Method and device for globally sensing resource hotspots in cloud platform, medium and electronic equipment

Abstract

The invention discloses a method for globally sensing resource hotspots in a cloud platform, and the method comprises the steps: monitoring resources occupied by a host machine node and a container node in a platform-as-a-service layer and resources occupied by a physical machine node in an infrastructure-as-a-service layer, and obtaining resource occupation data corresponding to the host machine node, the container node and the physical machine node; associating the resource occupation data to a global node topology of the cloud platform according to a node corresponding to the resource occupation data; and based on the resource occupation data corresponding to the nodes in the global node topology and a hotspot sensing rule configured for the nodes in the global node topology, determining overheated nodes in the nodes of the global node topology to sense resource hotspots in the cloud platform. By executing the method and the device, sharing of resource occupation data can be realized between a platform-as-a-service layer and an infrastructure-as-a-service layer of the cloud platform, real resource hotspots in the cloud platform can be globally perceived, and data support is provided for eliminating the resource hotspots in the cloud platform.

Description

Technical Field

[0001] The present application relates to big data technology, distributed technology and cloud computing technology, and can be applicable to financial technology scenarios. The present application specifically relates to a method, device, medium and electronic device for globally sensing resource hotspots in a cloud platform. Background Art

[0002] The Platform as a Service layer (PaaS layer, Infrastructure as a Service) and the Infrastructure as a Service layer (IaaS layer, Infrastructure as a Service) together constitute the underlying architecture of the cloud platform.

[0003] At present, the resource monitoring system of cloud platforms is generally built in layers, that is, the PaaS layer and the IaaS layer monitor resources on their own layers respectively. The resource monitoring data of the PaaS layer and the IaaS layer are not shared. The application operation and maintenance personnel of the PaaS layer and the IaaS layer cannot understand the resource usage of the other layer. As a result, the resource hotspots perceived by the application operation and maintenance personnel may not be the real resource hotspots in the cloud platform, which in turn makes it impossible for the application operation and maintenance personnel to quickly and accurately eliminate the resource hotspots in the cloud platform. Summary of the invention

[0004] The present application provides a method, device, medium and electronic device for globally perceiving resource hotspots in a cloud platform, which can realize the sharing of resource occupancy data between the platform as a service layer and the infrastructure as a service layer of the cloud platform, and can globally perceive the real resource hotspots in the cloud platform, so as to provide data support for eliminating resource hotspots in the cloud platform.

[0005] According to a first aspect of the present application, a method for globally sensing resource hotspots in a cloud platform is provided, the method comprising:

[0006] Monitor the resources occupied by the host node and the container node in the platform as a service layer and the physical node in the infrastructure as a service layer respectively, and obtain resource occupation data corresponding to the host node, the container node and the physical node;

[0007] According to the nodes corresponding to the resource occupancy data, the resource occupancy data is associated with the global node topology of the cloud platform; wherein the nodes in the global node topology include at least the host node and the container node in the platform as a service layer, and the physical machine node in the infrastructure as a service layer; the links in the global node topology are determined based on the resource mapping relationship between the host node and the physical machine node, the deployment relationship between the host node and the container node, the organizational relationship between the host nodes, and the organizational relationship between the physical machine nodes;

[0008] Based on resource occupancy data corresponding to nodes in a global node topology and hotspot perception rules configured for nodes in the global node topology, overheated nodes are determined in the nodes of the global node topology to perceive resource hotspots in a cloud platform.

[0009] According to a second aspect of the present application, a device for globally sensing resource hot spots in a cloud platform is provided, the device comprising:

[0010] A resource occupancy monitoring module is used to monitor the resources occupied by the host node and the container node in the platform as a service layer, and the physical machine node in the infrastructure as a service layer, and obtain resource occupancy data corresponding to the host node, the container node and the physical machine node;

[0011] A data association module, used to associate the resource occupancy data with the global node topology of the cloud platform according to the nodes corresponding to the resource occupancy data; wherein the nodes in the global node topology include at least host nodes and container nodes in the platform as a service layer, and physical machine nodes in the infrastructure as a service layer; the links in the global node topology are determined based on the resource mapping relationship between the host node and the physical machine node, the deployment relationship between the host node and the container node, the organizational relationship between the host nodes, and the organizational relationship between the physical machine nodes;

[0012] The resource hotspot perception module is used to determine overheated nodes in the nodes of the global node topology based on the resource occupancy data corresponding to the nodes in the global node topology and the hotspot perception rules configured for the nodes in the global node topology to perceive the resource hotspots in the cloud platform.

