Description:4. Description:
Field of Invention:
It is reported in literature that nearly 4.57 billion people access Internet, covering nearly 59% of global population as per 2020 statistics. With huge number of Internet users and large volumes of data, the need for secure and fault-tolerant web applications increases. Huge volumes of data are not only consumed, but are also converted and copied among multiple computing resources. Proposed is a Supervised Machine Learning Technique for efficient management of cloud resources. Development of container instance aid in automatic allocation and management of resources thereby maintaining standard quality of service. PaaS module is built with persistent storage, Docker Registry and HAProxy. Log Collector collects data from HA Proxy, Kubernetes and transfers to Influx DB in profiling phase. The resources are analyzed using Rule-Based Neural Network algorithm for efficient utilization of CPU Resources, throughput, waiting time and turn-around time as utilization metrics. Based on analysis, Decision Maker module takes the decision and the Controller passes the resource management decision to Kubernetes.
Background Art & Description:
CN109245916B invention belongs to the technical field of wireless communication networks, and discloses an intention-driven cloud access network system and method.A service application layer obtains a mobile network core application and a service appeal facing vertical industry application, and translation is completed through an intention northbound interface; an intention enabling layer builds a global dynamic management and arrangement control system based on intents by relying on a cloud network service plane, and realizes unified planning of service intents and unified scheduling of resources; the infrastructure layer forms a resource pool by utilizing a resource virtualization technology, and provides ubiquitous connection based on three 5G application scenes. The invention realizes network collaboration, resource sharing, interface opening and management intellectualization through the intent-driven cloud ubiquitous extreme intelligence access network, and constructs a reconfigurable resource management architecture. The invention also discloses an intention-driven method for realizing the cloud access network, which is used for cooperatively scheduling network resources and providing end-to-end service guarantee.
US11172022B2 First resources of a first cloud, a first dependency between the first resources, and second resources of a second cloud may be automatically discovered. Second resources of a second cloud may be discovered. A migration map between the first cloud and the second cloud may be generated based on the discovered first and second resources. The migration map may be recursively modified to increase accuracy of the migration map. The first resources may be migrated to the second cloud based on the modified migration map.
US10148757B2 Repository data fragments distributed across one or both of a first cloud and a network may be accessed. The repository data fragments may be combined into repository data. First resources of the first cloud, a dependency between the first resources, and second resources of a second cloud may be discovered. A migration map between the first cloud and the second cloud may be generated based on the discovered first and second resources and based on the repository data. The first resources may be migrated to the second cloud based on the migration map.
US9800470B2 Disclosed are methods and apparatus for implementing in an electronic device that includes a processor and memory. Virtual resources, which are associated with an execution of a user's applications in a cloud resource configuration including virtual machines, network services and storage, are identified. A first topology map of the virtual resources, including a plurality of nodes, is generated. The first topology map, including the nodes, is output. A vector, which is associated with each node, said vector including one or more features associated with each node, is generated. Based upon the vectors, a distribution of the plurality of nodes within two or more groups is determined. A second topology map, including each of the node groups in one of a collapsed format, wherein only a identifier of the node group is output or an expanded format, wherein a portion of the plurality of nodes the node group are output, is output.
CN107222531B invention provides a container cloud resource scheduling method which can improve the performance of a container cloud platform. The method comprises the following steps: acquiring a service request for applying for resources, which is submitted by a user; analyzing the obtained service request to obtain a resource request of each subtask in the task of the service request; the method includes the steps that current network resource state information and container load state information of a cloud data center are perceived through a cognitive ring, wherein the cognitive ring comprises the following steps: observing, orienting, deciding and acting links; generating a resource scheduling scheme according to the resource request of each subtask, the current network resource state information and the current container load state information of the cloud data center, which are perceived by the cognitive ring, wherein the resource scheduling scheme comprises the following steps: allocating a container for each subtask; and performing resource scheduling on each subtask according to the generated resource scheduling scheme. The invention relates to the technical field of cloud computing resource scheduling.
EP3455728A1 disclosure relates to an orchestrator, for a Virtual Network Platform as a Service (VNPaaS), which orchestrates the management of a Network Service (NS). The orchestrator is operative to select an orchestration zone for each of a plurality of Virtual Network Functions (VNFs) in the NS based on selected deployment locations, where each orchestration zone comprises at least one VNF. The orchestrator is operative to associate sub-services to the selected orchestration zones, the sub- services being obtained from a decomposition of the NS into a number of sub-services equal to a number of orchestration zones selected and each sub-service comprising at least one of the plurality of VNFs. The orchestrator is operative to initiate deployment of the sub-services in the selected orchestration zones.
US20210392049A1 Techniques are described herein for deploying, monitoring, and modifying network topologies comprising various computing and network nodes deployed across multiple workload resource domains. A deployment system may receive operational data from a network topology deployed across multiple workload resource domains, such as public or private cloud computing environments, on-premise data centers, and the like. The operational data may be provided to a trained machine-learning model, and output from the trained model may be used, along with constraint inputs and resource inventories of the workload resource domains, to determine updated topology models which may be deployed within the workload resource domains.
CN107070965A invention discloses a kind of Multi-workflow resource provision method virtualized under container resource, workflow is scheduled using intensified learning, provisioning resources are carried out, define resource utility index U, the supply-demand relationship between the task of operation and virtualization container resource in each scheduling of resource moment container cluster is established, the design of reward functions meets the requirement of the Multi-workflow resource generation using container cluster as granularity：It should ensure that the number amount and type of container cluster inner pressurd vessel unit meet the operational process of cloud workflow, avoid the workflow of different QoS requirements to violate service level agreements again, improve whole container cluster resource utilization.The status information of task in each container cluster can be obtained in real time, by workflow task distribution and the mutual collaboration of virtual resources supply.
