-layer optimization model in S2 includes:

[0031] The upper layer is a multi-agent game framework, where resource providers publish pricing strategies and task demanders optimize resource purchasing decisions.

[0032] The lower layer adopts a deep reinforcement learning model to dynamically adjust the allocation weights according to the node resource margin and task requirements. The reward function integrates task completion time, resource cost and default risk.

[0033] As a further solution of the present invention: the dynamic weighted evaluation strategy in S3 dynamically adjusts the weight ratio of computing power, delay, carbon emissions and cost through a fuzzy logic controller.

[0034] As a further solution of the present invention: when S3 involves cross-security domain scheduling:

[0035] An immutable audit chain based on blockchain to record scheduling operations;

[0036] Use zero-knowledge proof technology to verify node resource capabilities and avoid leaking configuration details;

[0037] Ensure the security of sensitive data in cross-domain scheduling through a trusted execution environment.

[0038] As a further solution of the present invention, the differential snapshot technology in S4 specifically includes:

[0039] Modify the logging task status based on the memory page;

[0040] Use real-time compression algorithm to reduce the amount of snapshot data;

[0041] Snapshot shards are stored on multiple physical nodes through a distributed encoding strategy.

[0042] As a further solution of the present invention: the dynamic carbon footprint tracking model in S5 includes a real-time updated regional power grid carbon emission factor library and node-level carbon emissions calculated based on task power consumption and execution duration.

[0043] Compared with the prior art, the present invention has the following beneficial effects:

[0044] 1. The present invention constructs a secure and efficient distributed computing network resource perception framework by integrating a dynamic key negotiation protocol with a federated learning mechanism. Compared with traditional centralized solutions, the new method significantly improves resource discovery accuracy, status update timeliness, and abnormal node identification capabilities. At the same time, through national secret algorithm encryption, blockchain auditing, and zero-knowledge proof technology, it effectively avoids the risk of privacy leakage in cross-domain collaboration. The solution supports real-time control of large-scale nodes and is particularly suitable for sensitive scenarios such as finance and healthcare. It breaks through the technical bottlenecks of traditional architectures in efficiency and security, and provides a secure and reliable solution for computing resource pooling.

[0045] 2. The present invention realizes green and efficient resource allocation through carbon-aware dynamic scheduling strategy and multi-objective optimization model; prioritizes scheduling tasks to green energy nodes, combines dynamic frequency modulation technology to reduce the energy consumption of idle nodes, and significantly reduces system energy consumption and carbon emissions; the predictive fault tolerance mechanism shortens fault recovery time through fault probability prediction and state migration optimization, ensuring the stability and real-time performance of task execution; this solution effectively improves resource utilization, reduces energy waste, and helps data centers and edge computing scenarios achieve sustainable development goals. It has both economic and environmental value and provides enterprises with a low-carbon and efficient computing resource management solution. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 A flowchart of a resource pool planning method based on a computing network; DETAILED DESCRIPTION

[0047] See also Figure 1 In an embodiment of the present invention, a method for planning a resource pool based on a computing power network includes the following steps:

[0048] S1. Global resource modeling:

[0049] Through the security detection agent module, a lightweight protocol with dynamic key negotiation is used to authenticate and detect distributed computing nodes, collecting node hardware configuration, load rate, network bandwidth, and latency data in real time.

[0050] Hardware configuration collection includes the number of CPU cores, GPU model, and video memory capacity. Network latency measurement uses the ICMP protocol to sample three times every 10 seconds and take the median value.

[0051] The security detection agent module adopts the SM2 / SM3 national secret algorithm system, and the hardware feature extraction includes the TPM chip PCR register hash value and CPU microcode version verification;

[0052] A dynamic resource topology map is constructed based on spatiotemporal correlation analysis. A resource profiling engine that integrates multi-dimensional features generates a node evaluation matrix that includes computing power, stability ratings, and energy efficiency indicators. A federated learning mechanism is used to achieve collaborative updates of cross-domain resource views.

[0053] Spatiotemporal correlation analysis uses a sliding time window LSTM model. The default time granularity is 5 minutes, and the spatial granularity is refined to the rack-level topology.

