Abstract: Embodiments allow automated provisioning of a plan upgrade for databases hosted in storage environments. A database is hosted in a shared storage environment according an existing plan, based upon consumption of available system resources (e.g., processing, I/O, memory, disk). An agent periodically issues requests for information relevant to database behavior (e.g., performance metrics, query logs, and/or knob settings). The agent collects the received information (e.g., via a domain socket), performing analysis thereon to predict whether future database activity is expected remain within the existing plan. Such analysis can include but is not limited to compiling statistics, and calculating values such as entropy, information divergence, and/or adjusted settings for database knobs. Based upon this analysis, the agent communicates a recommendation including a plan update and supporting statistics.

Abstract: Some embodiments are associated with systems and methods to provide effective cloud-resource scaling (e.g., for virtual machines and containers). A data center computing system may collect programmable-network real-time traffic statistics associated with enterprise resource planning users. A network and enterprise resource planning load for the enterprise resource planning users may then be predicted (e.g., using a mathematical regression approach) based on an analysis of past programmable-network real-time traffic statistics and current enterprise resource planning user activity. According to some embodiments, the analysis includes time-domain network traffic clustering based on a pre-processing query level categorization. The data center computing system may then dynamically allocate network resources for the enterprise resource planning users based on the predicted network and enterprise resource planning load.

Abstract: Some embodiments are associated with multi-tenant software defined data center network traffic management. A data center computing system may calculate a first value for a first traffic flow, associated with a first user, using a dynamic, distributed, and substantially real-time model. The system may calculate a second value for to a second traffic flow, associated with a second user, using the dynamic, distributed, and substantially real-time model. The system may then dynamically allocate network resources to the first and second traffic flows based on the first and second priorities. Some embodiments may establish a plurality of network device queues and perform queue selection for optimization. According to some embodiments, the first user may be categorized as a premium user while the second user is categorized as an enterprise user.

Abstract: A method includes monitoring a user login to a computer network, receiving a media access control (MAC) address associated with a logged-in user, comparing the MAC address to a table that associates the MAC address with one or more queries to which the user has access, wherein the table comprises information associated with other users who are currently logged-in to the computer network, determining whether the user and at least one of the other currently logged-in users have common access to one or more queries, identifying the user and the at least one of the other currently logged-in users determined to have common access to one or more queries as conflicting users, and restricting write or modify queries through the network for the conflicting users.

Abstract: Some embodiments are associated with multi-tenant software defined data center network traffic management. A data center computing system may assign a first priority to a first traffic flow, associated with a first customer, using a regression technique based on a first response time of a first query associated with the first traffic flow. The system may assign a second priority to a second traffic flow, associated with a second customer, using the regression technique based on a second response time of a second query associated with the second traffic flow. The system may then dynamically allocating network resources to the first and second traffic flows based on the first and second priorities. Some embodiments may establish a plurality of network device queues and calculate demand-supply scores for the first and second traffic flows. The first and second traffic flows may then be sent to queues based on the calculated demand-supply scores.

Abstract: A system and method is disclosed for recovering from any form of disaster-based deletion or loss of resource for a provisioner in a PaaS offering deployed on a cloud. Major architectural changes in the typical provisioner framework are provided along with algorithms to recover from any kind of disaster. Gap-points in existing recovery processes for a provisioner are solved with the algorithms described herein. In addition, disaster recovery for an entire tenant in a provisioner framework may be provided in accordance with some embodiments. In other words, a solution is described in which a user/customer is able to recover a previous state with zero-data loss and zero-re-configuration cost in any scenario of resource deletion.

Abstract: Some embodiments are associated with systems and methods to provide effective cloud-resource scaling (e.g., for virtual machines and containers). A data center computing system may collect programmable-network real-time traffic statistics associated with enterprise resource planning users. A network and enterprise resource planning load for the enterprise resource planning users may then be predicted (e.g., using a mathematical regression approach) based on an analysis of past programmable-network real-time traffic statistics and current enterprise resource planning user activity. According to some embodiments, the analysis includes time-domain network traffic clustering based on a pre-processing query level categorization. The data center computing system may then dynamically allocate network resources for the enterprise resource planning users based on the predicted network and enterprise resource planning load.

Abstract: A system and method are disclosed to facilitate a database tuning as a service offered by a cloud platform as a service provider. A throttling detection engine, associated with a database service instance, may periodically determine if an automated database tuning process should be performed. When it is determined that the automated database tuning process should be performed, the throttling detection engine may transmit database performance metrics. A database tuner as a service, coupled to the throttling detection engine, may access aggregated database performance metrics of the database service instance and determine a set of tunable parameters associated with the database service instance. The database tuner as a service may then execute the automated database tuning process to recommend, using an intelligent algorithm, a new set of configurations for the set of tunable parameters to be applied to the database service instance.

Abstract: A system and method is disclosed for recovering from any form of disaster-based deletion or loss of resource for a provisioner in a PaaS offering deployed on a cloud. Major architectural changes in the typical provisioner framework are provided along with algorithms to recover from any kind of disaster. Gap-points in existing recovery processes for a provisioner are solved with the algorithms described herein. In addition, disaster recovery for an entire tenant in a provisioner framework may be provided in accordance with some embodiments. In other words, a solution is described in which a user/customer is able to recover a previous state with zero-data loss and zero-re-configuration cost in any scenario of resource deletion.