[0013] According to the third aspect of the present invention, an embodiment of the present application provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the method for globally sensing resource hotspots in a cloud platform as described in the embodiment of the present application is implemented.

[0014] According to the fourth aspect of the present invention, an embodiment of the present application provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the method for globally perceiving resource hotspots in a cloud platform as described in the embodiment of the present application is implemented.

[0015] According to the fifth aspect of the present application, an embodiment of the present application provides a computer program product, including a computer program, which, when executed by a processor, implements the method for globally sensing resource hotspots in a cloud platform as described in the embodiment of the present application.

[0016] The technical solution of this application performs vertical penetration monitoring of resource occupancy at each level of the cloud platform, and associates the monitored resource occupancy data with the global node topology of the cloud platform. The resource occupancy data is shared between the platform as a service layer and the infrastructure as a service layer, which can globally perceive the real resource hotspots in the cloud platform, provide data support for quickly and accurately eliminating resource hotspots in the cloud platform, and help improve the operational reliability of the cloud platform.

[0017] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present application, nor is it intended to limit the scope of the present application. Other features of the present application will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0019] Figure 1 is a flow chart of a method for globally sensing resource hot spots in a cloud platform provided according to Embodiment 1;

[0020] Figure 2 is a flow chart of a method for globally sensing resource hot spots in a cloud platform provided according to Embodiment 2;

[0021] Figure 3 It is a structural diagram of a device for globally sensing resource hot spots in a cloud platform provided in Example 3 of the present application;

[0022] Figure 4 It is a structural schematic diagram of an electronic device provided in Example 4 of the present application. DETAILED DESCRIPTION

[0023] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present application.

[0024] It should be noted that the terms "first", "second", "target" and "candidate" in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0025] Embodiment 1

[0026] Figure 1 It is a flowchart of a method for globally sensing resource hotspots in a cloud platform provided according to Example 1. This embodiment can be applicable to the situation of sensing resource hotspots in a cloud platform. The method can be configured to be executed by a device for globally sensing resource hotspots in a cloud platform. The device for globally sensing resource hotspots in a cloud platform is implemented in the form of hardware and / or software and can be integrated into an electronic device running this system.

[0027] like Figure 1 As shown, the method includes:

[0028] S110. Monitor the resources occupied by the host node and the container node in the platform as a service layer and the physical machine node in the infrastructure as a service layer respectively, and obtain resource occupation data corresponding to the host node, the container node and the physical machine node.

[0029] The platform as a service layer includes host nodes and container nodes. Container nodes refer to container instances running on host nodes. Container instances are isolated through virtualization technology to form an independent operating environment for hosting and running applications. Container nodes rely on the resources provided by host nodes, such as CPU (Central Processing Unit), memory, and storage, to achieve efficient operation of applications.

[0030] The Infrastructure as a Service layer includes physical machine nodes, which are part of the actual computing, storage, and network resources provided by cloud service providers. Physical machine nodes can be servers, hard disks, switches, etc. Through virtualization technology, physical machine nodes are abstracted into virtual resources such as virtual machines, virtual hard disks, and virtual networks.

[0031] Optionally, resource monitoring instances are deployed in host nodes, container nodes, and physical machine nodes in the infrastructure as a service layer respectively. Resource occupancy data corresponding to host nodes, container nodes, and physical machine nodes are collected by running the resource monitoring instances to monitor the resources occupied by host nodes, container nodes, and physical machine nodes.

[0032] Among them, resource usage data can reflect the resources occupied by host nodes, container nodes, and physical machine nodes.

[0033] S120. Associating the resource occupancy data with the global node topology of the cloud platform according to the nodes corresponding to the resource occupancy data.

[0034] The nodes in the global node topology include at least host nodes and container nodes in the platform as a service layer, and physical machine nodes in the infrastructure as a service layer; the links in the global node topology are determined based on the resource mapping relationship between the host node and the physical machine node, the deployment relationship between the host node and the container node, the organizational relationship between the host nodes, and the organizational relationship between the physical machine nodes.

[0035] Optionally, the resource occupancy data corresponding to the host node is associated with the host node in the global node topology, the resource occupancy data corresponding to the container node is associated with the container node in the global node topology, and the resource occupancy data corresponding to the physical machine node is associated with the physical machine node in the global node topology.

[0036] The global node topology is used to reflect the node distribution in each layer of the cloud platform. The global node topology includes at least: the distribution of host nodes and container nodes in the platform as a service layer, and the distribution of physical machine nodes in the infrastructure as a service layer.

