Methods and systems for performing operations comprising: accessing one or more data objects including a data set that has been collected over a given span of time, the data set representing a plurality of parameters corresponding to resource utilization of a given server; computing first and second statistical measures based on the plurality of parameters; obtaining current resource utilization corresponding to at least a subset of the plurality of parameters; determining a first condition in which values of the current resource utilization exceed a first threshold associated with the first statistical measure; determining a second condition in which values of the data set corresponding to a time period associated with the current resource utilization exceed a second threshold associated with the second statistical measure; and triggering an anomaly detection operation in response to determining the first and second conditions.

Example aspects include techniques for implementing resource governance in multi-tenant environment. These techniques may include receiving a service request for a multi-tenant service from a client device, and predicting a resource utilization value (RUV) resulting from execution of the service request based on text of the service request, an amount of data associated with the client device at the multi-tenant service, and/or a temporal execution value. In addition, the techniques may include determining that the RUV is greater than a preconfigured threshold identifying an expensive request, and applying a load balancing strategy to the service request based on the RUV being greater than the preconfigured threshold.

Techniques are disclosed relating to implementing inter-application communication in a peer-to-peer fashion via a lease-based central registry. The central registry is configured to maintain application-layer routing information and to perform load balancing of the inter-application communication. A first application of a plurality of applications may send a lease request identifying the second application to the central registry. The first application receives a lease response that identifies a specific instance of a second application within a plurality of computing nodes and includes a resource allocation defining one or more limits on inter-application communication that the first application is permitted to perform with the specific instance of the second application. Based at least in part on the lease response, the first application performs inter-application communication directly with the specific instance of the second application subject to the one or more limits without requiring routing of the inter-application communication by another entity.

A data processing program causes a processing device, among a plurality of processing devices each executes predetermined processing on received data and outputs a result, to execute a process. The process includes, calculating, in a case where a notification that a resource is increased is received from a first processing device in a group of processing devices at a previous stage of the processing device among the plurality of processing devices, a ratio of an amount of data received from the first processing device to a total amount of data received from each of the group of processing devices, based on an amount of data received from each of the group of processing devices within a predetermined period, and determining whether or not to increase a resource of the processing device based on the calculated ratio and resource usage of the processing device.

A data processing program causes a processing device, among a plurality of processing devices each executes predetermined processing on received data and outputs a result, to execute a process. The process includes, calculating, in a case where a notification that a resource is increased is received from a first processing device in a group of processing devices at a previous stage of the processing device among the plurality of processing devices, a ratio of an amount of data received from the first processing device to a total amount of data received from each of the group of processing devices, based on an amount of data received from each of the group of processing devices within a predetermined period, and determining whether or not to increase a resource of the processing device based on the calculated ratio and resource usage of the processing device.

Systems are methods are described for predicting and forecasting a resource utilization on network device, particularly for handling multicast flows, by monitoring past resource consumption patterns. A system can include a plurality of multicast clients coupled to a network; and a network device coupled to the network. The network device may be a switch or a router that directs multicast traffic to the plurality of multicast clients. The network device can include a flow prediction controller that determines one or more real-time predictions relating to a demand of the network based on an analysis of an artificial intelligence (AI) forecasting model, such as an Autoregressive Integrated Moving Average (ARIMA) model. Also, the network device can include a resource optimizer that performs a resource management action that optimizes the resources of the network device based on the one or more real-time predictions of the demand of the network and a policy.

A system, method, and computer-readable storage medium configured to facilitate user purpose in a computing architecture.

Embodiments seek to protect privacy of potentially sensitive client resources in web transactions using crowd-disambiguation. Crowd-disambiguation machines can aggregate information about resources from multiple clients as resource fingerprints, and can use the fingerprints to provide crowd-sourced services in a privacy-protected manner. For example, embodiments can communicate a resource fingerprint as a fully ambiguated resource instance (FARI) and a partially disambiguated resource instance (PDRI). When one (or few) clients communicates the resource fingerprint, the identity of the resource remains obfuscated from the crowd-disambiguation machine. As more clients communicate fingerprints for the same resource (e.g., identified by the matching FARIs), respective, differently generated PDRIs of those fingerprints enable the crowd-disambiguation machine to resolve further portions of the resource, ultimately permitting the resource to be revealed and considered non-private (e.g., for use in hint generation or other crowd-sourced services).

As described herein, a system, method, and computer program are provided for network experience optimization using a residential network router. In use, an electronic calendar is accessed by a residential network router. Additionally, the residential network router determines a plurality of events saved to the electronic calendar, wherein the plurality of events require, at least in part, simultaneous network resource (e.g. bandwidth) usage. Further, the plurality of events are prioritized, using the residential network router. Still yet, available network resources are assigned among the plurality of events, using the residential network router, based on the prioritization.

A method implemented to provide a virtual network to tenants requiring bandwidth in a cloud computing environment is disclosed. The method starts with receiving a request for a task at a network device, the request including a first parameter indicating VMs required, a second parameter indicating bandwidths the required VMs need, a third parameter indicating a duration of the task, and a fourth parameter indicating a deadline of the task. The network device then selects a starting time and a bandwidth allocation of the task, where the bandwidth allocation is shrank to be smaller than the second parameter indicating, and where the selection aims at minimizing a measurement of cloud resource utilization considering consumptions of both VMs and bandwidth. Then the network device allocates VMs for the request at the starting time with the bandwidth allocated at a particular location in the cloud computing environment.