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Self-adaptive and self-optimising resource monitoring for dynamic grid environments

As the number of resources on the grid tends to be very dynamic and potentially large, it becomes increasingly important to discover and monitor them efficiently. This paper concentrates on the scalability, reliability and performance of grid discover and monitoring services, using autonomous concepts. Application scenarios are developed where the notification rate of data is dynamically modified, based on the overhead costs at the MDS3 Index Service. Previously collected performance benchmarks are also utilised to implement two self-adaptive algorithms which are the basis of the scenarios. The objective of these feedback algorithms is to sustain an adequate level of service for clients while minimising costs at the MDS. Different types of workload models are also used to assess the efficiency of the algorithms. Experimental results are subsequently shown when varying notification update mechanisms and decision parameters are used, ensuring that the MDS is scalable with accruing concurrent clients. Therefore, a policy is proposed where the notification rate is computed dynamically, thereby creating an autonomous grid monitoring service.