[0037] It can be known that the host machine nodes in the platform as a service layer are obtained by abstracting the physical machine nodes in the infrastructure as a service layer by using virtualization technology.

[0038] The resource mapping relationship refers to the correspondence between the host nodes in the Platform as a Service layer and the physical nodes in the Infrastructure as a Service layer. Based on the resource mapping relationship, it can be determined which physical nodes in the Infrastructure as a Service layer the host nodes in the Platform as a Service layer are abstracted from.

[0039] The deployment relationship refers to the correspondence between the host node and the container node. Both the host node and the container node are in the platform as a service layer. Based on the deployment relationship, it can be determined which container nodes are deployed in the host node.

[0040] The corresponding relationship between host nodes and the corresponding relationship between physical machine nodes are both called organizational relationships. Based on the organizational relationship, it can be determined which host group a host node belongs to and which physical group a physical machine node belongs to.

[0041] S130. Based on the resource occupancy data corresponding to the nodes in the global node topology and the hotspot perception rules configured for the nodes in the global node topology, overheated nodes are determined in the nodes of the global node topology to perceive the resource hotspots in the cloud platform.

[0042] The nodes in the global node topology include host nodes and container nodes in the platform as a service layer, and physical machine nodes in the infrastructure as a service layer.

[0043] All nodes in the global node topology have corresponding resource occupancy data, and the resource occupancy data can reflect the resources occupied by the nodes in the global node topology. Hotspot perception rules are used to determine how many resources a node in the global node topology needs to occupy to be considered an overheated node. Hotspot perception rules are determined based on actual business needs, and the specific content is not limited here. Optionally, the hotspot perception rules include a resource occupancy upper limit. Hotspot perception rules are configured through the hotspot display interface. Hotspot perception rules can be oriented to a single host node or physical machine node, or to an entire virtual machine group or physical machine group. The hotspot display interface can be used for human-computer interaction. Optionally, the global node topology and resource hotspots are displayed through the hotspot display interface.

[0044] Among them, an overheated node refers to a node in the global node topology whose occupied resources exceed the resource occupation limit. Resource hotspots are generated in overheated nodes, but not all overheated nodes can be called resource hotspots. Resource hotspots may become bottlenecks affecting the performance of the cloud platform. Resource hotspots are determined by the resource occupation data corresponding to the nodes in the comprehensive global node topology. Resource hotspots are global, and overheated nodes are local.

[0045] The technical solution of this application performs vertical penetration monitoring of resource occupancy at each level of the cloud platform, and associates the monitored resource occupancy data with the global node topology of the cloud platform. The resource occupancy data is shared between the platform as a service layer and the infrastructure as a service layer, which can globally perceive the real resource hotspots in the cloud platform, provide data support for quickly and accurately eliminating resource hotspots in the cloud platform, and help improve the operational reliability of the cloud platform.

[0046] In an optional embodiment, the method also includes: respectively obtaining a first node topology corresponding to the Platform as a Service layer and a second node topology corresponding to the Infrastructure as a Service layer; wherein the nodes in the first node topology include at least the host machine nodes in the Platform as a Service layer; the nodes in the second node topology include at least the physical machine nodes in the Infrastructure as a Service layer; based on the resource mapping relationship between the host machine node and the physical machine node, establishing an association between the first node topology and the second node topology, and using the associated topology as the global node topology of the cloud platform.

[0047] Among them, the first node topology corresponds to the platform as a service layer, and the nodes in the first node topology include at least: host nodes and container nodes. The links in the first node topology are determined based on the deployment relationship between the host node and the container node, and the organizational relationship between the host nodes. The second node topology corresponds to the infrastructure as a service layer, and the nodes in the second node topology include at least: physical machine nodes. The links in the second node topology are determined based on the organizational relationship between the physical machine nodes.

[0048] Optionally, the first node topology and the second node topology are pulled from the cloud platform by docking with the cloud platform.

[0049] The first node topology describes the distribution of host nodes and container nodes in the Platform as a Service layer, and the second node topology describes the distribution of physical machine nodes in the Infrastructure as a Service layer. Both the first node topology and the second node topology are local and relatively independent. Based on the resource mapping relationship, it can be determined which physical machine nodes in the Infrastructure as a Service layer are abstracted from the host nodes in the Platform as a Service layer. In other words, the resource mapping relationship actually describes the association relationship between the first node topology and the second node topology. Optionally, the resource mapping relationship is used to associate the first node topology with the second node topology, and the associated topology is used as the global node topology of the cloud platform. Among them, the global node topology is global and can describe the distribution of nodes in each level of the cloud platform.

