A Survey on Mobile Edge Computing: Focusing on Service Adoption and Provision
Table 3
Summary of literatures on MUs-oriented service adoption.
Work area
Related Work
Key Points
Single MUCO
Cloudlet based single MUCO [51–55] Base station based single MU CO [56–58]
[51] Application-aware cloudlet selection in multi-cloudlet environment [52] Cloudlet selection mechanism in a decentralized environment [53] A power and latency aware cloudlet selection in multi-cloudlet environment [54] MILP in the Multi-cloudlet environment [55] A deep learning approach [56] Offloading in task allocation and computational frequency scaling [57] Tradeoff between the latency and reliability in task offloading [58] Partial computation offloading using DVS
Multi-MUCO
Cloudlet based multi-MUCO [59] Base station based multi-MUCO [60–70]
[59] A Game-theoretic machine learning approach [60] MUCO game [61] Power-delay tradeoff in MU MEC systems [62] Multi-objective optimization [63] Sum energy consumption minimization [64] Joint computation offloading and interference management [65] Offloading in 5G heterogeneous networks [66] Joint offloading and computing optimization [67] Multiple knapsack problem for 5G mobile edge computing [68] Joint radio and computational resource management [69] Dynamic offloading approach [70] Joint task offloading and resource allocation
[71] An adaptive data offloading model. [72] Combination optimization in emerging VCPSs [50] Data offloading technologies (four taxonomies) [73] The UAV trajectory to offload traffic for BSs