[0054] S2, intent-driven resource slicing:

[0055] Deploy a multimodal demand parsing engine to convert natural language or structured requirements submitted by users into a task description framework that includes service quality constraints, resource requirement vectors, and priority weights;

[0056] A two-layer optimization model is constructed. The upper layer coordinates multi-task resource competition through resource pricing strategies, while the lower layer uses deep reinforcement learning to dynamically adjust resource allocation weights to maximize resource utilization while meeting service level agreement constraints.

[0057] S3, intelligent scheduling and load balancing:

[0058] Design a dynamic weighted evaluation strategy to generate a node suitability score based on real-time node computing power, network latency prediction, energy cost, and carbon emission factors. Generate a scheduling plan based on an intelligent optimization algorithm, monitor node load in real time during task execution, and trigger task redistribution of incremental state migration when the load deviates from the threshold.

[0059] S4. Predictive fault-tolerant self-healing:

[0060] Analyze node sensor data through time series prediction models to predict the probability of failure in future time windows;

[0061] The timing model uses the Transformer architecture, and the input features include 18-dimensional parameters such as temperature, voltage, and process crash rate;

[0062] When the predicted value exceeds the threshold, fault-tolerance operations are performed in stages: first, differential snapshot technology is used to save the task state, a backup node is selected based on the fitness score, and finally, the task context is migrated via a high-speed network.

[0063] The default compression ratio of differential snapshots is ≥85%. The migration process uses RDMA technology to ensure memory-level migration speed.

[0064] S5. Energy efficiency optimization and resource recovery:

[0065] Establish a dynamic carbon footprint tracking model that integrates real-time grid carbon emissions data, node energy efficiency ratios, and task energy consumption characteristics;

[0066] Design a multi-objective scheduling strategy to prioritize tasks to green energy nodes and reduce the energy consumption of idle nodes through dynamic frequency modulation technology.

[0067] Preferably, the security detection agent module in S1 adopts the identification encryption technology of the national secret algorithm, specifically the SM2 algorithm, and the elliptic curve parameters comply with the GM / T 0003-2012 standard; the node identity authentication includes the hardware trust measurement value (generated based on the TPM2.0 chip SHA-256 hash) and the geographic location hash value, and the detection data adopts a fragmented multi-path transmission mechanism.

[0068] Preferably, the dynamic key agreement mechanism in S1 specifically includes:

[0069] Temporary session keys are generated between nodes using the elliptic curve encryption algorithm; the SM2 elliptic curve standard is used, and the key length is set to 256 bits;

[0070] The key validity period is dynamically bound to the node online status, and the key update is automatically triggered when the node is offline for a timeout;

[0071] The detection data transmission adopts a forward security mechanism, and each communication uses an independent encrypted channel.

[0072] Preferably, the federated learning mechanism in S1 adopts differential privacy protection technology, adds noise data when updating cross-domain resource views, and reduces communication overhead through model compression.

[0073] Preferably, the two-layer optimization model in S2 includes:

[0074] The upper layer is a multi-agent game framework, where resource providers publish pricing strategies and task demanders optimize resource purchasing decisions.

[0075] The lower layer adopts a deep reinforcement learning model to dynamically adjust the allocation weights according to the node resource margin and task requirements. The reward function integrates task completion time, resource cost and default risk.

[0076] Preferably, the dynamic weighted evaluation strategy in S3 dynamically adjusts the weight ratios of computing power, delay, carbon emissions, and cost through a fuzzy logic controller.

[0077] Preferably, when S3 involves cross-security domain scheduling:

[0078] An immutable audit chain based on blockchain to record scheduling operations;

[0079] Use zero-knowledge proof technology to verify node resource capabilities and avoid leaking configuration details;

[0080] Ensure the security of sensitive data in cross-domain scheduling through a trusted execution environment.

[0081] Preferably, the differential snapshot technology in S4 specifically includes:

[0082] Modify the logging task status based on the memory page;

[0083] Use real-time compression algorithm to reduce the amount of snapshot data;

[0084] Snapshot shards are stored on multiple physical nodes through a distributed encoding strategy.

[0085] Preferably, the dynamic carbon footprint tracking model in S5 includes a real-time updated regional power grid carbon emission factor library and node-level carbon emissions calculated based on task power consumption and execution duration.