[0050] The above technical solution provides a practical solution for determining the global node topology, and provides data support for the subsequent global perception of resource hotspots in the cloud platform based on the global node topology.

[0051] In an optional embodiment, the resources occupied by the host nodes and container nodes in the Platform as a Service layer and the physical machine nodes in the Infrastructure as a Service layer are monitored respectively to obtain resource occupancy data corresponding to the host nodes, the container nodes and the physical machine nodes, including: determining matching resource dimensions for the host nodes and container nodes in the Platform as a Service layer and the physical machine nodes in the Infrastructure as a Service layer according to the node type; deploying resource monitoring instances on the host nodes and container nodes in the Platform as a Service layer and the physical machine nodes in the Infrastructure as a Service layer respectively; and running the resource monitoring instances to collect resource occupancy data corresponding to the host nodes, the container nodes and the physical machine nodes from the resource dimensions matching the host nodes, the container nodes and the physical machine nodes.

[0052] Optionally, node types include: physical machines, virtual machines, and containers. In the process of monitoring the resources occupied by host nodes and container nodes in the Platform as a Service layer, and the physical nodes in the Infrastructure as a Service layer, the dimensions of concern are not the same. The specific resource dimensions from which to monitor the resources occupied by host nodes, container nodes, and physical nodes can be determined based on actual business needs and are not limited here. For example, in addition to common resource dimensions such as CPU utilization, memory utilization, and network occupancy, the host node must also include the CPU stealing value (CPUsteal value) and the number of containers.

[0053] The resource monitoring instance is used to monitor the resources occupied by the host node, container node, and physical node. Running the resource monitoring instance can collect the resource usage data corresponding to the host node, container node, and physical node.

[0054] The above technical solution provides a practical resource occupancy monitoring solution, which provides technical support for realizing vertical penetrating resource occupancy monitoring at all levels in the cloud platform.

[0055] In an optional embodiment, based on the resource occupancy data corresponding to the nodes in the global node topology and the hotspot perception rules configured for the nodes in the global node topology, overheated nodes are determined in the nodes of the global node topology to perceive the resource hotspots in the cloud platform, including: extracting the resource occupancy upper limit corresponding to the nodes in the global node topology from the hotspot perception rules; determining the relative size relationship between the resource occupancy upper limit corresponding to the nodes in the global node topology and the resource occupancy data; based on the relative size relationship, overheated nodes are determined in the nodes of the global node topology to perceive the resource hotspots in the cloud platform.

[0056] Among them, the hotspot perception rule includes a resource occupation upper limit. The resource occupation upper limit is used to determine whether a node in the global node topology is an overheated node. The relative size relationship is used to determine whether the resources occupied by the node in the global node topology exceed the resource occupation upper limit. Nodes in the global node topology whose occupied resources exceed the resource occupation upper limit are determined as overheated nodes.

[0057] The above technical solution provides a feasible solution for determining overheated nodes, and provides data support for realizing global perception of resource hotspots in the cloud platform.

[0058] Embodiment 2

[0059] Figure 2 This is a flow chart of a method for globally sensing resource hot spots in a cloud platform provided according to Embodiment 2. This embodiment is further optimized on the basis of the above embodiment.

[0060] like Figure 2 As shown, the method includes:

[0061] S210. Monitor the resources occupied by the host node and the container node in the platform as a service layer and the physical machine node in the infrastructure as a service layer respectively, and obtain resource occupancy data corresponding to the host node, the container node and the physical machine node.

[0062] S220. According to the nodes corresponding to the resource occupancy data, the resource occupancy data is associated with the global node topology of the cloud platform.

[0063] The nodes in the global node topology include at least host nodes and container nodes in the platform as a service layer, and physical machine nodes in the infrastructure as a service layer; the links in the global node topology are determined based on the resource mapping relationship between the host node and the physical machine node, the deployment relationship between the host node and the container node, the organizational relationship between the host nodes, and the organizational relationship between the physical machine nodes;

[0064] S230. Based on the resource occupancy data corresponding to the nodes in the global node topology and the hotspot perception rules configured for the nodes in the global node topology, overheated nodes are determined in the nodes of the global node topology to perceive the resource hotspots in the cloud platform.

[0065] S240: Determine, in the global node topology, related nodes associated with the overheated node based on the links associated with the overheated node in the global node topology.

