Sap Se

Martin Parent

Alain Gauthier

Edgar Willi Lott

Marc Dorais

Joel Bao-Lan Tran

Nirmal Pothuraj

Mihai David

Ali Mahram Hassan Zada

Mackenzie Trenholm

Fuman Jin

Bianca Matei

In an example embodiment, a mechanism is provided that helps distinguish between email link clicks that are initiated by a human and those that are initiated by a bot. The mechanism provides three different processes for such detection, as well as processes for how to deal with clicks that are initiated by a bot, although it is not necessary to employ all three. These different processes include detection of high volumes of clicks from a particular address range in a short period of time, a hidden honey pot, and detection of duplicate clicks from different addresses. In additional example embodiments, a stall time is added to any evaluation of click metric to allow time for the aforementioned processes to detect and handle bot clicks. This stall time effectively keeps any clicks from 'counting' until the bot detection mechanism(s) can perform their tasks.

Email bot click detection

The present disclosure involves systems, software, and computer implemented methods for resource allocation and management. One example method includes receiving a request, including a first application priority, to run a task for an application. At least one second application priority is identified. A maximum number of parallel tasks per application priority is determined. Application priority weights are assigned to the first application priority and the second application priorities. Application priority divisors are determined, for the first application priority and the second application priorities, based on a respective application priority weight and a number of currently running applications of a respective application priority. A number of parallel tasks for the first application and other applications are determined based on the maximum number of allowable parallel tasks per application, an overall divisor, and a respective application priority weight. A number of parallel tasks are assigned to the first application.

Server resource balancing using a dynamic-sharing strategy

The present disclosure involves systems, software, and computer implemented methods for resource allocation and management. One example method includes receiving, in a dispatching layer, a request to run a first task for a first application, the request including a first application priority. At least one second application priority of at least one currently running application is identified. A maximum number of allowable parallel tasks per application is determined. Application priority weights are assigned to each of the first application priority and the at least one second application priority. A number of parallel tasks for the first application and the at least one currently running application are determined based on the maximum number of allowable parallel tasks per application and the assigned application priority weights. A first number of parallel tasks are assigned to the first application. The first application is executed using the assigned first number of parallel tasks.

Server resource balancing using a fixed-sharing strategy

The present disclosure involves systems, software, and computer implemented methods for resource allocation and management. One example method includes tracking assignments by a dispatcher of tasks to servers in a data structure. The data structure includes at least one entry for each server, with a number of entries per server being based on a capacity of the server. Servers with greater capacity have more entries than servers with lesser capacity. An entry represents either an assignment of a task to a server or an available slot indicating an availability of a server to execute a task. A first server has a lesser capacity than a second server. The tracking of assignment of tasks to servers in the data structure results in more tasks being assigned to the second server than the first server, due to faster task completion by the second server.

Intelligent server task balancing based on server capacity

The present disclosure involves systems, software, and computer implemented methods for resource allocation and management. One example method includes receiving, by a first dispatcher in a dispatching layer, a first request to run a first task for a first application, the first request including a first application priority. A determination is made that the first application priority is lower than at least one higher application priority of another application. Execution of the first application is suspended based on determining that the first application priority is lower than the at least one higher application priority. An indication that an application having a higher application priority has finished is received. A determination is made that the first application priority is a highest application priority of currently-running applications. The first task for the first application is dispatched to a first application server.

Server resource balancing using a suspend-resume strategy

The present disclosure involves systems, software, and computer implemented methods for resource allocation and management. One example method includes receiving a request to run a first task for a first application, the request including a first application priority. At least one second application priority of at least one currently running application is identified. A dispatching algorithm is executed, to dispatch the first task to a first application server based on the first application priority and the at least one second application priority. Destination information is returned, in response to the request, for the first application server, for execution of the first task for the first application, at the first application server, using the first resources.

Growing community of inventors

St. Joseph du Lac, Canada

Martin Parent