Abstract: Methods and systems described herein are directed to aggregating and querying log messages. Methods and systems determine event types of log message generated by event sources of the distributed computing system. The event types are aggregated into aggregated records for a shortest time unit and event types are aggregated into aggregated records for longer time units based on the aggregated records associated with the shortest time unit. In response to a query regarding occurrences of an event type in a query time interval, the query time interval is split into subintervals with time lengths that range from the shortest time unit to a longest time unit that lie within the query time interval. The method determines a total event count of occurrences of the event type in the query time interval based on the aggregated records with time stamps in the subintervals. The event count in the query time interval may be used to detect abnormal behavior of the event sources.

Abstract: A disclosed example to determine a migration recommendation of a service between geographic regions includes: a graph generator to generate an interaction graph, the interaction graph including first and second nodes and an edge therebetween, the first node representative of a first service in a first geographic region, the second node representative of a second service in a second geographic region, and the edge representative of a network path of interactions between the first and second services; a weighing engine to determine a weight value of the edge between the first and second services based on a count of network interactions between the first and second services and a real-time latency between the first and second services; and a recommendation engine to generate a migration recommendation to migrate the first service to the second geographic region based on the weight value of the edge.

Abstract: An example method is provided for recommending VM configurations, including one or more servers upon which one or more VMs can run. A user wishing to run these VMs can request a recommendation for an appropriate server or set of servers. The user can indicate a category corresponding to the type of workload that pertains to the VMs. The system can receive the request and identify a pool of servers available to the user. Using industry specifications and benchmarks, the system can classify the available servers into multiple categories. Within those categories, similar servers can be clustered and then ranked based on their levels of optimization. The sorted results can be displayed to the user, who can select a particular server (or group of servers) and customize the deployment as needed. This process allows a user to identify and select an optimized setup quickly and accurately.

Abstract: Methods and systems described herein are directed to classifying log messages generated by event sources of a distributed computing systems. Methods and systems generate a Grok expression and determine log-message metadata for each log message generated by the event sources. For each log message, the log message is classified based on the corresponding Grok expression and log-message metadata. Classified log messages may be used to perform troubleshooting and root cause analysis of the event sources.

Abstract: A maintenance recommendation for containerized services can find a time to perform maintenance on a particular service based on resource usage patterns such that the maintenance will have a reduced impact on dependent services. The dependent services can be determined for the particular service based on network interactions between the services.

Abstract: An example method is provided for recommending VM configurations, including one or more servers upon which one or more VMs can run. A user wishing to run these VMs can request a recommendation for an appropriate server or set of servers. The user can indicate a category corresponding to the type of workload that pertains to the VMs. The system can receive the request and identify a pool of servers available to the user. Using industry specifications and benchmarks, the system can classify the available servers into multiple categories. Within those categories, similar servers can be clustered and then ranked based on their levels of optimization. The sorted results can be displayed to the user, who can select a particular server (or group of servers) and customize the deployment as needed. This process allows a user to identify and select an optimized setup quickly and accurately.

Abstract: Disclosed are various embodiments for generating recommended replacement host machines for a datacenter. The recommendations can be generated based upon an analysis of historical workload usage across the datacenter. Clusters can be generated that cluster workloads together that are similar. Purchase plans can be generated based upon the identified clusters and benchmark data regarding servers.

Abstract: Examples described herein include systems and methods for optimizing the number of servers in a cluster. In one example, a number of application servers, a number of backend servers, and a first disk throughput of a backend server to be included in the cluster are determined. The first disk throughput is determined based on the storage capacity of the backend server and a first round trip time. Example systems and method can also include validating the number of application servers based on a cluster throughput and one of a network interface card bandwidth of an application server to be included in the cluster and a load bearing capacity of the application server. The systems and methods can further include determining a second disk throughput of the backend server and increasing the number of backend servers if the second disk throughput is less than the second disk throughput.

Abstract: An example apparatus includes memory, and at least one processor to execute instructions to assign first containers to a first cluster and second containers to a second cluster based on the first containers including first allocated resources that satisfy a first threshold number of allocated resources and the second containers including second allocated resources that satisfy a second threshold number of allocated resources, determine a representative interaction count value for a first one of the first containers, the representative interaction count value based on a first network interaction metric corresponding to an interaction between the first one of the first containers and a combination of at least one of the first containers and at least one of the second containers, and generate a priority class for the first one of the first containers based on the representative interaction count value.

Abstract: Various examples are disclosed for generating a prediction of server requirements needed to deploy an application. The application can be deployed in virtualized environment in which virtual machines can execute the application. The predicted server requirements can be generated based upon data from other deployments of the application in other virtualized environments.