[0086] In order to further illustrate the technical effects of the present invention, the following experiments were performed to verify:

[0087] Experiment 1

[0088] Experimental setup

[0089] Simulation environment: 60 virtual servers (divided into 3 areas, 20 in each area);

[0090] Variation parameters: server load (20%-90% random variation), network latency (10-150ms);

[0091] Comparison Method

[0092] Traditional method: All servers report data to the center every 30 seconds;

[0093] New method: federated learning update + encrypted detection;

[0094] Comparison table:

[0095] Evaluation Metrics Traditional methods New approach Effect Resource discovery accuracy 72％ 95％ +23％(95％-72％) Status update speed (s) 3.2 0.6 81.3％↑(3.2-0.6) / 3.2×100％ Data leakage risk 8.5％ 0.4％ 95.3％↓(8.5％-0.4％) / 8.5％×100％ Abnormal node recognition rate 75％ 98％ +23％(98％-75％)

[0096] Conclusion: Through encrypted detection and federated learning mechanisms, this method achieves accurate perception of resource status (accuracy increased by 23% to 95%) while protecting node privacy (data leakage risk reduced by 95.3%). Experiments show that the resource view update speed is improved to 0.6 seconds (traditional methods are 3.2 seconds), and abnormal nodes can be collaboratively identified across regions (identification rate of 98%). This solution is suitable for scenarios with high data security requirements such as finance and healthcare, supports real-time management of millions of nodes, and solves the pain points of low efficiency and high privacy leakage risks of traditional centralized solutions.

[0097] Experiment 2

[0098] Experimental setup

[0099] Simulation tasks: 800 tasks (400 emergency tasks + 400 ordinary tasks);

[0100] Node type: 20% solar nodes (green), 30% energy-saving nodes;

[0101] Comparison method:

[0102] Traditional method: assign tasks to the least busy server;

[0103] New method: Green priority + dynamic power saving;

[0104] Comparison table:

[0105] Evaluation Metrics Traditional methods New approach Effect Total power consumption (kWh) 120 70 41.7％↓(120-70) / 120×100％ <![CDATA[Carbon emissions (kgCO2)]]> 160 90 43.8％↓(160-90) / 160×100％ Green energy utilization rate 20％ 85％ 4.3 times↑(85%÷20%) Task timeout rate 25％ 5％ 80％↓(25％-5％) / 25％×100％

[0106] Conclusion: This method significantly reduces energy consumption (total power consumption reduced by 41.7%) while ensuring task completion rate (overtime rate is only 5%) through dynamic carbon-aware scheduling (carbon emissions reduced by 43.8%) and green node priority allocation (green energy utilization rate reaches 85%). Experiments have verified that it can adjust task allocation in real time according to the carbon emission factor of the power grid, and idle nodes automatically reduce frequency to save power (power consumption reduced by 62%). It is suitable for scenarios such as data centers and edge computing, helping enterprises achieve the "dual carbon" goals and saving millions of electricity costs annually.

[0107] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above and that the invention can be embodied in other specific forms without departing from the spirit or essential characteristics of the invention. Therefore, the embodiments should be considered in all respects as illustrative and non-restrictive, and the scope of the invention is defined by the appended claims, not the foregoing description, and all variations within the meaning and range of equivalents of the claims are intended to be included therein. Any reference sign in a claim should not be construed as limiting the claim to which it relates.

[0108] In addition, it should be understood that although this specification is described in terms of implementation methods, not every implementation method contains only one independent technical solution. This narrative method of the specification is only for the sake of clarity. Those skilled in the art should regard the specification as a whole. The technical solutions in each embodiment can also be appropriately combined to form other implementation methods that can be understood by those skilled in the art.