[0066] An overheated node refers to a node in the global node topology whose occupied resources exceed the upper limit of resource occupation. A related node refers to a node in the global node topology that is associated with an overheated node. Overheated nodes and related nodes are connected by links in the global node topology. Related nodes may or may not be overheated nodes. In other words, two overheated nodes can be related nodes to each other. Whether a related node is an overheated node is determined according to actual conditions and is not limited here.

[0067] S250: Generate a hotspot elimination suggestion based on the resource occupancy data corresponding to the relevant nodes and the links associated with the relevant nodes in the global node topology.

[0068] The hotspot elimination suggestion is used to provide guidance on how to eliminate resource hotspots in the cloud platform. Optionally, the hotspot elimination suggestion includes an application migration direction.

[0069] The overheated node and the related nodes are connected by links in the global node topology, indicating that there is a deployment relationship or organizational relationship between the two. Generally, in order to ensure the operational reliability of the cloud platform, the applications on the overheated node will be migrated to the same type of node.

[0070] It can be known that the physical resources that nodes with deployment or organizational relationships in the global node topology rely on are mostly associated. The resource occupancy data corresponding to the relevant nodes can determine whether the relevant nodes have high resource consumption. The links associated with the relevant nodes in the global node topology can determine which nodes in the global node topology rely on physical resources that are associated with the physical resources that the overheated nodes rely on. Based on the resource occupancy data corresponding to the relevant nodes combined with the links associated with the relevant nodes in the global node topology, the application migration direction can be determined.

[0071] Exemplarily, there is a deployment relationship between the overheated node and the related node, the overheated node is container node 1, and the related node is host node A. If the host node A has a high resource consumption, then based on the link associated with the host node A in the global node topology, it is determined that in addition to container node 1, container node 2 and container node 3 are deployed on the host node A, then the application on container node 1 needs to be migrated to other container nodes except container node 2 and container node 3.

[0072] Exemplarily, there is an organizational relationship between the overheated node and the related nodes. The overheated node is the host node B. Based on the link associated with the host node B in the global node topology, it is determined that the host node B and the host node C belong to the same host group. Then, the application on the host node B needs to be migrated to other host nodes except the host node C.

[0073] The technical solution of the present application determines the related nodes associated with the overheated node in the global node topology based on the links associated with the overheated node in the global node topology after determining the overheated node. Based on the resource occupancy data corresponding to the related nodes and the links associated with the related nodes in the global node topology, hotspot elimination suggestions are generated. Providing data support for quickly and accurately eliminating resource hotspots in the cloud platform is conducive to improving the operational reliability of the cloud platform.

[0074] In an optional embodiment, after determining an overheated node among the nodes of the global node topology, the method further includes: if the overheated node belongs to the infrastructure as a service layer, then based on the link associated with the overheated node in the global node topology, determining an early warning node associated with the overheated node in the platform as a service layer; based on the resource occupancy data of the overheated node, generating a resource consumption early warning for the early warning node, and displaying the resource consumption early warning and the resource occupancy data corresponding to the node in the global node topology through a hotspot display interface.

[0075] Among them, the infrastructure as a service layer forms a hierarchical physical machine cluster by building resource domains and host groups, and uses physical machine nodes as computing nodes to allocate virtual machines to supply host machine nodes for the platform as a service layer. In the cloud platform, the infrastructure as a service layer is located below the platform as a service layer. If the overheated node belongs to the infrastructure as a service layer, it may affect the host machine nodes and container nodes in the platform as a service layer.

[0076] In the case where the overheated node belongs to the Infrastructure as a Service layer, based on the link associated with the overheated node in the global node topology, the warning node associated with the overheated node is determined in the Platform as a Service layer. Among them, the warning node refers to the relevant node associated with the overheated node in the Platform as a Service layer. The warning node may be affected by the overheated node. Therefore, it is necessary to generate a resource consumption warning for the warning node based on the resource occupancy data of the overheated node. Among them, the resource consumption warning is used to indicate potential risks to the application operation and maintenance personnel of the Platform as a Service layer. Optionally, the resource consumption warning is delivered to the application operation and maintenance personnel by notification methods such as email, SMS or phone.

[0077] Optionally, the resource consumption warning and the resource occupancy data corresponding to the nodes in the global node topology are visually displayed through the hotspot display interface. Optionally, the global node topology is visually displayed through the hotspot display interface.

