Abstract: Techniques for using more-specific routing to perform scalable Layer-2 (L2) stretching of subnets across hybrid-cloud environments. Routing tables in a public cloud may allow for routes that are more specific than the default local route, and the more-specific routes may be used to send all traffic to a dedicated, cloud router. The more-specific routes are set up for a VPC where a subnet resides such that the more specific-routes cover at least a portion of subnet range. The next hop for the more-specific routes point to the cloud router which is capable of doing host routing and segmentation extension. Thus, traffic originating from endpoints in a VPC is routed to the cloud router, and the cloud router determines whether the traffic is to be re-routed back to a destination endpoint in the VPC (or another cloud location), or sent to a destination endpoint residing in the on-premises site.

Abstract: Heterogeneous cloud storage services offered by different cloud service providers have unique deliverable performance. One key challenge is to find the maximum achievable data transfer rate from one cloud service to another. The disclosure herein generally relates to cloud computing, and, more particularly, to a method and system for parameter tuning in cloud network. The system obtains optimum value of parameters of a source cloud and a destination cloud in a cloud pair, by performing a parameter tuning. The optimum value of parameters and corresponding data transfer rate is used as a training data to generate a data model. The data model processes real-time information with respect to cloud pairs, and predicts corresponding data transfer rate.

Abstract: This disclosure relates generally to configuring/building of applications. Typically, a deep learning (DL) application having multiple models composed and interspersed with corresponding transformation functions has no mechanism of efficient deployment on underlying system resources. The disclosed system accelerates the development of application to compose multiple models where each model could be a primitive model or a composite model itself. In an embodiment, the disclosed system optimally deploys a composable model application and transformation functions on underlying resources using performance prediction models, thereby accelerating the development and deployment of the application.

Abstract: This disclosure relates generally to configuring/building of applications. Typically, a deep learning (DL) application having multiple models composed and interspersed with corresponding transformation functions has no mechanism of efficient deployment on underlying system resources. The disclosed system accelerates the development of application to compose multiple models where each model could be a primitive model or a composite model itself. In an embodiment, the disclosed system optimally deploys a composable model application and transformation functions on underlying resources using performance prediction models, thereby accelerating the development and deployment of the application.