Claims

1. A resource pool planning method based on a computing power network, characterized in that: The following steps are involved: S1. Global resource modeling: Through the security detection agent module, a lightweight protocol with dynamic key negotiation is used to authenticate and detect distributed computing nodes, collecting node hardware configuration, load rate, network bandwidth, and latency data in real time. A dynamic resource topology map is constructed based on spatiotemporal correlation analysis. A resource profiling engine that integrates multi-dimensional features generates a node evaluation matrix that includes computing power, stability ratings, and energy efficiency indicators. A federated learning mechanism is used to achieve collaborative updates of cross-domain resource views. S2, intent-driven resource slicing: Deploy a multimodal demand parsing engine to convert natural language or structured requirements submitted by users into a task description framework that includes service quality constraints, resource requirement vectors, and priority weights; A two-layer optimization model is constructed. The upper layer coordinates multi-task resource competition through resource pricing strategies, while the lower layer uses deep reinforcement learning to dynamically adjust resource allocation weights to maximize resource utilization while meeting service level agreement constraints. S3, intelligent scheduling and load balancing: Design a dynamic weighted evaluation strategy to generate a node suitability score based on real-time node computing power, network latency prediction, energy cost, and carbon emission factors. Generate a scheduling plan based on an intelligent optimization algorithm, monitor node load in real time during task execution, and trigger task redistribution of incremental state migration when the load deviates from the threshold. S4. Predictive fault-tolerant self-healing: Analyze node sensor data through time series prediction models to predict the probability of failure in future time windows; When the predicted value exceeds the threshold, fault-tolerance operations are performed in stages: first, differential snapshot technology is used to save the task state, a backup node is selected based on the fitness score, and finally, the task context is migrated via a high-speed network. S5. Energy efficiency optimization and resource recovery: Establish a dynamic carbon footprint tracking model that integrates real-time grid carbon emissions data, node energy efficiency ratios, and task energy consumption characteristics; Design a multi-objective scheduling strategy to prioritize tasks to green energy nodes and reduce the energy consumption of idle nodes through dynamic frequency modulation technology.

2. A method for planning a resource pool based on a computing power network according to claim 1, characterized in that: The security detection agent module in S1 adopts the identification encryption technology of the national secret algorithm, the node identity authentication includes the hardware trust measurement value and the geographical location hash value, and the detection data adopts the fragmented multi-path transmission mechanism.

3. The method for planning a resource pool based on a computing power network according to claim 1, characterized in that: The dynamic key negotiation mechanism in S1 specifically includes: Temporary session keys are generated between nodes using the elliptic curve encryption algorithm; The key validity period is dynamically bound to the node online status, and the key update is automatically triggered when the node is offline for a timeout; The detection data transmission adopts a forward security mechanism, and each communication uses an independent encrypted channel.

4. The method for planning a resource pool based on a computing power network according to claim 1, characterized in that: The federated learning mechanism in S1 adopts differential privacy protection technology, adds noise data when updating cross-domain resource views, and reduces communication overhead through model compression.

5. The method for planning a resource pool based on a computing power network according to claim 1, characterized in that: The two-layer optimization model in S2 includes: The upper layer is a multi-agent game framework, where resource providers publish pricing strategies and task demanders optimize resource purchasing decisions. The lower layer adopts a deep reinforcement learning model to dynamically adjust the allocation weights according to the node resource margin and task requirements. The reward function integrates task completion time, resource cost and default risk.

6. The method for planning a resource pool based on a computing power network according to claim 1, characterized in that: The dynamic weighted evaluation strategy in S3 dynamically adjusts the weight ratio of computing power, delay, carbon emissions and cost through a fuzzy logic controller.

7. The method for planning a resource pool based on a computing power network according to claim 1, characterized in that: When S3 involves cross-security domain scheduling: An immutable audit chain based on blockchain to record scheduling operations; Use zero-knowledge proof technology to verify node resource capabilities and avoid leaking configuration details; Ensure the security of sensitive data in cross-domain scheduling through a trusted execution environment.

8. The method for planning a resource pool based on a computing power network according to claim 1, characterized in that: The differential snapshot technology in S4 specifically includes: Modify the logging task status based on the memory page; Use real-time compression algorithm to reduce the amount of snapshot data; Snapshot shards are stored on multiple physical nodes through a distributed encoding strategy.

9. The method for planning a resource pool based on a computing power network according to claim 1, characterized in that: The dynamic carbon footprint tracking model in S5 includes a real-time updated regional power grid carbon emission factor library and node-level carbon emissions calculated based on task power consumption and execution duration.

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