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Abstract:Cloud computing is a paradigm that provides platforms for scientific applications. In clouds, the users pay based on the usage and the quality of service (QoS). There are many workflow scheduling algorithms in the heterogeneous computing environment. But all these cannot be applied in cloud due to t service based resource managing method in cloud. This paper discusses a workflow scheduling problem which gives an optimized solution considering make span and deadline as two objectives. A novel hybrid algorithm of Memetic algorithm and Bee swarm optimization algorithm called Memetic Bee Optimization algorithm (MBOA) is proposed. Experiments on randomly generated workflow and real time workflows show that the schedules generated by MBOA gives more stability on most of workflow instances. The results also show that the algorithm gives significantly better solutions than existing QoS optimization algorithms.

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The human face is an important part of an individual’s body and plays an important role in knowing the individual’s mood. The face is where a human expresses all his basic emotions. In the existing system, they examine the mental state manually by assessing them but have many disadvantages like we cannot predict any accurate solutions based on the assessment score because we might be not sure what kind of emotions the human user would be all time. To overcome this problem, a novel system is proposed to suggest an effective solution for predicting the mental state dynamically, we propose a hybrid architecture invoking facial based emotion sequence, PEN test and IQ test. By consistent monitoring of a human’s emotion and subjecting to PEN and IQ tests, the human’s mental state is routed. Combination of the above three techniques provides promising results for mental state and self-control.