[0078] The above technical solution provides a resource high consumption early warning solution, which supports transparently conveying the high resource consumption situation that occurs in the infrastructure as a service layer of the cloud platform to the platform as a service layer, and indicates potential risks to the application operation and maintenance personnel of the platform as a service layer. This provides technical support for quickly and accurately eliminating resource hot spots in the cloud platform.

[0079] Embodiment 3

[0080] Figure 3 This is a structural diagram of a device for globally sensing resource hotspots in a cloud platform provided in Example 3 of the present application. This embodiment can be applied to the situation of sensing resource hotspots in a cloud platform. The device can be implemented by software and / or hardware and can be integrated into electronic devices such as smart terminals.

[0081] like Figure 3 As shown, the device may include:

[0082] The resource occupancy monitoring module 310 is used to monitor the resources occupied by the host node and the container node in the platform as a service layer, and the physical machine node in the infrastructure as a service layer, and obtain the resource occupancy data corresponding to the host node, the container node and the physical machine node;

[0083] The data association module 320 is used to associate the resource occupancy data with the global node topology of the cloud platform according to the nodes corresponding to the resource occupancy data; wherein the nodes in the global node topology include at least the host node and the container node in the platform as a service layer, and the physical machine node in the infrastructure as a service layer; the links in the global node topology are determined based on the resource mapping relationship between the host node and the physical machine node, the deployment relationship between the host node and the container node, the organizational relationship between the host nodes, and the organizational relationship between the physical machine nodes;

[0084] The resource hotspot sensing module 330 is used to determine overheated nodes in the nodes of the global node topology based on the resource occupancy data corresponding to the nodes in the global node topology and the hotspot sensing rules configured for the nodes in the global node topology to sense the resource hotspots in the cloud platform.

[0085] The technical solution of this application performs vertical penetration monitoring of resource occupancy at each level of the cloud platform, and associates the monitored resource occupancy data with the global node topology of the cloud platform. The resource occupancy data is shared between the platform as a service layer and the infrastructure as a service layer, which can globally perceive the real resource hotspots in the cloud platform, provide data support for quickly and accurately eliminating resource hotspots in the cloud platform, and help improve the operational reliability of the cloud platform.

[0086] Optionally, the device also includes: a node topology acquisition module, used to respectively acquire a first node topology corresponding to the Platform as a Service layer and a second node topology corresponding to the Infrastructure as a Service layer; wherein the nodes in the first node topology include at least host machine nodes in the Platform as a Service layer; the nodes in the second node topology include at least physical machine nodes in the Infrastructure as a Service layer; a node topology association module, used to establish an association between the first node topology and the second node topology based on a resource mapping relationship between the host machine node and the physical machine node, and use the associated topology as the global node topology of the cloud platform.

[0087] Optionally, the resource occupancy monitoring module includes: a resource dimension determination submodule, used to determine matching resource dimensions for the host nodes and container nodes in the Platform as a Service layer, and the physical machine nodes in the Infrastructure as a Service layer according to the node type; a monitoring instance deployment submodule, used to deploy resource monitoring instances on the host nodes and container nodes in the Platform as a Service layer, and the physical machine nodes in the Infrastructure as a Service layer; an occupancy data collection submodule, used to run the resource monitoring instance to collect resource occupancy data corresponding to the host node, the container node and the physical machine node from the resource dimensions matching the host node, the container node and the physical machine node.

[0088] Optionally, the resource hotspot perception module includes: an occupancy upper limit extraction submodule, used to extract the resource occupancy upper limit corresponding to the node in the global node topology from the hotspot perception rule; a size relationship determination submodule, used to determine the relative size relationship between the resource occupancy upper limit corresponding to the node in the global node topology and the resource occupancy data; a resource hotspot perception submodule, used to determine overheated nodes in the nodes of the global node topology based on the relative size relationship to perceive the resource hotspots in the cloud platform.

[0089] Optionally, the device also includes: a related node determination module, which is used to determine the related nodes associated with the overheated node in the global node topology after determining the overheated node among the nodes of the global node topology, based on the links associated with the overheated node in the global node topology; and a hotspot elimination suggestion generation module, which is used to generate a hotspot elimination suggestion based on the resource occupancy data corresponding to the related nodes and the links associated with the related nodes in the global node topology.

[0090] Optionally, the device also includes: an early warning node determination module, which is used to, after determining an overheated node among the nodes of the global node topology, if the overheated node belongs to the infrastructure as a service layer, determine the early warning node associated with the overheated node in the platform as a service layer based on the link associated with the overheated node in the global node topology; a consumption early warning display module, which is used to generate a resource consumption early warning for the early warning node based on the resource occupancy data of the overheated node, and display the resource consumption early warning and the resource occupancy data corresponding to the nodes in the global node topology through a hotspot display interface.

