The designed approach for optimization is It piles up complexity, if the centralized. system scales up (no. As discussed, a possible of users or applications increases). alternative is a decentralized approach, which comprises an such that N-agent system, each agent influence about the global optimal solution. However large communication overhead is a problem in this approach. Parameters for decision making such as and bandwidth, security, response time failure are sometimes hard to measure or acquire in a timely manner in practical systems. rate Therefore, the way to estimate and measure parameters need to be further investigated. 4G LTE communication network as a network model is used, but due to cost concerns there are other alternatives e.g. and which can provide good connectivity wifi bluetooth and throughput in certain scenarios. In designed methodology, a simplistic user QoS has been considered, in which user has equal preferences in terms of choosing solutions concerning the resources invested in cloud and network. This does not take into account, if user has some inherent preferences like in view of its high bandwidth demand subscriber context. Effect of has not been considered. Similarly, the link signaling channels overhead between (EPC) and the cloud is also assumed to be perfect. LTE evolved packet core was assumed in the Other scheduler e.g. Round robin scheduler network context. has not been simulated. proportional fair, max SINR and best CQI Effect of handovers has not been taken into account. User high mobility can cause excessive handovers, which might lead to missing QoS latency requirements. Future Work In view of the critical review, we outline the following directions of the future work. Rate of change of channel conditions, type of channel (Gaussian, Rayleigh, Pedestrian, strongly influence the optimal solution. In this way, a Vehicular), cloud and network load is proposed in the section 3.4, which can comprehensive single user optimization model be extended further to accommodate in future to extend the current multi user scenario optimization framework. In the similar way, the single user optimization framework can be extended to a scenario by scaling up the network and cloud resources. In view of the increased complexity of multi-user model and dynamic programming Collaborative game theory
The designed approach for optimization is centralized. It piles up complexity, if the system scales up (no. of users or applications increases). As discussed, a possible alternative is a decentralized approach, which comprises an N-agent system, such that each agent influence about the global optimal solution. However large communication overhead is a problem in this approach. Parameters for decision making such as bandwidth, security, response time and failure rate are sometimes hard to measure or acquire in a timely manner in practical systems. Therefore, the way to estimate and measure parameters need to be further investigated. 4G LTE communication network as a network model is used, but due to cost concerns there are other alternatives e.g. wifi and bluetooth which can provide good connectivity and throughput in certain scenarios. In designed methodology, a simplistic user QoS has been considered, in which user has equal preferences in terms of choosing solutions concerning the resources invested in cloud and network. This does not take into account, if user has some inherent preferences like high bandwidth demand in view of its subscriber context. Effect of signaling channels overhead has not been considered. Similarly, the link between LTE evolved packet core (EPC) and the cloud is also assumed to be perfect. Round robin scheduler was assumed in the network context. Other scheduler e.g. proportional fair, max SINR and best CQI has not been simulated. Effect of handovers has not been taken into account. User high mobility can cause excessive handovers, which might lead to missing QoS latency requirements. Future Work In view of the critical review, we outline the following directions of the future work. Rate of change of channel conditions, type of channel (Gaussian, Rayleigh, Pedestrian, Vehicular), cloud and network load strongly influence the optimal solution. In this way, a comprehensive single user optimization model is proposed in the section 3.4, which can be extended further to accommodate multi user scenario in future to extend the current optimization framework. In the similar way, the single user optimization framework can be extended to a scenario by scaling up the network and cloud resources. In view of the increased complexity of multi-user model dynamic programming and Collaborative game theory
Dr.Asad-Javied-PhD.docx