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In enterprise compliance solutions, a server, which runs job on agents, has to be informed of job status so that the results can be pulled from agents. The server polls agents regularly to get status updates & causes huge overhead on the network bandwidth usage and processes running on server. This invention aims at reducing network bandwidth usage & increasing server performance by using machine learning to predict agent polling time. Previous job running times can be used to estimate future running times using machine learning.
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Intelligent Polling Mechanism for Client Server Architecture Using Grey System Theory
In agent based enterprise solutions used for calculating compliance, the server, which triggers and monitors the compliance job on agents, is informed of the status of the job runs on the agents so that timely results are retrieved from the agents. This is done either by ‘data push’ or ‘data pull’ mechanism. Data push mechanism is tightly coupled with agent based compliance solutions since it requires the agents to push the data to the server after the job completion. The drawbacks of this mechanism are lack of predictability of incoming data on the server side since this data is entirely decided by the agents. Also, the push mechanism requires that the server is first contacted by the agents. This is possible only after the firewall on the server’s side has authenticated the agent connection. But,...