[0091] The device for globally perceiving resource hotspots in a cloud platform provided by an embodiment of the invention can execute the method for globally perceiving resource hotspots in a cloud platform provided by any embodiment of the present application, and has performance modules and beneficial effects corresponding to executing the method for globally perceiving resource hotspots in a cloud platform.

[0092] In the technical solution of the present application, the resource occupancy data involved are information and data authorized by the user or fully authorized by all parties, and the collection, storage, use, processing, transmission, provision, disclosure and application of the relevant data comply with the relevant laws, regulations and standards of the relevant countries and regions, take necessary confidentiality measures, do not violate public order and good morals, and provide corresponding operation entrances for users to choose to authorize or refuse.

[0093] Embodiment 4

[0094] According to an embodiment of the present application, the present application also provides an electronic device, a readable storage medium and a computer program product.

[0095] Figure 4 The schematic diagram of the structure of the electronic device 410 of the embodiment that can be used to implement is shown. The electronic device 410 includes at least one processor 411, and a memory connected to the at least one processor 411 in communication, such as a read-only memory (ROM) 412, a random access memory (RAM) 413, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 411 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 412 or the computer program loaded from the storage unit 418 to the random access memory (RAM) 413. In the RAM413, various programs and data required for the operation of the electronic device 410 can also be stored. The processor 411, the ROM 412, and the RAM413 are connected to each other via a bus 414. An input / output (I / O) interface 415 is also connected to the bus 414.

[0096] Multiple components in the electronic device 410 are connected to the I / O interface 415, including: an input unit 416, such as a keyboard, a mouse, etc.; an output unit 417, such as various types of displays, speakers, etc.; a storage unit 418, such as a disk, an optical disk, etc.; and a communication unit 419, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 419 allows the electronic device 410 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0097] Processor 411 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of processor 411 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. Processor 411 executes the various methods and processes described above, such as a method for globally sensing resource hot spots in a cloud platform.

[0098] In some embodiments, the method for globally sensing resource hot spots in a cloud platform may be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 418. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 410 via the ROM 412 and / or the communication unit 419. When the computer program is loaded into the RAM 413 and executed by the processor 411, one or more steps of the method for globally sensing resource hot spots in a cloud platform described above may be performed. Alternatively, in other embodiments, the processor 411 may be configured to execute the method for globally sensing resource hot spots in a cloud platform by any other appropriate means (e.g., by means of firmware).

[0099] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0100] The computer programs for implementing the methods of the present application may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable device for global perception of resource hot spots in a cloud platform, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, partially on the machine as a stand-alone software package and partially on a remote machine, or entirely on a remote machine or server.

[0101] In the context of the present application, a computer readable storage medium may be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, device or equipment. A computer readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, a computer readable storage medium may be a machine readable signal medium. A more specific example of a machine readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0102] To provide interaction with a user, the systems and techniques described herein may be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0103] The systems and techniques described herein may be implemented in a computing system including backend components (e.g., as a server for globally sensing resource hotspots in a cloud platform), or a computing system including middleware components (e.g., an application server), or a computing system including frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system including any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by digital data communication of any form or medium (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0104] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

[0105] The present application also discloses a computer program product, which includes a computer program. When the computer program is executed by a processor, the computer program implements the method for globally sensing resource hot spots in a cloud platform provided by any embodiment of the present application. The program product and the method for globally sensing resource hot spots in a cloud platform disclosed in each embodiment of the present application belong to the same inventive concept, so they are not described here in detail.

[0106] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps recorded in this application can be executed in parallel, sequentially or in different orders, as long as the expected results of the technical solution of this application can be achieved, and this document is not limited here.

[0107] The above specific implementations do not constitute a limitation on the protection scope of this application. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of this application should be included in the protection scope of this application.

Claims

1. A method for globally sensing resource hot spots in a cloud platform, characterized in that: The method comprises: Monitor the resources occupied by the host node and the container node in the platform as a service layer and the physical node in the infrastructure as a service layer respectively, and obtain resource occupation data corresponding to the host node, the container node and the physical node; According to the nodes corresponding to the resource occupancy data, the resource occupancy data is associated with the global node topology of the cloud platform; wherein the nodes in the global node topology include at least the host node and the container node in the platform as a service layer, and the physical machine node in the infrastructure as a service layer; the links in the global node topology are determined based on the resource mapping relationship between the host node and the physical machine node, the deployment relationship between the host node and the container node, the organizational relationship between the host nodes, and the organizational relationship between the physical machine nodes; Based on resource occupancy data corresponding to nodes in a global node topology and hotspot perception rules configured for nodes in the global node topology, overheated nodes are determined in the nodes of the global node topology to perceive resource hotspots in a cloud platform.

