Optimal Cloudlet Placement and User to Cloudlet Allocation in Wireless Metropolitan Area Networks

By Mike Jia, Jiannong Cao, Weifa Liang. IEEE Transactions on Cloud Computing, 2017

422 citations recorded by the source corpus.

Subfields: Computer Networks and Communications

View Paper: https://www.semanticscholar.org/paper/1b76c259fdf9150cc72b5be0db0d255c984e2fc9

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

1. Peng Li
2. Xiaofei Liao
3. Shiwen Mao
4. Hai Jin
5. Xuemin Shen
6. Max Mühlhäuser
7. Cem Ersoy
8. Li Lin

Citations in the corpus

• How Can Edge Computing Benefit From Software-Defined Networking: A Survey, Use Cases, and Future Directions (2017, background): “Although there are studies focus on this area [231], [232], server placement in the Metropolitan area need to be studied in detail and analyzed”
• Delay-Aware Microservice Coordination in Mobile Edge Computing: A Reinforcement Learning Approach (2019, background): “3 On the network side, mobile edge computing emerges as a new network architecture, where edge clouds are deployed at base stations by endowing the latter with cloud functionalities [6], [7], [8], [9], [10], [11].”
• Computation Offloading Toward Edge Computing (2019, background): “[104], [105] proposed the heuristic algorithms to enable the optimal cloudlet placement to minimize the average wait time of user requests.”
• Online Resource Allocation for Arbitrary User Mobility in Distributed Edge Clouds (2017, background): “On the other hand, attentions have been paid very recently on resource management in an edge cloud computing system with multiple edge clouds [3], [4], [13], [27]–[29].”
• Edge server placement in mobile edge computing (2019, background): “[18] proposed an offloading system model for multiuser mobile task offloading, and they studied cloudlet placement and mobile user allocation to the cloudlet.”
• On Multi-Access Edge Computing: A Survey of the Emerging 5G Network Edge Cloud Architecture and Orchestration (2017, neutral): “An optimization study for determining the edge cloud positions that best fit given mobility patterns and fluctuating resource demands is elaborated in [133] and [134].”
• All One Needs to Know about Fog Computing and Related Edge Computing Paradigms: A Complete Survey (2018, background): “Design and Planning Infrastructure Design [27, 121, 128–130, 134, 210–220]”
• Online job dispatching and scheduling in edge-clouds (2017, methodology): “, via a metropolitan area network) so as to enhance the services provided to the mobile users by sharing and balancing the workloads among the participating edge-clouds [5]–[7].”
• Multiobjective Optimization for Computation Offloading in Fog Computing (2018, methodology): “Weassume that the requests generated from MD i , i ∈ N , follow a Poisson process with an average arrival rate of λ i [13].”