Abstract: Various examples are directed to systems and methods for managing network traffic between a user query machine and a database query machine. A data center network controller may generate a plurality of query type scores describing database query type types directable from the user query machine to the database query machine. The data center network controller may generate a first database element score for a first database element. The first database element score may be based at least in part on a set of the query type scores associated with database queries to be executed at the first database element and on a first schema weight of a first schema associated with the first database element. The data center network controller may assign a first database element traffic flow to a first network appliance queue based at least in part on the first database element score.

Abstract: Some embodiments are associated with multi-tenant software defined data center network traffic management. A data center computing system may calculate a first value for a first traffic flow, associated with a first user, using a dynamic, distributed, and substantially real-time model. The system may calculate a second value for to a second traffic flow, associated with a second user, using the dynamic, distributed, and substantially real-time model. The system may then dynamically allocate network resources to the first and second traffic flows based on the first and second priorities. Some embodiments may establish a plurality of network device queues and perform queue selection for optimization. According to some embodiments, the first user may be categorized as a premium user while the second user is categorized as an enterprise user.

Abstract: Methods and system are disclosed that manage access to a logical object in software defined networks. In one aspect, managing access to the logical objects may include determining whether to retain or release a lock object on a corresponding logical object in the software defined network. The software defined network may receive multiple requests to access applications or logical objects. In response, a front-end engine may be instantiated. The front-end engine may communicate with a back-end engine to determine an availability of TCP communication ports, lock objects, etc., to access the requested logical objects. A network monitoring engine working in cooperation with the front-end engine and the back-end engine may receive the above information on the TCP communication ports, the lock objects, etc., and may determine whether to retain a lock object on a corresponding logical object or to release the lock object and the corresponding logical object.

Abstract: Some embodiments are associated with multi-tenant software defined data center network traffic management. A data center computing system may assign a first priority to a first traffic flow, associated with a first customer, using a regression technique based on a first response time of a first query associated with the first traffic flow. The system may assign a second priority to a second traffic flow, associated with a second customer, using the regression technique based on a second response time of a second query associated with the second traffic flow. The system may then dynamically allocating network resources to the first and second traffic flows based on the first and second priorities. Some embodiments may establish a plurality of network device queues and calculate demand-supply scores for the first and second traffic flows. The first and second traffic flows may then be sent to queues based on the calculated demand-supply scores.

Abstract: A method includes monitoring a user login to a computer network, receiving a media access control (MAC) address associated with a logged-in user, comparing the MAC address to a table that associates the MAC address with one or more queries to which the user has access, wherein the table comprises information associated with other users who are currently logged-in to the computer network, determining whether the user and at least one of the other currently logged-in users have common access to one or more queries, identifying the user and the at least one of the other currently logged-in users determined to have common access to one or more queries as conflicting users, and restricting write or modify queries through the network for the conflicting users.

Abstract: Methods and system are disclosed that optimize an allocation of virtual machines or instances of virtual machines or computing resources. In one aspect, a software defined network includes a front-end query classifier to monitor user traffic (e.g. requests or queries for executing operations) generated from multiple virtual machines (e.g., tenants). The monitored queries may be classified into multiple levels or categories (e.g., L1, L2, L3, etc.) by a back-end query classifier. The classified queries are clustered by a query-clustering engine based on a time stamp information associated with the queries. A virtual machine optimizer may optimize an allocation of virtual machines based on a computation that includes a combination of density of clustered queries and weights assigned to the queries.

Abstract: Methods and system are disclosed that manage access to a logical object in software defined networks. In one aspect, managing access to the logical objects may include determining whether to retain or release a lock object on a corresponding logical object in the software defined network. The software defined network may receive multiple requests to access applications or logical objects. In response, a front-end engine may be instantiated. The front-end engine may communicate with a back-end engine to determine an availability of TCP communication ports, lock objects, etc., to access the requested logical objects. A network monitoring engine working in cooperation with the front-end engine and the back-end engine may receive the above information on the TCP communication ports, the lock objects, etc., and may determine whether to retain a lock object on a corresponding logical object or to release the lock object and the corresponding logical object.

Abstract: Methods and system are disclosed that optimize an allocation of virtual machines or instances of virtual machines or computing resources. In one aspect, a software defined network includes a front-end query classifier to monitor user traffic (e.g. requests or queries for executing operations) generated from multiple virtual machines (e.g., tenants). The monitored queries may be classified into multiple levels or categories (e.g., L1, L2. L3, etc.) by a back-end query classifier. The classified queries are clustered by a query-clustering engine based on a time stamp information associated with the queries. A virtual machine optimizer may optimize an allocation of virtual machines based on a computation that includes a combination of density of clustered queries and weights assigned to the queries.

Abstract: Various examples are directed to systems and methods for network virtualization for web applications. For example, a WAN controller may receive from a backend query processor, a first tenant user query table that describes a first set of query types for a first user. The first set of query types may comprises a first query type having a first computational weight and a second query type having a second computational weight less than the first computational weight. The WAN controller may determine that a first network path to a web application data center is less congested that a second network path to the web application data center. The WAN controller may send a first flow entry to a first network appliance on the first network path instructing the first network appliance on the first network path to send packets associated with queries of the first set of query types to a second network appliance on the first network path.