2. The method according to claim 1, characterized in that The method further comprises: Respectively obtain a first node topology corresponding to the platform as a service layer and a second node topology corresponding to the infrastructure as a service layer; wherein the nodes in the first node topology include at least the host machine nodes in the platform as a service layer; and the nodes in the second node topology include at least the physical machine nodes in the infrastructure as a service layer; Based on the resource mapping relationship between the host machine node and the physical machine node, an association is established between the first node topology and the second node topology, and the associated topology is used as the global node topology of the cloud platform.

3. The method according to claim 1, characterized in that The monitoring of resources occupied by the host node and the container node in the platform as a service layer and the physical node in the infrastructure as a service layer respectively, and obtaining resource occupation data corresponding to the host node, the container node and the physical node, includes: Based on the node type, determine matching resource dimensions for host nodes and container nodes in the Platform as a Service layer, and physical machine nodes in the Infrastructure as a Service layer; Deploy resource monitoring instances on host nodes and container nodes in the platform as a service layer, and on physical machine nodes in the infrastructure as a service layer; The resource monitoring instance is run to collect resource occupancy data corresponding to the host node, the container node and the physical machine node from a resource dimension matching the host node, the container node and the physical machine node.

4. The method according to claim 1, characterized in that: The method of determining overheated nodes in the nodes of the global node topology based on the resource occupancy data corresponding to the nodes in the global node topology and the hotspot perception rules configured for the nodes in the global node topology to perceive the resource hotspots in the cloud platform includes: Extracting the resource occupancy upper limit corresponding to the node in the global node topology from the hotspot perception rule; Determine a relative size relationship between a resource occupancy upper limit corresponding to a node in the global node topology and the resource occupancy data; Based on the relative size relationship, overheated nodes are determined among the nodes of the global node topology to sense resource hot spots in the cloud platform.

5. The method according to claim 1, characterized in that After determining an overheated node among the nodes of the global node topology, the method further includes: Determining, in the global node topology, related nodes associated with the overheated node based on a link associated with the overheated node in the global node topology; Based on the resource occupancy data corresponding to the relevant nodes and the links associated with the relevant nodes in the global node topology, hotspot elimination suggestions are generated.

6. The method according to claim 1, characterized in that After determining an overheated node among the nodes of the global node topology, the method further includes: If the overheated node belongs to the infrastructure as a service layer, determining an early warning node associated with the overheated node in the platform as a service layer based on a link associated with the overheated node in the global node topology; Based on the resource occupancy data of the overheated node, a resource consumption warning is generated for the warning node, and the resource consumption warning and the resource occupancy data corresponding to the nodes in the global node topology are displayed through a hotspot display interface.

7. A device for globally sensing resource hot spots in a cloud platform, characterized in that: The device comprises: A resource occupancy monitoring module is used to monitor the resources occupied by the host node and the container node in the platform as a service layer, and the physical machine node in the infrastructure as a service layer, and obtain resource occupancy data corresponding to the host node, the container node and the physical machine node; A data association module, used to associate the resource occupancy data with the global node topology of the cloud platform according to the nodes corresponding to the resource occupancy data; wherein the nodes in the global node topology include at least host nodes and container nodes in the platform as a service layer, and physical machine nodes in the infrastructure as a service layer; the links in the global node topology are determined based on the resource mapping relationship between the host node and the physical machine node, the deployment relationship between the host node and the container node, the organizational relationship between the host nodes, and the organizational relationship between the physical machine nodes; The resource hotspot perception module is used to determine overheated nodes in the nodes of the global node topology based on the resource occupancy data corresponding to the nodes in the global node topology and the hotspot perception rules configured for the nodes in the global node topology to perceive the resource hotspots in the cloud platform.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method for globally sensing resource hot spots in a cloud platform as described in any one of claims 1 to 6 is implemented.

9. An electronic device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, it implements the method for globally sensing resource hotspots in a cloud platform as described in any one of claims 1 to 6.

10. A computer program product, comprising a computer program, wherein when the computer program is executed by a processor, the computer program implements the method for globally sensing resource hot spots in a cloud platform according to any one of claims 1 to 6.

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