Abstract: Example methods and apparatus to determine container priorities in virtualized computing environments are disclosed herein. Examples include: a cluster controller to classify a first container into a cluster based on the first container having a number of distinct allocated resources within a threshold number of distinct allocated resources corresponding to a second container; a container ranking generator to: determine resource utilization rank values for a resource usage type of a number of distinct allocated resources, the resource utilization rank values indicative that the first container utilizes the resource usage type more than the second container; determine an aggregated resource utilization rank value for the first container based on aggregating ones of the resource utilization rank values corresponding to the first container; and a container priority controller to generate a priority class for the first container based on the aggregated resource utilization rank value.

Abstract: Examples described herein include systems and methods for optimizing the number of servers in a cluster. In one example, a number of application servers, a number of backend servers, and a first disk throughput of a backend server to be included in the cluster are determined. The first disk throughput is determined based on the storage capacity of the backend server and a first round trip time. Example systems and method can also include validating the number of application servers based on a cluster throughput and one of a network interface card bandwidth of an application server to be included in the cluster and a load bearing capacity of the application server. The systems and methods can further include determining a second disk throughput of the backend server and increasing the number of backend servers if the second disk throughput is less than the second disk throughput.

Abstract: Example methods and apparatus to determine container priorities in virtualized computing environments are disclosed herein. Examples include: a cluster controller to classify a first container into a cluster based on the first container having a number of distinct allocated resources within a threshold number of distinct allocated resources corresponding to a second container; a container ranking generator to: determine resource utilization rank values for a resource usage type of a number of distinct allocated resources, the resource utilization rank values indicative that the first container utilizes the resource usage type more than the second container; determine an aggregated resource utilization rank value for the first container based on aggregating ones of the resource utilization rank values corresponding to the first container; and a container priority controller to generate a priority class for the first container based on the aggregated resource utilization rank value.

Abstract: Various examples are disclosed for generating a prediction of server requirements needed to deploy an application. The application can be deployed in virtualized environment in which virtual machines can execute the application. The predicted server requirements can be generated based upon data from other deployments of the application in other virtualized environments.

Abstract: Various examples are disclosed for workload placement and workload balancing across a cluster of nodes. Workloads can be migrated between nodes to free sufficient CPU and memory resources to place a new workload onto a cluster. Workloads can also be migrated between pods to balance CPU and memory utilization of nodes in a cluster.

Abstract: A disclosed example to determine a migration recommendation of a service between geographic regions includes: a graph generator to generate an interaction graph, the interaction graph including first and second nodes and an edge therebetween, the first node representative of a first service in a first geographic region, the second node representative of a second service in a second geographic region, and the edge representative of a network path of interactions between the first and second services; a weighing engine to determine a weight value of the edge between the first and second services based on a count of network interactions between the first and second services and a real-time latency between the first and second services; and a recommendation engine to generate a migration recommendation to migrate the first service to the second geographic region based on the weight value of the edge.

Abstract: Disclosed are various embodiments for automating the prediction of workload tenures in datacenter environments. In some embodiments, parameters are identified for a plurality of workloads of a software defined data center. A machine learning model is trained to determine a predicted tenure based on parameters of the workloads. A workload for the software defined data center is configured to include at least one workload parameter. The workload is processed using the trained machine learning model to determine the predicted tenure. An input to the machine learning model includes the at least one workload parameter.

Abstract: Various examples are disclosed for generating a prediction of server requirements needed to deploy an application. The application can be deployed in virtualized environment in which virtual machines can execute the application. The predicted server requirements can be generated based upon data from other deployments of the application in other virtualized environments.

Abstract: Examples described herein include systems and methods for optimizing the number of servers in a cluster. In one example, a number of application servers, a number of backend servers, and a first disk throughput of a backend server to be included in the cluster are determined. The first disk throughput is determined based on the storage capacity of the backend server and a first round trip time. Example systems and method can also include validating the number of application servers based on a cluster throughput and one of a network interface card bandwidth of an application server to be included in the cluster and a load bearing capacity of the application server. The systems and methods can further include determining a second disk throughput of the backend server and increasing the number of backend servers if the second disk throughput is less than the second disk throughput.

Abstract: An example method is provided for recommending VM configurations, including one or more servers upon which one or more VMs can run. A user wishing to run these VMs can request a recommendation for an appropriate server or set of servers. The user can indicate a category corresponding to the type of workload that pertains to the VMs. The system can receive the request and identify a pool of servers available to the user. Using industry specifications and benchmarks, the system can classify the available servers into multiple categories. Within those categories, similar servers can be clustered and then ranked based on their levels of optimization. The sorted results can be displayed to the user, who can select a particular server (or group of servers) and customize the deployment as needed. This process allows a user to identify and select an optimized setup quickly and accurately.