Moreover we will also take into account learning and prediction and optimization algorithm. of parameters to further improve the performance. An overview of the proposed algorithm is presented below: Steps Description Contextual User profile (subscription and social profile, battery status, Information resolution, processing and storage) Network (CQI, channel type Number of Users) ζ, Cloud (Utilization status VM’s) η, Application Application profile (Packet sizes, packet interarrival constraints) ẞ Profile Cloud Blocking Probability, VM’s Load, Number of Customers ~ ẞ SNR ~ CQI, (Throughput response, BLER) ~ ζ Optimization Determine the optimal amount of Network, Cloud and User resources in view of the Application Profile. Offline According to rate of channel variation, cloud loading variation application neural network is used. Use of statistical information about User, modeling Network and Cloud to predict the variability in network, cloud and user context. Summary In this chapter, we aim to investigate the behavior of cloud, network, application and user QoS aspects in terms of corresponding input and output performance parameters. In section 3.1-4, we have formulated mathematical models of the mentioned entities of an end to end A comprehensive model of each entity is presented in the form of communication framework. mathematical equations which relate the input resources and output gains. A CANU framework has been presented in this way which glues all the entities. We have applied multi-objective optimization on our framework and tried to find out the effects of certain parameters on the other. We also have analyzed and simulated the accuracy of multi-objective optimization algorithm in the section 3.8.2 and 3.8.3. We have observed the parameters of certain entities are beyond the control of user. For example, wireless channel behavior and cloud blocking probability are beyond the control of user. This
and optimization algorithm. Moreover we will also take into account learning and prediction of parameters to further improve the performance. An overview of the proposed algorithm is presented below: Steps Description Contextual Information Application Profile User profile (subscription and social profile, battery status, resolution, processing and storage) Network (CQI, channel type ζ, Number of Users) Cloud (Utilization status η, VM’s) Application profile ẞ (Packet sizes, packet interarrival constraints) Cloud Blocking Probability, VM’s Load, Number of Customers ~ ẞ SNR ~ CQI, (Throughput response, BLER) ~ ζ Optimization Offline application modeling Determine the optimal amount of Network, Cloud and User resources in view of the Application Profile. According to rate of channel variation, cloud loading variation neural network is used. Use of statistical information about User, Network and Cloud to predict the variability in network, cloud and user context. Summary In this chapter, we aim to investigate the behavior of cloud, network, application and user QoS aspects in terms of corresponding input and output performance parameters. In section 3.1-4, we have formulated mathematical models of the mentioned entities of an end to end communication framework. A comprehensive model of each entity is presented in the form of mathematical equations which relate the input resources and output gains. A CANU framework has been presented in this way which glues all the entities. We have applied multi-objective optimization on our framework and tried to find out the effects of certain parameters on the other. We also have analyzed and simulated the accuracy of multi-objective optimization algorithm in the section 3.8.2 and 3.8.3. We have observed the parameters of certain entities are beyond the control of user. For example, wireless channel behavior and cloud blocking probability are beyond the control of user. This