Abstract:
Methods and systems for deriving a scoring function, for use in ordering a set of search results, are described. Consistent with some embodiments, a simulation platform includes an offline simulator module that receives search result sets for search queries that result in the conclusion of a transaction. The result set is then processed by the offline simulation platform to derive a set of weighting factors for use with one or more parameters in a parametric scoring function. The weighting factors are determined, for example, by specifying an equation setting one or more constraints and then solving the equation for the weighting factors.
Abstract:
Methods and systems for deriving a scoring function, for use in ordering a set of search results, are described. Consistent with some embodiments, a simulation platform includes an offline simulator module that receives search result sets for search queries that result in the conclusion of a transaction. The result set is then processed by the offline simulation platform to derive a set of weighting factors for use with one or more parameters in a parametric scoring function. The weighting factors are determined, for example, by specifying an equation setting one or more constraints and then solving the equation for the weighting factors.
Abstract:
A system to detect a network deficiency, in one example embodiment, comprises a receiving module to receive utilization metrics of at least one network resource, wherein the utilization metrics are collected and made available at a network level, an aggregator to aggregate the utilization metrics of at least one network resource with the utilization metrics of a plurality of the network resources, a processing module to determine a level of aggregated utilization metrics of the network resources, and a detecting module to detect a precursor indicative of a network traffic deterioration based on the level of the aggregated utilization metrics.
Abstract:
In various example embodiments, a system and method for managing a server cluster are provided. An example method may include scheduling a first job on a first node, using a first resource manager, establishing a service for a second resource manager on a second node, wherein the service is allocated node resources of the second node, and attempting to schedule a second job on the first node, using the first resource manager. The method may include preempting the service on the second node, using the second resource manager, in response to the attempt to schedule the second job on the first node, and deallocating the node resources of the second node from the service. The method may include advertising, using a node manager of the first resource manager, available node resources of the second node, and scheduling the second job on the second node, using the first resource manager.
Abstract:
A load balancer receives a sequence of requests for computing service and distributes the requests for computing service to a computing node in an ordered list of computing nodes until the computing node reaches its maximum allowable compute capability. Responsive to an indication that the computing node has reached its maximum allowable compute capability, the load balancer distributes subsequent requests for computing service to another computing node in the ordered list. If the computing node is the last computing node in the ordered list, the load balancer distributes a subsequent request for computing service to a computing node other than one of the computing nodes in the ordered list of computing nodes. If the computing node is not the last computing node in the ordered list, the load balancer distributes a subsequent request for computing service to another computing node in the ordered list of computing nodes.
Abstract:
A data storage system may be configured to allocate replica-sets in a balanced manner and mark some of these balanced replica-sets as being spares. As one or more drives or machines fail, the data storage system may move all copies of an affected replica-set to a marked spare replica-set and mark the affected replica-set as being inactive or invalid. As the failed drives are replaced, the data storage system may reconfigure those inactive replica-sets and use them as new spares. The data storage system may implement a coordinator module that handles the balancing and allocation of spares within a sub-cluster. The coordinator may also reallocate entire replica-sets across sub-clusters to maintain balance at the cluster level.
Abstract:
A system for intelligent feature degradation in response to a network deficiency detection, in one example embodiment, comprises a monitoring module to monitor a utilization of a plurality of network resources, a processing module to determine whether the utilization of the plurality of the network resources is above a threshold, to determine a category of an application level using entity, and to determine a revenue generated by the application level using entity, and a degrading module to degrade at least one application level functionality available to the using entity based on the category and the revenue generated by the application level using entity unless the utilization of the plurality of the network resources drops below the first threshold.
Abstract:
A system for intelligent request refusal in response to a network deficiency detection, in one example embodiment, comprises an aggregator to aggregate revenue generated by a requesting entity with a revenue generated by requesting entities homogenous to the requesting entity, and a filtering module to filter a response to a service request when an aggregated revenue-to-network-resource-utilization ratio is below a second threshold unless utilization of a plurality of network resources drops below a first threshold.
Abstract:
A provisioning machine may receive a request that an application be executed while distributed according to a distribution constraint among various devices. The provisioning machine may access a topological model that represents multiple devices configured as a single cloud-based application server and defines a first group of devices that have the same redundancy status (e.g., active or backup). In addition, the topological model may define a second group of devices that have the same functional role (e.g., executing a particular component of the application). A device may be a member of both the first group and the second group. The provisioning machine may determine a size of the first group according to the distribution constraint. Based on the determined size of the first group, the provisioning machine may configure (e.g., provision) the first group of devices as a subset of the multiple devices of the server.
Abstract:
Methods and systems for simulating a search, for the purpose of evaluating one or more scoring functions used in ordering item listings for presentation in a search results page are described. Consistent with some embodiments, a simulation platform includes a real-time simulation module that receives search result sets for search queries that result in the conclusion of a transaction. The result set is then processed by the simulation platform with one or more test scoring functions, such that the resulting position of the item listing that has resulted in the transaction can be compared with the actual position at which the item listing was displayed in the actual search results. For each test scoring function, an average rank shift metric is determined, and displayed, thereby providing a metric with which to base decisions about which scoring functions to use in the production system.