Abstract: A solution is proposed for implementing a self-adapting reconciliation process. Multiple user accounts are defined on different endpoints for accessing protected resources thereof; a server centralizes the definition of the user accounts on a single point of control which is then synchronized with user account definitions on the endpoints. A dynamic reconciliator, interposed between the server and the endpoints, receives any request from the server for a reconciliation process to be completed in a predefined time-frame. The reconciliator collects the required information from the endpoints and determines an optimal time pattern for the processing of the information by the server over the time-frame according to an estimated amount of information expected to be received and an estimated workload distribution of the server in the time-frame. The reconciliator transmits the information to the server according to this time pattern.

Abstract: A system and computer program product for monitoring a data processing system is proposed. The system and computer program product involve the measuring of state parameters of the system. Indicators of the performance of the system are then inferred from the state parameters by applying fuzzy-logic rules. The proposed solution is based on the idea of estimating a trust value, based on the effectiveness of the corrective actions. If the previous corrective actions prove to be effective than the trust value is enhanced and the system is allowed a higher level of autonomy. Otherwise the intervention of an operator might be invoked.

Abstract: A method (300) for monitoring a data processing system is proposed. The method involves the measuring (306) of state parameters of the system. Indicators of the performance of the system are then inferred (324-330) from the state parameters by applying fuzzy-logic rules. The proposed solution is based on the idea of estimating (336) the expected value of each performance indicator in the future from a set of corresponding samples. In this way, it is possible to predict the evolution of the system and then detect (351) any problem in advance.

Abstract: A system and computer program product for monitoring a data processing system is proposed. The system and computer program product involve the measuring of state parameters of the system. Indicators of the performance of the system are then inferred from the state parameters by applying fuzzy-logic rules. The proposed solution is based on the idea of estimating a trust value, based on the effectiveness of the corrective actions. If the previous corrective actions prove to be effective than the trust value is enhanced and the system is allowed a higher level of autonomy. Otherwise the intervention of an operator might be invoked.

Abstract: A method for monitoring a data processing system is proposed. The method involves the measuring of state parameters of the system. Indicators of the performance of the system are then inferred from the state parameters by applying fuzzy-logic rules. The proposed solution is based on the idea of estimating the expected value of each performance indicator in the future from a set of corresponding samples. In this way, it is possible to predict the evolution of the system and then detect any problem in advance.

Abstract: A method for monitoring a data processing system is proposed. The method involves the measuring of state parameters of the system. Indicators of the performance of the system are then inferred from the state parameters by applying fuzzy-logic rules. The proposed solution is based on the idea of estimating a trust value, based on the effectiveness of the corrective actions. If the previous corrective actions prove to be effective than the trust value is enhanced and the system is allowed a higher level of autonomy. Otherwise the intervention of an operator might be invoked.

Abstract: A solution is proposed for implementing a self-adapting reconciliation process. Multiple user accounts are defined on different endpoints for accessing protected resources thereof; a server centralizes the definition of the user accounts on a single point of control which is then synchronized with user account definitions on the endpoints. A dynamic reconciliator, interposed between the server and the endpoints, receives any request from the server for a reconciliation process to be completed in a predefined time-frame. The reconciliator collects the required information from the endpoints and determines an optimal time pattern for the processing of the information by the server over the time-frame according to an estimated amount of information expected to be received and an estimated workload distribution of the server in the time-frame. The reconciliator transmits the information to the server according to this time pattern.

Abstract: A method (300) for monitoring a data processing system is proposed. The method involves the measuring (306) of state parameters of the system. Indicators of the performance of the system are then inferred (324-330) from the state parameters by applying fuzzy-logic rules. The proposed solution is based on the idea of estimating (336) the expected value of each performance indicator in the future from a set of corresponding samples. In this way, it is possible to predict the evolution of the system and then detect (351) any problem in advance.

Abstract: A method (300) for monitoring a data processing system is proposed. The method involves the measuring (306) of state parameters of the system. Indicators of the performance of the system are then inferred (324-330) from the state parameters by applying fuzzy-logic rules. The proposed solution is based on the idea of estimating (336) the expected value of each performance indicator in the future from a set of corresponding samples. In this way, it is possible to predict the evolution of the system and then detect (351) any problem in advance.

Abstract: A method (300) for monitoring a data processing system is proposed. The method involves the determination (306–360) of an indicator of the severity of any critical condition being detected in the system (for example, though the use of fuzzy-logic rules). In this way, it is possible to associate (366–381) a desired intensity to a corrective action corresponding to the critical condition; for example, this operation is performed by setting (366) an intensity parameter to the severity indicator, and then updating (369–381) it according to predefined policies, a power factor selected manually, and the feedback of any previous application of the corrective action. The corrective action is then applied (384) by modulating its effects according to the desired intensity.

Abstract: A method for monitoring a data processing system is proposed. The method involves the measuring of state parameters of the system. Indicators of the performance of the system are then inferred from the state parameters by applying fuzzy-logic rules. The proposed solution is based on the idea of estimating a trust value, based on the effectiveness of the corrective actions. If the previous corrective actions prove to be effective than the trust value is enhanced and the system is allowed a higher level of autonomy. Otherwise the intervention of an operator might be invoked.

Abstract: A method (300) for monitoring a data processing system is proposed. The method involves the determination (306-360) of an indicator of the severity of any critical condition being detected in the system (for example, though the use of fuzzy-logic rules). In this way, it is possible to associate (366-381) a desired intensity to a corrective action corresponding to the critical condition; for example, this operation is performed by setting (366) an intensity parameter to the severity indicator, and then updating (369-381) it according to predefined policies, a power factor selected manually, and the feedback of any previous application of the corrective action. The corrective action is then applied (384) by modulating its effects according to the desired intensity.

Abstract: A method (300) for monitoring a data processing system is proposed. The method involves the measuring (306) of state parameters of the system. Indicators of the performance of the system are then inferred (324-330) from the state parameters by applying fuzzy-logic rules. The proposed solution is based on the idea of estimating (336) the expected value of each performance indicator in the future from a set of corresponding samples. In this way, it is possible to predict the evolution of the system and then detect (351) any problem in advance.