Mobile edge computing (MEC) has been considered as a promising technology to handle computation-intensive and latency-sensitive tasks for mobile user equipments (UEs) in next-generation mobile networks. Mobile UEs can offload these tasks to nearby edge servers, which are typically deployed on base stations (BSs) that are equipped with computation resources. Thus, the task execution latency as well as the energy consumption of mobile devices can be reduced. Mobility management has played a fundamental role in MEC, which associates UEs with the appropriate BSs. In the existing handover decision-making process, the communication costs dominate. However, in edge scenario, the computation capacity constraints should also be considered. Due to user mobility, mobile UEs are nonuniformly distributed over time and space. Edge servers in hot-spot areas can be overloaded while others are underloaded. When edge servers are densely deployed, each UE may have multiple choices to offload its tasks. Instead, if edge servers are sparsely deployed, UEs may only have one option for task offloading. This aggravates the unbalanced workload of the deployed edge servers. Therefore, how to serve the dynamic hot-spot areas needs to be addressed in different edge server deployment scenarios. Considering these two scenarios discussed above, two problems are addressed in this thesis: 1) with densely deployed edge servers, for each mobile UE, how to choose the appropriate edge servers independently without full system information is inves- tigated, and 2) with sparsely deployed edge servers, how to serve dynamic hot-spot areas in an efficient and flexible way is emphasized. First, with BSs densely de- ployed in hot-spot areas, mobile UEs can offload their tasks to one of the available edge servers nearby. However, precise full system information such as the server workload can be hard to be synchronized in real time, which also introduces extra signaling overhead for mobility management decision-making. Thus, a user-centric reinforcement-learning-based mobility management scheme is proposed to handle sys- tem uncertainties. Each UE observes the task latency and automatically learns the optimal mobility management strategy through trial and feedback. Simulation results show that the proposed scheme manifests superiority in dealing with system uncer- tainties. When compared with the traditional received signal strength (RSS)-based handover scheme, the proposed scheme reduces the task execution latency by about 30%. Second, fixed edge servers that are sparsely deployed around mobile UEs are not flexible enough to deal with time-varying task offloading. Dispatching mobile servers is formulated as a variable-sized bin-packing problem with geographic constraints. A novel online unmanned aerial vehicle (UAV)-mounted edge server dispatching scheme is proposed to provide flexible mobile-to-mobile edge computing services. UAVs are dispatched to the appropriate hover locations by identifying the hot-spot areas sequen- tially. Theoretical analysis is provided with the worst-case performance guarantee. Extensive evaluations driven by real-world mobile requests show that, with a given task finish time, the mobile dispatching scheme can serve 59% more users on aver- age when compared with the fixed deployment. In addition, the server utilization reaches 98% during the daytime with intensive task requests. Utilizing both the fixed and mobile edge servers can satisfy even more UE demands with fewer UAVs to be dispatched and a better server utilization. To sum up, not only the communication condition but also the computation lim- itation have an impact on the edge server selection and mobility management in MEC. Moreover, dispatching mobile edge servers can be an effective and flexible way to supplement the fixed servers and deal with dynamic offloading requests. / Graduate

IoT enables profitable communication between sensor/actuator devices and the cloud. Slow network causing Edge data to lack Cloud analytics hinders real-time analytics adoption. VRebalance solves priority-based workload performance for stream processing at the Edge. BO is used in VRebalance to prioritize workloads and find optimal resource configurations for efficient resource management. Apache Storm platform was used with RIoTBench IoT benchmark tool for real-time stream processing. Tools were used to evaluate VRebalance. Study shows VRebalance is more effective than traditional methods, meeting SLO targets despite system changes. VRebalance decreased SLO violation rates by almost 30% for static priority-based workloads and 52.2% for dynamic priority-based workloads compared to hill climbing algorithm. Using VRebalance decreased SLO violations by 66.1% compared to Apache Storm's default allocation.