Cisco Technology, Inc.

Karthik Kulkarni

Raghunath Nambiar

Rajesh Shroff

Sean Mckeown

Disclosed are systems, methods, and computer-readable storage media for infrastructure aware adaptive resource allocation. A resource manager can receive a request to allocate a first container instance in a network. The resource manager can determine a set of candidate computing nodes in the network that are capable of hosting the first container instance and request, from an infrastructure monitor that received infrastructure data from the network, health metrics for the set of candidate computing nodes. The resource manager can select, based on the health metrics for the set of candidate computing nodes, an optimal computing node to host the first container instance, and allocate the first container instance on the optimal computing node.

Infrastructure aware adaptive resource allocation

A system and a method for prioritizing network traffic using application awareness and network awareness in a network environment is disclosed. An exemplary method can include receiving a network traffic priority for network traffic associated with a job performed by a distributed application; and based on the network traffic priority, assigning the network traffic to a network classification model, such as an access control list, associated with a network element for directing the network traffic in a network. The network classification model is associated with a network traffic priority class having defined quality of service (QoS) features, such that the network traffic traverses the network according to the defined QoS features.

Distributed application framework for prioritizing network traffic using application priority awareness

The subject technology provides a drillable time-series heat map, which combines information of separate network element (e.g., switch, router, server or storage) and relates them together through impact zones to correlate network wide events and the potential impact on the other units in the network. The subject technology also brings together the network and its components, the distributed application(s) and a heat map controller to proactively communicate with one another to disseminate information such as failures, timeouts, new jobs, etc. For an individual job (e.g., for a distributed application), the subject technology may monitor consumption of resources during different phases of execution to provide individual job profile data that could be presented as a drillable heat map. The heat map, in this regard, nay include resource utilization heat metrics of resources such as CPU, Input/Output (I/O), memory, etc., in the heat map or graphs and presented along with network activity.

Annotation of network activity through different phases of execution

A distributed application framework, along with related systems and/or methods described herein, can intelligently place data using network knowledge. An exemplary method can include receiving data placement information from a distributed application that identifies a source node of data in a network and a list of potential destination nodes in the network for the distributed application to place the data; for each potential destination node, determining a network latency associated with transferring the data from the source node to the potential destination node using network metrics associated with the network; and sending the determined network latencies to the distributed application, such that the distributed application can assign the data to one of the potential destination nodes based on the determined network latencies.

Distributed application framework that uses network and application awareness for placing data

Systems, methods, and computer program products to perform an operation comprising receiving a priority of a distributed computing job, an intermediate traffic type of the distributed computing job, and a set of candidate compute nodes available to process the distributed computing job, the candidate compute nodes each available to process at least one input split of the distributed computing job, and selecting a mapper node from the candidate compute nodes, for one of the input splits, wherein the mapper node is selected based on the priority and the intermediate traffic type of the distributed computing job, wherein the mapper compute node is further selected upon determining that the mapper node is not affected by an error, and a resource utilization score for the mapper node does not exceed a utilization threshold.

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Fremont, CA, United States of America

Karthik Kulkarni