US10262019B1 apparatus in one embodiment comprises a processing platform implementing an Internet of Things (IoT) distributed management system accessible to a plurality of user devices over at least one network. The processing platform is configured to determine IoT infrastructure for a given IoT deployment in at least one particular usage context, to control placement of multiple workflow processes for the IoT deployment over a plurality of distributed locations within the IoT infrastructure, and to manage execution of the workflow processes at the distributed locations in accordance with one or more constraints of the particular usage context. The IoT deployment comprises one or more IoT platforms each configured to interact with a different set of IoT devices. The placement of multiple workflow processes over the plurality of distributed locations illustratively provides a designated distribution of data, services, applications and analytics for the IoT deployment in the particular usage context.
US11102281B2 invention is directed towards a container orchestration system such as Kubernetes in which pods monitor themselves to determine if they are likely to require additional resources or vertical scaling within a given timeframe. If the pod determines that it will need additional processing power it notifies the state manager to begin allocating these resources on the same node or a different virtual or physical node before the CPU usage reaches 99%+. The state manager receives this request and allocates the necessary resources ahead of time. When the pod's CPU usage reaches 99%+, the state manager will remove the pod from the existing node and moves the application to the new pod on a different node in which sufficient resources are allocated. This invention brings about efficient utilization of nodes.
US10530740B2 virtual domain name system function is created in a data and analytic component of a platform disposed in a network in a cloud environment. The virtual domain name system function is implemented as a plurality of collector sites distributed throughout a plurality of network edges. A plurality of virtualized network functions are instantiated where each instantiation of the plurality of virtualized network function represents an active virtualized network function. Data is received at the plurality of collector sites from each active virtualized network function; and each instantiation is mapped so that each of the active virtualized network functions are reachable by any other of the plurality of virtualized network functions.
US20220231924A1 In a communication system, a first network node is configured to execute at least one service application executing a first service and at least one analytics application executing at least part of a distributed analytics service. The first network node obtains information about a new telecommunication service and transmits, to a second network node in the communication system, a request for a policy for the new telecommunication service. The first network node receives, from the second network node, the policy for the new telecommunication service and updates a currently applied policy on the basis of the received policy. The updated policy rebalances resources allocated from a shared computing resource pool of the first network node between the new telecommunication service and the at least one analytics application such that the new telecommunication service maintains adherence to the one or more requirements of a service level agreement.
It is reported in literature that nearly 4.57 billion people access Internet, covering nearly 59% of global population as per 2020 statistics. With huge number of Internet users and large volumes of data, the need for secure and fault-tolerant web applications increases. Huge volumes of data are not only consumed, but are also converted and copied among multiple computing resources. Proposed is a Supervised Machine Learning Technique for efficient management of cloud resources. Development of container instance aid in automatic allocation and management of resources thereby maintaining standard quality of service. PaaS module is built with persistent storage, Docker Registry and HAProxy. Log Collector collects data from HA Proxy, Kubernetes and transfers to Influx DB in profiling phase. The resources are analyzed using Rule-Based Neural Network algorithm for efficient utilization of CPU Resources, throughput, waiting time and turn-around time as utilization metrics. Based on analysis, Decision Maker module takes the decision and the Controller passes the resource management decision to Kubernetes.

Claims:
In this invention on Supervised Machine Learning Technique for Efficient Management of Cloud Resources, we claim that
1. It is reported in literature that nearly 4.57 billion people access Internet, covering nearly 59% of global population as per 2020 statistics. With huge number of Internet users and large volumes of data, the need for secure and fault-tolerant web applications increases. Huge volumes of data are not only consumed, but are also converted and copied among multiple computing resources. Proposed is a Supervised Machine Learning Technique for efficient management of cloud resources. Development of container instance aid in automatic allocation and management of resources thereby maintaining standard quality of service.
2. As a system in Claim 1, PaaS module is built with persistent storage, Docker Registry and HAProxy. Log Collector collects data from HA Proxy, Kubernetes and transfers to Influx DB in profiling phase. The resources are analyzed using Rule-Based Neural Network algorithm for efficient utilization of CPU Resources, throughput, waiting time and turn-around time as utilization metrics.
3. As a system in Claim 2, Based on analysis, Decision Maker module takes the decision and the Controller passes the resource management decision to Kubernetes.
Description of Drawings:
For the detailed understanding of the invention the explanations with reference to the figures are given below.
Figure 1: represents the block diagram of the proposed system
Figure 2: represents a working of the proposed system
, Claims:In this invention on Supervised Machine Learning Technique for Efficient Management of Cloud Resources, we claim that
1. It is reported in literature that nearly 4.57 billion people access Internet, covering nearly 59% of global population as per 2020 statistics. With huge number of Internet users and large volumes of data, the need for secure and fault-tolerant web applications increases. Huge volumes of data are not only consumed, but are also converted and copied among multiple computing resources. Proposed is a Supervised Machine Learning Technique for efficient management of cloud resources. Development of container instance aid in automatic allocation and management of resources thereby maintaining standard quality of service.
2. As a system in Claim 1, PaaS module is built with persistent storage, Docker Registry and HAProxy. Log Collector collects data from HA Proxy, Kubernetes and transfers to Influx DB in profiling phase. The resources are analyzed using Rule-Based Neural Network algorithm for efficient utilization of CPU Resources, throughput, waiting time and turn-around time as utilization metrics.
3. As a system in Claim 2, Based on analysis, Decision Maker module takes the decision and the Controller passes the resource management decision to Kubernetes.